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ChatGPT, Copilot, Gemini, SciSpace and Wolfram versus higher education assessments: an updated multi-institutional study of the academic integrity impacts of Generative Artificial Intelligence (GenAI) on assessment, teaching and learning in engineering

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Australasian Journal of engineering Education 2024年第2期29卷 126-153页

作者： Nikolic, Sasha Sandison, Carolyn Haque, Rezwanul Daniel, Scott Grundy, Sarah Belkina, Marina Lyden, Sarah Hassan, Ghulam M. Neal, Peter Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia School of Science Technology and Engineering University of the Sunshine Coast Sippy Downs Australia School of Professional Practice & Leadership University of Technology Sydney Sydney Australia School of Chemical Engineering University of New South Wales Sydney Australia College Western Sydney University Sydney Australia School of Engineering University of Tasmania Hobart Australia School of Computer Science & Software Engineering University of Western Australia Perth Australia

More than a year has passed since reports of ChatGPT-3.5’s capability to pass exams sent shockwaves through education circles. These initial concerns led to a multi-institutional and multi-disciplinary study to assess the performance of Generative Artificial Intelligence (GenAI) against assessment tasks used across 10 engineering subjects, showcasing the capability of GenAI. Assessment types included online quiz, numerical, oral, visual, programming and writing (experimentation, project, reflection and critical thinking, and research). Twelve months later, the study was repeated using new and updated tools ChatGPT-4, Copilot, Gemini, SciSpace and Wolfram. The updated study investigated the performance and capability differences, identifying the best tool for each assessment type. The findings show that increased performance and features can only heighten academic integrity concerns. While cheating concerns are central, opportunities to integrate GenAI to enhance teaching and learning are possible. While each GenAI tool had specific strengths and weaknesses, ChatGPT-4 was well-rounded. A GenAI Assessment Security and Opportunity Matrix is presented to provide the community practical guidance on managing assessment integrity risks and integration opportunities to enhance learning. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Artificial intelligence

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An influence maximization algorithm based on community detection using topological features 11

An influence maximization algorithm based on community detec...

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11th International Conference on computer engineering and Knowledge, ICCKE 2021

作者： Aghaee, Zahra Fatemi, Afsaneh Faculty of Computer Engineering Department of Software Engineering Isfahan Iran

ISBN: (纸本)9781665402088

Due to the growning use of social networks and the use of viral marketing in these networks, finding influential people to maximize information diffusion is considered. This problem is Influence Maximization Problem on social networks. The main goal of this Problem is to select a set of influential nodes to maximize the influence spread in a social network. Researchers in this field have proposed different algorithms, but finding the influential people in the shortest possible time is still a challenge that has attracted the attention of researchers. Therefore, in this paper, the IMPT-C algorithm is presented with a focus on graph pre-processing in order to reduce the search space based on community structure. The approach of this algorithm is to take advantage of the topological properties of the graph to identify influential nodes. The experiment results indicate that the IMPT-C algorithm has a great influence spread with low run time compared the state-of-the-art algorithms consist least 2.36% improve than PHG in term the influence spread. © 2021 IEEE.

关键词： Commerce

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data Systems 9

Towards Eco-Sustainability and Green Analytics Model to Meas...

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9th International Conference on Information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

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Thalassemia Diagnosis Through Medical Imaging: A New Artificial Intelligence-Based Framework

Thalassemia Diagnosis Through Medical Imaging: A New Artific...

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2022 International Conference on Smart Systems and Power Management, IC2SPM 2022

作者： Zaylaa, Amira J. Makki, Mohammad Kassem, Rola Beirut Arab University Beirut Faculty of Engineering Department of Electrical and Computer Engineering Beirut Lebanon Creoshift Company Software Devalopement Lebanon

ISBN: (数字)9781665452915

ISBN: (纸本)9781665452915

Thalassemia is the most common single-gene disorder throughout the world and represents a major public health problem resulting in abnormal ratios of hemoglobin subunits. It is widely spread throughout the Mediterranean region, Middle East, Southeast Asia, and some parts of Africa. There are different types of Thalassemia characterized by abnormal hemoglobin production. Most of the current hemoglobinopathy screening methods include High Performance Liquid Chromatography (HPLC), hemoglobin electrophoresis, screening of Polymerase Chain Reaction (PCR) mutations, and Deoxyribonucleic Acid (DNA) tests. However, all these methods are costly and require specialized instrumentation and trained technicians. Despite some studies used Artificial Intelligence (AI) they focused merely on Machine Learning (ML) and results were not optimal. This study aims to design a new AI-based framework for Thalassemia diagnosis using Deep Learning (DL) and a new combination of metrics for evaluation. This was achieved through the development and evaluation of a supervised semantic image segmentation model, and the implementation of different data engineering methods such as data annotation, augmentation, pre-processing, and preparation. Transfer learning was utilized and the Prediction Time Augmentation (PTA) was employed to get smoother and more accurate predictions. Quantitative results showed that, the mean Intersection Over Union (IoU) score of prediction of Thalassemia was 88% with PTA and 82% without PTA. Results also showed that as the combined metric of loss scores decreases the prediction of Thalassemia increases. Qualitative results showed that the final prediction of Thalassemia focuses on the Codocytes and labels the other unidentified cells as the background. Also, the resulting image was smoother and less bulky than the original annotated ground truth, and thus could be feasibly diagnosed. As a future prospect we aim to implement more algorithms and extend the diagnosis to incl

关键词： Diagnosis

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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Research on Topological Deformation of 3D Human Image Based on Laplace Optimization 4

Research on Topological Deformation of 3D Human Image Based ...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Zhou, Dongdong Chen, Xiaobing Zhang, Chuangchuang Guo, Shuxin Ashim, Khadka Lin, Jianchu Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China Gandaki College of Engineering and Science Pokhara University Pokhara Nepal

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

Image deformation technology is an important technology in computer graphics image processing, which can satisfy people's requirements for visual and sensory effects, and its research results have been widely used in film and television animation, game development, virtual reality and other fields. Mesh-based deformation technology has been a research emphasis in image deformation technology, through the virtual model for the mesh division, the use of mesh nodes control, to achieve the deformation of the model. However, the more complex the model is, the greater number of mesh control nodes, which greatly increases the complexity of mesh deformation. In this paper, the deformation of 3D virtual character model is studied based on the method of image topological deformation. In order to solve the problem of large number of network nodes in 3D model, the Laplace deformation method is further combined to reduce the number of control nodes in the network. The experimental results show that the deformation can be effectively completed using a small number of control nodes by using the laplace coordinate system for adjacent topological mesh node control. © 2022 IEEE.

关键词： Virtual reality

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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A Sparsifier Model for Efficient Information Retrieval

A Sparsifier Model for Efficient Information Retrieval

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Viacheslav Dobrynin Mark Sherman Roman Abramovich Alexey Platonov Faculty of Software Engineering and Computer Systems ITMO University Saint Petersburg Russian Federation

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

The constant development of dense neural models leads to improved search quality. It is crucial to adapt these models to meet performance requirements. Solutions like SPLADE or SparseEmbed address this by solving the ranking task, whereas our work proposes addressing the simplified task of sparsifying dense vector representations. This approach facilitates the faster adaptation of new dense models for use with efficient inverted indexes. The importance of the independence property for sparse space features, achieved through the use of iVAE, is demonstrated. Additionally, the model is trained to maintain the ranking properties of the dense model, which in our case was a BERT model. As a result, the obtained model showed search quality close to the original BERT model. The proposed sparsification approach can be applied to other tasks requiring sparse spaces by adding new or replacing existing properties of the sparse space. Thus, the paper describes the main aspects of a sparsifier model applied to the task of information retrieval.

关键词： Adaptation models Information retrieval Transformers Vectors Information and communication technology Indexes

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