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关键词： Machine learning   Computer science  

摘要： Government policies, generous funding and a pipeline of AI graduates have helped Chinese firms create advanced LLMs.

关键词： Codes (symbols)  

摘要： The increasing complexity of computer science research projects demands more effective tools for deploying code repositories. Large Language Models (LLMs), such as Anthropic Claude and Meta Llama, have demonstrated significant advancements across various fields of computer science research, including the automation of diverse software engineering tasks. To evaluate the effectiveness of LLMs in handling complex code development tasks of research projects, particularly for NLP/CV/AI/ML/DM topics, we introduce CSR-Bench, a benchmark for Computer Science Research projects. This benchmark assesses LLMs from various aspects including accuracy, efficiency, and deployment script quality, aiming to explore their potential in conducting computer science research autonomously. We also introduce a novel framework, CSR-Agents, that utilizes multiple LLM agents to automate the deployment of GitHub code repositories of computer science research projects. Specifically, by checking instructions from markdown files and interpreting repository structures, the model generates and iteratively improves bash commands that set up the experimental environments and deploy the code to conduct research tasks. Preliminary results from CSR-Bench indicate that LLM agents can significantly enhance the workflow of repository deployment, thereby boosting developer productivity and improving the management of developmental workflows. © 2025, CC BY-NC-ND.

关键词： Computer science   Machine learning  

摘要： The model produces cited, pages-long reports that might be helpful for generating literature reviews.

关键词： Computer crime  

摘要： Computer science research sometimes brushes with the law, from red-team exercises that probe the boundaries of authentication mechanisms, to AI research processing copyrighted material, to platform research measuring the behavior of algorithms and users. U.S.-based computer security research is no stranger to the Computer Fraud and Abuse Act (CFAA) and the Digital Millennium Copyright Act (DMCA) in a relationship that is still evolving through case law, research practices, changing policies, and legislation. Amid the landscape computer scientists, lawyers, and policymakers have learned to navigate, anti-fraud laws are a surprisingly under-examined challenge for computer science research. Fraud brings separate issues that are not addressed by the methods for navigating CFAA, DMCA, and Terms of Service that are more familiar in the computer security literature. Although anti-fraud laws have been discussed to a limited extent in older research on phishing attacks, modern computer science researchers are left with little guidance when it comes to navigating issues of deception outside the context of pure laboratory research. In this paper, we analyze and taxonomize the anti-fraud and deception issues that arise in several areas of computer science research. We find that, despite the lack of attention to these issues in the legal and computer science literature, issues of misrepresented identity or false information that could implicate anti-fraud laws are actually relevant to many methodologies used in computer science research, including penetration testing, web scraping, user studies, sock puppets, social engineering, auditing AI or socio-technical systems, and attacks on artificial intelligence. We especially highlight the importance of anti-fraud laws in two particular research fields that are of great policy importance in the current moment: attacking or auditing AI systems, and research involving legal identification. Finally, guided by principles in research ethi

关键词： Vectors   Codes   Upper bound   Training   Electronic mail   Data mining   Artificial intelligence   Reed-Solomon codes   Computer science   Polynomials  

摘要： The purpose of this note is to correct an error made by Con et al. (2023), specifically in the proof of Theorem 9. Here we correct the proof but as a consequence we get a slightly weaker result. In Theorem9, we claimed that for integers k and n such that $k \lt n/9$ , there exists an $[n,k]_{q}$ RS code that can decode from $n-2k+1$ insdel errors where $q = O\left ({{k^{5} \left ({{ \frac {en}{k-1} }}\right)^{4k-4}}}\right)$ . Here we prove the following. Theorem 1: For integers n and $k \lt n/9$ , there exists an $[n,k]_{q}$ RS-code, where $q=O\left ({{k^{4} \cdot \left ({{\frac {4en}{4k-3}}}\right)^{4k-3}}}\right)$ is a prime power, that can decode from $n - 2k + 1$ adversarial insdel errors. Note that the exponent of n is $4k-3$ whereas in Theorem 9 it is $4k-4$ . For constant dimensional codes, the field size is of order $O(n^{4k-3})$ , and in particular, for $k=2$ the field size is of order $O(n^{5})$ .

关键词： 产教融合   课程改革   人才培养  

摘要： 探讨在产教融合背景下高等职业院校中改革计算机专业课程教学的策略。分析产教融合为计算机教育带来的新机遇和挑战,指出当前教学中存在的问题,并提出优化教学内容、改变教学方式、加强教师团队建设、改革评价体系以及改善实验条件等一系列教学改革策略。通过这些方法的实施,促使该课程培养体系更贴近行业需求,为社会培养出适应产业发展需求的人才。

关键词： 校企合作   研创双驱   课程改革   项目化教学   职业教育  

摘要： 在经济和产业结构升级背景下,对高素质技能型人才的需求激增,而现有教育模式未能充分满足企业实际需求,导致毕业生就业难题。研究响应国家政策,以武汉职业技术学院人工智能学院用户界面设计课程为案例,通过问卷调查的方式了解计算机类专业课程与实际工作需求脱节、实践性不足、教学方法单一等问题。针对这些问题提出一系列校企合作下的课程改革策略,包括实训课程升级、毕业设计研发、跟岗学习经验积累、课程内容重构、项目化教学实施、信息技术融合教学,以及岗位能力导向评价体系构建。研究结果表明,校企联动模式能有效缩小教育与行业需求间的差距,丰富课程内容,提升教学质量,并激发学生的创新能力,对推动职业教育改革具有重要意义。

关键词： 新质生产力   人才培养   校企合作   创新实践  

摘要： 以提升学生创新实践能力和解决复杂工程问题能力为目标,基于“教育链-创新链-产业链”三链深度融合机制,聚焦教育实践体系的顶层设计,重构“3-4-4-3”人才培养体系和“基础-综合-专业”三层次能力课程体系,实施卓越工程师人才培养。同时,构建“产学研一体”实践体系,提出“多边化”师资能力提升机制,打造“多元协同”创新实践中心,优化效果评价体系,从各个维度打造多级有效衔接、全过程融合实施的创新实践教育生态体系。实践证明,改革取得了良好成效,人才培养质量明显提升,创新实践体系建设和具体实施效果可供其之学校借鉴。