Generative AI for Data Science 101: Coding Without Learning to Code by Jacob Bien, Gourab Mukherjee

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Integrating Generative AI into Legal Education: From Casebooks to Code, Opportunities and Challenges by Aswathy Prakash G, Vishnu Nair

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Generative Adversarial Network-Based Distortion Reduction Adapted to Peak Signal-to-Noise Ratio Parameters in VVC by Weihao Deng, Zhenglong Yang

“…In order to address the issues of image quality degradation and distortion that arise in the context of video transmission coding and decoding, a method based on an enhanced version of CycleGAN is put forth. …”
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Peculiarities of use of generative artificial intelligence in codes of academic integrity in higher education institutions of Singapore by Mariia Shchedrina

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EX-CODE: A Robust and Explainable Model to Detect AI-Generated Code by Luana Bulla, Alessandro Midolo, Misael Mongiovì, Emiliano Tramontana

“…Distinguishing whether some code portions were implemented by humans or generated by a tool based on artificial intelligence has become hard. …”
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Adversarial training driven malicious code detection enhancement method by Yanhua LIU, Jiaqi LI, Zhengui OU, Xiaoling GAO, Ximeng LIU, Weizhi MENG, Baoxu LIU

“…To solve the deficiency of the malicious code detector’s ability to detect adversarial input, an adversarial training driven malicious code detection enhancement method was proposed.Firstly, the applications were preprocessed by a decompiler tool to extract API call features and map them into binary feature vectors.Secondly, the Wasserstein generative adversarial network was introduced to build a benign sample library to provide a richer combination of perturbations for malicious sample evasion detectors.Then, a perturbation reduction algorithm based on logarithmic backtracking was proposed.The benign samples were added to the malicious code in the form of perturbations, and the added benign perturbations were culled dichotomously to reduce the number of perturbations with fewer queries.Finally, the adversarial malicious code samples were marked as malicious and the detector was retrained to improve its accuracy and robustness of the detector.The experimental results show that the generated malicious code adversarial samples can evade the detector well.Additionally, the adversarial training increases the target detector’s accuracy and robustness.…”
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The use of generative AI for coding in academia by Simon Wills, Sanson T. S. Poon, Arianna Salili‐James, Ben Scott

“…We highlight how the integration of generative AI tools such as ChatGPT holds immense promise for advancing academic research, revolutionising coding education and practices, promoting inclusivity, and facilitating knowledge dissemination. …”
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Using pseudo-AI submissions for detecting AI-generated code by Shariq Bashir

“…IntroductionGenerative AI tools can produce programming code that looks very similar to human-written code, which creates challenges in programming education. …”
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Integrating LLM-based code optimization with human-like exclusionary reasoning for computational education by Yi Rong, Tianfeng Du, Roubing Li, Wenting Bao

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Generative pre-trained transformers: ‘Ctrl+alt+create’ or ‘ctrl+alt+delete’ for IT education? by Samer Abaddi

Subjects: “…Generative pre-trained transformer…”
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EduFuncSum: a function-wise progressive transformer for code summarization in undergraduate programming education by Yi Rong, Manping Xu, Roubing Li, Lin Xin, Wenting Bao

Subjects: “…Code summarization…”
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A Polar-Coded PAPR Reduction Scheme Based On Hybrid Index Modulation by Si-Yu Zhang, Xinwei Yue, Behnam Shahrrava, Yuexia Zhang, Gongpu Wang

“…Therefore, in this paper, a polar-coded PAPR reduction scheme based on hybrid index modulation (PC-HIM) for OFDM-IM is proposed. …”
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Generating Distractors for Code Completion Problems: Can LLM Assist Instructors? by Mohammad Hassany, Kamil Akhuseyinoglu, Arun Balajiee Lekshmi Narayanan, Arav Agarwal, Jaromir Savelka, Peter Brusilovsky

“…While there is a growing body of research in computing education on the use of large language models (LLMs) to support learning content development, the use of LLMs for producing high-quality code completion problems has not yet been explored. …”
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NEURAL NETWORKS OF THE FINAL RING BASED ON THE REDUCTION SCHEME OF THE POSITION-MODULAR-CODE TRANSFORMATION by N. I. Chervyakov, A. A. Kolyada, N. A. Kolyada, V. A. Kuchukov, S. U. Protasenia

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An Ultra-Wideband Lossy Polarization Conversion Metasurface for RCS Reduction by Baoqin Lin, Wenzhun Huang, Jianxin Guo, Zuliang Wang, Kaibo Si, Xiaohua Zhou

“…In addition, the phase of its co-polarized reflection coefficients can be changed freely by the Pancharatnam-Berry phase generated by rotating its unit cell structure. When the unit cell structure is rotated by 0°, 45°, 90°, or 135°, the lossy PCM can be used as one of the four types of coding elements for a 2-bit absorptive coding metasurface (ACM). …”
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Integrated Technique of Natural Language Texts and Source Codes Authorship Verification in the Academic Environment by Aleksandr Romanov, Anna Kurtukova, Anastasiia Fedotova, Alexander Shelupanov

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A GPT-Based Code Review System With Accurate Feedback for Programming Education by Dong-Kyu Lee, Inwhee Joe

“…These results indicate that the system provides more accurate feedback than conventional automated code reviews, while achieving token efficiency and supporting self-directed learning through educational feedback. …”
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A DSL-Driven Solution and Code Generation for Accelerated Tourism Decision Support System Development by Adnane Souha, Charaf Ouaddi, Lamya Benaddi, Hamza Abdelmalek, El Mahi Bouziane, Abdeslam Jakimi

“…To address these challenges, this paper introduces a Domain-Specific Language (DSL), a graphical decision rule modeling tool, and code generation templates designed to streamline DSS development for smart tourism. …”
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Evaluating LLMs for Code Generation in HRI: A Comparative Study of ChatGPT, Gemini, and Claude by Andrei Sobo, Awes Mubarak, Almas Baimagambetov, Nikolaos Polatidis

“…This study investigates the effectiveness of Large Language Models (LLMs) in generating code for Human-Robot Interaction (HRI) applications. …”
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A Bibliometric Exposition and Review on Leveraging LLMs for Programming Education by Joanah Pwanedo Amos, Oluwatosin Ahmed Amodu, Raja Azlina Raja Mahmood, Akanbi Bolakale Abdulqudus, Anies Faziehan Zakaria, Abimbola Rhoda Iyanda, Umar Ali Bukar, Zurina Mohd Hanapi

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