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The role of moral intention and moral obligation in predicting attitudes toward avoiding AIgiarism: A protection motivation theory perspective

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Computers in Human Behavior Reports 2025年 18卷

作者： Ali, Ismail Mohamed Arpaci, Ibrahim Faculty of Computing SIMAD University Somalia Department of Software Engineering Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Balikesir Turkey Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea

Traditional methods of combating plagiarism have proven ineffective due to the dual use of AI chatbots to plagiarize and avoid detection. Therefore, there is a growing rationale to focus on moral responsibility towards protecting academic integrity. This study investigated the impact of students' moral intentions and moral obligations on their attitudes toward avoiding AI-assisted plagiarism (AIgiarism). Data was collected from a representative group of 263 students and analyzed using “partial least squares structural equation modeling” (PLS-SEM) and “artificial neural network” (ANN). The results revealed that students' moral intentions and obligations positively influenced their attitudes towards avoiding AIgarism. “Protection motivation theory” (PMT) constructs positively predicted students' moral intentions. Moreover, the results of the ANN analysis showed that moral obligation and intention were the most critical factors influencing students’ attitudes toward avoiding plagiarism. This study will help educational institutions develop AI-supported anti-plagiarism solutions in areas such as identification, instilling moral responsibility, and creating an environment of trust and support. © 2025 The Authors

关键词： AIgiarism Moral intention Moral obligation Plagiarism Protection motivation theory

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Differential-Trust-Mechanism Based Trade-off Method Between Privacy and Accuracy in Recommender Systems

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5054-5068页

作者： Xu, Guangquan Feng, Shicheng Xi, Hao Yan, Qingyang Li, Wenshan Wang, Cong Wang, Wei Liu, Shaoying Tian, Zhihong Zheng, Xi Qingdao Huanghai University School of Big Data Qingdao China Tianjin University College of Intelligence and Computing Tianjin300350 China KLISS and School of Software Beijing100084 China Sichuan University School of Cyber Science and Engineering Chengdu610207 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China East China Normal University Shanghai200062 China Hiroshima University School of Informatics and Data Science Higashihiroshima739-8511 Japan Guangzhou University Cyberspace Institute of Advanced Technology Guangdong Key Laboratory of Industrial Control System Security Huangpu Research School of Guangzhou University China Macquarie University School of Computing SydneyNSW2109 Australia

In the era where Web3.0 values data security and privacy, adopting groundbreaking methods to enhance privacy in recommender systems is crucial. Recommender systems need to balance privacy and accuracy, while also having the ability to overcome cold start problems. The Differential Trust Mechanism (DTM) introduced in this paper is such an approach. The DTM provides a unique use of Gaussian distributions in modeling trust relationships within data, offering a novel way to balance recommendation accuracy with user privacy. This mechanism innovatively applies differential privacy principles, using Gaussian noise addition to protect individual user data from inference attacks, while maintaining the integrity and utility of the overall dataset. Unlike traditional anonymization techniques that often compromise data utility or vulnerability to reverse engineering, DTM provides a robust solution by dynamically adjusting privacy levels based on the trustworthiness of data requests. By combining DTM with existing mainstream recommendation algorithms, the prediction accuracy of MAE and RMSE increases by at least 6.60% and 2.69%, respectively. This dual benefit positions DTM as a significant advancement in secure data processing, especially relevant for online businesses and platforms where personalized recommendations are crucial yet privacy concerns are paramount. © 2005-2012 IEEE.

关键词： Sensitive data

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Loss Functions in Deep Learning: A Comprehensive Review

arXiv

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arXiv 2025年

作者： Elharrouss, Omar Mahmood, Yasir Bechqito, Yassine Serhani, Mohamed Adel Badidi, Elarbi Riffi, Jamal Tairi, Hamid Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Informatics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models. © 2025, CC BY.

关键词： Deep learning

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Designing Quantum-Safe Lattice-Based Multi-Authority CP-ABE Scheme for Blockchain-Enabled IoT-Based Consumer Healthcare Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Bagchi, Prithwi Bisht, Abhishek Das, Ashok Kumar Saxena, Neetesh Hossain, M. Shamim International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Cardiff University School of Computer Science and Informatics CardiffCF24 4AG United Kingdom King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh13273 Saudi Arabia

Despite edge computing reducing communication delays associated with cloud computing, privacy concerns remain a significant challenge when sharing data from edge-based consumer electronics (CE) or Internet-of-Things (IoT) devices. Ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic tool that facilitates intricate and refined access control. It can deliver more flexible, secure, compact, and effective access control policies for the cloud data. In the case of a conventional CP-ABE scheme, the keys can only be issued and disseminated by a trusted central authority (CA). However, if the CA is compromised, the entire system becomes more susceptible to assaults or failures, which leads to a single-point failure. In this article, we propose a Ring-Learning with Errors (Ring-LWE)-based MA-CP-ABE scheme that is effectively resolved by the multi-authority CP-ABE (MA-CP-ABE), and it ensures the security against quantum attacks. The threshold secret sharing by Shamir and the Lagrange interpolation formula are applied in the key-generation and decryption procedures of the proposed scheme, which make it easier to segment and restore the private keys. The proposed scheme is implemented in the CE-enabled IoT-based smart healthcare applications using the blockchain technology as a secure storage. A detailed comparative study, security analysis and experimental results with the existing relevant schemes shows that the proposed scheme exhibits superior security and better efficiency as compared to other schemes, demonstrating its feasibility in practical IoT-based healthcare applications. © 2025 IEEE.

关键词： Quantum cryptography

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Deep Learning and IoT-Enabled Accident Detection and Reporting for Smart Cities Domain

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KSII Transactions on Internet and Information Systems 2025年第4期19卷 1140-1166页

作者： Javed, Rawal Mazhar, Tehseen Aoun, Muhammad Shahzad, Tariq Al-AlShaikh, Halah A. Ghadi, Yazeed Yasin Saudagar, Abdul Khader Jilani Khan, Muhammad Amir School of Automation Central South University Changsha China School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of CS IT Ghazi University Punjab Dera Ghazi Khan Pakistan School of Electrical Engineering Dept. of Electrical and Electronic Eng. Science University of Johannesburg Johannesburg2006 South Africa Riyadh11432 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi12555 United Arab Emirates School of Computing Sciences College of Computing Informatics and Mathematics University Technology MARA Selangor Shah Alam40450 Malaysia

Even though numerous ICT-based solutions have been put forth for the detection of accidents and rescue missions, they often suffer from compatibility issues with different vehicles and are accompanied by high costs. This paper introduces a cutting-edge intelligent system designed for accident detection and rescue, mirroring human cognitive functions through integrating the Internet of Things (IoT) and artificial intelligence. A specialized kit for IoT has been created to identify accidents and gather comprehensive incident-related data, such as pressure, position, gravitational force, and speed. This information is then transmitted to the cloud for processing. A deep learning model is implemented in the cloud environment to verify the IoT module's output and activate the rescue mechanism when an accident is detected. The DL module, employing ensemble transfer learning with dynamic weights, aims to minimize false detection rates. Because there isn't an appropriate dataset available, a personalized dataset is generated from diverse online videos. To validate the proposed method, a comparative analysis is conducted on Yolo V4. Experimental results indicate that Yolo V4 outperforms, achieving training, validation, and test accuracies of 98.2% each. To assess the system's real-world applicability, it undergoes validation using a robot car model. Copyright © 2025 KSII.

关键词： Deep learning

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ActivityMamba: A CNN-Mamba hybrid neural network for efficient human activity recognition

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IEEE Transactions on Mobile Computing 2025年

作者： Luo, Fei Li, Anna Jiang, Bin Khan, Salabat Wu, Kaishun Wang, Lu Great Bay University School of Computing and Information Technology Dongguan China Lancaster University School of Computing & Communications United Kingdom University of Petroleum College of Oceanography and Space Informatics Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Guangzhou China

Current research in human activity recognition primarily emphasizes enhancing accuracy, with limited exploration into computational efficiency and hardware compatibility. Recently, Mamba has sparked substantial interest within the realm of deep learning. Mamba is a hardware-aware algorithm enabling very efficient training and inference. Researchers are applying Mamba to various tasks, demonstrating significant promise in both language and vision tasks. It is worthwhile to investigate the use of Mamba for efficient human activity recognition. In this paper, we proposed a hybrid neural network that integrates CNN and visual Mamba, called ActivityMamba. The SE-Mamba block in ActivityMamba utilizes both CNN's local and Mamba's global context modeling while keeping computation and memory efficiency. We evaluated the ActivityMamba on five public benchmark datasets collected by using three different sensing techniques. ActivityMamba achieved higher performance than vision transformers, vision Mamba, and CNNs with fewer FLOPs and parameters. It sets a new SOTA on all five datasets, which are 91.78% OA and 89.13% F1 on the USC-HAD dataset, 99.19% OA and 98.64% F1 on the UT-HAR dataset, 99.82% OA and F1 on the DIAT dataset, 98.59% OA and 98.65% F1 on the UCI-HAR dataset, and 95.41% OA and 93.14% F1 on the UniMib dataset. Our work is the first to investigate the CNN-Mamba hybrid network for efficient human activity recognition. © 2002-2012 IEEE.

关键词： Active learning

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Analysis of Legged Wheels with Spring Suspension for Stair-Climbing Robotics

Analysis of Legged Wheels with Spring Suspension for Stair-C...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Islam Magomedov Timur Aygumov Josè Luis Ordõnez-Avila Bissnes informatics Kadyrov Chechen state university Grozny Russia Department at of software engineering computer technology and automated systems Dagestan State Technical University Dagestan Russia Universidad Tecnol'ogica Centroamericana(UNITEC) San Pedro Sula Honduras

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The following article looked into legged wheels with spring suspension. The idea behind the work was to see how adding springs in various places and their stiffness would impact the robot's ability to navigate through stairs. Due to the unconventional appearance of legged wheels, they are not suitable for even terrain passage. Hence, the idea was to add springs to minimize shock occurrence each time a leg touches the even environment. It was also important to equip springs throughout the whole path. As the robot progresses through steps, a greater disturbance of the structure occurs. Two different ideas were tested. In the first scenario, springs were attached between the holder of the wheels and the main body. In the second scenario, springs were directly added to the legs. The extruded part of the leg was divided into two, and a spring was embedded between these two parts. Different values of spring stiffness were also examined. The results illustrated that the structure would go further by implementing springs in all four wheels or in the rear. However, the structure was tested only on an upward incline. With other setups, it might be different with the rearrangement of the springs. It was also noted that with higher stiffness of the springs, the structure became unstable. In the second scenario, results illustrated the slightest progress. For instance, when the front wheels' legs were modified, they showed better results. To understand the influence in more depth, further investigation of the problem must be conducted.

关键词： Legged locomotion Industries Navigation Service robots Electric shock Wheels Stairs Boundary conditions Springs

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HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation

arXiv

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arXiv 2025年

作者： Wang, Cheng Kong, Lingxin Tamborski, Massimiliano Albrecht, Stefano V. The School of Engineering and Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom The School of Automation and Software Engineering Shanxi University Taiyuan030031 China The School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom

Simulation-based testing has emerged as an essential tool for verifying and validating autonomous vehicles (AVs). However, contemporary methodologies, such as deterministic and imitation learning-based driver models, struggle to capture the variability of human-like driving behavior. Given these challenges, we propose HAD-Gen, a general framework for realistic traffic scenario generation that simulates diverse human-like driving behaviors. The framework first clusters the vehicle trajectory data into different driving styles according to safety features. It then employs maximum entropy inverse reinforcement learning on each of the clusters to learn the reward function corresponding to each driving style. Using these reward functions, the method integrates offline reinforcement learning pre-training and multi-agent reinforcement learning algorithms to obtain general and robust driving policies. Multi-perspective simulation results show that our proposed scenario generation framework can simulate diverse, human-like driving behaviors with strong generalization capability. The proposed framework achieves a 90.96% goal-reaching rate, an off-road rate of 2.08%, and a collision rate of 6.91% in the generalization test, outperforming prior approaches by over 20% in goal-reaching performance. The source code is released at https://***/RoboSafe-Lab/Sim4AD. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning Methods 19thth

Efficient Classification of Live Sensor Data on Low-Energy...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Hanreich, Martin Krauss, Oliver Zwettler, Gerald Research and Development Research group AIST University of Applied Sciences Upper Austria Hagenberg4232 Austria Faculty for Informatics Communications and Media Department of Media Technology and Design University of Applied Sciences Upper Austria Hagenberg4232 Austria Faculty for Informatics Communications and Media Department of Software Engineering University of Applied Sciences Upper Austria Hagenberg4232 Austria

ISBN: (纸本)9783031829598

This paper addresses the implementation of time series classification on Internet of Things (IoT) devices with minimal resource usage while maintaining required accuracy levels. Although IoT devices have grown more powerful, optimizing resource usage remains essential to reduce costs and extend battery life. This work explores the practical aspects of developing and deploying time series classification models on IoT devices, emphasizing non-neural network models due to their lower resource demands. A detection system utilizing multiple predictions and a voting-based mechanism is constructed to enhance prediction stability. The input data comprises multivariate sensor streams of the same length. All computations are performed on the device, leveraging edge computing principles without external server dependency. This approach demonstrates how to balance accuracy and resource efficiency in IoT-based time series classification, offering a practical framework for similar applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy-Efficiency IoT-Devices Machine Learning Time-Series-Classification

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Wheelchair Wheel Design with Transformable “Wheel-Leg” Configuration for Climbing Stairs and Various Surfaces

Wheelchair Wheel Design with Transformable “Wheel-Leg” Con...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Islam Magomedov Timur Aygumov Anzor A. Kanametov Bissnes informatics Kadyrov Chechen state university Grozny Russia Department of software engineering computer technology and automated systems Dagestan State Technical University Dagestan Russia Department of Electronics and Digital Information Technologies Kabardino-Balkarian State University Nalchik Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The dynamic and uneven terrain of our environment introduces complexity for locomotion vehicles to navigate easily across different settings. Wheelchairs belong to the category of vehicles that require locomotion across various uneven terrains, such as stairs and other partitions. Therefore, the aim of this work is to design a wheel that can travel through various environments without using complex mechanisms. This aim was achieved by creating a novel transformable wheel. This wheel can turn into a legged wheel without using a complex mechanism and, when unnecessary, transform back into a normal wheel. Several simulations were carried out to understand the design's capability to overcome obstacles. In this case, the stair was the obstacle, and the results illustrated that the wheel is capable of climbing stairs without serious issues. Different parameters, such as speed and force, were examined with various values. The results showed that the weight on the wheelchair plays a significant role in climbing. For instance, with lighter weight, the structure moves without major slipping. The roughness of the surface is also related to the wheelchair's ability to reach the top of the stairs. A finer depth of cuts on the surface of the stairs increases the chance of the legged wheel firmly gripping and moving up the stairs.

关键词： Shape Navigation Wheelchairs Wheels Rotors Transforms Stairs Surface roughness Rough surfaces Vehicle dynamics

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