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1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | leSEPx6X35 | decision | 1,731,919,884,310 | 1eajzjMKeW | [
"everyone"
] | [
"tsinghua.edu.cn/THU/2024/Fall/AML/Program_Chairs"
] | decision: Strong Accept (Long Presentation)
comment: **Enhancing Embedded Agents' Text-based Reasoning Abilities in Human Environments through LLM-based Multi-Agent Collaboration**
**2.4.1 Key Innovations**
1. Developing a collaborative framework for multi-agent systems
**2.4.2 Additional Key Information**
None
**2.4.3 Advantages**
1. Potential applications in dynamic indoor environments
**2.4.4 Areas for Improvement**
1. Provide a clearer explanation of why individual agents struggle to effectively handle real-world problems
2. Define how the collaborative framework can achieve self-improvement
3. Clearly outline the conditions and boundaries for triggering human-agent collaboration within the framework
**2.4.5 Recommendations**
1. Differentiate between "agents" and "embodied intelligence" in the descriptions
2. Review existing research in this domain
3. Investigate how traditional multi-agent research can inform and contribute to this study
title: Paper Decision |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | ksvTRGftyH | review | 1,731,338,164,746 | 1eajzjMKeW | [
"everyone"
] | [
"~KAI_JUN_TEH1"
] | title: A clear problem statement and a well-defined research plan
review: This paper proposes a multi-agent reasoning framework that leverages large language models (LLMs) to enhance the capabilities of embodied agents, such as robots, in executing text-based tasks in human-populated environments like offices.
First, the article points out that in office-like environments, robots or embedded systems still face challenges such as dynamic reasoning, limited physical abilities, and human interaction. Therefore, the article proposes an LLM-based multi-agent reasoning framework to tackle the aforementioned issues. It also plans to build a simulated real-world environment to validate the effectiveness of this framework. In my view, this is a very solid piece of work.
However, my concern is that the article does not clarify the advantages of multi-agent systems, which led the authors to choose them over other systems. Thanks!
rating: 10
confidence: 4 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | gNBZBOuIF7 | review | 1,730,899,999,684 | 1eajzjMKeW | [
"everyone"
] | [
"~Liutao7"
] | title: An Innovative and Promising Multi-Agent Collaboration Framework
review: This proposal presents a multi-agent collaboration framework that utilizes LLMs to enhance the reasoning capabilities of embodied agents for executing text-based tasks in human environments. I think: 1. The proposal has good integrity; 2. The proposal is highly creative, providing a solution to address the limitations of embodied agents in dynamic reasoning, physical capabilities, and human-robot interaction; 3. The workload is reasonable.
Areas for improvement: The implementation plan and evaluation criteria need further refinement.
rating: 9
confidence: 4 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | bj3MWhnYRP | review | 1,731,142,868,227 | 1eajzjMKeW | [
"everyone"
] | [
"~Yuanda_Zhang1"
] | title: Interesting question, great idea
review: The proposal presents an innovative study that aims to improve the reasoning capabilities of embodied agents for solving text-based tasks in human-populated environments. By leveraging Large Language Models (LLMs) within a multi-agent system, the authors intend to create a more effective framework for task execution and human-robot interaction, with a focus on reducing human intervention in domestic settings.
Pros:
1）The research question addressed by the proposal is both novel and intriguing, with significant practical implications for the field of robotics and embodied AI.
2）The literature review is comprehensive, covering a wide range of relevant research and demonstrating the authors' thorough understanding of the current state of the art.
3）The proposed methodology, which involves designing a multi-agent reasoning framework and conducting tests in simulated and real-world environments, is promising and has the potential to advance the capabilities of embodied agents significantly.
Cons:
1）While the methodology section outlines a clear plan for testing and evaluation, there is a lack of preliminary results or insights from any pilot experiments that would support the feasibility of the proposed approach.
rating: 10
confidence: 4 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | Ym3kLKKySu | review | 1,730,904,806,019 | 1eajzjMKeW | [
"everyone"
] | [
"~Daniel_Wang4"
] | title: Promising Framework for Enhancing Embodied Agents
review: This proposal presents a creative and well-rounded approach, using LLM-powered multi-agent collaboration to improve how embodied agents handle text-based tasks in human environments. It’s innovative in tackling the real challenges these agents face, like adapting to unexpected changes, handling physical tasks, and interacting effectively with people.
However, there are areas that could use a bit more clarity. The methodology feels a bit vague in parts, leaving questions about how exactly the multi-agent system will work in complex, real-world situations. Additionally, the evaluation criteria could be more detailed to make it easier to see how success will be measured across various tasks and environments.
Overall, the proposal is thoughtfully put together and has strong potential to advance the effectiveness of these agents in practical settings.
rating: 9
confidence: 3 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | V1vq4OXYnr | review | 1,731,064,560,528 | 1eajzjMKeW | [
"everyone"
] | [
"~Un_Lok_Chen1"
] | title: An LLM-based Multi-Agent Framework for Complex Robotic Tasks
review: Summary:
This project proposal aims to explore an innovative multi-agent framework powered by LLMs to enable robots to handle dynamic tasks that require more reasoning abilities and interact with humans and external environment. It also outlines a preliminary plan to iteratively implement and validate the framework in simulated and real-world scenarios such as an office.
Pros:
1. The proposal has well presented the problem to be solved and the motivation behind their objectives. The research problem is considered critical and concrete.
2. The language use is professional and clear.
3. The structure of the proposal is reasonable enough to guide the readers from problem introduction to related works and at last the methodology. Reading experience is smooth.
Cons:
A. Major issues:
1) There are at least 3 goals enlisted in the proposal: 1) to incorporate LLM to allow for human interaction with text-based instruction; 2) to develop reasoning abilities of the robot for dynamic tasks with LLM-based agents; 3) to implement mechanisms for the robot to seek for human assistance actively. Additionally, these goals are supposed to be tested on robots in real complex scenes. The objective of the work may be too ambitious and the workload may be too high for a course project.
B. Minor issues:
1) If possible, consider adding more references to previous works to support the claims presented in the Introduction and Problem Statement sections.
2) Please comply with the maximum length requirement in the proposal guideline (2 pages).
3) Consider elaborate more on what specific techniques may be used in the Multi-Agent Collaboration Framework.
4) Are there any specific baseline methods on this task to compare with?
rating: 8
confidence: 4 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | SRc3thp8Mp | review | 1,731,413,437,384 | 1eajzjMKeW | [
"everyone"
] | [
"~Maanping_Shao1"
] | title: Review
review: The proposal, "Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents' Reasoning Capabilities," presents an innovative approach to improve robotic task execution in human-populated environments. By employing a multi-agent framework with Large Language Models (LLMs), the authors aim to enhance robots’ reasoning abilities and reduce human intervention.
Strengths:
Innovative Approach: Using LLMs for multi-agent collaboration could greatly improve robots' adaptability and human interaction capabilities.
Real-World Impact: The research targets critical challenges, with potential applications in dynamic, indoor environments.
Methodology: The proposed testing in simulated and real environments, combined with iterative improvement, is well-conceived.
Weaknesses:
Technical Detail: The integration of LLMs with agents could be more clearly defined.
Evaluation Metrics: More comprehensive success metrics would benefit the evaluation.
rating: 9
confidence: 3 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | N3KO4laMHj | review | 1,731,401,746,518 | 1eajzjMKeW | [
"everyone"
] | [
"~Chaoqun_Yang2"
] | title: Clear problem definition
review: **Summary:**
The proposal addresses the challenge of enhancing robots' ability to interpret and execute text-based instructions in dynamic human environments. The authors propose an LLM-based Multi-Agent Reasoning Framework to improve robots' dynamic reasoning, physical capabilities, and human interaction skills. This framework aims to enable robots to autonomously handle a variety of tasks while minimizing the need for human intervention, particularly in office settings. The proposal outlines the challenges faced by current robots, such as dynamic reasoning and limited physical abilities, and suggests a collaborative system where LLM-driven agents assist robots in processing human instructions more effectively.
**Highlights:**
1. **Comprehensive Objectives:** The research objectives are well-defined and cover the development, validation, and iterative improvement of the proposed framework, ensuring a thorough approach to solving the problem.
2. **Interdisciplinary Approach:** The proposal brings together elements of robotics, AI, and human-robot interaction, showcasing an interdisciplinary approach that is crucial for advancing embodied agents' capabilities.
**Advice:**
1. **Methodological Details:** It would be better if more details on the methodology and experimentation is provided, such as the specific baseline approaches for comparison, and the implementation details of the proposed method.
2. **The innovation of the framework:** What's the innovation of the proposed framework? How is it different from the common multi-agent cooperative framework? What idea is adopted to solve the problem to be solved in this paper?
rating: 9
confidence: 4 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | BzIHpBobcn | review | 1,731,417,310,057 | 1eajzjMKeW | [
"everyone"
] | [
"~Zihan_Wang7"
] | title: Hard and Meaning Task
review: **Summary**
This research proposal aims to enhance robots' capabilities in human environments through an LLM-based multi-agent reasoning framework. Through a multi-LLM agent collaboration system, it helps robots better understand and execute text-based tasks while intelligently seeking human assistance when needed.
**Highlights**
1. Key problems to be addressed: limitations in dynamic reasoning in real-time situations, physical constraints in complex task execution, difficulties in human-robot interaction and assistance-seeking
2. Research methodology: design of multi-agent collaboration framework, testing in both simulated and real-world environments, evaluation based on task completion rates and human intervention metrics, iterative improvement based on feedback
**Advice**
1. Include more detailed metrics for measuring "human intervention minimization"
2. Define specific scenarios for testing the framework
rating: 9
confidence: 4 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | BaY7eNRHUM | review | 1,731,142,225,206 | 1eajzjMKeW | [
"everyone"
] | [
"~André_Moreira_Leal_Leonor1"
] | title: Enhancing embodied agents' reasoning through LLM-based multi-agent collaboration for text tasks
review: This proposal reflects the aim of the research: using a multi-agent framework with large language models to enhance reasoning and task execution in embodied agents, with a special focus on text-based tasks in human-populated settings. It really represents a collaborative approach in nature and aims at solving real-world dynamic environment challenges.
rating: 10
confidence: 4 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | 14FXyGTrGN | review | 1,731,309,282,173 | 1eajzjMKeW | [
"everyone"
] | [
"~Chenxi_Hu4"
] | title: Promising Topic but Limited Clarity in Multi-Agent Collaboration Framework
review: The proposal offers a promising solution to enhance embodied agents’ capabilities in human-populated environments through LLM-based multi-agent collaboration. While the research objectives and methodology are well-defined, further clarification on LLM agent roles, human-robot interaction specifics, and detailed evaluation metrics is needed. But still, this project is a novel topic with the potential to have a significant impact on the real world.
rating: 8
confidence: 4 |
1eajzjMKeW | 【Proposal】Leveraging LLM-based Multi-Agent Collaboration to Enhance Embodied Agents’ Reasoning Capabilities for Solving Text-based Tasks in Human-populated Environments | [
"Nan Sun",
"Chengming Shi",
"Yuwen Dong"
] | This proposal explores the design of a reasoning framework leveraging LLM-based multi-agent collaboration to enhance the reasoning capabilities of embodied agents. By improving their understanding and execution of text-based instructions in complex, human-populated environments, the system aims to improve robots' dynamic reasoning, interaction with humans, and task completion. The proposed framework will enable robots to handle tasks autonomously while efficiently seeking human assistance when needed, ensuring task completion with minimal intervention. | [
"Multi-agent System",
"LLM-based Agent",
"Autonomous Robot",
"Human-robot Interaction",
"Embodied AI"
] | https://openreview.net/pdf?id=1eajzjMKeW | 0Ns3khMnKw | review | 1,731,400,685,045 | 1eajzjMKeW | [
"everyone"
] | [
"~jin_wang30"
] | title: A detailed and practical proposal
review: This proposal explores a multi-agent collaboration framework driven by a large language model, aiming to enhance the reasoning ability of robots to perform tasks in complex environments. The system proposed in this paper is suitable for robot assistance in text-command tasks, and improves the performance of robots in dynamic and changeable real-world scenarios through multi-agent collaboration.
Advantages:
This paper focuses on applying the reasoning ability of LLM to environments with intensive human-computer interaction, such as offices and homes. The research content is forward-looking and in line with the current development trend in the field of artificial intelligence and robotics. In addition, this paper designs a multi-agent collaboration framework that enables different agents to collaborate and divide labor in robot tasks, which has good innovation and practical value. The roles and tasks of each agent are described in detail, and the framework design is reasonable. The structure of the paper is very clear, and multi-dimensional evaluation indicators are proposed, including task completion rate, human-computer collaboration efficiency, and the degree of reduction of human intervention, which ensures the reliability of the research results.
Disadvantages:
Although the article proposes a theoretical framework, it lacks actual test or experimental data to verify its effectiveness. If it can be combined with actual experimental data and specific test results, it will be more convincing. In addition, the article lacks detailed descriptions of the specific interaction mode and data flow between LLM and multi-agents. It would be better if the details were properly enriched.
Overall, this is a very good proposal, and I look forward to the subsequent research progress.
rating: 10
confidence: 4 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | zm7rX9EuJQ | review | 1,731,113,242,568 | 0r81vMYJcz | [
"everyone"
] | [
"~Xiying_Huang2"
] | title: Evaluation of “GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System”
review: The quality of the paper is commendable, as it addresses a complex problem in legal multi-agent collaboration with a well-structured approach that integrates advanced AI methodologies such as planning, memory, and reflection within the GLM-4 framework. The paper is logically organized, with each section providing clarity on the model’s components and their roles within the system.
The clarity of the paper is generally high, though a few technical aspects could benefit from additional explanation. Terms like “dynamic feedback from local planning” and the specific functionalities of agents (e.g., “Memorizer” and “Reflector”) are well introduced, but some readers may find these ideas abstract without further clarification. Including illustrative examples could enhance readability and help audiences understand the practical application of each component.
The paper shows originality by developing a multi-agent legal system that combines local and global strategies to manage complex legal inquiries. Leveraging GLM-4 for such applications demonstrates a novel approach, especially through the use of reinforcement mechanisms and multi-agent collaboration for legal services.
This work is significant as it targets the under-served area of legal assistance through intelligent systems. The proposed method’s capability to enhance legal consultation access, particularly where resources are limited, is impactful. The combination of agents to address planning, memory, and adaptability has the potential to set a foundation for future developments in legal multi-agent systems.
Pros
1. Innovative Approach: Utilizes GLM-4 and multi-agent collaboration, which are advanced concepts in AI, to tackle challenges in the legal domain.
2. Clear System Design: The roles of Processor, Memorizer, Actor, Judge, and Reflector agents are well-defined, each contributing uniquely to the overall goal.
3. Addresses Real-World Needs: The system could significantly impact regions with limited legal resources by improving access to legal services.
Cons
1. Limited Practical Examples: The absence of detailed examples of real-world scenarios could make it challenging for readers to fully grasp the application potential.
2. Clarity in Technical Details: Some technical descriptions are overly abstract, which may hinder understanding for readers less familiar with multi-agent frameworks.
3. Scalability Concerns: Although the paper describes a promising model, the scalability of GoalAct in larger, more complex legal systems remains unexplored.
rating: 8
confidence: 3 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | vsthtwogC0 | review | 1,731,141,846,147 | 0r81vMYJcz | [
"everyone"
] | [
"~André_Moreira_Leal_Leonor1"
] | title: Strengths in Multi-Agent Design with Room for Technical Depth and Risk Considerations
review: The proposal presents a clear, well-structured vision of the GoalAct legal multi-agent system based on GLM4, for improving legal services through adaptive collaboration. One of the strengths of this work is its clear architecture with differentiated agent roles and an innovative combination of global and local planning, reflection, and memory. This multi-agent approach will enhance the accuracy of responses and adaptability, establishing the system as quite useful in the area of legal service automation.
The proposal could be improved by providing more detailed technical descriptions—especially on error-handling mechanisms and memory utilization—to be applicable legally. Further, addressing potential risks with respect to ethical concerns in legal decision-making and API limitations would make the proposal much stronger.
rating: 8
confidence: 4 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | t0EcuOVqcg | review | 1,731,425,254,750 | 0r81vMYJcz | [
"everyone"
] | [
"~Chendong_Xiang1"
] | title: review
review: The paper presents GoalAct, a globally adaptive multi-agent system leveraging the GLM-4 language model for dynamic legal service provision.
Strengths
1.Adaptive Collaboration: GoalAct’s multi-agent system enables efficient collaboration by assigning specialized tasks to different agents (Processor, Memorizer, Actor, Judge, Reflector), which enhances both task accuracy and overall system adaptability.
2.Memory Integration: By utilizing both short- and long-term memory, the system accumulates past experiences and improves decision-making over time, contributing to more robust and contextually accurate legal services.
3.Dynamic Reflection and Adjustment: GoalAct continuously reflects on planning and execution, dynamically adjusting strategies based on user feedback, which enhances reliability and responsiveness.
questions：
dose authors show ablation of system design that claim of Processor, Memorizer, Actor, Judge, Reflector act critically?
rating: 7
confidence: 3 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | o5PEGS51yk | review | 1,731,399,119,895 | 0r81vMYJcz | [
"everyone"
] | [
"~jin_wang30"
] | title: Good proposal
review: This article provides an innovative legal multi-agent collaboration system, which has theoretical research value.
Advantages：
The article adopts a standard academic structure, including background introduction, definition, related work and methods, with clear levels and easy for readers to understand.
In addition, the article cites a large number of recent studies in the field of LLM, such as Chain-of-Thought, ReAct, Reflexion and other methods, showing the author's understanding and use of cutting-edge technologies in the field.
As for the specific implementation of the GoalAct system, the role and process of each agent (Processor, Memorizer, Actor, Judge, Reflector) are clearly described, reflecting the logical rigor of the system.
Disadvantages：
The innovation of the article does not seem to be enough. The article is mainly based on GLM-4 and calls the relevant database API interface, but there is no innovation in the improvement of the model itself. In addition, this article lacks a discussion of potential challenges. The multi-agent collaboration system may have challenges in scalability and real-time performance, but the article does not explore these potential problems and solutions in depth.
rating: 9
confidence: 4 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | l7Z1eyApq7 | review | 1,731,421,639,304 | 0r81vMYJcz | [
"everyone"
] | [
"~Zhu_Zhang6"
] | title: A good proposal for legal multi-agent system
review: **Summary:**
The proposal outlines "GoalAct," a globally adaptive dynamic legal multi-agent collaboration system based on the GLM-4 language model. The system is designed to provide efficient and accurate legal services by integrating global and local information. By using API calls to access legal databases, GoalAct addresses legal service challenges, such as handling complex inquiries, managing irrelevant information, and developing a robust self-correction mechanism. The system employs five types of agents—Processor, Memorizer, Actor, Judge, and Reflector—that collaboratively tackle user demands by breaking down and processing tasks.
**Strengths:**
1. **Innovative Agent Design:** The structured multi-agent system, with distinct agents for processing, memory management, action, judgment, and reflection, allows for a highly modular approach that is potentially scalable and adaptable to complex legal inquiries.
2. **Memory Utilization:** The integration of both short-term and long-term memory is particularly promising for improving decision accuracy over time and could reduce error recurrence.
**Weaknesses:**
1. **Evaluation Metrics Not Defined:** There is minimal information on how the system’s success or effectiveness would be measured, making it difficult to assess its real-world impact.
**Questions:**
1. How will GoalAct handle potential conflicts or redundancies between agents, especially in complex or ambiguous cases?
2. What specific metrics or benchmarks will be used to evaluate the system’s effectiveness, particularly for different legal domains?
rating: 7
confidence: 4 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | htsOqqsFb1 | review | 1,731,139,432,460 | 0r81vMYJcz | [
"everyone"
] | [
"~Yuanda_Zhang1"
] | title: Innovative Multi-agent System
review: The proposal introduces GoalAct, a novel dynamic legal multi-agent collaboration system that integrates global and local strategies to provide efficient legal services. The system leverages the GLM4 language model and API calls to legal databases, aiming to enhance accuracy and adaptability in legal assistance.
Pros:
1)The integration of global and local planning, reflection, and memory in the multi-agent system is a sophisticated approach to addressing the complexity of legal inquiries.
2)The use of API calls to access legal databases suggests a practical application that can significantly improve the accessibility and cost-effectiveness of legal services.
3)The system's design to filter irrelevant information and avoid local search loops demonstrates an understanding of the challenges in legal AI and proposes valid solutions.
Cons:
1)The proposal could benefit from a more detailed explanation of how the system handles the diversity of legal issues and the types of errors it can correct in user queries.
2)The balance between local task accuracy and global objective alignment is mentioned as a challenge, but the proposal does not provide a clear strategy for achieving this balance.
rating: 8
confidence: 4 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | dpbT69BXX9 | review | 1,731,416,933,935 | 0r81vMYJcz | [
"everyone"
] | [
"~Zihan_Wang7"
] | title: Reflexion LLM of Law
review: **Summary**
This paper proposes GoalAct, a legal multi-agent collaboration system based on GLM-4 that combines global and local information processing to provide legal services. The system employs five specialized agents (Processor, Memorizer, Actor, Judge, and Reflector) working collaboratively to handle legal queries while maintaining a balance between local task accuracy and global objectives.
**Highlights**
- Reflector: In addition to conventional memory and execution agents, the system incorporates a reflection module to optimize and improve solutions
- Technical Foundation: Built upon recent advances such as CoT, ReAct, and Reflexion frameworks
**Advice**
The paper could benefit from more specific details:
- Lacks investigation into legal database APIs
- Missing specific mechanisms for legal-domain reflection
rating: 8
confidence: 5 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | a5pDWSd87P | review | 1,731,310,595,730 | 0r81vMYJcz | [
"everyone"
] | [
"~Chengming_Shi1"
] | title: Review
review: ### Summary
The proposal “GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System” introduces a sophisticated multi-agent system designed to provide legal services using the GLM-4 language model. The system integrates global and local information processing, planning, reflection, and memory to enhance accuracy and adaptability in legal consultations. The goal is to improve the efficiency and accessibility of legal services through advanced AI technology.
### Pros
1. **Integration of LLMs**: Leveraging Large Language Models like GLM-4 for legal services is innovative and has the potential to revolutionize the legal industry.
2. **Multi-Agent Collaboration**: The use of different types of agents (Processor, Memorizer, Actor, Judge, and Reflector) can effectively decompose complex legal tasks into manageable subtasks.
3. **Adaptability**: The system’s ability to dynamically adjust plans and learn from interactions can lead to more accurate and reliable legal.
4. **Memory Functionality**: Incorporating short-term and long-term memory can enhance the system’s problem-solving capabilities and improve over time.
5. **Global and Local Integration**: The approach of balancing local accuracy with global objectives is a novel strategy that could lead to more effective multi-agent systems.
### Cons
1. **Complexity**: The system’s complexity may make it difficult to implement and maintain, potentially leading to high costs and technical challenges.
2. **Ethical and Legal Considerations**: The use of AI in legal services raises ethical concerns about the quality of advice and the potential for malpractice.
3. **Dependence on Data Quality**: The system’s performance is highly dependent on the quality and relevance of the legal databases it accesses.
4. **User Trust**: Building trust in AI-driven legal services may be challenging, especially given the sensitive nature of legal matters.
5. **Scalability**: The system may face scalability issues, as it needs to be adaptable to a wide range of legal issues and jurisdictions.
rating: 8
confidence: 4 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | Z9N9n9P8pD | review | 1,731,137,212,922 | 0r81vMYJcz | [
"everyone"
] | [
"~Rosalie_Butte1"
] | title: Review of “GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System”
review: The paper proposes a method to leverage a multi-agent collaboration system to handle complex legal inquiries. The use of multiple type of agents, helps to break down the problem and enhance the accuracy of the solution by using memory and feedback. To further enhance the quality of the generated solution, the paper proposes to balance the local accuracy of the agents with the global alignment to the overall objective.
The paper targets an important real-world problem to improve accessibility of legal services. It shows a well-structured approach by defining the set of agents and their respective tasks, as well as how these agents work together to solve the overall objective. It also combines an innovative approach to use feedback and a mix of global and local accuracies to further enhance the quality and correctness of the generated solution, which are necessary in the field of legal services.
However, the paper is a bit vague in the description of the technical details, for example how to manage global and local accuracy and how to adjust the global goal. Additionally, the paper could benefit from formulating a plan on how to evaluate and compare the results of GoalAct.
rating: 8
confidence: 4 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | HS5nX4AakU | review | 1,731,400,741,227 | 0r81vMYJcz | [
"everyone"
] | [
"~Chaoqun_Yang2"
] | title: A practical application
review: **Summary:**
The paper introduces a novel approach to legal service provision through a multi-agent system that leverages the capabilities of Large Language Models (LLMs). The system, named GoalAct, is designed to address the complexities of legal inquiries by integrating global and local information, utilizing APIs to access legal databases. The proposed system consists of five types of agents: Processor, Memorizer, Actor, Judge, and Reflector, each with specific roles in processing user demands and providing solutions.
**Highlights:**
1. **Practical Application:** The paper emphasizes the practical implications of the system, suggesting that it could significantly impact the legal industry by making services more efficient and accessible.
2. **Comprehensive System Design:** The system's design, which includes five distinct types of agents, is comprehensive and addresses multiple facets of legal service provision, from processing and memory to action planning and reflection.
**Advice:**
1. **Related Work Depth:** The review of related work is somewhat brief. Expanding on this section to include a more detailed analysis of existing systems and their limitations would provide a stronger context for the proposed method. Additionally, discussing how GoalAct overcomes these limitations would be valuable.
4. **Innovative Methodology:** It would be better if the proposed method can demonstrate differences from existing techniques. For instance, what's the difference between the proposed method and the current mainstream multi-agent collaboration approach?
rating: 8
confidence: 4 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | EKxopcjr2t | review | 1,731,045,007,571 | 0r81vMYJcz | [
"everyone"
] | [
"~王俊逸1"
] | title: Innovative Approach to Legal Services through Multi-Agent Collaboration
review: The proposal for "GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System" presents a forward-thinking approach to leveraging Large Language Models (LLMs) for legal services. It addresses the complexity of user inquiries and the need for a robust, self-correcting system capable of accessing legal databases through API calls. The integration of planning, reflection, and memory at both global and local levels is a significant strength, enhancing the system's accuracy and adaptability.
The proposal's methodology, which includes a diverse set of agents—Processor, Memorizer, Actor, Judge, and Reflector—shows promise in streamlining legal processes and improving decision-making. The system's ability to dynamically adjust plans based on feedback and to learn from past experiences is particularly innovative, potentially leading to more efficient and effective legal services.
However, the proposal could benefit from a more detailed discussion on how the system will handle the confidentiality and sensitivity of legal data, as well as the ethical implications of automating legal advice. Additionally, while the system's adaptability is a key feature, it is crucial to ensure that the system's global objectives do not compromise the autonomy and creativity of individual agents.
Overall, the proposal for GoalAct is a commendable effort to revolutionize the legal sector through advanced AI technology. It presents a comprehensive system that could significantly improve the accessibility and quality of legal services, provided that the implementation addresses the associated ethical and data security concerns.
rating: 8
confidence: 4 |
0r81vMYJcz | 【Proposal】GoalAct: A Globally Adaptive Dynamic Legal Multi-agent Collaboration System | [
"Junjie Chen",
"Ruowen Zhao",
"ZhiYuan Feng"
] | This paper presents our proposed multi-agent collaboration system based on GLM-4, which employs a strategy that combines global and local information to provide legal services by accessing relevant legal databases through API calls. The strength of our approach lies in integrating planning, reflection and memory globally and locally, thereby enhancing both the accuracy and adaptability. | [
"Multi-agent",
"Tool Learning",
"Legal Assistant",
"Planning",
"Reflection",
"Memory"
] | https://openreview.net/pdf?id=0r81vMYJcz | 7hQsXV7H54 | review | 1,731,119,366,426 | 0r81vMYJcz | [
"everyone"
] | [
"~Yufei_Zhuang1"
] | title: Good idea but need more specific details needed
review: On one hand, the detailed description of the functions of each agent, including the Processor, Memorizer, Actor, Judge, and Reflector, and the overall collaborative process framework is well - structured. It theoretically elaborates how the system should operate to handle user requests and solve problems. For instance, it clearly states the tasks of different agents in each stage. This shows a systematic design concept.
However, on the other hand, there is a lack of more specific implementation details. When it comes to the GoalAct strategy for balancing local and global aspects, although it describes what to do in each dimension, details such as how to precisely adjust the global plan based on dynamic feedback and how to quantitatively judge whether the overall objective is met during reflection are not provided.
rating: 7
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | yIeQMQ3hvs | review | 1,731,312,284,205 | 0CfLQLw5yV | [
"everyone"
] | [
"~Jin_Zhu_Xu1"
] | title: Well explained
review: The guidelines are clear enough
rating: 10
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | vDKS1oNQTL | review | 1,731,379,710,220 | 0CfLQLw5yV | [
"everyone"
] | [
"~Zou_Dongchen1"
] | title: Got the information
review: Clear guidelines, thanks for the information.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | ujJNxeVjIl | review | 1,731,339,793,817 | 0CfLQLw5yV | [
"everyone"
] | [
"~XueZeng1"
] | title: Review
review: Clear and helpful
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | rq3dZnNWsh | review | 1,730,905,047,165 | 0CfLQLw5yV | [
"everyone"
] | [
"~Daniel_Wang4"
] | title: Clear Guidelines
review: The AML project guideline is well-structured, covering all submission stages and using the OpenReview platform. Includinga reviewer guideline would help us understand clearly what we are expected to do.
Overall, the guideline is very clear.
rating: 9
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | nEeQSNQoqA | review | 1,731,115,005,749 | 0CfLQLw5yV | [
"everyone"
] | [
"~Aleksandr_Algazinov1"
] | title: Clear and helpful
review: Thank you for well-written and well-structured guidelines!
rating: 10
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | mKeFlayhzR | review | 1,731,407,878,793 | 0CfLQLw5yV | [
"everyone"
] | [
"~Han-Xi_Zhu1"
] | title: Great Guideline for the Course
review: This work briefly introduced the guideline of the AML course. The layout is clear and easy to follow. The paper is well organized. Thank you!
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | lNk7TyAhaJ | review | 1,731,406,841,035 | 0CfLQLw5yV | [
"everyone"
] | [
"~Kairong_Luo1"
] | title: Great Guideline!
review: 1. The topic list is inspiring and insightful;
2. The requirement is very clean;
3. The instructions are easy to follow.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | hq3sXAgnUx | review | 1,731,048,336,248 | 0CfLQLw5yV | [
"everyone"
] | [
"~Peidong_Zhang1"
] | title: Extremely helpful
review: The guideline is very clear and helps a lot.
rating: 7
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | gs9LKOwY1R | review | 1,731,248,637,685 | 0CfLQLw5yV | [
"everyone"
] | [
"~Chentian_wei1"
] | title: clear
review: clear
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | gUsBfofSNK | review | 1,731,424,689,109 | 0CfLQLw5yV | [
"everyone"
] | [
"~Grace_Xin-Yue_Yi1"
] | title: Review
review: Clear and helpful guidelines
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | g0u1IesIKx | review | 1,731,254,500,368 | 0CfLQLw5yV | [
"everyone"
] | [
"~Tianxing_Yang1"
] | title: Clear Guideline
review: It's a clear guideline for students.
rating: 10
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | cAywduyf8W | decision | 1,731,915,707,044 | 0CfLQLw5yV | [
"everyone"
] | [
"tsinghua.edu.cn/THU/2024/Fall/AML/Program_Chairs"
] | decision: Best Paper
comment: **Advantages:**
1. Clear task objectives
2. Provides guidance
3. Plays a significant role in selecting research topics
**Disadvantages:**
1. Inconsistent formatting between Chinese and English
2. Needs more specific research objectives for each topic
3. Lacks relevant guidelines for reviewers
title: Paper Decision |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | bZrgC7iVJt | review | 1,731,304,756,772 | 0CfLQLw5yV | [
"everyone"
] | [
"~Gausse_Mael_DONGMO_KENFACK1"
] | title: Clear Guidelines
review: It provides clear guidelines with relevant details, explains the exercise well, and includes examples that are especially helpful for those who may not have a specific subject in mind.
rating: 9
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | ZT4XuBw4B2 | review | 1,731,343,515,408 | 0CfLQLw5yV | [
"everyone"
] | [
"~KAI_JUN_TEH1"
] | title: A perfect handbook of guidelines
review: The guidelines are very clear and have been very helpful for my research progress as well as the writing of my proposal.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | YrfZELaU1S | review | 1,731,227,658,235 | 0CfLQLw5yV | [
"everyone"
] | [
"~Xun_Wang10"
] | title: GOOD GUIDELINES
review: It is a clear guidelines.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | Wfrj7ZUbHp | review | 1,731,337,577,480 | 0CfLQLw5yV | [
"everyone"
] | [
"~Jiaxiang_Liu7"
] | title: Thanx
review: Exceptionally clear.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | V3eh7B9hzy | review | 1,731,137,564,235 | 0CfLQLw5yV | [
"everyone"
] | [
"~Anton_Johansson1"
] | title: Helpful and clear guidelines
review: Good and well-structured guidelines, thanks!
rating: 9
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | UBJ6XmZwpE | review | 1,731,209,029,829 | 0CfLQLw5yV | [
"everyone"
] | [
"~Jia-Nuo_Liew1"
] | title: Clear guidelines
review: Well structured, straightforward and clear guidelines for the proposal.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | SzNmSDWFXX | review | 1,731,257,282,090 | 0CfLQLw5yV | [
"everyone"
] | [
"~Matteo_Jiahao_Chen1"
] | title: Clear guidelines
review: Well-structured and clear guidelines
rating: 9
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | Sz5nDEN0UD | review | 1,731,418,508,175 | 0CfLQLw5yV | [
"everyone"
] | [
"~jin_wang30"
] | title: Wonderful guidance
review: Very specific, clear and helpful.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | R6aCEWusil | review | 1,731,125,344,248 | 0CfLQLw5yV | [
"everyone"
] | [
"~Yanchen_Wu1"
] | title: Helpful guidelines
review: This guideline will help us complete the final project of the course!
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | Q7djqWiyDy | review | 1,731,376,549,273 | 0CfLQLw5yV | [
"everyone"
] | [
"~Eddy_Yue1"
] | title: Good
review: Easy to understand
rating: 9
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | Q4EnzVVxGu | review | 1,731,419,830,016 | 0CfLQLw5yV | [
"everyone"
] | [
"~Fei_Long3"
] | title: Wonderful Guideline!
review: This guideline is exceptionally well-crafted, offering clear guidance and insightful explanations that make clear guidance accessible to learners!
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | OCh0Ql0188 | review | 1,731,336,796,462 | 0CfLQLw5yV | [
"everyone"
] | [
"~Michael_Hua_Wang1"
] | title: Review
review: The guidelines are very helpful with respect to identifying a project to work on and to how to write the proposal. However, they are somewhat ambiguous with respect to what the expectations are with respect to the contents of each review.
rating: 8
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | NpmgDI8D29 | review | 1,731,414,980,851 | 0CfLQLw5yV | [
"everyone"
] | [
"~Kittaphot_Saengprachathanarak1"
] | title: Well-made and detail guidelines
review: Provide detailed description of every part in the paper and good examples!
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | NUNRIBWNKr | review | 1,731,420,480,928 | 0CfLQLw5yV | [
"everyone"
] | [
"~Zhu_Zhang6"
] | title: Excellent guideline
review: This is an excellent guideline, providing a clear understanding of the tasks to be completed and highlighting the key areas of focus in the AML course. We greatly appreciate the teaching assistants' patience and dedication in designing and organizing the course assignments, which enhances our learning experience and helps us to grasp the essential aspects of the curriculum more effectively. Thank you for all the effort and support!
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | N0WN2lmcam | review | 1,731,381,864,202 | 0CfLQLw5yV | [
"everyone"
] | [
"~Mingdao_Liu1"
] | title: Review
review: Awesome guideline.
~~(But why this is assigned?)~~
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | LCr4Gr36QP | review | 1,731,413,404,505 | 0CfLQLw5yV | [
"everyone"
] | [
"~Gangxin_Xu1"
] | title: review
review: ? but !
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | KH04nH0z0v | review | 1,731,324,613,877 | 0CfLQLw5yV | [
"everyone"
] | [
"~Changsong_Lei2"
] | title: Clear Guidelines
review: The guideline is clear enough for me to understand AML, very helpful!
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | JvYBRzDnLD | review | 1,731,319,697,367 | 0CfLQLw5yV | [
"everyone"
] | [
"~Jiajun_Xu3"
] | title: Helpful Guidelines
review: It's very nice of the TA to provide such clear and well constructed guidelines, helping us understand the requirements of this project.
rating: 9
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | FZNlsCNVnm | review | 1,731,320,297,008 | 0CfLQLw5yV | [
"everyone"
] | [
"~Huajun_Bai1"
] | title: Thanks
review: The guidelines are clear enough
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | EAFXcAWPUp | review | 1,731,401,864,796 | 0CfLQLw5yV | [
"everyone"
] | [
"~Wanlan_Ren1"
] | title: Great guidelines!
review: This guideline is helpful and clear.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | E86J7xE2Vm | review | 1,730,898,537,529 | 0CfLQLw5yV | [
"everyone"
] | [
"~Zijun_Liu2"
] | title: Thanks for the Guidelines
review: The guidelines for the proposal is clear and informative. However, it will be better to include guidelines for reviewers, e.g., what should be included in a review. Thank TAs for the effort again.
rating: 9
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | DTFJbKglgy | review | 1,731,425,247,978 | 0CfLQLw5yV | [
"everyone"
] | [
"~Yifan_Luo2"
] | title: Good
review: Good
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | DPbmd6Dfld | review | 1,731,413,257,617 | 0CfLQLw5yV | [
"everyone"
] | [
"~Justinas_Jučas3"
] | title: Great work!
review: State of the art guidelines!
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | CWUiAKwhH1 | review | 1,731,208,926,157 | 0CfLQLw5yV | [
"everyone"
] | [
"~Yunghwei_Lai1"
] | title: Review
review: Well structured guidelines, thanks for the information.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | C1KIkmH6NE | review | 1,731,231,588,633 | 0CfLQLw5yV | [
"everyone"
] | [
"~Rosalie_Butte1"
] | title: clear guidelines
review: Clear and well-structured guidelines!
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | 7oNY12LPo5 | review | 1,731,300,047,533 | 0CfLQLw5yV | [
"everyone"
] | [
"~Hector_Rodriguez_Rodriguez1"
] | title: Clear Project Proposal Guidelines but Could Use More Detail
review: The project proposal guidelines are clear and provide a good foundation to write the project proposal. However, they could benefit from including a more detailed explanation of the evaluation criteria. For example, a checklist could be added to standarize the peer review process.
rating: 8
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | 67zLFyD0j4 | review | 1,731,400,915,845 | 0CfLQLw5yV | [
"everyone"
] | [
"~Zihan_Wang7"
] | title: Clear and bilingual
review: Summarize:
The enclosed document serves as a comprehensive instructional manual on the formulation of a proposal.
Summary Of Strengths:
Clear and bilingual.
Summary Of Weaknesses:
Inconsistent typography between Chinese and English, cloud have better page breaks.
rating: 9
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | 4CJpQwhtAf | review | 1,731,373,233,912 | 0CfLQLw5yV | [
"everyone"
] | [
"~Ruitao_Jing1"
] | title: A Nice Guideline
review: Very specific, clear and helpful.
rating: 10
confidence: 5 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | 3r1vj7WM0C | review | 1,731,137,665,280 | 0CfLQLw5yV | [
"everyone"
] | [
"~Tim_Bakkenes1"
] | title: Clear guidelines
review: The guidelines are very clear, and they helped us greatly in understanding how to write the proposal. The examples provided were also very helpful in finding an interesting project.
rating: 9
confidence: 4 |
0CfLQLw5yV | 【AML】Project Proposal Guidelines for Advanced Machine Learning Course | [
"Jinhua Du"
] | This project guideline explains the requirements and evaluation criteria for the major assignment in the Advanced Machine Learning course. Each team, consisting of 2-3 students, must solve a practical problem using machine learning methods. The topic can be related to large models or any area of interest within machine learning, including tasks from Kaggle. The required submissions include a Proposal, Mid-term Report, Final Report, PPT, and implementation code. All report materials must be written in English and formatted according to the NeurIPS conference paper guidelines. The grading criteria cover the Proposal, peer reviews, TA and instructor assessments, and provide detailed instructions for registering and submitting via the OpenReview platform. Additionally, the guideline suggests several potential research directions and resources, offering clear guidance for students on their research and writing process. | [
"Advanced Machine Learning",
"major assignment",
"topic selection",
"Proposal",
"NeurIPS format",
"Kaggle tasks",
"peer review",
"evaluation criteria",
"OpenReview platform",
"research directions"
] | https://openreview.net/pdf?id=0CfLQLw5yV | 0JP3GiXnFV | review | 1,731,158,396,511 | 0CfLQLw5yV | [
"everyone"
] | [
"~Lei_Wu17"
] | title: Perfect!
review: I think this explanation for proposal is complete and understandable enough.
rating: 10
confidence: 5 |
xGeMHst5Mz | Chinese vocabulary teaching in Spain: a proposal for the localisation of the International Standard for Chinese Language Levels | [] | With the publication and implementation of Guoji Zhongwen Jiaoyu Zhongwen Shuiping Dengji Biaozhun (国际中文教育中文水平等级标准) [Chinese Proficiency Grading Standards for International Chinese Language Education] (GF0025-2021) by the Center for Language Education and Cooperation of the Chinese Ministry of Education, a number of changes are taking place in Chinese language teaching and assessment globally, so existing materials for learning Chinese as a foreign language may not meet the current needs of international learners. Although the new standards have been designed in line with other international standards such as the CEFR to adapt them to local teaching, there is still a lot of work to be done. Taking vocabulary teaching in Spain as an example, in addition to the need to re-examine the difficulty of the lexis included, it is also necessary to consider the usage possibilities and learning needs of the words in teaching Chinese as a foreign language in each context. In this sense, we believe that the frequency information provided by the corpora can help to determine the vocabulary that should form the core of the teaching. Accordingly, this study aims to examine the vocabulary list proposed in these new Chinese language standards, the Common European Framework of Reference for Languages (CEFR) guidelines and a sample of widespread ELE-related teaching materials and graded readings designed following this framework, as well as using empirical data from the existing prestigious balanced corpora, namely CNC, BLCU, CCL in Chinese and CREA in Spanish, to identify the dilemmas and problems facing the localisation of Chinese lexical teaching in Spain, thus offering the possibility of adapting its teaching to the new standards. | [
"corpus linguistics; Chinese and Spanish corpora; Chinese as a foreign language; vocabulary teaching; localisation"
] | https://openreview.net/pdf?id=xGeMHst5Mz | VhCbFFbEGc | decision | 1,717,400,131,150 | xGeMHst5Mz | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Reject
comment: Dear author/s
Our reviewers have deemed the abstract submitted not sufficiently focussed for our conference and we regret to tell you that it has not been accepted this time.
Please log on to www.openreview.net to see the comments given.
We encourage you to think whether you might like to have a poster at DRDHum instead.
Thank you very much for the time and effort spent and we hope that we can welcome in you in December: Please follow the link to register : https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
https://sites.uef.fi/drd-hum-2024/
title: Paper Decision |
w43pRid3GL | Letter_2_Santa.py – Tapping Big Data from the Arctic Circle | [] | POSTER
Our poster presents the results of a pilot project which aims at building the Santa Claus Letter Corpus. These letters – sent to Finland from around the world – feature text and art, mostly handwriting enriched with drawings. The senders are primarily children. The physical collection at the National Archives contains 25 shelf meters of letters. So far they have been catalogued only in bunches, according to country and year of origin.
We have started to examining the collection in 2023, digitised parts of it, enriched the cataloguing metadata, run tests for quantitiative analyses, and carried out first qualitative analyses. Our original focus has been on letters which we expected to be written in either German, Finnish, Swedish, or Russian. But we found out immediately that the language diversity is higher than the sender’s poststamp suggests, e.g. letters sent in Finnish from Sweden or in English from Germany.
The main results of our pilot were: 1) The documentation of workflows and data standards for digitisation, 2) Preliminary (manual) indexing according to language, artwork, and texttype, 3) Experimenting with computational methods for indexing the letters (format-, language-, and text recognition), 4) Pragmatic analysis of a subset of German-language letters (name anonymised, in press).
REFERENCES
name anonymised (in press) „Briefe an den Weihnachtsmann in Finnland – eine unerforschte Textsorte. Kategorisierung und textpragmatische Auswertung“ | [
"Finland",
"Linguistic Data Science",
"Cultural Studies",
"Art Education",
"Pragmatics"
] | https://openreview.net/pdf?id=w43pRid3GL | kUN6g7jp6S | decision | 1,717,159,031,684 | w43pRid3GL | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author/s
Congratulations. This is to let you know that we are happy to accept your proposed poster.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
sqKR8HwlE2 | NLP-based Topical Analysis and Comparison of "Molokai" by Alan Brennert and "Night Calypso" by Lawrence Scott | [] | This research presents a method for literary analysis that employs automatic topical analysis. It examines the novels "Molokai" by Alan Brennert and "Night Calypso" by Lawrence Scott. By utilizing natural language processing (NLP) techniques, this study delves into and compares how each novel addresses the themes of belief systems, human relationships, health care, and the body. Our approach also allows for a more detailed analysis by subdividing the themes into sub-themes. For instance, human relationships are further divided into those dealing with relatives, friendships or authorities.
We use the outputs of the NLP system to analyze the historical, societal, and cultural context of leprosy colonies. We then utilize a postcolonial theoretical perspective to support the analysis. This approach allows us to reveal the insightful ways Brennert and Scott delve into the human condition under extraordinary circumstances. Both novels offer a remarkable account of life within a leprosy colony. Furthermore, they uncover the external factors that shape the communities' response to leprosy.
The comparative analysis underscores the relevance of the selected themes as lenses through which the authors explore the multifaceted experiences of their characters, shedding light on public attitudes towards the ill and the marginalized and colonized subjects. This study enhances our understanding of the thematic richness of "Molokai" and "Night Calypso" and highlights the potential of NLP tools to help uncover insights into literary texts. By combining NLP-based topical analysis with the postcolonial theoretical perspective, this study contributes to digital humanities and literary studies, offering a model for future research to explore the complex interplay of themes in literary works. | [
"Molokai",
"Alan Brennert",
"Night Calypso",
"Lawrence Scott",
"natural language processing",
"automatic topical analysis",
"postcolonial theory",
"leprosy"
] | https://openreview.net/pdf?id=sqKR8HwlE2 | CqQtA0F8vV | decision | 1,717,403,419,913 | sqKR8HwlE2 | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
sOXirsQpVr | Rethinking Algorithm/AI Studies | [] | If media have been studied in three aspects: production, media text, and reception, algorithms have now become the de facto media text of digital platforms.
Ontologically, three features of algorithms complicate researching them:
Hyper-modulation: Algorithms do not have a fixed textuality;
Invisibility: They are infrastructural and thus invisible to users;
Inextricability: They are interwoven with one another, with platforms’ core code, and with user data.
Given the distinct and disruptive ontology of algorithms and challenges of a positivist epistemology, this paper proposes a pragmatist epistemology and thereby a conceptual model which views platforms as two core intertwined processes: datafication and personalization.
Datafication consists of surveillance and categorization and is oriented to the present time. Surveillance links human life to digits, resulting in a modulating relation which can be called life-digits or data. Categorization is linking these life-digits (data) to each other.
Personalization is oriented to the near future and consists of two sub-processes of prediction and allocation. Prediction is a re-categorization toward the future; it is a speculative reconfiguration of the links between life-digits, or data relations, based on the existing categories. Allocation is a future-oriented reversal of surveillance, a process in which predictions (which are themselves relations between data relations) are disentangled down toward life qualities.
This cyclic model of platforms calls for different research methods. Given how platforms have become infrastructures of sociality, the paper proposes a renewal of ethnomethodological breaching experiments that disrupt the platforms’ personalized affordances to make them visible.
For instance, in my current research project on the domestication of algorithmic listening on Spotify, I have asked my participants to use Spotify accounts of other unknown people for a few weeks before they are allowed to use their own accounts again. In each phase, I’m interviewing them (coupled with the walkthrough method) about their experiences and practices, particularly those that have become visible through the experiment. | [
"Algorithms",
"platforms",
"methodology",
"epistemology",
"ethnomethodology",
"theory",
"personalization",
"datafication",
"figuration",
"pragmatism"
] | https://openreview.net/pdf?id=sOXirsQpVr | 6031w4ZIzu | decision | 1,717,401,298,612 | sOXirsQpVr | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept with Revisions
comment: Dear author/s
Congratulations. Our reviewers have rated your paper and, provided the necessary revisions are made, we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
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title: Paper Decision |
rimlrAyMPI | Comparing French and Swedish web registers using multilingual word vectors | [] | Comparing French and Swedish web registers using multilingual word vectors
Saara Hellström (University of Turku)
The web features a wide variety of registers (Biber, 1988), i.e., situationally defined language use with different purposes (e.g., blogs, news, recipes), in numerous languages. Yet online language use in other languages than English (Biber & Egbert, 2018) remains largely unexplored. Moreover, comparisons across languages are manually conducted which is time-consuming and prone to subjective interpretations. Our study expands web register research to French and Swedish and examines the register characteristics using multilingual word vectors allowing the analysis of registers in one multilingual space without manual comparison. Our research aims at answering the following questions: 1) What kind of keyword groupings does the clustering of the word embeddings reveal? and 2) What (dis)similarities does the clustering of the word embeddings reveal about the languages and registers?
Our data consists of the newly established FreCORE and SweCORE corpora including similarly register-annotated web documents. In our analysis, we first extract the keywords, i.e., the statistically overrepresented words indicating what the texts are about (Scott & Tribble, 2006, pp. 55-59), from the corpora using text dispersion keyness (Egbert & Biber, 2019) to get the language specific characteristics for the registers. Then, using the fastText tools, we transform the keywords into word vectors, i.e., linguistically motivated, numerical representations of words derived from a language model. The word vectors present words in one multilingual space where semantically similar words are represented by similar vectors even across languages. Finally, to examine the cross-lingual similarities of the keywords and what they tell about the registers, we cluster the word vectors with KMeans.
Nineteen clusters offer the best fit to the data. Our analysis shows that the clusters group keywords based on their topical or grammatical features: e.g., the cluster POLITICS/POWER (topic) includes pouvoir – makt (power; authority), people – folket (people) while the cluster STANCE (grammar) features pense – tänker (thinks), vrai – sant (true). Moreover, the keywords in each cluster tend to belong to certain dominant registers, and these prominent registers and clusters are often the same in both French and Swedish. The keywords within a register group coherently which suggests that clustering could be a viable method to group keywords computationally instead of the laborious manual grouping. These findings suggest that there are more cross-linguistic similarities than dissimilarities between the French and Swedish web registers.
References
Biber, D. (1988). Variation Across Speech and Writing. Cambridge University Press.
Biber, D. & Egbert, J. (2018). Register Variation Online. Cambridge University Press.
Egbert, J. & Biber, D. (2019). Incorporating text dispersion into keyword analyses. Corpora, 14(1), 77–104.
Scott, M. & Tribble, C. (2006). Textual Patterns: Key Words and Corpus Analysis in Language Education. John Benjamins Publishing Company. | [
"web register",
"keywords",
"multilingual word vectors",
"clustering"
] | https://openreview.net/pdf?id=rimlrAyMPI | tJtv8md0zE | decision | 1,717,403,297,195 | rimlrAyMPI | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
qbighRDDN0 | Tweets in Karelian: from data collection to the content analysis | [] | The internet in general and social media in particular offer a new domain for the use of minority languages, which is important from the perspective of language vitality and language revitalisation. In our presentation we focus on the visibility of the Karelian language on X (formerly known as Twitter). Karelian is an endangered minority language spoken in Russia and Finland. According to the latest census and research, the total number of Karelian speakers is roughly about 20,000 people (Sarhimaa 2017; Federal State Statistics Service 2021).
We present our data collection strategy based on the use of language-related keywords and hashtags. The data was scraped from X (Twitter) using the Postman API software (Postman, 2023). The multilingual dataset combines many different languages, with Finnish dominating. Our final data consists of 2625 entries written entirely or partially in Livvi, South and Viena Karelian. The language and Karelian dialects were labelled manually by the first author of the study, who is a native Livvi-Karelian speaker. The visibility of Karelian on X has increased significantly in recent years, with Livvi-Karelian being the most prominent dialect (Moshnikov and Rykova 2023). Automatic language detection (Jauhiainen et al. 2022) identified Livvi-Karelian (or a mix of dialects including Livvi-Karelian) as such with 99.7% sensitivity, and South Karelian and Viena Karelian as Livvi-Karelian with 90% and 73.8% sensitivity, respectively.
We also analysed the topics of Twitter (X) entries written in Karelian. Ten main topics were identified manually by close reading each entry. Since the data was collected using keywords and hashtags related to the Karelian language itself, most of the entries are related to the language and vocabulary in sense of translation or language learning. However, personal tweets are the most numerous among the original entries. Tweets about the status of the Karelian language and the process of language revitalisation are particularly interesting from a research perspective as well as individual use of the language. In our data it is also possible to analyse tweets about the Karelian language written in Finnish and Russian.
REFERENCES
Federal State Statistics Service. (2021). Vserossijskaja perepis’ naselenija 2020 [Russian Census 2020]. https://rosstat.gov.ru/vpn/2020.
Jauhiainen, T., Jauhiainen, H., & Lindén, K. (2022). HeLI-OTS, Off-the-shelf language identifier for text. Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 3912–3922. Marseille, France. European Language Resources Association. https://aclanthology.org/2022.lrec-1.416/.
Moshnikov, I. & Rykova, E. (2023). Little Big Data: Karelian Twitter Corpus. Proceedings of the 10th International Conference on CMC and Social Media Corpora for the Humanities (CMC-Corpora 2023), 14–15 September 2023, University of Mannheim, Germany, pp. 142–147. https://doi.org/10.14618/1z5k-pb25.
Postman. (2023). Postman API Tool. https://www.postman.com/.
Sarhimaa, A. (2017). Vaietut ja vaiennetut. Karjalankieliset karjalaiset Suomessa [Silent and being forced to be silent: Karelian-speaking Karelians in Finland]. Tietolipas 256. Helsinki: Finnish Literature Society. | [
"automatic language recognition",
"data scraping",
"Karelian",
"minority languages",
"X (Twitter)."
] | https://openreview.net/pdf?id=qbighRDDN0 | bF1QW0A1Du | decision | 1,717,402,966,393 | qbighRDDN0 | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author/s,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
oH06kDe076 | A cancer of Finnish or a great happiness to us all? A corpus-assisted discourse study on the English language in the Suomi24 discussion forum | [] | Increased use of English has brought about a massive change in the sociolinguistic landscape of the Nordic countries during the past few decades (see Peterson & Beers Fägersten 2024). This change is not welcomed by all: in Finland, the prominent role of English in the public sphere is also a cause for concern and produces heated discussion in the media as well as among the general public, whose views both reflect and contribute to the broader discourses pertaining to English (Saarinen & Ennser-Kananen 2020). In this study, we examine a popular online discussion forum to uncover layman views related to the English language in Finland and the emerging attitudes and ideologies.
The study utilizes digital methods and data in the form of corpus-assisted discourse analysis. Our focus is on the word englanti ‘English (language)’ and its common collocates in Suomi24 Corpus (Lagus et al. 2016), based on Finnish language discussions on the Suomi24 online forum in the years 2018-2020. Through the study of collocates, i.e. commonly co-occurring words (see Scott & Tribble 2006), it is possible to identify recurring themes, which are then further analyzed qualitatively through the lens of language ideologies and discourses. Our research questions are:
1) What are the most common collocates of the word englanti in the Suomi24 Corpus and, based on the collocates, what are the central topics of discussion on English in Finland?
2) What kinds of discourses can be identified around these collocates, and what do the findings reveal about linguistic ideologies and the current sociolinguistic climate in Finland?
The emerging discourses reflect language speakers’ reactions to and attitudes towards the changing linguistic sphere in Finland. While Finland as well as other Nordic countries have always been multilingual, English has fairly rapidly begun to occupy the space of national languages, which causes tensions. One of the most prevalent themes in the discussions is English as a threat vs. opportunity, which is also observed in public opinions in other Nordic countries (Mortensen 2024). By comparing the findings to studies conducted in other Nordic countries, it is possible to gain a broader perspective into the public discussion on the role of English in Finland.
References:
Lagus, K., Pantzar, M., Ruckenstein, M. & Ylisiurua, M. 2016. Suomi24. Muodonantoa aineistolle. Valtiotieteellisen tiedekunnan julkaisuja, Nro 10. Helsinki: Helsingin Yliopisto.
Mortensen, J. 2024. Beyond threat or opportunity: English and language ideological tensions in the Nordic countries. In Peterson, E. & Beers Fägersten, K. (eds.), English in the Nordic Countries. Connections, Tensions, and Everyday Realities. New York & London: Routledge, 104-124.
Peterson, E. & Beers Fägersten, K. (eds.) 2024. English in the Nordic Countries. Connections, Tensions, and Everyday Realities. New York & London: Routledge.
Saarinen, T. & Ennser-Kananen, J. 2020. Ambivalent English: what we talk about when we think we talk about language. Nordic Journal of English Studies 19(3): 115–129.
Scott, M. & Tribble, C. 2006. Textual patterns. Key words and corpus analysis in language education. Amsterdam: John Benjamins. | [
"Corpus-assisted discourse analysis",
"language ideologies",
"English language"
] | https://openreview.net/pdf?id=oH06kDe076 | ny2X3mb62k | decision | 1,717,154,654,729 | oH06kDe076 | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear Lea & Heli -
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
nPtiffcdmE | Gendered recruiting in social media: a case study in network marketing | [] | My doctoral dissertation explores the novel phenomenon of recruiting new workers on social media platforms such as Instagram. In particular, I focus on analyzing those personal narratives that recruiters share on social media in order to attract potential workers and build communities. Being interested in ’life writing’ practices, I study self-presentation and authoring of one’s own story from a new angle - as a social recruiting practice in the direct selling and network marketing industry. Social media offer recruiters the chance to tap into the passive candidates market to approach new talents and those who are not actively seeking new job opportunities. To recruit new people, direct selling professionals build long-term relationships with the audiences that function as affectively engaging communities. My theoretical starting point for approaching social media recruiting is the affect theory. The theory offers an alternative understanding of the ‘life writing’ practices that focus on a moment of recognition of the need for change in one’s work-life relationship. I explore how affects and emotions are evoked, triggered, and build up in social media practices and what insights the theory of affect brings on hiring in the digital age and recruiting passive candidates. I examine a contemporary form of the direct selling industry as a form of the gig economy and female direct selling professionals as independent contractors. The gig economy is a part of a larger transformation of the economy, where all kinds of online platforms take center stage. My research widens the understanding of the gig economy and brings a more gendered perspective to the debates by focusing on a women-centered industry and its practices. In my empirical study, I develop the concept of gendered social recruiting. The methodological approach of this research combines data from digital ethnography and qualitative semi-structured interviews with direct sellers from three EMEA region countries (Finland, the United Kingdom, and South Africa). I focus on countries that belong to the same region inside the company and according to my observations share similar social recruiting practices while also open up tensions when get into comparison. It allows studying the international societal phenomenon that is important both in a global and national context. | [
"Digital labour",
"gender",
"affect",
"social media",
"digital ethnography",
"gendered social recruiting"
] | https://openreview.net/pdf?id=nPtiffcdmE | btzc0PmNRt | decision | 1,717,403,473,481 | nPtiffcdmE | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
mrMkmSNzwb | Towards Open Source Ecosystem for European Music Data | [] | The Open Music Europe project aims to reshape the European music data landscape by identifying data sources, developing data collection methods, and crafting policy-relevant indicators to underscore significance of data. The core scientific focus of the project is on enhancing data interoperability and accessibility through the integration of best practices in data science into an open source software ecosystem. The project pioneers best practices in data science and integrates them into an accessible open source software ecosystem that enables non-specialist stakeholders to gather and utilize data from multiple sources effectively. This software ecosystem, which includes a set of open source components and interactive cloud-based applications, has been implemented and is actively maintained. We demonstrate the use of these tools, and in particular the use of the eurostat R package in data retrieval and analysis. We show how users can add metadata by utilizing special data containers where additional metadata contents can be obtained from the Eurostat SDMX API. The framework supports conversions to various linked data standards and formats, greatly facilitating interoperability between data standards and openly available methodology and advancing data provenance, data citations, and reproducible research. Analysis of the European music industry complements the ongoing research efforts focusing of other forms of cultural production in the field of computational humanities. Our work demonstrates in particular how interoperability across data standards can significantly contribute to the advancement of FAIR and open data initiatives, helping to ensure more open sharing and utilization of music data in academic research as well as more broadly in society. | [
"Computational Humanities",
"Open Music",
"Open Source Ecosystem"
] | https://openreview.net/pdf?id=mrMkmSNzwb | RcLRsDswtf | decision | 1,717,401,604,602 | mrMkmSNzwb | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept with Revisions
comment: Dear author/s,
Congratulations. Our reviewers have rated your paper and, provided the necessary (minor, we hasten to add) revisions are made, we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
mNmlXi1HFm | A Comparative Corpus-based Discursive News Values Analysis of Liz Truss’ and Rishi Sunak’s representation in the British Press | [] | Although the number of women in relevant political roles on the international stage is growing, politics still seems a public sphere dominated by men (Liu, 2019). The gender gap in politics is visible and women represent a minority in this field. The representation of female politicians in and out of the media has always been influenced by gender stereotypes. Media can play a significant role in the reiteration of these stereotypes backgrounding other important aspects such as politicians’ political agenda (Zamfirache, 2010).
This paper focuses on journalistic discourse concerning Truss’ and Sunak’s representation in the British press. The main aim of the analysis is to investigate if these politicians are represented similarly or differently through the employment of specific gender stereotypes. Moreover, the analysis aims to understand if their representation as gendered social actors is intertwined with particular news values. The data were collected on LexisNexis and include all the articles mentioning Truss and Sunak in headlines and lead paragraphs in British national newspapers during five specific days (their candidacy and election, and Truss’ resignation). We followed Bednarek’s and Caple’s (2017) Discursive News Values Analysis approach in combination with qualitative (Machin and Mayr, 2023) and quantitative (Partington, Duguid and Taylor, 2013) tools aiming to identify which news values were used and paying particular attention to the gendered representation. Specifically, the quantitative approach (Partington, Duguid and Taylor, 2013) will be carried out through the software Sketch Engine (Kilgarriff et al., 2014) that has proven to be a very useful digital resource in the field of linguistics over the years, especially in combination with qualitative approaches allowing a more complete interpretation of data (Tognini-Bonelli, 2010: 17–18; Baker and McEnery, 2015: 2).
The preliminary results of the analysis highlight that, from a general perspective, the selected timespans influence the presence of certain news values. Another significant general trend is for tabloids to convey news values especially through images. Whereas, from a more specific perspective, some news values seem to be connected to specific gender stereotypes (e.g., the news value of personalisation is connected to Truss’ role as mother and wife) by both broadsheets and tabloids.
References
Baker, P. and McEnery, T. (2015). Introduction. In P. Baker and T. McEnery (Eds.), Corpora and Discourse Studies: Integrating Discourse and Corpora (pp. 1–19). Palgrave Macmillan. https://doi.org/10.1057/9781137431738_1
Bednarek, M. and Caple, H. (2017). The Discourse of News Values: How News Organizations Create Newsworthiness. Oxford University Press.
Kilgarriff, A., Baisa, V., Bušta, J., Jakubíček, M., Kovář, V., Michelfeit, J., Rychlý, P. and Suchomel, V. (2014). The Sketch Engine: ten years on. Lexicography, 1(1), pp. 7–36. https://doi.org/10.1007/s40607-014-0009-9
Liu, S.-J. S. (2019). Cracking Gender Stereotypes? Challenges Women Political Leaders Face. Political Insight, 10(1), pp.12–15. https://doi.org/10.1177/2041905819838147
Machin, D. and Mayr, A. (2023). How to Do Critical Discourse Analysis. A Multimodal Introduction. Sage.
Partington A., Duguid A. and Taylor C. (2013). Patterns and Meanings in Discourse: Theory and practice in corpus-assisted discourse studies (CADS). John Benjamins Publishing Company. https://doi.org/10.1075/scl.55
Tognini-Bonelli, E. (2010). Theoretical overview of the evolution of corpus linguistics. In A. O’Keeffe and M. McCarthy (Eds.), The Routledge Handbook of Corpus Linguistics (pp. 14–27). Routledge. https://doi.org/10.4324/9780203856949-3
Zamfirache, I. (2010) ‘Women and politics – the glass ceiling’. Journal of Comparative Research in Anthropology and Sociology, 1(1), pp. 175–185. | [
"CADS",
"DNVA",
"gender",
"Truss",
"Sunak"
] | https://openreview.net/pdf?id=mNmlXi1HFm | JW8SluwEyS | decision | 1,717,403,919,792 | mNmlXi1HFm | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
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title: Paper Decision |
ja4wR3c33N | Enhancing TikTok Content Success Prediction through Multimodal Fusion | [] | This research introduces a Multimodal Ensemble Architecture for predicting TikTok content success by combining visual and audio features. The study utilizes a diverse dataset of TikTok videos, with detailed information about its size and characteristics. Visual embeddings are extracted through an unsupervised ConvLSTM Autoencoder, capturing spatial and temporal features. Simultaneously, audio embeddings are obtained using the Whisper ASR model, which transcribes spoken content. These embeddings are integrated into a dual Transformer-based regression model for comprehensive multimodal analysis.
The dataset, comprising a substantial number of TikTok videos, is processed and analyzed using PyTorch, Torch-vision, and Scikit-learn. Hardware resources include NVIDIA GPUs and CPUs, with considerations for VRAM limitations during the training phase. The challenges related to tensor sizes, given the variability in video lengths, are adeptly addressed through techniques such as fixed-size padding, average pooling, and sequential handling strategies.
In addition to the model architecture, this research introduces the standard normalization of target values to enhance model generalization. The evaluation metrics encompass both Mean Squared Error (MSE) and Mean Absolute Error (MAE), providing a comprehensive assessment of model performance. Furthermore, the research delves into the impact of outliers on MSE and MAE, highlighting the importance of robust loss functions in handling extreme values in the dataset.
The results showcase the effectiveness of the ConvLSTM Autoencoder in learning meaningful visual representations, with decreasing loss over epochs. The multimodal ensemble regression model outperforms baseline models, including a 3D convolution model and Swin Transformer, in terms of MSE and MAE. A classifier variation, transforming the regression problem into quartile-based classification, provides additional insights into the complexity of predicting video success.
This research not only contributes to the advancement of multimodal deep regression but also underscores the significance of handling real-world challenges posed by heterogeneous datasets. The detailed exploration of the dataset, along with model architecture and evaluation metrics, lays a robust foundation for future work in the realm of TikTok content success prediction and beyond. The implications extend to applications in audio-visual recognition and multi-source data integration. | [
"Multimodal",
"Deep Learning",
"ConvLSTM",
"Transformer",
"Audio-Visual Fusion",
"TikTok"
] | https://openreview.net/pdf?id=ja4wR3c33N | TMcwteC4yI | decision | 1,717,402,678,534 | ja4wR3c33N | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
jXeQ9HKQwn | The forgotten 33%? Finland-Swedish literature from a database perspective | [] | In the digital era, one could say, that literary history is partly written and shaped in databases and digital archives, via metadata about authors and literature. What is the impact of such resources on the visibility – or invisibility – of authors of literary work? And to what extent is the Finland-Swedish minority literature “forgotten” seen from a database perspective?
In my paper, I will approach these questions with starting point in my research on the amount of database references, which in turn indicates the amounts of secondary literature about the group of Finland-Swedish authors writing in 1830–1930 (Biström 2021). The study builds on data compiled especially from the database Finna (finna.fi), and bibliographies. I have analyzed the amount of database references about authors, with the use of Excel, in comparison with data such as the authors gender and year of birth.
The database or archive on which quantitative studies are based, is however not complete, as has been pointed out by Katherine Bode (2014: 7–25) in her critique of Franco Morettis’ claims to accuracy and objectivity. I have approached this issue with the concept of “database visibility” – which represents not only the actual amount of literature about an author, but rather the visibility and accessibility of this literature. In my paper, I will also develop this concept a bit further against the background of my data, as different databases give different perspectives on the visibility of authors.
My ongoing research focuses “invisible authors” – those with no relevant database refer-ences in Finna-searches with the authors’ name as subject, exploring the question what the Finland-Swedish literary field looks like from the point of view of invisible authors, with a theoretical starting point in the concept of cultural memory (Assmann 2010). Among other things, my data however indicates that these forgotten authors represent – not 99% (Moretti 2013)- but around 33% of all the authors, which supports a point made by Kristina Malmio (2021) about Finland-Swedish literary history and the year of modernist debutants 1916: As my results also indicate, the minority literature (in this case to some extent a privileged one) – is not always forgotten, but on the contrary made more visible due to its importance for the identity of the minority.
References:
Assmann, Aleida (2010). “Canon and Archive”. In Astrid Erll & Ansgar Nünning
(Eds.), A Companion to Cultural Memory Studies (97–107). De Gruyter. https://doi.org/10.1515/9783110207262
Biström, Anna (2021). ”Forskarnas favoriter och det stora outforskade. En grovgenom
gång av finländska skönlitterära författare på svenska 1830–1930 och deras syn-lighet i databasreferenser och sekundärlittertur”. Samlaren. Tidskrift för svenska och annan nordisk litteratur, 142, 188–239.
Bode, Katherine (2014). Reading by Numbers. Recalibrating the Literary Field, Anthem
Press.
Malmio, Kristina (2022). ”99%? En kvantitativ studie av litteratur publicerad på svenska
i Finland året 1916”. In J. Bradley (Ed), Tonavan Laakso: Eine Festschrift für Johanna Laakso, Central European Uralic Studies 2 (538–567). Praesens Verlag.
Moretti, Franco (2013 [2000]). ”The Slaughterhouse of Literature”, In Franco Moretti,
Distant Reading. Verso. | [
"Finland-Swedish literature",
"databases",
"digital archives",
"cultural memory",
"minorities"
] | https://openreview.net/pdf?id=jXeQ9HKQwn | 6nbakglFvm | decision | 1,717,401,762,960 | jXeQ9HKQwn | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author/s,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
hvweBRgzFf | IN SEARCH OF THE INVISIBLE: GPT in An Investigation of Hidden Semantic Information | [] | The surge of interest in artificial intelligence (AI) stems from the remarkable fluency demonstrated by Large Language Models (LLMs) in language comprehension. Our study focuses on uncovering hidden semantic information, particularly how OpenAI’s Generative Pre-trained Transformer (GPT) 4.0 model excels in this area. Informed by documented obstacles in neural machine translation (MT) (Koehn and Knowles, 2017; Wan et al., 2022), challenges in text-generation models (Wang et al., 2023), recent advancements in leveraging LLMs for natural language processing (NLP) tasks (Riemenschneider and Frank, 2023) and human-like translation strategies (He et al., 2024), our aim to evaluate GPT’s proficiency in discerning and articulating semantic nuances, comparing its performance with human judgement.
Employing a comparative analysis framework, our study scrutinises a selection of translated sentences from literary and audiovisual texts that have been extensively studied within the framework of Talmy’s (2000) force dynamics by Wiśniewska (2022 and 2023). This conceptual framework illuminates how language expresses causality and dynamic relationships between entities, often using the metaphor of physical forces acting on objects. For instance, in the sentence She persuaded him to go, the force exerted by she leads to the action of him going.
Methodologically, our approach is multifaceted, integrating quantitative measures with qualitative analysis to assess the fidelity of translations in capturing hidden semantic information. Custom prompts are employed to elicit translation and self-evaluation of GPT output as metadata. We explore relationships between English, Finnish, and Polish, with a focus on verb phrases and sentences embedded with force dynamics meanings. Evaluation encompasses idiomatic correctness and language conventions, providing technical details alongside a nuanced discussion of semantics in JSON format.
Anticipated findings indicate that while GPT demonstrates remarkable proficiency in rendering surface-level meanings, its ability to identify and articulate hidden semantic information varies based on linguistic context, complexity, and the specificity of prompts. Human translation evaluators display a more nuanced understanding, leveraging linguistic intuition to analyse translations rich in hidden meanings.
This study contributes to the growing body of research on AI-assisted translation by shedding light on the capabilities and limitations of LLMs in uncovering implied semantic information. By elucidating the interplay between human translators, AI, and MT systems, this research advances our understanding of translation studies in the digital age of NLP. Drawing on insights from studies such as Alzahrani et al. (2024), it emphasises the importance of rigorous methodology and careful consideration of evaluation metrics in assessing the performance of translation models. | [
"artificial intelligence",
"cognitive semantic theories",
"LLMs",
"translation",
"translation evaluation"
] | https://openreview.net/pdf?id=hvweBRgzFf | vTW29Rq2tn | decision | 1,717,404,102,026 | hvweBRgzFf | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked as one of the best paper abstracts. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
gpRXCNXOJA | AI Literacy for study and working life – University students’ experiences from the pilot course | [] | This presentation introduces a pilot course “AI Literacy for study and working life” at the University of Jyväskylä (JYU), Centre for Multilingual Academic Communication (Movi). The voluntary course was held in Spring, 2024 and it was for all students in JYU. The course covered topics such as AI literacy and ethics, AI in research and writ-ing process, and machine translation and AI in language learning process. In this study, we focus on AI ethics and the students’ perceptions of how they think AI literacy skills could benefit them in their studies and in their future working life. The topic is studied through the following research questions:
1. What are students’ opinions about AI and AI Ethics a) at the beginning of the course b) at the end of the course?
2. How do the students see AI literacy skills in their a) studies b) in their future work-ing life?
The data was collected through the course’s online platform, Howspace. There was also an ethnographical touch since the researchers have been planning and teaching the course alongside other teachers. The current data (N = 38) can be considered small, but it is the first part of a longitudinal study. The data consists of students’ online discus-sions and mind maps, reports, reflective written task, and teachers’ notes. During the content analysis, the data is first coded into smaller units, which are then combined into categories and finally, to larger thematic areas. The aim is to find not only simi-larities but also differences between students and their perceptions. The content analy-sis is carried out by two researchers in several phases using atlas.ti software.
The results are expected to give new insights on university students’ perceptions of AI ethics and AI literacy since there are not so many empirical studies in the field (Laupichler, et al., 2022). The aim is to increase general understanding of how univer-sity teaching and teacher education should be developed to promote students’ AI-related future skills for working life (e.g., Dignum, 2021). On a larger scale, the re-sults bring added value to the research of this topic and stimulate discussion about the development of higher education AI-pedagogy and how to keep up to date in a rapidly changing working life.
REFERENCES
Dignum, V. (2021). The role and challenges of education for responsible AI. London review of education. 19(1), 1-11. DOI:10.14324/LRE.19.1.01
Laupichler, M., Aster, A., Schirch, J., & Raubach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence 3(1). DOI:10.1016/j.caeai.2022.100101 | [
"AI Ethics",
"AI Literacy",
"AI Pedagogy",
"Future Skills",
"Higher Education"
] | https://openreview.net/pdf?id=gpRXCNXOJA | g6HBvuIhKs | decision | 1,717,402,446,149 | gpRXCNXOJA | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author/s,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
gaUvx3ccaT | Navigating the ethical and legal dimensions of Human-AI co-creativity in Interaction Design | [] | In our rapidly evolving contemporary landscape, characterized by the pervasive influence of technological progress, artificial intelligence (AI) emerges as a significant catalyst driving transformative shifts within society(Serbanescu, 2024; Serbanescu & Nack, 2024). However, amidst its potential lies a veil of uncertainty regarding the ethical and legal responsibilities incumbent upon its creators and users. While numerous ethical guidelines have been promulgated by diverse entities, spanning from corporate entities (IBM, 2019; FLI, 2021; Microsoft, 2022) to governmental bodies (EU, 2023; United Nations, 1948; Wiewiórowski & Wojciech, 2022), a noticeable dearth of commensurate legal frameworks governing the actions of designers within the realm of human-AI co-creativity persists. This contribution seeks to shed light on the ethical aspects surrounding human-AI co-creativity, delving into the theoretical underpinnings through a critical examination of existing literature. By analyzing two case studies—DesignPal (Rezwana & Maher, 2023) and AniThings (Marenko & Van Allen, 2016)— the aim is to elucidate the ethical considerations arising from the collaborative nexus between humans and AI within the creative process. As the dynamics of human-AI co-creativity are scrutinized, pivotal inquiries surface: What ethical implications emerge from the symbiotic relationship between humans and AI in creative endeavors? How should the mantle of responsibility be shared between human designers and AI systems throughout the creative process?
However, the delineation of a designer's accountability in the development of AI systems remains opaque, encompassing not only ethical but also legal dimensions. Thus, this study endeavors to elucidate the extent of a designer's responsibility within the existing scholarly discourse, aiming to clarify the ethical and legal obligations inherent in co-creating with AI support systems. By traversing the blurred boundary between ethical considerations and legal obligations in human-AI co-creativity, this study contributes to a more comprehensive understanding of the complex interplay between technology and ethics, paving the way for informed decision-making and responsible innovation in interaction design.
Bibliography
EU. (2023). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex%3a52021pc0206
IBM, (2019). Fundamentals. IBM Design for AI. https://www.ibm.com/design/ai/fundamentals
FLI, (2021). AI Principles. 2022(August 16,). https://futureoflife.org/2017/08/11/ai-principles/
Marenko, B., & Van Allen, P. (2016). Animistic design: How to reimagine digital interaction between the human and the nonhuman. Digital Creativity, 27(1), 52–70.
Microsoft, (2022). Microsoft responsible AI standard, V2.
Rezwana, J., & Maher, M. L. (2023). User Perspectives on Ethical Challenges in Human-AI Co-Creativity: A Design Fiction Study. 62–74.
Serbanescu, A. (2024a). Human-AI Co-Creativity. Understanding the Relationship between Designer and AI systems in the field of Interactive Digital Narrative. Politecnico di Milano.
Serbanescu, A., & Nack, F. (2024). Towards an analytical framework for AI-powered creative support systems in interactive digital narratives. Journal of Entrepreneurial Researchers.
United Nations. (1948). Universal declaration of human rights. https://www.un.org/en/about-us/universal-declaration-of-human-rights
Wiewiórowski & Wojciech. (2022). EDPS Homepage (European Data Protection Supervisor). https://edps.europa.eu/_en | [
"Human-AI co-creativity",
"ethical AI",
"creative design process",
"Interaction Design"
] | https://openreview.net/pdf?id=gaUvx3ccaT | quzPzu0hwX | decision | 1,717,403,671,310 | gaUvx3ccaT | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
fkHGnd94Vm | Corpus-assisted critical discourse analysis on LGBTQ+ segregation and internal migration in Finland | [] | Although an inclusive city is generally the normative framework for urban development, opportunities for inclusion are not the same for everyone. This presentation focuses on urban places and meaning-making of these places for Finns whose gender and sexual identity are considered non-normative. Furthermore, the discourses of inclusion and exclusion and how these have influenced people’s willingness to stay in certain places or move away are discussed (see e.g., Gerhards 2010; Poston et al. 2017). These experiences are linked to LGBTQ+ segregation, LGBTQ+ ghettos (see e.g., Aldrich 2004), and the stigmatisation of certain places; identity issues and fear of discrimination can lead to self-segregation and the choice of environments that offer social protection, which in turn can lead to the segregation of those areas (Ghaziani 2014).
The presentation takes a critical approach to the experiences and meaning-making associated with urban places, and the notions of inclusion, equality, and rights that they evoke. The research is based on two datasets; the first from a survey conducted via the Webropol application (521 responses), and the second from the Suomi24 Corpus (City Digital Group, 2021), which is a collection of posts from the ‘Finland24’ discussion forum. The Webropol survey asked participants about their experiences of the places where they have lived as a child and an adolescent, reasons for settling in their current location, possible reasons for internal migration, and experiences that have affected the quality of life. The Suomi24 Corpus was chosen to represent “general” opinion, as the forum ranks among one of the most popular for discussions among the general public in Finland.
The data was analysed using corpus-assisted discourse analysis, i.e. a combination of quantitative corpus and qualitative discourse analysis methods. The data shows there is a clear trend for internal migration towards large urban areas and growth centres; one of the motivating factors for this being the sense of inclusion in a community that supports identity. The creation of such places is also carried out by members of minorities themselves through self-segregation and strategies to avoid stigmatised places. In turn, the Suomi24 data shows that the internal migration of LGBTQ+ is seen as problematic in the “general” discussion (mostly among non-LGBTQ+ people), as certain urban areas, such as Helsinki, are seen to be adversely overcrowded with people from minority groups.
References
Aldrich 2004: Homosexuality and the City: An Historical Overview – Urban Studies 41:9.
City Digital Group 2021: The Suomi24 Sentences Corpus 2001-2020. The Language Bank of Finland.
Gerhards 2010: Non-Discrimination towards Homosexuality – International Sociology 25:1.
Ghaziani 2014: There Goes the Gayborhood? Princeton, NJ: Princeton University Press.
Poston, Compton, Xion & Knox 2017: The Residential Segregation of Same-Sex Household in Metropolitan USA – Population Review 56:2. | [
"CACDA",
"inclusion",
"internal migration",
"LGBTQ+ segregation",
"urban areas"
] | https://openreview.net/pdf?id=fkHGnd94Vm | LzWNItvT0p | decision | 1,717,158,957,353 | fkHGnd94Vm | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author/s
Congratulations. This is to let you know that we are happy to accept your proposed poster.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
ej1ZSSN2Og | Finland's navigation towards NATO: How is it portrayed in Turkish digital media? | [] | Putin’s invasion of Ukraine triggered a chain of events prompting Finland to reassess its longstanding policy of military non-alignment. Consequently, Finland made the decision to seek NATO membership. Türkiye, whose NATO membership dates to 1951, largely affected Finland’s path towards NATO integration (Kanniainen 2022; Visala & Kajander 2023). This research investigates how Finnish NATO membership is portrayed in Turkish digital media, employing a linguistic framework. The data for this study is collected from web texts of online news reports and internet forum within the Turkish media sources. These texts encompass all references to Finland with specific words such as “Finlandiya” (Finland), “Fin” and “Finli” (Finnish) published between 24 February 2022 when Ukraine was invaded, and 4 April 2023 when Finland officially joined NATO. Sourced from both pro-government and anti-government Turkish digital media, these web texts present a diverse range of perspectives that shape political and public discourse surrounding Finland's NATO membership. The analysis employs Corpus-assisted Discourse Studies (CADS) (Partington et al. 2013), combining quantitative corpus linguistics methods such as keyword analysis and qualitative methods like discourse analysis to examine language patterns, discourse structures, and lexical choices. Initial findings reveal a divergence in content focus of news reports, primarily centred on NATO, and interactive discussions, which extend to broader topics including Finnish culture and language. The divergence between the two contents is further reflected in discourse structures and lexical choices. Moreover, the study identifies potential intersections between political and public discourse, providing a comprehensive perspective on the topic. The research contributes to understanding Finland’s recent history and national identity while offering insights into media literacy.
REFERENCES
Kanniainen, V. (2022). Gallup Democracy in Exercising the NATO Membership Option: The Cases of Finland and Sweden. CESifo Economic Studies 68(3), 281-296.
Partington, A., Duguid, A. & Taylor, C. (2013). Patterns and meanings in discourse. Theory and practice in corpus-assisted discourse studies (CADS). John Benjamins Publishing.
Visala, H. & Kajander, R. (2023 January 30). Turkki on hankala mutta Natolle tärkeä, koska sen avulla voidaan estää laaja sota, sanoo asiantuntija. Yle. Retrieved from https://yle.fi/a/74-20015325 . | [
"Turkish digital media",
"Finland’s NATO membership",
"web texts",
"political and public discourse",
"Corpus-assisted Discourse Studies."
] | https://openreview.net/pdf?id=ej1ZSSN2Og | J9OR0YS190 | decision | 1,717,066,149,027 | ej1ZSSN2Og | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear Selcen,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
Kind regards,
Michael
title: Paper Decision |
d3LPd6v4Bl | DIGITAL EMBODIMENT AND SPATIALITY BY USING 3D-GAME WORLDS AS A DIGITAL LEARNING ENVIRONMENT? | [] | DIGITAL EMBODIMENT AND SPATIALITY BY USING 3D-GAME WORLDS AS A DIGITAL LEARNING ENVIRONMENT?
The presented project is an ERASMUS+ project that combines sustainability issues and interdisciplinary learning of foreign languages (English, German and French) and STEM In the project, students from different countries collaborate online in virtual 3D-sandbox
game worlds. The questions of sustainability are related to different STEM subjects, so the language learning and the communicational use of the target language are integrated
to interdisciplinary learning content following the CLIL (LOTE= Languages Other Than
English; e.g. Mehisto, P., Marsh, D., & Frigols-Martín, M. J. 2008) concept in a project-,
phenomenon- and game-based setting . The project based approach entails in this case
multilingual information retrieval, critical reading skills, reviewing and processing the
information found in different sources and languages, joint discussions of alternatives,
negotiating and adapting the outcome of the planning work to the concrete building
projects in 3D-gameworlds, which includes digital spatial excperiences. Finally, the
experiences and results are reported.
During student actions their language use is observed, recorded and analyzed, including
student feedback and interviews.The used applications Minecraft and the open source
equivalent Minetest are 3D video game platforms, which offer multimodal, even embodied, holistic and spatial experiences, because the user moves through his avatar actions within the virtual 3D world (cf. anonymized author 2014; 2015; 2020; 2022). This paper focuses on the multilingual and multimodal, embodied and spatial experiences of the project participants,
described in the student feedback and interviews and/ or observed during the project
collaboration. The data collection and qualitative content analysis presented in this paper
are running at the moment .
Bibliography
Mehisto, P., Marsh, D., & Frigols-Martín, M. J. (2008). Uncovering CLIL: Content and
language integrated learning in bilingual and multilingual education. Macmillan Education. Anonymized author 2014.
Anonymized author 2015.
Anonymized author 2020.
Anonymized author 2022. | [
"none given"
] | https://openreview.net/pdf?id=d3LPd6v4Bl | bGT89KKt5h | decision | 1,717,400,724,184 | d3LPd6v4Bl | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Reject
comment: Dear author/s
Our reviewers have deemed the abstract submitted not sufficiently focussed for our conference and we regret to tell you that it has not been accepted this time.
Please log on to www.openreview.net to see the comments given.
We encourage you to think whether you might like to have a poster at DRDHum instead.
Thank you very much for the time and effort spent and we hope that we can welcome in you in December: Please follow the link to register : https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
https://sites.uef.fi/drd-hum-2024/
title: Paper Decision |
cGasO7Cluc | Understanding Document Internal Variation in Eighteenth-Century English Texts | [] | Historical language databases are invaluable resources for linguists and historians, yet their utility is hindered by factors such as complexity, text variety, and lack of register information. Registers, i.e., situationally defined varieties with specific purposes, are important predictors of linguistic variation (Biber 2012). While prior research has predominantly examined registers at the document level, recent studies (Egbert and Gracheva 2023) have revealed that longer documents exhibit features from different registers in different sections due to shifts in e.g. audience or purpose. In this study, we investigate document internal variation in eighteenth-century English texts. Specifically, we 1) explore the impact of different text segments on the classification of registers and 2) inspect the linguistic characteristics associated with the text segments. First, we inspect how different parts of texts (e.g., beginnings vs. middle parts) affect the classification of registers using a BERT-based text classification model. BERT utilizes deep learning to comprehend language context, aiding in the analysis of text segments based on their linguistic characteristics. We fine-tune the model with the Corpus of Founding Era American English (COFEA) that comprises manually register-annotated legal and everyday texts. For testing purposes, we use another subset of COFEA and a small hand-annotated section of the Goldsmiths'-Kress Library of Economic Literature (GKL). To inspect and compare the linguistic characteristics and key features across different text parts and registers, we employ the Stable Attribution Class Explanation (SACX; Rönnqvist et al. 2022) method. SACX provides explanations of text classes in the form of keyword lists that are derived from input attribution (Integrated Gradients; Sundararajan, Taly & Yan 2017) from the BERT model. Our findings suggest varying degrees of influence among different text segments on register classification. Echoing past findings (Laippala et al. 2023), particularly text beginnings seem to produce more reliable classification results. Furthermore, the findings suggest that certain features exhibit connections with specific parts of documents, akin to genre markers (Biber and Conrad 2019), while others demonstrate a more pervasive influence across the document.
References:
Biber, D. (2012), ‘Register as a predictor of linguistic variation’, Corpus Linguistics and Linguistic Theory, 8(1), 9–37.
Biber, D. & S. Conrad (2019), Register, Genre, and Style, 2nd edn., Cambridge: Cambridge University Press.
Egbert, J. & Gracheva, M. (2023). ‘Linguistic variation within registers: granularity in textual units and situational parameters’, Corpus Linguistics and Linguistic Theory, 19(1), 115–143.
Laippala, V., S. Rönnqvist, M. Oinonen, A.-J. Kyröläinen, A. Salmela, D. Biber, J. Egbert & S. Pyysalo (2023), ‘Register Identification from the Unrestricted Open Web Using the Corpus of Online Registers of English’, Language Resources and Evaluation 57, 1045–1079.
Rönnqvist, S., A.-J. Kyröläinen, A. Myntti, F. Ginter & V. Laippala. (2022), ‘Explaining Classes through Stable Word Attributions’, Findings of the Association for Computational Linguistics: ACL 2022, 1063–1074.
Sundararajan, M., A. Taly & Q. Yan (2017), ‘Axiomatic Attribution for Deep Networks’, Proceedings of the 34th International Conference on Machine Learning, 5109–5118. | [
"register",
"text classification",
"BERT",
"keywords"
] | https://openreview.net/pdf?id=cGasO7Cluc | BvdgXcBmWw | decision | 1,717,403,851,191 | cGasO7Cluc | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
c9xliiDAYx | Handwritten Text Recognition (HTR) model for historical documents from 17th to 20th centuries – Using TrOCR | [] | Handwritten historical documents remain an important part of research materials in different fields even today. When historical documents are digitized, modern text analysis methods can be applied to make them easier to use and analyse. However, texts must first be recognized from images containing text and converted into machine-typed form. In a research project conducted at the National Archives of Finland, we utilize the pretrained Transformer-based Optical Character Recognition (TrOCR) model developed by Microsoft (Li et al. 2022). It combines an image Transformer encoder and a text Transformer decoder for optical character recognition, replacing traditional CNN- and RNN-based approaches and eliminating the need for additional language models for post-processing accuracy. TrOCR is pre-trained on synthetic data and fine-tuned on human-labeled datasets, demonstrating superior performance on both printed and handwritten text recognition tasks.
In this research, we aim to compare the performance of various HTR models developed specifically for the handwriting styles of individual centuries against a super model trained on a comprehensive dataset from the 1600s to 1900s. Another goal of the research is to train an HTR model to perform with sufficient accuracy on documents in both Finnish and Swedish languages. With the help of high-performing HTR models, The National Archives can make handwritten historical documents more accessible and easier to use as source materials in many fields, such as historical or linguistic research.
This research has access to 26800 pages of annotated data. Annotation here refers to the transcription of texts and the marking lines around text lines. On average, one page consists of 30 lines of text. The data is randomly divided into training, validation, and test datasets. The training dataset is used for training the HTR model, while the validation set is automatically used by TrOCR for model validation to identify the best model configuration. The test dataset is used to compare different models against each other. The test data has been randomly selected and weighted across different centuries and languages (Swedish and Finnish) to ensure a sufficiently representative sample from each century and both languages. Different models are compared, and model accuracy is evaluated using the Character Error Rate (CER) value. | [
"handwritten text recognition",
"machine learning",
"historical documents"
] | https://openreview.net/pdf?id=c9xliiDAYx | ERDbRTSkpn | decision | 1,717,403,351,944 | c9xliiDAYx | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
aqds7UOjpz | Hiking with Machine’s Eyes: A Computer Vision Exploration of Nature Photography in Instagram | [] | This paper explores the methodological and epistemological implications of using computer vision to analyse visual representations of Finnish recreational nature sites in social media. The study focuses on the Instagram imagery of two nature sites in Finland, while also being informed by ethnographic walking interviews that focus on how the uses of digital media transform the representations and experiences of nature and its fragility. By combining and contrasting machine learning techniques with qualitative inquiry (cf. Maltezos et al., 2024), the study aims at making sense of how the complex interplay between algorithmic visual cultures and the quotidian uses of technology shapes our environmental relations.
Through scraping the Instagram API with a hashtag-based approach, a large dataset of images was collected about the two fieldwork sites: Patvinsuo National Park in Lieksa, Finland, and Viiankiaapa Mire Reserve in Sodankylä, Finland. The images were analysed using Google's Inception v3 API for image embeddings and further through unsupervised machine learning methods (hierarchical clustering, principal component analysis) in Orange data mining platform. These methods facilitated constructing a visual taxonomy of nature representations as well as a set of dichotomous factors that supposedly describe a part of the dataset's variance as captured by the embedding algorithm. This visual taxonomy highlights AI's proficiency in object detection, while the categorisations of landscapes images were harder to interpret in a cultural context. Notably, the process of hierarchical clustering creates pairings of which some are predictable but others very unexpected ("selfies and canoes"), challenging us to consider the embedded values, assumptions, and often invisible data labour that shape AI's understanding (cf. Denton et al., 2021; Carah et al., 2022).
The study asserts that the proliferation of digital photography on social media in combination with ethnographic approaches provides a rich basis for exploring how boundaries between virtual and on-site nature are currently being blurred, and how this entanglement transforms our relations with nature. Algorithms not only categorise but co-create our visual digital cultures, and thus there is a need to critically assess their underlying tendencies and biases. The research also underscores the AI's methodological limitations in visual content analysis. While AI offers an efficient method to manage and categorise large image datasets, the interpretative nuance of human analysis remains essential, particularly for contextually rich images. A mixed-method approach can thus yield a more holistic understanding of nature's digital representations.
References:
Carah, N., Angus, D., & Burgess, J. (2022). Tuning machines: an approach to exploring how Instagram’s machine vision operates on and through digital media’s participatory visual cultures. Cultural Studies, 36(3), 456-478. https://doi.org/10.1080/09502386.2022.2042578
Denton, E., Hanna, A., Amironesei, R., Smart, A., & Nicole, H. (2021). On the genealogy of machine learning datasets: A critical history of ImageNet. Big Data & Society, 8(1), 1-15. https://doi.org/10.1177/20539517211035955
Maltezos, V., Luhtakallio, E., & Meriluoto, T. (2024). Bridging ethnography and AI: a reciprocal methodology for studying visual political action. International Journal of Social Research Methodology, 27(2), 234-249. https://doi.org/10.1080/13645579.2024.2330057 | [
"nature imagery",
"computer vision",
"machine learning",
"ethnography",
"social media studies",
"algorithmic cultures",
"cultural representation",
"visual culture"
] | https://openreview.net/pdf?id=aqds7UOjpz | dqOsgmPYfo | decision | 1,717,140,981,956 | aqds7UOjpz | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear Juhana,
Very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory. Please resubmit a pdf formatted according to the template (details for which will be send out in due course).
You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
(Please chose the bottom option, being part of the organiser committee). :-)
title: Paper Decision |
ZA33QLj0BE | An Investigation on the Impact of Digital Media on International Postgraduate Overseas Studies: A Case Study in New Zealand | [] | In an increasingly interconnected world, digital media plays a significant role in shaping various aspects of society, including education. This study explores the impact of digital media on international postgraduate studies through a comprehensive case study approach. The research investigates how digital media influences the decision-making process, experiences, and outcomes of international postgraduate students pursuing studies abroad. It examines the utilization of digital media platforms such as social media, online forums, and educational websites in facilitating information exchange, networking, and support among students during their overseas studies. Through qualitative data collection methods including interviews, surveys, and document analysis, the study aims to uncover the multifaceted dimensions of digital media's impact on international postgraduate students' experiences. It explores how digital media influences students' perceptions of academic institutions, cultural adaptation, social integration, and overall satisfaction with their overseas study experience. Furthermore, the research delves into the challenges and opportunities presented by digital media in the context of international postgraduate studies. It investigates issues such as information overload, the authenticity of online information, the digital divide, and the role of digital literacy in navigating digital media platforms effectively during the study abroad process.
The case study approach allows for an in-depth examination of specific instances and contexts, providing rich insights into the complex interplay between digital media and international postgraduate overseas studies. By analyzing the experiences of a diverse group of international postgraduate students across different academic disciplines and geographical locations, the study seeks to identify patterns, trends, and best practices in leveraging digital media to enhance the study abroad experience. Ultimately, the findings of this research contribute to a deeper understanding of the evolving role of digital media in shaping international education landscapes and provide valuable insights for academic institutions, policymakers, and students seeking to optimize the benefits of digital media in the context of overseas studies. | [
"Digital Media",
"international Postgraduate students",
"Case Study"
] | https://openreview.net/pdf?id=ZA33QLj0BE | M8V8nor8Lw | decision | 1,717,400,247,074 | ZA33QLj0BE | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Reject
comment: Dear author,
Our reviewers have deemed the abstract submitted not sufficiently focussed for our conference and we regret to tell you that it has not been accepted this time.
Please log on to www.openreview.net to see the comments given.
We encourage you to think whether you might like to have a poster at DRDHum instead.
Thank you very much for the time and effort spent and we hope that we can welcome in you in December: Please follow the link to register : https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
https://sites.uef.fi/drd-hum-2024/
title: Paper Decision |
YSPYipW5EX | The three universities' cooperated management studies in the specialist training in medicine and dentistry | [] | The three universities' cooperated management studies in the specialist training in medicine and dentistry
Mirkka Forssell (TAU), Marjo Tourula (OU), Anna Liisa Suominen (UEF), Hanna Tenhunen (UEF), Elias Vaattovaara (OU)
Specialist training in medicine and dentistry in Finland includes a compulsory ten-credit course in management. National teaching modules are human resource management, communication, structure, organization, legislation, and financing of social and health care services.
In 2024, three universities (UEF, Oulu, and Tampere) entitled to provide specialist training organized a pilot during which each university provided a one-day webinar common to all participants. Each university is responsible for hosting one of the three webinars.
The aim of the research
The research aims to get information about the experiences of doctors and dentists in specialist training from the joint webinars at three universities. The purpose is to develop management training to meet the changing needs of the healthcare service system.
Methodology in-brief
In this research, information is collected using a structured questionnaire from specializing doctors and dentists in connection with three pilot sessions, which will be carried out from February 2024 to December 2024. Participation in the study is voluntary. There were 172/296 participants aged from under 30 to 65 who answered the first questionnaire. The structured questionnaire contained multiple-choice questions and open questions. The open questions have been analyzed using the Affinity diagram method.
Preliminary findings
The preliminary research results showed issues related to accessibility occurred during the webinar day. Despite the accessibility issues, 86% of the participants considered the lecture implemented as a webinar to be the best arrangement, and only 10% on-site teaching and 3% independent study online. Of the respondents, 52% reported problems with sound quality and audibility, and 19% with network connections. The open-ended answers to the cause of why students report webinars as the best lecture option, analyzed with an affinity diagram, revealed five clusters: no need to commute, flexibility, interaction between participants, work and family life balance, and ability to concentrate. The research revealed that 77% of participants considered the engaging and most educative part of the webinar to be the discussion with other participants who were geographically located in different parts of Finland. | [
"management",
"medicine",
"dentistry",
"sustainability"
] | https://openreview.net/pdf?id=YSPYipW5EX | 3sNfb8hrky | decision | 1,717,400,294,602 | YSPYipW5EX | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author/s
Congratulations. This is to let you know that we are happy to accept your proposed poster.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
VPXSpO4BhF | (T) Word proximity and dependencies in parliamentary discourse in Finnish parliament | [] | (T) Word proximity and dependencies in parliamentary discourse in Finnish parliament
In digital humanities, so-called distant reading often relies on methods based on statistics on proximity of words used in the given textual context. Prior to the data analysis, textual data is typically lemmatized and stop words are removed, which from linguistic perspective erases a layer of textual meaning as well as the actual informational content from the sentences (see e.g. Lambrecht 1996). Even if the content words can provide an overall comprehension of discourse entities mentioned in a given text, the majority of research questions in the fields of humanities and social sciences are concerned with how the entities are spoken about as well as the explicated relations between the entities, both typically expressed grammatically: with function words, inflection and word order.
This study sets out to evaluate the extent to which a computational analysis based on grammatical relations (as in syntactic dependencies) - instead of word proximity - can capture central features of temporal relations expressed in parliamentary debate discourse and elaborate methodological approaches to parliamentary data and political temporality. Parliamentary discourse is nothing but straightforward; discourse entities and processes MPs refer to during parliamentary debates are typically abstract and expressed with complex noun phrases or infinitive constructions. Due to this, the rhetorical wordings under scrutiny can comprise the core of the expression as well as stand as a more distant frame or a modifier for what actually is stated. We report three cases where a noun referring to time is used in different syntactic positions: head of a noun phrase, modifier of a noun phrase and as one of the main arguments in the clause (subject, object). We focus on plenary sessions of Finnish parliament, a showcase for highly inflectional language with flexible word order.
The data set consists of the official records of Finnish parliamentary debates from 1980 to 2022 (see Andrushchenko et al. 2021) and is dependency-parsed with the Finnish neural parser (Turku NLP, Kanerva et al. 2018). Universal Dependencies provide a language independent framework thus it also enables systematic comparisons between languages used in different parliaments. The analyses are obtained from a machine-learned parser in a standardized syntax tree format. They are then sent to a rule-based pattern matching tool, which finds subtrees and sequences of trees satisfying relevant conditions such as "sentence where the word 'future' is used as subject or object", or "sequence of sentences in the past tense".
References
Andrushchenko, M., Sandberg, K., Turunen, R., Marjanen, J., Hatavara, M., Kurunmäki, J., Nummenmaa, T., Hyvärinen, M., Teräs, K., Peltonen, J. & Nummenmaa, J. (2021) Using parsed and annotated corpora to analyze parliamentarians' talk in Finland. Journal of the Association for Information Science and Technology (JASIST) https://doi.org/10.1002/asi.24500
Kanerva, J., Ginter, F., Miekka, N., Leino, A., Salakoski, T. (2018). Turku neural
parser pipeline: An end-to-end system for the conll 2018 shared task. Proceedings of the conll 2018 shared task: Multilingual parsing from raw text to universal dependencies.
Association for Computational Linguistics. Retrieved from http://www.aclweb.org/anthology/K18-2013
Lambrecht, K. (1996) Information Structure and Sentence Form : Topic, Focus, and the Mental Representations of Discourse Referents. Cambridge: Cambridge University Press | [
"universal dependencies",
"parliamentary records",
"temporality"
] | https://openreview.net/pdf?id=VPXSpO4BhF | xSFqRV4tG5 | decision | 1,717,401,524,139 | VPXSpO4BhF | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept with Revisions
comment: Dear authors,
Congratulations. Our reviewers have rated your paper and, provided the necessary revisions are made, we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
U3j1UfTdYl | (P) Collecting digital research data using smart devices from deaf and hard of hearing children training speechreading | [] | Speechreading, or lipreading, aids speech recognition in everyday social interaction, especially in deaf and hard of hearing (DHH) people. Speechreading training in childhood could increase the benefits for speech recognition, and the systematicity of training could be improved with digital solutions. We developed a mobile speechreading training application Optic Track (openly available after the research period) and aimed to find out how the amount and quality of using the app is related to the change of the speechreading skills of Finnish DHH children aged 8–11 years. Children participating in the study can use either Android or Apple devices (cellular phones, tablet computers) for training for eight weeks. Users of the app look at videos of people silently speaking single words, sentences and short narratives and accomplish discrimination and matching tasks and compile three-word-sentences. The app provides different categories and gamified practice modes to promote active usage. During the training period, the research version of the app collects data such as the time used for practicing, and the success in accomplishing different tasks.
After the training period, the data collected by the Optic Track are transferred to the researcher’s device without saving any sensitive data. Data transfer is done either via USB cable or wirelessly. In the wireless option, the data are transferred using QR-code created based on a FileSender link, a secure filesharing service. The binary file is then converted into a .CSV file to have the data in a more easily readable text format and to allow further processing with statistical programs.
The preliminary results show that the collected data can be used in exploring the relationships between, for example, the training time with the Optic Track app, the improvement across time in the tasks the app contains and the possible changes in speechreading skills tested with a speechreading test. | [
"deaf and hard of hearing",
"intervention study",
"mobile application",
"speechreading training"
] | https://openreview.net/pdf?id=U3j1UfTdYl | yq0I8eeFAQ | decision | 1,717,400,530,212 | U3j1UfTdYl | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept with Revisions
comment: Dear author/s,
Congratulations. Our reviewers have rated your paper and, once the necessary revisions are made, we can accept it as a paper at our conference.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
T5IvoRoYrP | Event-based Experience Sampling of Music Listening with the MuPsych app | [] | In the age of the internet and smartphones, music listening has become a more portable, accessible, and personalised experience. As this personal style has unique potential for influencing the emotions and well-being of listeners, it is important to understand the complete range of variables involved. An innovative solution to this challenge comes from the mobile app MuPsych, which utilises the experience sampling method (ESM) to capture music listening experiences as they occur in everyday life. The app presents participants with a series of questions at the moment they start listening to music on their phone, allowing for real-time and ecologically valid data measurement of listening experiences. Data from these music reports are combined with individual variables, through a battery of psychological surveys presented within the app. To complement these sources of self-report data, the app can also collect track and artist data, physiological data from wearable devices, and weather data. The main purpose of research using the MuPsych app has been to develop a comprehensive model of how music influences emotional states, through a complex interaction of music, listener, and context variables. The app is also available to all music researchers, as a tool to investigate various phenomena related to the listening experience, through custom studies. In the future, the data collected by MuPsych will be used to develop a music recommender, which will create playlists based on listener mood, activity, and reason for listening, while supporting emotional health and well-being. | [
"Experience sampling",
"Music and Emotions",
"Data collection"
] | https://openreview.net/pdf?id=T5IvoRoYrP | iI7U8OUaO9 | decision | 1,717,402,387,453 | T5IvoRoYrP | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author/s,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
RYo1fmtFcS | Digital Methodologies in Forensic Linguistic Authorship Analysis: Social Media Data and Computational Approaches in Geolinguistic Profiling | [] | (T/presentation)
Research and case work in forensic authorship profiling focuses on inferring social characteristics of unknown authors from their texts, such as age, gender or first language influence, while drawing on foundational work laid in sociolinguistics (Nini, 2018). However, inferring the regional background of an author has received limited attention, although one of the most prominent cases in forensic authorship profiling was resolved recognising the regionalism “devil strip” in a ransom note (see Shuy, 2001).
With computational methods and large corpora of natural language data being available, this study moves away from the traditional approach to geolinguistic profiling by spotting regionalisms and using dictionaries or dialect atlases in the hopes of placing the word in question. For this the study employs a corpus of 21 million German social media posts from the platform Jodel (Hovy & Purschke, 2018) and provides an evaluation of the regionally distributed data in the corpus. Given that geolocated social media data is often sparse and centred on cities, the study uses ordinary kriging (see Wackernagel, 2003), i.e. geospatial statistics, to interpolate the data for unobserved locations, thus enhancing the resolution for location prediction while visualising the results to make them more accessible. Further, the study presents an algorithm to predict the dialect region of an author in question and discusses both the explainability of the prediction in the forensic context and the accuracies reached. The study finds that apart from being a reference tool for qualitative analyses in forensic investigations, this corpus also allows us to extract linguistic features relevant for forensic analyses that are not based on previous knowledge.
Not only does this research advance the field of forensic authorship profiling by reducing the reliance on an analyst’s expertise to spot regionalisms, it also illustrates how interdisciplinary research in linguistics, NLP, digital technologies and forensic science can improve the delivery of justice.
References:
Hovy, D., & Purschke, C. (2018). Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting. Proceedings of the 2018 Conference on EMNLP, 4383–4394.
Nini, A. (2018). Developing forensic authorship profiling. Language and Law / Linguagem e Direito, 5(2), 38–58.
Shuy, R. W. (2001). DARE’s role in linguistic profiling. DARE Newsletter, 4(3), 1–5.
Wackernagel, H. (2003). Multivariate Geostatistics: An Introduction with Applications. Springer. | [
"corpus linguistics",
"spatial statistics",
"authorship profiling",
"forensic linguistics",
"dialect classification",
"nlp",
"geolinguistic profiling",
"authorship analysis"
] | https://openreview.net/pdf?id=RYo1fmtFcS | 2poiEknqgn | decision | 1,717,403,738,672 | RYo1fmtFcS | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
QweJnjf8ql | Exploring the Potential of AI-Generated Texts to Replace Human-Written Content in Language Education | [] | The incorporation of AI-generated texts into educational materials is an emerging topic of interest, particularly concerning the potential application of AI in crafting tasks for language teaching. The goal of our research is to examine the capability of AI-generated texts to replicate the characteristics of English coursebook text samples used in language instruction. This analysis enables assessing the viability of replacing traditional human-written instructional content with AI-generated texts in educational settings. Our investigation is set against the background that textbook texts, conventionally employed as exemplars in language education, are specifically tailored and revised to match a certain level of difficulty, and thus do not fully represent authentic language usage in everyday scenarios. Nevertheless, these coursebook texts exhibit a distinct form of human authorship, shaped by the instructional requirements of students learning a second language. The ability of AI to produce simplified texts that are on par with those created by humans remains an open question. To fill this gap, we conducted a Multi-Dimensional Analysis (Biber, 1988, 1995; Berber Sardinha & Veirano Pinto, 2014, 2019) of our English Language Teaching textbook corpus (ELTT corpus), encompassing 106,840 words from 500 texts across 19 different registers. These texts, sourced from 43 books by major publishers over 25 years (1996 to 2021), spans B2 and C1 levels, with an equal number of texts from each level. Five dimensions were identified, namely (1) Persuasion, speaker engagement, and personal opinion vs Expression of analysis and technical information; (2) Expressive, interactive, speculative discourse with stance marking; (3) Formal, informative, detailed composition; (4) Narrative and descriptive accounts; (5) Summarized abstracted overviews. Each dimension comprises a set of correlated grammatical features performing the major functions corresponding to the dimensions. As a comparison sample, we created an AI-generated corpus (AI-ELTT corpus) using ChatGPT to simulate textbook texts, resulting in 500 comparable texts. In general, the results showed that AI EFL coursebook text models are different from human counterparts. First, AI struggles with producing texts that emphasize persuasion, speaker engagement, and personal opinion. Instead, AI-generated texts are characterized by the expression of analysis and technical information. Secondly, AI faces difficulties in producing language that is expressive, interactive, and speculative with stance marking, reducing the incidence of these features. Given these differences, it was possible to successfully differentiate AI from human texts in more than 80% of cases. | [
"Multi-Dimensional Analysis",
"Language Teaching",
"Artificial Intelligence"
] | https://openreview.net/pdf?id=QweJnjf8ql | ifYnZCxB21 | decision | 1,717,403,642,429 | QweJnjf8ql | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked as the top paper. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
QEs1MFNJjT | The Wanders of the Invisible World. Astrology and magic-superstitious beliefs on social networks | [] | In the complex web of contemporary society, an increasingly widespread phenomenon is beginning to be rather conspicuous: a sharp contrast can be observed between the decline of traditional religious practices and the increasingly evident rise of beliefs, from the most classical to the most exotic, of a magical or superstitious nature. Such a dichotomy is jarring, and stimulates reflections of various kinds: not only can it serve as a witness to an ongoing cultural change, but it could also be a way of highlighting the dynamism and variety of spiritual and philosophical perspectives that are developing, in a subterranean and non-institutionalised manner. Energy crystals, tarot cards, wicca, just to mention a few of the most widespread and growing phenomena to date, are now a leitmotif for many social networking users, especially Instagram and Tik Tok, in inverse proportion to the number of traditional believers. In this context, Adorno's foundational work comes to mind, in which he analysed the irrationality of the horoscope, using it as a litmus test to describe contemporary society. The textual analysis developed refers to 1950s America, a world that was totally different in many respects, certainly including technological and communicational ones, which is why the aim of this work is to verify whether the conclusions reached by the Frankfurt author remain valid today. The aim of this paper is to verify whether Adorno's approach remains valid in the contemporary context. Analysing the Italian context, we intend to replicate the methodology employed in "The Stars Down To Earth" taking as reference four Italian-language Instagram profiles dedicated to horoscopes, selected on the basis of their popularity. These profiles will be subjected to a netnography with the aim of building a dataset of texts, to cover the duration of an entire year (2023). These texts will then be compared and cross-analysed with the intention of trying to find recurrences or dissonances, in order to deepen our knowledge of the beliefs behind this practice in today's context. | [
"Astrology",
"Digital Religion",
"Digital humanities",
"Supersition",
"Digital Methods"
] | https://openreview.net/pdf?id=QEs1MFNJjT | 83o4L6tuh0 | decision | 1,717,402,766,378 | QEs1MFNJjT | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author/s,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
Q7ppKe8WHQ | Imaginaries of ownership and sustainability: A corpus-assisted study | [] | In recent years, public discussion on economic change or, rather, a deliberate transformation of the economy, has become increasingly common. This study examines the idea on ownership in the context of these discourses of economic change. In particular, the study focuses on how the role of ownership of material property is shaped in relation to “futures of sustainability” (Adloff & Neckel, 2019; Degens, 2021) and other imaginaries of future good life.
The corpus of this study was formed by retrieving both news texts and social media texts from a large collection of Finnish online textual materials aggregated by the company Legentic. The search term used was omistaminen (‘ownership’) and it was also required that the text mentioned a word referring to economic change, namely kiertotalous (‘circular economy’), jakamistalous (‘sharing economy’), or alustatalous (‘platform economy’). The search terms were treated as lemmas. The final corpus totals approximately 426,000 running words and spans the years 2015-2023.
This study takes a corpus-assisted discourse studies approach (e.g. Ancarno, 2020; Mautner, 2016), analysing keywords, as well as n-grams and collocates related to ownership and the new economy concepts.
In this way, this chapter sheds light on, among other things, who we talk about as owners in Finland, what are essential things to own, and how the social meaning of ownership is described. The focus is on what kind of change is being talked about in the context of these dimensions of meaning and how change is linked to sustainability. There is a broad consensus in the data that ownership is and will become less desirable and will be replaced by services, sharing, and other forms of co-use. In the corpus, the importance of ownership for sustainability is seen narrowly, as mainly related to the ownership of goods, whereby real estate, land and financial property, as well as new forms of property such as carbon quota are excluded.
References:
Adloff, F., & Neckel, S. (2019). Futures of sustainability as modernization, transformation, and control: a conceptual framework. Sustainability Science, 14(4), 1015–1025. https://doi.org/10.1007/s11625-019-00671-2
Ancarno, C. (2020). Corpus-Assisted Discourse Studies. In A. Georgakopoulou & A. De Fina (Eds.), The Cambridge Handbook of Discourse Studies (pp. 165–185). Cambridge University Press. https://doi.org/DOI: 10.1017/9781108348195.009
Degens, P. (2021). Towards sustainable property? Exploring the entanglement of ownership and sustainability. Social Science Information, 60(2), 209–229. https://doi.org/10.1177/05390184211011437
Mautner, G. (2016). Checks and balances: How corpus linguistics can contribute to CDA. In R. Wodak & M. Meyer (Eds.), Methods of critical discourse studies (3rd ed., pp. 122–143). SAGE Publications. | [
"corpus-assisted discourse studies; discourse; future; imaginary; ownership; property"
] | https://openreview.net/pdf?id=Q7ppKe8WHQ | LXGtW828Dt | decision | 1,717,403,868,508 | Q7ppKe8WHQ | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
PlV2XbR4TO | Unwanted in the homeland? The image of Chinese international students on Chinese social media Zhihu | [] | The outbreak of COVID-19 and the surging nationalism and populism sentiments in China made Chinese international students (CIS) targets of online vigilantism on Chinese social media and they face alienation in the homeland apart from discrimination overseas. The first step to address a problem is to understand it. Thus, this research investigates how CIS are presented and discursively alienated on Chinese social media. Concepts and frameworks in Critical Discourse Analysis (CDA) can yield valuable insights into this topic. I adopt the corpus-assisted CDA approach, and use the Discourse-Historical Approach (DHA), a major approach in CDA that often serves as an analytical framework for problem-oriented social research (Wodak, 2015), as the analytical framework.
328 posts consisting of 280995 Chinese characters published on a major Chinese social media Zhihu were collected. Major referential expressions of CIS in the corpus were identified and classified by browsing the general word and keyword lists and examining their concordances. Predication analysis was conducted by examining and classifying concordances of 留学生（们）international student(s), the most frequent referential expressions of CIS in the corpus. To check whether there are any differences between in-group and out-group presentations of CIS, I also distinguished between comments from CIS themselves and those from other Zhihu users establishing two sub-corpora, and compared the results of referential and predication analysis of the two corpora through chi-square tests.
It is found CIS were alienated and stigmatised as the problematic “other” through frames of trouble or degenerate, meritocracy, nationalism, populism, collectivism, and misogyny in the corpus though some comments try to challenge those frames and depict CIS as well-behaved people, victims, the socioculturally marginalized, patriots, ordinary people without privileges or high socioeconomic status, talents, individuals with rights, and cosmopolitans. Comparative analysis of comments from Chinese international students and other Zhihu users reveals both groups produce stigmatising discourses in their presentation of CIS, indicating tensions not only exist between CIS and non-CIS but also within the group of CIS. The major difference is that CIS group are more likely to object to the “trouble or degenerate” and “meritocracy” frames, present CIS as “socio-culturally marginalized or isolated”, recount reverse culture shocks CIS experienced, and depict CIS as cosmopolitans while non-CIS group is more likely to oppose the “victim” frame, stigmatize CIS as trouble or degenerates, position them in a meritocratic hierarchy, and perceive them from a collectivism (pro-collectivism in particular) stance. | [
"Corpus linguistics; DHA; media image; Chinese international students; Covid-19"
] | https://openreview.net/pdf?id=PlV2XbR4TO | f1WqoM0VEq | decision | 1,717,403,124,430 | PlV2XbR4TO | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
Please understand that we cannot provide further assistance for travel arrangements beyond what can be found on the website: https://sites.uef.fi/drd-hum-2024/practical-information/
title: Paper Decision |
OrJDtlFzvG | Bodies of Media Education – Towards Digital Pedagogies of Feeling | [] | This paper discusses the starting points of the forthcoming postdoc project Bodies of Media Education – Towards Digital Pedagogies of Feeling (BoME). The paper introduces the project’s objectives, methodological approaches, and theoretical baselines. BoME’s aim is to develop new approaches to studying the relationship between visual culture and bodies through analyzing self-shooting, the digital practice of creating selfies and other still and moving images of oneself, as a digital pedagogy of feeling. Drawing on four bodies of work – social media studies; scholarship on media education; feminist, queer, fat studies and disability studies theories of corporeality; and theorizations of affect – BoME analyses the dynamics, values, limits, and rules of self-shooting as a vehicle of exploration of one’s body and its feelings. The project hypothesizes that digital pedagogies of feeling afford breakthroughs in theorizations of the body’s relation to the media and visual culture.
BoME asks what kinds of information the corporeal practices of self-shooting produce and how this information helps media users to navigate the digital landscapes of body ideals. To answer this question, it explores media education and self-shooting practices from the viewpoint of subjects often deemed “the most vulnerable” to body ideals – teenagers and young people, but also adults taking part in body positive activities and struggling with their body image. BoME examines media education resources offered by Finnish and European organizations, interview materials with teenagers and educators on the experiences of media education on body ideals, and ethnographic materials gathered at body positive photography meetups. Through combining textual analysis and close reading practices with conceptual analysis, interviews, and ethnographic observation, BoME pushes for creative methodologies for unravelling the co-constructive nature of bodies and images. The project coins the term digital pedagogies of feeling to map the role of digital devices as instruments for “feeling one’s body” in media education. In this way, BoME builds ground to a novel approach for studying visual digital cultures as corporeal practices. | [
"digital pedagogy",
"self-shooting",
"feeling",
"corporeality",
"body image"
] | https://openreview.net/pdf?id=OrJDtlFzvG | 36nG20ZzT2 | decision | 1,717,401,736,609 | OrJDtlFzvG | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept
comment: Dear author,
Congratulations. Our reviewers have rated your paper well, and we are happy to accept your talk.
Please log on to www.openreview.net to see the comments given.
Please take the reviewers’ comments into account and revise the paper accordingly before resubmission by 16/08/2024. Please resubmit a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
Op5Ss5M3sM | #WOMENINSTEM: A Corpus-Based Multimodal Critical Discourse Analysis of STEM Identity Construction and Advocacy Performance on Instagram | [] | Digital spaces have paradigmatically altered the way we communicate, giving people the opportunity to speak freely and reach potentially infinite audiences (Sergeant & Tagg, 2014).
Such affordances, typical of social media, have been progressively and intrinsically ex-ploited by minorities, thus allowing them to resist social, cultural and institutional power (Buktus, 2023) and renegotiate identities.
Historically, women have been quite marginalised in STEM (Science, Technology, Engi-neering, Mathematics) fields: still in 2023, they made up on average only 28% of the STEM workforce globally (Piloto, 2023); prejudices and biases are actually at the core of the persistence of such status quo, resulting in the perception of STEM fields as male-dominated (Lee, 2008).
Nevertheless, in recent years an increasing number of STEM women has undoubtedly started to take advantage of social networks affordances (Montgomery, 2018) to con-struct public counter-discourses against patriarchal institutions and culture, as it is the case with hashtag feminism (Linabary et al., 2020; Semenzin 2022).
Indeed, by recounting their day-to-day experiences, women aim to ‘own’ the narrative of what being a woman in STEM actually is and implies, redefining therefore their STEM identity (Kim et al., 2018), providing at the same time genuine representation and inspiration for the “next STEMM gen”.
This works aims at analysing how STEM identity and advocacy have been both discur-sively and visually constructed and performed during the two weeks surrounding both the International Day of Women and Girls in Science (11th February) and the Interna-tional Women’s Day (8th March).
Our dataset stems from a search of the #womeninstem: hanging out on Instagram, we identified several accounts of women working both in Academia and Industry in differ-ent STEM fields; we therefore selected 15 accounts belonging to the former category. Firstly, the linguistic and visual content of the posts selected for that day were analysed employing Multimodal Critical Discourse Analysis tools (Machin & Mayr, 2012); after-wards, an ad hoc corpus of comments of the posts was created to observe wordlists, collocates, and frequencies (Hunston, 2022): both methods combined helped us investi-gate how both textual and visual content were used to perform STEM identity and ad-vocacy and helped us reach the preliminary conclusion that such identity is performed discursively (with women constantly appealing to the women in the STEM community), multimodally (with visual elements fostering diverse representation) and lastly by means of co-construction through the comment section.
Keywords: Corpus Linguistics, Identity, Instagram, Multimodal Critical Discourse Analysis, STEMinism
REFERENCES
Buktus, C. M. (2023). Social Media, Marginalised Identity and Liminal Publics [Doctoral Thesis, University of Technology Sydney]. Opus Lib Uts Edu Repository. https://opus.lib.uts.edu.au/handle/10453/173479
Hunston, S. (2022). Corpora in Applied Linguistics (2nd ed.). Cambridge University Press.
Kim, A., Sinatra, G. M. & Seyranian, V. (2018). Developing a STEM Identity Among Young Women: A Social Identity Perspective. Review of Educational Research, 20(10), 1-37. https://doi.org/10.3102/0034654318779957
Lee, J. A. (2008). Gender equity issues in technology education: A qualitative approach to uncovering the barriers. [Doctoral Thesis, Carolina State University]. NC Repository. https://repository.lib.ncsu.edu/items/0c73fd52-3730-49a3-acde-9269c97dd8de
Linabary, J.R., Corple, D.J. & Cooky, C. (2020). Feminist activism in digital space: Postfeminist contradictions in# WhyIStayed. New Media & Society, 22(10), 1827–1848. https://doi.org/10.1177/1461444819884635
Machin, D. & Mayr, A. (2012). How to do Critical Discourse Analysis. A Multimodal Introduction. Sage Publications Ltd.
Montgomery, B. L. (2018). Building and Sustaining Diverse Functioning Networks Using Social Media and Digital Platforms to Improve Diversity and Inclusivity. Frontiers in Digital Humanities, 5(22), 1-11. https://doi.org/10.3389/fdigh.2018.00022
Piloto, C. (2023, March 13). The Gender Gap in STEM: Still Gaping in 2023. MIT Professional Education. https://professionalprograms.mit.edu/blog/leadership/the-gender-gap-in-stem
Seargeant P. & Tagg C. (Eds). (2014). The Language of Social Media Identity and Community on the Internet. Palgrave.
Semenzin, S. (2022). “Swipe up to smash the patriarchy”: Instagram feminist activism and the necessity of branding the self. AG AboutGender, 11(21), 113-141. https://doi.org/10.15167/2279-5057/AG2022.11.21.1990 | [
"Corpus Linguistics",
"Multimodal Critical Discourse Analysis",
"STEM Identity",
"Instagram"
] | https://openreview.net/pdf?id=Op5Ss5M3sM | BCppubIIRb | decision | 1,717,403,546,279 | Op5Ss5M3sM | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
NCYLNBKjSp | Automating data curation for the Finnish national bibliography Fennica | [] | The consortium Digital History for Literature in Finland (Research Council of Finland, 2022–26) differs from earlier research on Finnish literary history by making use of digital collections and new methods in data science, which enable the use of the collection as a whole.1 Here we present a dedicated bibliographic data science framework tailored for the specific context of the consortium research purposes. We will examine how data science methods can improve our understanding of literary history and how it’s told, and how reliable the information can be. [1,2,3] The Finnish National Bibliography, Fennica, consists of over one million records from 1488 to the present and includes diverse data types such as books, newspapers, maps, and other documents from 1488 to the present day. The source data contains ambiguous information, missing or erroneous entries, however. Any refinement efforts will include context-specific choices that depend on the research use case. We have previously shown how selected subsets of the collection can be refined automatically to support large-scale statistical analyses of book printing during the years 1500-1800[1, 3]. In our present version of the workflow2, we’ve scaled up the previous analyses of 70 thousand records to cover all Fennica records and improved the data curation workflows. To cater to the research objectives of the project, we've integrated signum data and created a focused subset covering the years 1809-1917 for each metadata category. Furthermore, we've added a genre subfield derived from a broader leader field to enable genre identification at a bibliographic level, specifically focusing on books. Our aim has been to replicate a curated list, adhering to predefined criteria such as UDC classification, language, genre, and signum data. This automated process mirrors and supports the manual list creation method utilized by the Literary History subproject within the consortium. Future efforts could encompass the integration of further complementary sources, such as the rich information on authors, publishers, and geographic places available in the public domain. These efforts contribute to achieving the consortium's goals, which involve leveraging digital collections and methodologies to broaden the conventional understanding of Finnish literary history. Specifically, we aim to map Finnish and Swedish language fiction from the 19th century into an enriched format conducive to large-scale statistical analyses and the development of reproducible data science workflows. | [
"digital humanities",
"workflow",
"bibliography"
] | https://openreview.net/pdf?id=NCYLNBKjSp | eqyuwptLTo | decision | 1,717,403,973,600 | NCYLNBKjSp | [
"everyone"
] | [
"uef.fi/University_of_Eastern_Finland/DRDHum/2024/Conference/Program_Chairs"
] | decision: Accept (Best Paper)
comment: Dear author/s
We are very happy to accept your paper. As you can see from the reviews, this is ranked amongst the top papers. Please log on to www.openreview.net to see the comments given.
You may want to take the reviewers’ comments into account and revise the paper but it is not obligatory.
Please resubmit, by 16/08/2024, a photo-ready document using the template (details for which will appearing on the https://sites.uef.fi/drd-hum-2024/call-for-papers/ page). You can already register for the conference here: Please follow the link to register: https://registration.contio.fi/uef/Registration/Login?id=7500-T_7500-8717
Please note that there will be a Pre-Conference Workshop FIN-CLARIAH tools to make sense of web data, open to all attendees, on Tuesday morning, 10:15 – 12:30. If you like to attend, please tick the relevant box on the registration form.
title: Paper Decision |
Subsets and Splits