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27th International Conference on Pattern Recognition, ICPR 2024

作者： Vavilthota, Venkata Ragavendra Ramanathan, Ranjith Aakur, Sathyanarayanan N. Department of Computer Science Oklahoma State University StillwaterOK74078 United States Department of Animal and Food Sciences Oklahoma State University StillwaterOK74078 United States Department of Computer Science and Software Engineering Auburn University Auburn36849 United States

ISBN: (纸本)9783031781063

Analyzing sequential data is crucial in many domains, particularly due to the abundance of data collected from the Internet of Things paradigm. Time series classification, the task of categorizing sequential data, has gained prominence, with machine learning approaches demonstrating remarkable performance on public benchmark datasets. However, progress has primarily been in designing architectures for learning representations from raw data at fixed (or ideal) time scales, which can fail to generalize to longer sequences. This work introduces a compositional representation learning approach trained on statistically coherent components extracted from sequential data. Based on a multi-scale change space, an unsupervised approach is proposed to segment the sequential data into chunks with similar statistical properties. A sequence-based encoder model is trained in a multi-task setting to learn compositional representations from these temporal components for time series classification. We demonstrate its effectiveness through extensive experiments on publicly available time series classification benchmarks. Evaluating the coherence of segmented components shows its competitive performance on the unsupervised segmentation task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spatio-temporal data

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Using Social Robots to Enhance Cognitive Health in Older Adults with Mild Cognitive Impairment 20

Using Social Robots to Enhance Cognitive Health in Older Adu...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Gao, Yuan Kerse, Ngaire MacDonald, Bruce Broadbent, Elizabeth University of Auckland Department of Psychological Medicine Auckland New Zealand University of Auckland Department of General Practice and Primary Healthcare Auckland New Zealand University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350378931

Mild cognitive impairment (MCI) is an early stage of cognitive decline that significantly increases the risk of dementia, making early interventions crucial for maintaining cognitive health in older adults. Our research takes a co-design approach to understand, design, and evaluate how socially assistive robots and a virtual human can promote lifestyle changes for people with MCI (pwMCI), potentially improving their cognitive health. Through an iterative refinement process, we aim to develop adaptable, user-friendly, and sustainable technologies that foster long-term engagement across diverse settings. Ultimately, our goal is to improve cognitive health, quality of life, emotional well-being, and loneliness in pwMCI. © 2025 IEEE.

关键词： Printed circuit design

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A Meta-Model to Support Compatibility Testing of Cross-Browser Web Application

A Meta-Model to Support Compatibility Testing of Cross-Brows...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Mishal Muneer Uzair Rasheed Muhammad Majid Hussain Department of Computer Science & IT The University of Lahore Lahore Pakistan Department of Computer and Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Ensuring the user interface (UI) compatibility of web applications across diverse client-side configurations, including various operating systems and browsers, is a significant challenge due to the extensive range of possible combinations. Traditional tools often struggle to address this complexity, leading to visual inconsistencies and malfunctions. To improve compatibility testing, we propose a meta-model that initiates the process with a checklist covering critical configurations across different browsers. This checklist is then translated into Interaction Flow Modeling Language (IFML) constructs, enabling a model-driven approach to compatibility assessment. By leveraging this checklist-IFML integration, we systematically generate test cases that target compatibility issues more effectively. Our approach is validated through the Laptop-Web Case Study, where we assess compatibility by comparing checklist items with IFML constructs within the case study’s domain model. The results show that our approach enhances the accuracy and efficiency of compatibility testing, addressing a core gap in current methods. This research introduces a structured, model-driven method for compatibility testing, providing a more reliable framework for identifying and mitigating UI compatibility issues in web applications across varying client-side configurations.

关键词： Visualization Translation Flow production systems Computational modeling Operating systems User interfaces Reliability engineering Browsers Complexity theory Testing

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Monitoring of Fire-Affected Buildings and Air-Quality Assessment: A Remote-Sensing Study Using Ground-Based interferometric Radar and Google-Earth-Engine

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Rangeland Ecology and Management 2025年 101卷 28-42页

作者： Zhang, Hao Zheng, Xiangtian Ghaffar, Bushra Quddoos, Abdul Naz, Iram Aslam, Rana Waqar Fatima, Zoia Abdullah-Al-Wadud, M. Zulqarnain, Rana Muhammad China Academy of Safety Science and Technology Beijing 100012 China Nanjing Institute of Technology School of Computer Engineering Nanjing 211167 China Department of Environmental Science Faculty of Sciences International Islamic University Islamabad 44000 Pakistan State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (LIESMARS) Wuhan University Wuhan 430079 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Department of Mathematics Saveetha School of Engineering SIMATS Thandalam Tamilnadu Chennai - 602105 India

This study investigates the environmental and structural impacts of crop and rangeland residue burning in Punjab during April and May 2023, utilizing ground-based interferometric radar alongside atmospheric and land use datasets. The radar system was employed to monitor structural deformations in buildings affected by fire, providing critical insights into the broader impacts of the fires on infrastructure. Multiple ground-based interferometric radars for distributed monitoring can effectively obtain the three-dimensional deformation components of structural targets in real fire scenes. The monitoring results conform to the displacement variation law of existing portal steel frame structures during fire deformation, and can provide effective data support for building structure collapse warning under fire. This approach offers a novel method for assessing the structural integrity of buildings during and after fire events, complementing traditional environmental monitoring. Environmental data show a marked increase in atmospheric pollutants such as NO₂, CH₄, and CO in May 2023, directly linked to the residue burning. Active fire data from FIRMS and MODIS revealed a 98% increase in fire events compared to April, while land surface temperature analysis showed a rise of 1.54%, with affected areas reaching up to 45.9°C. LULC analysis indicated a 23.3% decrease in cropland and rangeland areas, reflecting extensive land clearing. These findings highlight the urgent need for sustainable agricultural management and policies to mitigate the environmental and infrastructural damage caused by residue burning in Punjab. © 2025 The Society for Range Management

关键词： Agriculture Fire & Air Quality Distributed monitoring Google Earth Engine Ground-based interferometric radar Sentinel-2

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SPUFChain: Permissioned Blockchain Lightweight Authentication Scheme for Supply Chain Management Using PUF of IoT

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IEEE Access 2025年 13卷 88662-88682页

作者： Assaqty, Mohammad Iqbal Saryuddin Gao, Ying Alqarni, Abeer D. Khan, Siraj Chakraborty, Mohuya Siddiqi, Hafied Nur Ahmad, Sadique South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Universitas Nahdlatul Ulama Indonesia Faculty of Engineering and Computer Science Jakarta10320 Indonesia College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Department of Information Technology PO Box 84428 Riyadh11671 Saudi Arabia Shenzhen University College of Computer Science & Software Engineering Shenzhen518060 China University of Engineering and Management Cybersecurity Centre of Excellence Kolkata700160 India Prince Sultan University EIAS Data Science and Blockchain Laboratory CCIS Riyadh12435 Saudi Arabia

The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a security mechanism that exploits the unique, unrepeatable physical characteristics of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The unique identification of IoT devices along the supply chain is implemented by using PUFs as tamper-resistant IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its potential to combat several attack scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing

关键词： Supply chain management

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SegOTA: Accelerating Over-the-Air Federated Learning with Segmented Transmission

arXiv

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arXiv 2025年

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Department of Electrical and Computer Engineering University of Toronto Canada Department of Electrical Computer and Software Engineering Ontario Tech University Canada Ericsson Canada

—Federated learning (FL) with over-the-air computation efficiently utilizes the communication resources, but it can still experience significant latency when each device transmits a large number of model parameters to the server. This paper proposes the Segmented Over-The-Air (SegOTA) method for FL, which reduces latency by partitioning devices into groups and letting each group transmit only one segment of the model parameters in each communication round. Considering a multi-antenna server, we model the SegOTA transmission and reception process to establish an upper bound on the expected model learning optimality gap. We minimize this upper bound, by formulating the per-round online optimization of device grouping and joint transmit-receive beamforming, for which we derive efficient closed-form solutions. Simulation results show that our proposed SegOTA substantially outperforms the conventional full-model OTA approach and other common alternatives. Copyright © 2025, The Authors. All rights reserved.

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An Efficient Deep Learning-Based Framework for Predicting Cyber Violence in Social Networks

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Complexity 2025年第1期2025卷

作者： Fayand Fathabad, Younes Balafar, Mohammad Ali Golzari Oskouei, Amin Koohi, Kamal Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of IT and Computer Engineering Urmia University of Technology Urmia Iran Department of Software Engineering Faculty of Engineering and Natural Science İstinye University Istanbul Turkey Department of Social Sciences Faculty of Law and Social Sciences University of Tabriz Tabriz Iran

The widespread use of the internet has led to the rapid expansion of social networks, making it easier for individuals to share content online. However, this has also increased the prevalence of cyber violence, necessitating the development of automated detection methods. Deep learning-based algorithms have proven effective in identifying violent content, yet existing models often struggle with understanding contextual nuances and implicit forms of cyber violence. To address this limitation, we propose a novel deep multi-input recurrent neural network architecture that incorporates neighborhood-based contextual information during training. The Jaccard similarity metric is employed to construct neighborhoods of input texts, allowing the model to leverage surrounding context for improved feature extraction. The proposed model combines Bi-LSTM and GRU networks to capture both sequential dependencies and contextual relationships effectively. The proposed model was evaluated on a real-world cyber violence dataset, achieving an accuracy of 94.29%, recall of 81%, precision of 72%, and an F1-score of 76.23% when incorporating neighborhood-based learning. Without contextual information, the model attained an accuracy of 89.15%, recall of 72.00%, precision of 71.5%, and an F1-score of 71.74%. These results demonstrate that neighborhood-based learning contributed to an average improvement of 5.14% in accuracy and 4.49% in F1-score, underscoring the importance of contextual awareness in cyber violence detection. These results highlight the significance of contextual awareness in deep learning-based text classification and underscore the potential of our approach for real-world applications. Copyright © 2025 Younes Fayand Fathabad et al. Complexity published by John Wiley & Sons Ltd.

关键词： Recurrent neural networks

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Feature Extraction and Analysis for GPT-Generated Text

arXiv

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arXiv 2025年

作者： Selvioğlu, Aydın Adanova, Venera Atagoziev, Maksat Department of Computer Engineering TED University Ankara Turkey Department of Software Engineering OSTIM Technical University Ankara Turkey

With the rise of advanced natural language models like GPT, distinguishing between human-written and GPT-generated text has become increasingly challenging and crucial across various domains, including academia. The long-standing issue of plagiarism has grown more pressing, now compounded by concerns about the authenticity of information, as it is not always clear whether the presented facts are genuine or fabricated. In this paper, we present a comprehensive study of feature extraction and analysis for differentiating between human-written and GPT-generated text. By applying machine learning classifiers to these extracted features, we evaluate the significance of each feature in detection. Our results demonstrate that human and GPT-generated texts exhibit distinct writing styles, which can be effectively captured by our features. Given sufficiently long text, the two can be differentiated with high accuracy. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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Evaluating Feature Prioritization in a Reduced Feature Set Using Explainable AI for Intracranial Aneurysm Classification

Evaluating Feature Prioritization in a Reduced Feature Set U...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Iram Abdullah Ali Javed Department of Computer Science Air University Kamra Pakistan Department of Software Engineering University of Engineering & Technology Taxila Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

The timely management of an unruptured aneurysm can prevent hemorrhage. Multiple anatomical, morphological, and hemodynamic features associated with an aneurysm can predict the risk of rupture and classify the nature of the aneurysm as either ruptured or unruptured. However, considering all features may create ambiguities in the model due to inter-dependencies, such as the correlation between aneurysm height and width to aneurysm area. In computer-aided medical diagnostics, focusing on relevant features that diagnose the disease is more crucial than using an abundance of features that may not contribute to the diagnosis and may cause unnecessary complexity. The use of independent or sparsely correlated features improves the accuracy of the classifier. The proposed research analyzes the interdependency of features by calculating the correlation between features. Based on the analyses, the high correlation threshold is determined through rigorous experimentation. From the highly correlated features, one feature is selected. Consequently, the reduced feature set is recommended. The reduced feature set performs better with simpler classifiers. Instead of choosing a classifier that struggles with a broad range of features, an appropriately optimized feature set can enhance performance and improve accuracy. The proposed approach is tested on a publicly available dataset of aneurysms, leading to an improvement in classification accuracy from 82.6% to 87%, due to the use of an appropriate feature set. Moreover, the best model was evaluated using two different explainable AI frameworks to assess the contribution of each feature in prediction. However, the conflicting feature prioritization in each explainable framework suggests that these models prioritize different features for the same trained model, leading to ambiguity. This inconsistency indicates that the explainable models lack generalization and fail to consistently analyze feature importance.

关键词： Accuracy Correlation Explainable AI Computational modeling Redundancy Training data Aneurysm Predictive models Medical diagnosis Random forests

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