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LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education arxiv.org/abs/2411.10455 .CY .SE

LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education

There is a great need for data in computing education research. Data is needed to understand how students behave, to train models of student behavior to optimally support students, and to develop and validate new assessment tools and learning analytics techniques. However, relatively few computing education datasets are shared openly, often due to privacy regulations and issues in making sure the data is anonymous. Large language models (LLMs) offer a promising approach to create large-scale, privacy-preserving synthetic data, which can be used to explore various aspects of student learning, develop and test educational technologies, and support research in areas where collecting real student data may be challenging or impractical. This work explores generating synthetic buggy code submissions for introductory programming exercises using GPT-4o. We compare the distribution of test case failures between synthetic and real student data from two courses to analyze the accuracy of the synthetic data in mimicking real student data. Our findings suggest that LLMs can be used to generate synthetic incorrect submissions that are not significantly different from real student data with regard to test case failure distributions. Our research contributes to the development of reliable synthetic datasets for computing education research and teaching, potentially accelerating progress in the field while preserving student privacy.

arXiv.org

Evolutionary Multi-agent Reinforcement Learning in Group Social Dilemmas arxiv.org/abs/2411.10459 -bio.PE .MA

Evolutionary Multi-agent Reinforcement Learning in Group Social Dilemmas

Reinforcement learning (RL) is a powerful machine learning technique that has been successfully applied to a wide variety of problems. However, it can be unpredictable and produce suboptimal results in complicated learning environments. This is especially true when multiple agents learn simultaneously, which creates a complex system that is often analytically intractable. Our work considers the fundamental framework of Q-learning in Public Goods Games, where RL individuals must work together to achieve a common goal. This setting allows us to study the tragedy of the commons and free rider effects in AI cooperation, an emerging field with potential to resolve challenging obstacles to the wider application of artificial intelligence. While this social dilemma has been mainly investigated through traditional and evolutionary game theory, our approach bridges the gap between these two by studying agents with an intermediate level of intelligence. Specifically, we consider the influence of learning parameters on cooperation levels in simulations and a limiting system of differential equations, as well as the effect of evolutionary pressures on exploration rate in both of these models. We find selection for higher and lower levels of exploration, as well as attracting values, and a condition that separates these in a restricted class of games. Our work enhances the theoretical understanding of evolutionary Q-learning, and extends our knowledge of the evolution of machine behavior in social dilemmas.

arXiv.org

Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary arxiv.org/abs/2411.10461 .HC .AI

Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary

Recent advances in AI models have increased the integration of AI-based decision aids into the human decision making process. To fully unlock the potential of AI-assisted decision making, researchers have computationally modeled how humans incorporate AI recommendations into their final decisions, and utilized these models to improve human-AI team performance. Meanwhile, due to the ``black-box'' nature of AI models, providing AI explanations to human decision makers to help them rely on AI recommendations more appropriately has become a common practice. In this paper, we explore whether we can quantitatively model how humans integrate both AI recommendations and explanations into their decision process, and whether this quantitative understanding of human behavior from the learned model can be utilized to manipulate AI explanations, thereby nudging individuals towards making targeted decisions. Our extensive human experiments across various tasks demonstrate that human behavior can be easily influenced by these manipulated explanations towards targeted outcomes, regardless of the intent being adversarial or benign. Furthermore, individuals often fail to detect any anomalies in these explanations, despite their decisions being affected by them.

arXiv.org

Residual Multi-Task Learner for Applied Ranking arxiv.org/abs/2411.09705 .IR .LG

Residual Multi-Task Learner for Applied Ranking

Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their ranking systems. However, existing multi-task learning methods encounter two main challenges: some lack explicit modeling of task relationships, resulting in inferior performance, while others have limited applicability due to being computationally intensive, having scalability issues, or relying on strong assumptions. To address these limitations and better fit our real-world scenario, pre-rank in Shopee Search, we introduce in this paper ResFlow, a lightweight multi-task learning framework that enables efficient cross-task information sharing via residual connections between corresponding layers of task networks. Extensive experiments on datasets from various scenarios and modalities demonstrate its superior performance and adaptability over state-of-the-art methods. The online A/B tests in Shopee Search showcase its practical value in large-scale industrial applications, evidenced by a 1.29% increase in OPU (order-per-user) without additional system latency. ResFlow is now fully deployed in the pre-rank module of Shopee Search. To facilitate efficient online deployment, we propose a novel offline metric Weighted Recall@K, which aligns well with our online metric OPU, addressing the longstanding online-offline metric misalignment issue. Besides, we propose to fuse scores from the multiple tasks additively when ranking items, which outperforms traditional multiplicative fusion. The code is released at https://github.com/BrunoTruthAlliance/ResFlow

arXiv.org

AI-Driven Feedback Loops in Digital Technologies: Psychological Impacts on User Behaviour and Well-Being arxiv.org/abs/2411.09706 .CY .AI .HC

AI-Driven Feedback Loops in Digital Technologies: Psychological Impacts on User Behaviour and Well-Being

The rapid spread of digital technologies has produced data-driven feedback loops, wearable devices, social media networks, and mobile applications that shape user behavior, motivation, and mental well-being. While these systems encourage self-improvement and the development of healthier habits through real-time feedback, they also create psychological risks such as technostress, addiction, and loss of autonomy. The present study also aims to investigate the positive and negative psychological consequences of feedback mechanisms on users' behaviour and well-being. Employing a descriptive survey method, the study collected data from 200 purposely selected users to assess changes in behaviour, motivation, and mental well-being related to health, social, and lifestyle applications. Results indicate that while feedback mechanisms facilitate goal attainment and social interconnection through streaks and badges, among other components, they also enhance anxiety, mental weariness, and loss of productivity due to actions that are considered feedback-seeking. Furthermore, test subjects reported that their actions are unconsciously shaped by app feedback, often at the expense of personal autonomy, while real-time feedback minimally influences professional or social interactions. The study shows that data-driven feedback loops deliver not only motivational benefits but also psychological challenges. To mitigate these risks, users should establish boundaries regarding their use of technology to prevent burnout and addiction, while developers need to refine feedback mechanisms to reduce cognitive load and foster more inclusive participation. Future research should focus on designing feedback mechanisms that promote well-being without compromising individual freedom or increasing social comparison.

arXiv.org

Space-Air-Ground Integrated MEC-Assisted Industrial Cyber-Physical Systems: An Online Decentralized Optimization Approach arxiv.org/abs/2411.09712 .SY .GT .SY

Space-Air-Ground Integrated MEC-Assisted Industrial Cyber-Physical Systems: An Online Decentralized Optimization Approach

Cloud computing and edge/fog computing are playing a pivotal role in driving the transformation of industrial cyber-physical systems (ICPS) towards greater intelligence and automation by providing high-quality computation offloading services to Internet of Things devices (IoTDs). Recently, space-air-ground integrated multi-access edge computing (SAGIMEC) is emerging as a promising architecture combining edge computing and cloud computing, which has the potential to be integrated with ICPS to accelerate the realization of the above vision. In this work, we first present an SAGIMEC-assisted ICPS architecture that incorporates edge computing and cloud computing through seamless connectivity supported by satellite networks to achieve determinism in connectivity, networked computing, and intelligent networked control. Then, we formulate a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation, and UAV trajectory control optimization problem (JSC4OP) to maximize the quality of service (QoS) of IoTDs. This problem considers both the dynamics and uncertainties of the system environment, as well as the limited resources and energy of UAVs. Given the complexity of JSC4OP, we propose an online decentralized optimization approach (ODOA) to solve the problem. Specifically, JSC4OP is first transformed into a real-time decision-making optimization problem (RDOP) by leveraging Lyapunov optimization. Then, to solve the RDOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the ODOA, while the simulation results demonstrate that the proposed ODOA outperforms other alternative approaches in terms of overall system performance.

arXiv.org

Integrating Fuzzy Set Theory with Pandora Temporal Fault Trees for Dynamic Failure Analysis of Complex Systems arxiv.org/abs/2411.09717 .SY .PR .SY

Integrating Fuzzy Set Theory with Pandora Temporal Fault Trees for Dynamic Failure Analysis of Complex Systems

Pandora temporal fault tree, as one notable extension of the fault tree, introduces temporal gates and temporal laws. Pandora Temporal Fault Tree(TFT) enhances the capability of fault trees and enables the modeling of system failure behavior that depends on sequences. The calculation of system failure probability in Pandora TFT relies on precise probabilistic information on component failures. However, obtaining such precise failure data can often be challenging. The data may be uncertain as historical records are used to derive failure data for system components. To mitigate this uncertainty, in this study, we proposed a method that integrates fuzzy set theory with Pandora TFT. This integration aims to enable dynamic analysis of complex systems, even in cases where quantitative failure data of components is unreliable or imprecise. The proposed work introduces the development of Fuzzy AND, Fuzzy OR, Fuzzy PAND, and Fuzzy POR logic gates for Pandora TFT. We also introduce a fuzzy importance measure for criticality analysis of basic events. All events in our analysis are assumed to have exponentially distributed failures, with their failure rates represented as triangular fuzzy numbers. We illustrate the proposed method through a case study of the Aircraft Fuel Distribution System (AFDS), highlighting its practical application and effectiveness in analyzing complex systems. The results are compared with existing results from Petri net and Bayesian network techniques to validate the findings.

arXiv.org

Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems arxiv.org/abs/2411.09720 .IT .LG .IT

Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems

The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) systems with large antenna arrays allow resolving finer details of channel behavior, we investigate how machine learning can be applied to time series data of beam measurements in the Fifth Generation (5G) New Radio (NR) system to improve the HO procedure. This paper introduces the Early-Scheduled Handover Preparation scheme designed to enhance the robustness and efficiency of the HO procedure, particularly in scenarios involving high mobility and dense small cell deployments. Early-Scheduled Handover Preparation focuses on optimizing the timing of the HO preparation phase by leveraging machine learning techniques to predict the earliest possible trigger points for HO events. We identify a new early trigger for HO preparation and demonstrate how it can beneficially reduce the required time for HO execution reducing channel quality degradation. These insights enable a new HO preparation scheme that offers a novel, user-aware, and proactive HO decision making in MIMO scenarios incorporating mobility.

arXiv.org
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