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关键词： Attention mechanisms   Training   Games   Intelligent transportation systems   Data mining   Computer science   Sports   Knowledge engineering   Human-machine systems   Deep reinforcement learning   Multi-agent reinforcement learning   cooperation   group-specific attention   mixed tasks  

摘要： The cooperation in mixed cooperative-competitive tasks has drawn significant attention in multi-agent deep reinforcement learning. Agents need to cooperate with their teammates while competing against their opponents. However, most existing works treat the cooperative agents and competitive agents equally as they perform the same operation on all the agents. As a result, without distinguishing between cooperative and competitive agents, they may suffer from information disorder in learning an optimally cooperative policy and struggle to decide on the next step action. To address the above issues, we decompose the final Q-value into a weighted combination of three parts: the Q-values of the cooperative agents, the competitive group, and the current agent. A theoretical proof of the correctness of the decomposition is provided. With this decomposition, we are able to consider cooperative and competitive agents separately. Accordingly, we propose a multi-agent actor-critic algorithm called actor-double-attention-critic (ADAC) under centralized training and decentralized execution according to the decomposition. In ADAC, networks with group-specific attention and an attentional weighting network are specially designed. With the designed double-attention structure, ADAC can capture the distributions from different agents and improve cooperation performance. Extensive experiments are conducted in three scenarios with nine settings against six representative methods. The results demonstrate the superiority of the proposed ADAC model against state-of-the-art methods in various mixed cooperative-competitive tasks. The code is available at https://***/CrazyBayes/ADAC

关键词： STEM education   Cognitive abilities   Grit   High school   Excelling students   Math   Physics   Computer science  

摘要： BackgroundThe current study aimed to investigate the associations between cognitive abilities, grit, and academic achievement in high school STEM studies. The study utilized a natural sample of excelling STEM students (N = 814) who underwent a selection process to join elite undergraduate STEM programs. Through this selection process, the students reported their high school grades in Math, Physics, and Computer science, as well as their Psychometric Entrance Test (PET) scores, which are equivalent to the SAT. They also completed a Grit questionnaire and took various cognitive ability *** study highlights the significant role of grit in STEM achievements, particularly for students with moderate cognitive abilities. We found that while cognitive abilities were strong predictors of academic STEM success, grit weakly contributed to predicting achievements in STEM studies beyond cognitive abilities alone. The study's main finding revealed that grit moderates the association between cognitive abilities and academic STEM grades, with higher levels of grit diminishing the impact of cognitive abilities on STEM success. Specifically, students with high grit could leverage their determination and passion to bridge cognitive gaps, effectively reducing performance *** findings underscored the multifaceted nature of cognitive skills and non-cognitive factors like grit in contributing to STEM success. The study highlights the importance of fostering grit, particularly among students with moderate cognitive abilities. Higher grit can especially benefit these students in achieving higher STEM grades. According to Social Cognitive Career Theory (SCCT), higher achievements could promote one's self-efficacy and encourage students to pursue STEM careers after high school. This underscores the importance of integrating both cognitive and non-cognitive factors in educational and career guidance programs.

关键词： Tensors   Clustering algorithms   Feature extraction   Image segmentation   Data mining   Transforms   Technological innovation   Matrix decomposition   Computer science   Urban areas   Low-rank tensor   multiview clustering   scale-driven   unsupervised learning  

摘要： Real-world data tends to exhibit an inherent hierarchical structure, providing a natural multiview perspective where features at different scales can be treated as distinct views. However, most existing multiview clustering algorithms primarily focus on the inter-sample relationships at a single level. These methods overlook the hierarchical structures present in the data and are specifically designed for native multiview data. This article introduces a comprehensive multiview clustering framework that transforms both typical data and images into a unified multiview feature representation. The framework allows for extracting multiscale features from the raw data and clustering different types of data with the same algorithm. A novel scale-driven pre-processing approach unifies the feature structure across various data types and explores local relationships among samples at multiple scales. Features at larger scales delineate the global cluster contours, while features at smaller scales reveal fine-grained local details. Subsequently, the proposed method learns the view-specific partitions from different scales of views and derives consensus features through tensor low-rank representation. By optimizing these consensus features, the approach effectively captures the precise cluster shapes from coarse to fine-grained levels. The final label indicator matrix is directly obtained from these consensus features. To demonstrate the effectiveness and versatility of the proposed method, we conducted experimental comparisons with state-of-the-art (SOTA) algorithms in both multiview clustering and image segmentation across diverse datasets. The source code and datasets are released at https://***/ChuanbinZhang/SDTR

摘要： The COVID pandemic has significantly influenced educational methodologies, leading to a shift towards more interactive and technology-integrated teaching approaches. Live-coding, which involves real-time coding demonstrations, has gained recognition as a valuable tool in computer science education. This study investigates the impact of the pandemic on the popularity and application of live-coding. By conducting a comprehensive literature review of 22 research papers, this study categorizes the papers based on their publication date relative to the pandemic: pre-COVID (2017 to 2019), during COVID (2020 to 2022), and post-COVID (2023 to 2024). The papers were selected using a specific search query for live-coding in introductory computer science education on Google Scholar. A systematic literature review was performed to determine their sentiment towards live-coding, categorizing the sentiments as positive, neutral, or negative. The sentiment data were then statistically analyzed using Fisher's Exact Test to assess significant differences across the three periods. Results from this manuscript indicate shifts in positive sentiment towards live-coding during and after the pandemic.

摘要： Most computing departments offer one or two undergraduate programs, but the CSIT Department at a mid-south state university offers three, all three accredited by the Accreditation Board for Engineering and Technology (ABET): Computer Information Systems, Computer Information Technology, and Computer Science. In 2018, an integrated 3-in-1 B.S. program structure was adopted, which earned ABET accreditation in 2021--2022. With about 650 majors and 100--150 graduates annually, the streamlined curriculum has optimized resources and contributed to the department's success.

关键词： Python  

摘要： Large Language Models (LLMs), such as GitHub Copilot and ChatGPT have become popular among programming students. Students use LLMs to assist them in programming courses, including generating source code. Previous work has evaluated the ability of LLMs in solving introductory-course programming assignments. The results have shown that LLMs are highly effective in generating code for introductory Computer Science (CS) courses. However, there is a gap in research on evaluating LLMs’ ability to generate code that solves advanced programming assignments. In this work, we evaluate the ability of four LLM tools to solve programming assignments from advanced CS courses in three popular programming languages, Java, Python, and C. We manually select 12 problems, three problems from introductory courses as the baseline and nine programming assignments from second- and third-year CS courses. To evaluate the LLM-generated code, we generate a test suite of 1000 test cases per problem and analyze the program output. Our evaluation shows that although LLMs are highly effective in generating source code for introductory programming courses, solving advanced programming assignments is more challenging. Nonetheless, in many cases, LLMs identify the base problem and provide partial solutions that may be useful to CS students. Furthermore, our results may provide useful guidance for teachers of advanced programming courses on how to design programming assignments. © 2025, CC BY.

关键词： Bioinformatics   Computer science  

摘要： The security of medical information has become a significant challenge with the move from traditional filing systems to electronic records. This study proposes the use of blockchain technology to address these concerns. The system registers patients and medical staff with unique IDs and stores patient diagnoses as immutable records on the blockchain. A central interplanetary file system stores the collected data, which can be accessed by authorized users like nurses, pharmacists, and patients via special access details. Users must log in before accessing medical records through the Electronic Record Management system. This approach can be scaled to multiple hospitals. After testing, the system showed some latency issues with 100 nodes, but performance improved with more nodes (300-500), demonstrating better scalability as the system handles more data and hospitals. Overall, the proposed blockchain-based system offers a secure, scalable solution for managing and accessing medical records.

关键词： Natural sciences   Applied sciences   Computer science  

摘要： This paper constructs three bi-class multi-relational heterogeneous graphs based on real-world data, and proposes a semantic and relation aware neural network model (SRA-BMHN) designed for bi-class multi-relational heterogeneous graphs. SRA-BMHN consists of two core modules: first, the semantic-aware module, which integrates diverse relational semantic information using a non-linear mapping function and attention mechanism tailored to specific relationship semantics;second, the relation-aware module, which employs a hierarchical bipartite subgraph aggregation strategy. This module first divides the bi-class multi-relational heterogeneous graphs into multiple bipartite subgraphs based on different edge relationship types and captures the topological information within them. It then merges these subgraphs into a relation-weighted sub graph and captures the relational features of nodes on the weighted heterogeneous subgraph. Finally, SRABMHN fuses the features from the semantic-aware module and the relation-aware module to generate the final node embeddings. Experimental results on three datasets demonstrate the superiority of the proposed method.

关键词： Health sciences   Medicine   Computer science   Artificial intelligence applications  

摘要： Deep learning holds great promise in drug discovery, yet its application is hindered by high labeling costs and limited datasets. Developing algorithms that effectively learn from sparsely labeled data is crucial. Capsule networks (CapsNet), introduced in 2017, solve the spatial information loss in traditional neural networks and excel in handling small datasets by capturing spatial hierarchical relationships among features. This capability makes CapsNet particularly promising for drug discovery, where data scarcity is a common challenge. Various modified CapsNet architectures have been successfully applied to drug design and discovery tasks. This review provides a comprehensive analysis of CapsNet's theoretical foundations, its current applications in drug discovery, and its performance in addressing key challenges in the field. Additionally, the study highlights the limitations of CapsNet and outlines potential future research directions to further enhance its utility in drug discovery, offering valuable insights for researchers in both computational and pharmaceutical sciences.

关键词： Students  

摘要： This study explores the integration of Large Language Models (LLMs) into the grading and appeal resolution process in computer science education. We introduce AI-PAT, an AI-powered assessment tool that leverages LLMs to evaluate computer science exams, generate feedback, and address student appeals. AI-PAT was used to assess over 850 exam submissions and handle 185 appeal cases. Our multi-model comparison (ChatGPT, Gemini) reveals strong correlations between model outputs, though significant variability persists depending on configuration and prompt design. Human graders, while internally consistent, showed notable inter-rater disagreement, further highlighting subjectivity in manual evaluation. The appeal process led to grade changes in 74% of cases, indicating the need for continued refinement of AI evaluation strategies. While students appreciated the speed and detail of AI feedback, survey responses revealed trust and fairness concerns. We conclude that AI-PAT offers scalable benefits for formative assessment and feedback, but must be accompanied by transparent grading rubrics, human oversight, and appeal mechanisms to ensure equitable outcomes. Copyright © 2025, The Authors. All rights reserved.