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

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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Secure kNN For Distributed Cloud Environment Using Fully Homomorphic Encryption

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IEEE Transactions on Cloud Computing 2025年

作者： Fukuchi, Yuuya Hashimoto, Sota Sakai, Kazuya Fukumoto, Satoshi Sun, Min-Te Ku, Wei-Shinn Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo Japan Tokyo Metropolitan University Department of Computer Science Tokyo Japan National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan Auburn University Department of Computer Science and Software Engineering AuburnAL United States

Privacy-preserving k-nearest neighbor (PPkNN) classification for multiple clouds enables categorizing queried data into a class in keeping with data privacy, where the database and key servers jointly perform cryptographic operations. The existing solutions, unfortunately, take a long time and incur a large amount of traffic between the database and key servers. Therefore, in this paper, we propose a fast and secure kNN classification protocol, namely FSkNN, over distributed databases deployed in multiple clouds under the semi-honest model. Particularly, we focus on optimizing the network-related operations during kNN classification. That is, the proposed cryptographic protocol reduces the number of interactions between the servers by using a fully homomorphic encryption scheme and eliminates unnecessary traffic by applying mathematical techniques. In addition, the indistinguishability-based security of FSkNN is proven. We implemented FSkNN with C++ and the testbed experiments demonstrate that the proposed scheme significantly facilitates the query response time and reduces the communication cost. © 2013 IEEE.

关键词： Differential privacy

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Decentralized Medical Image Sharing: A Blockchain Based Approach with Subject Sensitive Hashing for Enhanced Privacy and Integrity

IET Blockchain

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IET Blockchain 2025年第1期5卷

作者： Arafat, Yeasir Siam, Abu Sayem Md. Chowdhury, Md Muzadded Hasan, Md Mehedi Jobayer, Sayed Hossain Shatabda, Swakkhar Islam, Salekul Mukta, Saddam Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Electrical and Computer Engineering Department North South University Dhaka Bangladesh Department of Software Engineering LUT University Lappeenranta Finland

This research presents a blockchain-based framework for secure and efficient medical image sharing, prioritizing data integrity and privacy. The framework involves two key phases: image compression with feature extraction and image encryption with storage on the InterPlanetary File System (IPFS). Medical images are compressed using the JPEG algorithm to reduce file size while maintaining diagnostic value. A deep neural network-based subject sensitive hashing (SSH) algorithm ensures feature map integrity by extracting consistent features from both original and compressed images. Encrypted images, along with SSH-generated hashes, are securely stored in the IPFS server. The encryption key and hash sequence are used for secure image retrieval, with smart contracts validating access requests based on the hash sequence. This multi-stage feature extraction approach demonstrates robust image integrity, security, and privacy, as verified by experimental results. Achieving an average correctness rate of 98% across multiple datasets, the framework significantly enhances healthcare data management by addressing the challenges of secure, scalable, and private medical image sharing. This research contributes to the development of more efficient, reliable, and privacy-conscious solutions for medical image handling in healthcare systems. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Data integrity

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TangibleMoments: Embedding XR Memories onto Physical Objects

TangibleMoments: Embedding XR Memories onto Physical Objects

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Omar Khan Zaid Ahmed Hyeongil Nam Kangsoo Kim Department of Computer Science University of Calgary Department of Electrical and Software Engineering University of Calgary

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

The increasing prevalence of Extended Reality (XR) and head-mounted displays (HMDs), alongside rapid advancements in 3D reality capture technology, unlocks a new paradigm for capturing and reliving past memories/experiences through XR. Current methods for accessing and interacting with these "XR Memories" still lack the ability to fully leverage the range of capabilities afforded by XR and HMDs. We introduce TangibleMoments, a novel framework that enables users to embed XR memories onto physical objects, transforming those objects into "Moments"—tangible user interfaces for accessing and interacting with XR memories. We describe and illustrate five interaction methods as part of this framework: Creating Moments, Recalling Moments, Sharing Moments, Copying Moments, and Clearing Moments. We showcase an initial prototype and discuss possible extensions.

关键词： Three-dimensional displays Head-mounted displays Extended reality Conferences Prototypes Mixed reality User interfaces Cultural differences Artificial intelligence

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Investigating Visual Guide Cues in VR: Impacts of Virtual Humans and Symbol-Based Navigation on Real-World Performance and Experience

Investigating Visual Guide Cues in VR: Impacts of Virtual Hu...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Omar Khan Anh Nguyen Hyeongil Nam Kangsoo Kim Department of Computer Science University of Calgary Department of Electrical and Software Engineering University of Calgary

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Virtual experiences can significantly influence our perception and behavior in the real world, shaping how we interact with and navigate physical environments. In this paper, we examine the impact of learning navigation routes in an immersive virtual environment (IVE) on navigation performance and user experience in a corresponding real-world indoor setting. We developed a guide system with two distinct audiovisual representations: a human agent guide and a symbol-based guide. A preliminary user study (N = 10) was conducted to evaluate the system. While no significant differences were observed between the two guide conditions, the findings reveal valuable insights into user-perceived confidence and enjoyment during real-world navigation tasks. Contrary to our expectations, the symbol-based guide elicited slightly higher positive scores compared to the human agent guide. We discuss these findings and outline directions for future research.

关键词： Visualization Three-dimensional displays Navigation Conferences Virtual environments User interfaces User experience

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"I look like a gorilla, but don’t move like one!": Impact of Avatar-Locomotion Congruence in Virtual Reality

"I look like a gorilla, but don’t move like one!": Impact o...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Omar Khan Hyeongil Nam Kangsoo Kim Department of Computer Science University of Calgary Department of Electrical and Software Engineering University of Calgary

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

As virtual reality (VR) continues to expand, particularly in social VR platforms and immersive gaming, understanding the factors that shape user experience is becoming increasingly important. Avatars and locomotion methods play central roles in influencing how users identify with their virtual representations and navigate virtual spaces. However, little is known about the impact of congruence between these two factors. We conducted a user study with 30 participants, employing two avatar types (human and gorilla) and two locomotion methods (human-like arm-swing and gorilla-like arm-roll), to assess the effects of avatar-locomotion congruence. Our results indicate that congruent avatar-locomotion conditions enhance avatar identification and user experience.

关键词： Three-dimensional displays Shape Navigation Avatars Conferences User interfaces User experience Usability

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An empirical study of manual abstraction between class diagrams and code of open-source systems

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software and Systems Modeling 2025年 1-27页

作者： Zhang, Wenli Zhang, Weixing Strüber, Daniel Hebig, Regina Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden Department of Software Science Radboud University Nijmegen Netherlands Institute of Computer Science University of Rostock Rostock Germany

Models play a crucial role in software design, analysis, and supporting new maintainers. However, over time, the benefits of models can diminish as system implementations evolve without corresponding updates to the original models. Reverse engineering methods and tools can help maintain alignment between models and implementation code. Yet, automatically reverse-engineered models often lack abstraction and contain extensive details that hinder comprehension. Recent advancements in AI-based content generation suggest that we may soon see reverse engineering tools capable of human-grade abstraction. To guide the design and validation of such tools, we need a principled understanding of manual abstraction—a topic that has received limited attention in existing literature. In pursuit of this goal, our paper presents a multiple-case study of model-to-code differences, examining nine substantial open-source software projects obtained through repository mining. We manually matched source code from projects comprising 4983 classes, 26k attributes, and 54k operations to 523 model elements (including classes, attributes, operations, and relationships). These mappings precisely capture discrepancies between provided class diagram designs and actual implementation code. By analyzing these differences in detail, we derive a taxonomy of difference types and provide a well-organized list of cases corresponding to identified differences. Our findings have the potential to contribute to improved reverse engineering methods and tools, propose new mapping rules for model-to-code consistency checks, and offer guidelines to avoid over-abstraction and over-specification during the design process. © The Author(s) 2025.

关键词： Abstraction Modeling software design

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An Experimental Study on Dynamic Lifelong Learning with GPT for Mitigating Catastrophic Forgetting in Aspect-Based Sentiment Analysis

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IEEE Access 2025年

作者： Huang, Huang Asemi, Adeleh Universiti Malaya Faculty of Computer Science Information Technology Department of Software Engineering Kuala Lumpur Malaysia

GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for individuals and small businesses, underscoring the need for efficient, domainspecific alternatives. In Aspect-Based Sentiment Analysis (ABSA), existing models are typically optimized for single domains, facing challenges in performing effectively across multiple domains. A key issue, known as "catastrophic forgetting," arises when models trained on one domain lose previously learned knowledge upon exposure to new domain data. This leads to two significant problems: limited cross-domain generalization and difficulty in retaining prior knowledge while learning domain-specific information. To address these challenges, we introduce the Dynamic Lifelong Learning Aspect-Based Sentiment Analysis GPT (DllaGPT), a model designed to handle multiple domains while mitigating catastrophic forgetting. Leveraging datasets from four ABSA domains—Laptops, Restaurants, Tweets, and Finance—this study fine-tunes a pretrained GPT model from HuggingFace sequentially across domains. DllaGPT employs a mechanism to retain real data from earlier domains during new domain training, effectively preserving prior knowledge. Experimental results highlight that DllaGPT achieves an average accuracy of 0.85 and a Backward Transfer (BWT) score of -0.09 across the four domains, showcasing its high accuracy and robust lifelong learning capabilities. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Contrastive Learning

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TRANSIT-EEG - A Framework for Cross-Subject Classification with Subject Specific Adaptation

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Ahuja, Chirag Sethia, Divyashikha Delhi Technology University Department of Computer Science and Engineering Delhi India Delhi Technology University Department of Software Engineering Delhi India

Electroencephalography (EEG) is pivotal in monitoring and analysing cerebral activity across diverse domains, including medical diagnostics, cognitive neuroscience, and brain-computer interfaces. However, the inherent intricacy of EEG signals and their subject-specific characteristics pose formidable challenges in devising robust and generalisable classification models. Traditional EEG signal classification paradigms rely on extensive subject-specific datasets. Also, the domain adaption for new subjects often leads to "catastrophic forgetting,"thereby diminishing the performance of model trained on prior subjects. This paper proposes a novel framework, Transfer and Robust Adaptation of New Subjects in EEG Technology (TRANSIT-EEG), designed to adapt adeptly to new subjects. TRANSIT-EEG demonstrates resilience to subject-specific artefacts by integrating synthetic data generation using the proposed subject-specific augmentation model - Individualised Diffusion Probabilistic Model (IDPM). Also, it employs a robust Self Organising Graph Attention Transformer (SOGAT) that dynamically constructs a graph for each subject, fostering a more accurate classification. Moreover, TRANSIT-EEG introduces adapter-based finetuning using Low-Rank Adaptation (LoRA) for new subjects, enriching the adaptation process. The TRANSIT-EEG framework presents a promising avenue for advancing the realm of EEG signal classification. Evaluation of widely studied datasets, specifically focusing on two significant tasks, SEED for Emotion Recognition and PhyAat for Auditory Activity Recognition, substantiates the efficacy and versatility of TRANSIT-EEG. This validation indicates a substantial stride toward achieving more generalisable and accurate EEG signal classification. © 2016 IEEE.

关键词： Graph neural networks

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Batch Execution of Microbenchmarks for Efficient Performance Testing

Batch Execution of Microbenchmarks for Efficient Performance...

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International Conference on software Testing, Verification, and Validation, ICST

作者： Mostafa Jangali Kundi Yao Yiming Tang Diego Elias Costa Weiyi Shang Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Software Engineering Rochester Institute of Technology USA Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798331508142

ISBN: (纸本)9798331508159

Performance microbenchmarking is essential for ensuring software quality by providing granular insights into code efficiency. While automated performance microbenchmark generation tools (e.g., ju2jmh) are proposed to alleviate practitioners from manually curating microbenchmarks, the high volume of generated benchmarks can lead to protracted benchmarking execution time, as many of the generated benchmarks are too short in nature to be valuable for evaluating performance. In this paper, we present a novel approach that optimizes microbenchmark execution through a batching strategy, i.e., grouping benchmarks with similar code coverage and treating them as a single unit to 1) reduce execution overhead and 2) reduce the bias from microbenchmarks that are too short. We evaluate the effectiveness of this enhancement across various Java projects, comparing the execution times of clustered and individual micro benchmarks. Our findings demonstrate substantial improvements in execution efficiency, reducing execution time by up to 89.81% while preserving high microbenchmark stability.

关键词： software testing Java Codes software quality software performance Benchmark testing

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