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Embrace Opportunities and Face Challenges: Using ChatGPT in Undergraduate Students' Collaborative Interdisciplinary Learning

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arXiv 2023年

作者： Zhu, Gaoxia Fan, Xiuyi Hou, Chenyu Zhong, Tianlong Seow, Peter Shen-Hsing, Annabel Chen Rajalingam, Preman Yew, Low Kin Poh, Tan Lay Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Nanyang Technological University Singapore Division of Psychology Nanyang Technological University Singapore Centre for Teaching Learning & Pedagogy Nanyang Technological University Singapore Nanyang Technological University Singapore School of Materials Science & Engineering Nanyang Technological University Singapore

ChatGPT, launched in November 2022, has gained widespread attention from students and educators globally, with an online report by Hu (2023) stating it as the fastest-growing consumer application in history. While discussions on the use of ChatGPT in higher education are abundant, empirical studies on its impact on collaborative interdisciplinary learning are rare. To investigate its potential, we conducted a quasi-experimental study with 130 undergraduate students (STEM and non-STEM) learning digital literacy with or without ChatGPT over two weeks. Weekly surveys were conducted on collaborative interdisciplinary problem-solving, and physical and cognitive engagement, and individuals reflected on their ChatGPT use. Analysis of survey responses showed significant main effects of topics on collaborative interdisciplinary problem-solving and physical and cognitive engagement, a marginal interaction effect between disciplinary backgrounds and ChatGPT conditions for cognitive engagement, and a significant interaction effect for physical engagement. Qualitative analysis of students' reflections generated eight positive themes about ChatGPT use, including efficiency, addressing knowledge gaps, and generating human-like responses, and eight negative themes, including generic responses, lack of innovation, and counterproductive to self-discipline and thinking. Our findings suggest that ChatGPT use needs to be optimized by considering the topics being taught and the disciplinary backgrounds of students rather than applying it uniformly. These findings have implications for both pedagogical research and practices. © 2023, CC BY.

关键词： Students

Benchmarking Cryptographic Protocols for (IoT) Malware Defence

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Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

Research on Conversion of 3D CAD Model Into Monte Carlo Models Based on SALOME

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Research on Conversion of 3D CAD Model Into Monte Carlo Mode...

Computational Modeling, Simulation and Data Analysis (CMSDA), International Conference on

作者： Chengyan Chang Tao Yu Zhenping Chen Huajian Zhang Lanjun Kuang College of Computer Science School University of South China Hengyang China Virtual Simulation Experiment Teaching Center on Nuclear Energy and Technology University of South China Hengyang China School of Nuclear Science and Technology University of South China Hengyang China

In nuclear reactor design analysis, Monte Carlo (MC) programs are widely used for simulation calculations of particle transport. Traditionally, MC program input files are constructed by manual creation, but with the increasing complexity of advanced nuclear reactor models, the traditional input file construction method can no longer meet the application requirements, and it is important to develop an auxiliary modeling program for MC programs. In this paper, based on the open source platform SALOME, we conducted a study on the conversion of CAD 3D geometric model to MC computational model using decomposition-based BREP-CSG conversion algorithm and cavity generation algorithm, and developed an auxiliary modeling program MOSRT. Based on the MOSRT program, the user can create or import CAD 3D geometry models and convert them into MC program input files corresponding to the geometry through the program. To verify the effectiveness of the MOSRT program model conversion function, the NEA published benchmark example C5G7 model and the critical experimental device model were used for geometric verification. The results show that the MOSRT program developed based on the method of this paper can accurately convert the CAD 3D geometric model into the corresponding MC program calculation model, which verifies the correctness and reliability of the methods and program of this paper.

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DEEP GENERATIVE MODELING ON LIMITED DATA WITH REGULARIZATION BY NONTRANSFERABLE PRE-TRAINED MODELS

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arXiv 2022年

作者： Zhong, Yong Liu, Hongtao Liu, Xiaodong Bao, Fan Shen, Weiran Li, Chongxuan Gaoling School of AI Renmin University of China Beijing China Beijing Key Lab of Big Data Management and Analysis Methods Beijing China Department of Computer Science Technology Tsinghua University Beijing China

Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the classical perspective of the bias-variance tradeoff, we propose regularized deep generative model (Reg-DGM), which leverages a nontransferable pre-trained model to reduce the variance of generative modeling with limited data. Formally, Reg-DGM optimizes a weighted sum of a certain divergence and the expectation of an energy function, where the divergence is between the data and the model distributions, and the energy function is defined by the pre-trained model w.r.t. the model distribution. We analyze a simple yet representative Gaussian-fitting case to demonstrate how the weighting hyperparameter trades off the bias and the variance. Theoretically, we characterize the existence and the uniqueness of the global minimum of Reg-DGM in a non-parametric setting and prove its convergence with neural networks trained by gradient-based methods. Empirically, with various pre-trained feature extractors and a data-dependent energy function, Reg-DGM consistently improves the generation performance of strong DGMs with limited data and achieves competitive results to the state-of-the-art methods. Our implementation is available at https://***/ML-GSAI/Reg-ADA-APA. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval

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arXiv 2022年

作者： Zhan, Jingtao Ai, Qingyao Liu, Yiqun Mao, Jiaxin Xie, Xiaohui Zhang, Min Ma, Shaoping Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China

Recent advance in Dense Retrieval (DR) techniques has significantly improved the effectiveness of first-stage retrieval. Trained with large-scale supervised data, DR models can encode queries and documents into a low-dimensional dense space and conduct effective semantic matching. However, previous studies have shown that the effectiveness of DR models would drop by a large margin when the trained DR models are adopted in a target domain that is different from the domain of the labeled data. One of the possible reasons is that the DR model has never seen the target corpus and thus might be incapable of mitigating the difference between the training and target domains. In practice, unfortunately, training a DR model for each target domain to avoid domain shift is often a difficult task as it requires additional time, storage, and domain-specific data labeling, which are not always available. To address this problem, in this paper, we propose a novel DR framework named Disentangled Dense Retrieval (DDR) to support effective and flexible domain adaptation for DR models. DDR consists of a Relevance Estimation Module (REM) for modeling domain-invariant matching patterns and several Domain Adaption Modules (DAMs) for modeling domain-specific features of multiple target corpora. By making the REM and DAMs disentangled, DDR enables a flexible training paradigm in which REM is trained with supervision once and DAMs are trained with unsupervised data. Comprehensive experiments in different domains and languages show that DDR significantly improves ranking performance compared to strong DR baselines and substantially outperforms traditional retrieval methods in most scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

TFSWA-UNet: Temporal-Frequency and Shifted Window Attention Based UNet For Music Source Separation

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TFSWA-UNet: Temporal-Frequency and Shifted Window Attention ...

Culture-Oriented science and Technology (CoST), International Conference on

作者： Zhenyu Yao Yuping Su Honghong Yang Xiaojun Wu Yumei Zhang School of Computer Science Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Shaanxi Normal University Xi’an China Key Laboratory of Modern Teaching Technology Ministry of Education Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an China

ISBN: (数字)9798350380347

ISBN: (纸本)9798350380354

The performance of music source separation (MSS) has been greatly improved in recent years due to the rapid development of various neural network architectures. Spectrogram features are widely used in MSS tasks which consists of both time and frequency information. However, the time and frequency correlations and the local patterns of spectrogram have not been fully explored. In this paper, we propose a novel U-Net architecture named TFSWA-UNet in which a temporal-frequency and shifted window attention (TFSWA) based block is designed as bottleneck block. In the proposed TFSWA block, time sequence attention (TSA) block and frequency sequence attention (FSA) block are used to capture the global correlation of music spectrogram features within time and frequency sequences respectively. To further capture the local correlations of spectrogram features, shifted window attention based Swin transformer is also introduced into the TFSWA module, which computes self-attention within local non-overlapping windows and capture correlations from both temporal and frequency dimensions. Experiment results on MUSDB18 dataset indicate that the proposed TFSWA-UNet model outperforms the state-of-the-art MSS methods with SDR of 9.16dB on vocals.

关键词： Time-frequency analysis Correlation Source separation Costs Computational modeling Neural networks computer architecture

Left inferior parietal lobe and auditory cortex jointly contribute to sound knowledge retrieval

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Brain Stimulation 2025年第4期18卷 1037-1047页

作者： Kuhnke, Philipp Numssen, Ole Voeller, Johannah Cheung, Vincent K.M. Weise, Konstantin Kiefer, Markus Hartwigsen, Gesa Wilhelm Wundt Institute for Psychology Leipzig University Germany Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) Leipzig Germany Methods and Development Group Brain Networks MPI CBS Leipzig Germany TMS Group Department of Neuroscience and Biomedical Engineering Aalto University Finland TMS Lab Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Boston United States Department of Experimental Psychology University of Granada Spain Mind Brain and Behavior Research Center (CIMCYC) University of Granada Spain Sony Computer Science Laboratories Tokyo Japan Leipzig University of Applied Sciences HTWK Leipzig Germany Department of Psychiatry Ulm University Germany

Background: Conceptual knowledge is central to human cognition. Neuroimaging studies suggest that conceptual processing relies on the joint contribution of modality-specific perceptual-motor and multimodal brain regions. In particular, the multimodal left inferior parietal lobe (IPL) couples with auditory association cortex during sound knowledge retrieval and with somatomotor cortex during action feature retrieval. However, it remains unknown whether the interaction between modality-specific and multimodal cortices is causally relevant for conceptually-guided behavior. methods: To tackle this issue, we applied offline transcranial magnetic stimulation (TMS) over modality-specific cortex (somatomotor, auditory, or sham), before 24 healthy participants received online TMS over multimodal cortex (IPL, or sham) during action and sound knowledge tasks on written words. For the first time in a TMS study on higher cognition, TMS targeting and dosing were optimized using individual a priori computational electric field simulations. Results: We found that combined stimulation of auditory association cortex and multimodal IPL selectively impaired sound judgments, but not action judgments, on low sound–low action words (BF₁₀ = 5.316). Unifocal TMS over auditory cortex or IPL alone did not disrupt behavior, suggesting adaptive compensatory plasticity. Conclusions: These results indicate that the joint contribution of multimodal IPL and auditory cortex is causally relevant for sound knowledge retrieval: The functional relevance of left IPL depends on the integrity of the auditory cortex, and vice versa, as single perturbation of either region did not disrupt performance. Our findings support “hybrid theories” of conceptual processing, which propose that task-dependent functional interactions between modality-specific and multimodal regions are crucial. © 2025 The Authors

关键词： Compensation Computational modeling Concepts Plasticity Semantics TMS

Configuration Testing of an Artificial Pancreas System Using a Digital Twin

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SSRN

SSRN 2024年

作者： Somers, Richard J. Walkinshaw, Neil Hierons, Robert M. Elliott, Jackie Iqbal, Ahmed Walkinshaw, Emma Computer Science Department University of Sheffield SheffieldS1 4DP United Kingdom Sheffield Teaching Hospitals NHS Foundation Trust Diabetes and Endocrine Centre Northern General Hospital SheffieldS5 7AU United Kingdom University of Sheffield United Kingdom

The recent spread of wearable medical devices has improved the quality of life of people with medical conditions. Traditionally, users configure these systems using physical trials, putting themselves in potentially dangerous scenarios. Digital twins present an opportunity to perform configuration testing in simulated environments, reducing the potential risk. In this paper, we perform an evaluative case study to assess the capabilities of a digital twin in the configuration testing of an artificial pancreas system (APS) control algorithm. These systems regulate the blood glucose levels in people with type 1 diabetes mellitus (T1DM), which if misconfigured, can cause severe hypoglycaemia or hyperglycaemia, which can be life-threatening. We tested the OpenAPS control algorithm against 156 people's clinical data. We found that our digital twin provided an accurately simulated environment to perform configuration testing and was capable of predicting blood glucose-insulin behaviour. We were able to evaluate different APS configurations, identifying a potentially unsafe configuration without the risks associated with a physical trial. We uncovered how modelling clinical data is not always consistent, which could lead to misinterpretations in configuration testing, and the challenges associated with test reliability when modelling human body dynamic uncertainty. © 2024, The Authors. All rights reserved.

关键词： Software testing

Diffusion-convection reaction equations with sign-changing diffusivity and bistable reaction term

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arXiv 2021年

作者： Berti, Diego Corli, Andrea Malaguti, Luisa Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Italy Department of Mathematics and Computer Science University of Ferrara Italy

We consider a reaction-diffusion equation with a convection term in one space variable, where the diffusion changes sign from the positive to the negative and the reaction term is bistable. We study the existence of wavefront solutions, their uniqueness and regularity. The presence of convection reveals several new features of wavefronts: according to the mutual positions of the diffusivity and reaction, profiles can occur either for a single value of the speed or for a bounded interval of such values;uniqueness (up to shifts) is lost;moreover, plateaus of arbitrary length can appear;profiles can be singular where the diffusion *** Codes 35K65 (Primary) 35C07, 34B40, 35K57 (Secondary) © 2021, CC BY.

关键词： Diffusion