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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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Body-part Identification Learning for Preschool Children Using Internet of Things 5

Body-part Identification Learning for Preschool Children Usi...

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5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023

作者： Suwastika, Novian Anggis Sitohang, Paulus Berliz Yasirandi, Rahmat Masrom, Maslin Qonita, Qori Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Telkom University School of Computing Bandung Indonesia King Mongkut's Institute of Technology Ladkrabang School of Information Technology Ladkrabang Thailand Public Vocational High School 10 Bandung Broadcast and Film Area of Expertise Bandung Indonesia

ISBN: (纸本)9798350303414

Children can identify body parts either on dolls or on their bodies. The inability of toddlers (aged 2-3 years) to demonstrate body parts can indicate potential delays in language, cognitive, and body scheme development. The integration of teaching aids for body part identification with the Internet of Things (IoT) offers several advantages, including automation and customization of activities, digital storage of activity results for easy accessibility, real-time and accurate evaluation and feedback, as well as the freedom to repeat activities without limitations. This study focuses on implementing the integration of body part identification trainers with IoT, following the five phases of IoT development: initialization, analysis, design, implementation, and evaluation. The initialization phase involves problem identification, and the position of this research is based on a literature study. The analysis phase establishes parameters for measuring the functionality and performance of the system, as well as requirements for software and hardware components. The design phase produces the system architecture and system flowchart. The implementation phase results in a prototype integrating hardware and software components. Evaluation results based on the predefined functionality and performance parameters set during the analysis phase indicate that all system functionalities have been successfully achieved. The system demonstrates a response time below 1 second for sensor buttons and LED lights, and the feedback system shows 100% accuracy. This prototype highlights the potential and opportunities of implementing IoT to support learning at various levels, specifically at the pre-school level, as shown in this study. © 2023 IEEE.

关键词： Internet of things

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A Comparison of Regression Techniques for Prediction of Air Quality in Smart Cities 1

A Comparison of Regression Techniques for Prediction of Air ...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Garg, Kamal Deep Gupta, Manik Sharma, Bhisham Dhaou, Imed Ben Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The expansion of the internet of things (IoT) and Big Data are two factors that have contributed to the rise in popularity of smart cities. The ability to anticipate the quality of the air in an area with precision and efficiency is one of the fundamental building blocks of a smart city. The amount of polluted air found in smart cities throughout the world has been gradually growing. Because of this, there has been a rise in the concentration of several air pollutants in the environment, including particulate matter (PM 10), sulphur dioxide (SO2), and PM 2.5, amongst others. Because of the possibility of uncontrollable repercussions, such as an increase in the severity of asthma and cardiovascular disease, this situation poses a risk to the country and to the people who live in it. Heavy industry and vehicle exhaust have been major contributors to the growth of air pollution in smart cities such as New Delhi, Bombay, Chandigarh, and Bengaluru in India. The purpose of this investigation is to compare and contrast the efficiency of a variety of machine learning methods in order to assess the precision of the air quality index (AQI) projection of PM 2.5 in Chandigarh, India. Models for predicting AQI are trained and tested using a variety of statistical techniques like Linear regression, Lasso regression, KNN regression, and Random Forest regression This Root Mean Square Error (RMSE) found for Linear regression, Lasso regression, KNN regression, and Random Forest regression are 31.01, 29,45, 37.09 and 28.3. From all four models, random forest regression was more accurate than the other three regression models in estimating PM 2.5 levels in India's smart city. © 2023 IEEE.

关键词： Diseases

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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Review of AI Technologies for Enhancing the Lives of Visually Impaired Individuals: Applications, Outcomes, and Future Directions

Review of AI Technologies for Enhancing the Lives of Visuall...

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Information and Communication technology for Development for Africa (ICT4DA), International Conference on

作者： Fitsum Gizachew Deriba Betelhem Zewdu Wubineh Mengistu Zenebe Teka School of Computing University of Eastern Finland Joensuu Finland Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland School of Computing and Informatics Wachemo University Hosaena Ethiopia

ISBN: (数字)9798331527792

ISBN: (纸本)9798331527808

Imagine an environment where individuals with disabilities enjoy a significantly improved quality of life, not only because of the support they receive but also due to advancements in artificial intelligence (AI). While existing studies focus on exploring specific aspects of AI technologies for visually impaired (VI) individuals, there is limited research exploring the outcomes of designed and implemented AI technologies on these individuals. This study examines the current state of AI technologies in supporting VI individuals. We systematically reviewed 263 articles published between 2008 and 2023, of which 33 met the inclusion criteria for our study. This rigorous selection process ensured that only the relevant studies conducted with AI technology for VI were included in our analysis. The results identified several applications developed for various purposes, predominantly 45% focused on AI technology for independent navigation, followed by technology that addresses daily activities issues (21%). Our findings indicate that limited AI technologies have been developed for educational purposes and use in finance and e-commerce. The outcomes of using AI technology demonstrate improvements in navigation and mobility, as well as enhanced independence and quality of life. While our study identifies gaps in existing AI technology for VI, it also contributes to various stakeholders, including developers, users, and researchers, by providing a comprehensive understanding of the current state of AI technology for individuals with VI.

关键词： Ethics Navigation Reviews Finance Africa Information and communication technology Electronic commerce Stakeholders Artificial intelligence

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IoMT-Based Smart Healthcare of Elderly People Using Deep Extreme Learning Machine

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Computers, Materials & Continua 2023年第7期76卷 19-33页

作者： Muath Jarrah Hussam Al Hamadi Ahmed Abu-Khadrah Taher M.Ghazal School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia Center for Cyber-Physical Systems Khalifa University127788Abu DhabiUAE

The Internet of Medical Things(IoMT)enables digital devices to gather,infer,and broadcast health data via the cloud *** phenomenal growth of the IoMT is fueled by many factors,including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based *** is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising *** IoMT is a novel reality transforming our daily *** can renovate modern healthcare by delivering a more personalized,protective,and collaborative approach to ***,the current healthcare system for outdoor senior citizens faces new *** healthcare systems are inefficient and lack user-friendly technologies and interfaces appropriate for elderly people in an outdoor ***,in this research work,a IoMT based Smart Healthcare of Elderly people using Deep Extreme Learning Machine(SH-EDELM)is proposed to monitor the senior citizens’*** performance of the proposed SH-EDELM technique gives better results in terms of 0.9301 accuracy and 0.0699 miss rate,respectively.

关键词： ICT ML FN DELM SH-EDELM

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Towards an Enthymeme-Based Communication Framework 21

Towards an Enthymeme-Based Communication Framework

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21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022

作者： Panisson, Alison R. McBurney, Peter Bordini, Rafael H. Department of Computing UFSC Araranguá Brazil Department of Informatics KCL London United Kingdom School of Technology PUCRS Porto Alegre Brazil

ISBN: (纸本)9781713854333

In this work, we give an operational semantics for speech acts that BDI agents can use to communicate enthymemes. The approach uses argumentation schemes as common organisational knowledge to guide the construction of enthymemes by the proponents of arguments. Such schemes are also used to guide the reconstruction of the intended argument by the recipients of such enthymemes. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Semantics

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A Hybrid Computational Framework Using NLP and ML for Emotion Analysis in Sinhala Songs

A Hybrid Computational Framework Using NLP and ML for Emotio...

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Advanced Research in computing (ICARC), International Conference on

作者： Malinda Peiris Deshan Sumanathilaka School of Computing Informatics Institute of Technology Colombo Sri Lanka School of Mathematics and Computer Science Swansea University Wales United Kingdom

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

This study Rasathura explores the emotional dimensions of Sri Lankan music, specifically Sinhala songs, by developing an emotional classification model using both audio features and lyrical analysis. The research addresses the limitations in emotional classification using advanced Natural Language Processing (NLP) techniques for Sinhala music. For audio analysis, JAudio was used to extract musical data, and machine learning (ML) techniques, including Random Forest, Sequential Minimal Optimization (SMO), Naive Bayes, Decision Tree, and Logistic Regression, were evaluated for classification. Relief-based and correlation-based attribute selection were utilized to enhance classification accuracy. The SMO algorithm achieved an accuracy of 75.92% using JAudio features, which increased to 79.01% with Relief-based attribute selection. The BERT model (built on XLM-RoBERTa) was used for lyrical analysis, achieving a maximum accuracy of 61.22%. TFIDF, Word2Vec with n-grams, and BERT embeddings were evaluated for feature extraction in lyrical data. The findings could benefit the future music industry by creating personalized music recommendation systems, enhancing music therapy applications, and contributing to preserving cultural heritage through digital music archiving.

关键词： Analytical models Accuracy Machine learning algorithms Music Medical treatment Feature extraction Natural language processing Recommender systems Random forests Optimization

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A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior

arXiv

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arXiv 2025年

作者： Jayasuriya, Nikil Sumanathilaka, Deshan School of Computing Informatics Institute of Technology Colombo006 Sri Lanka School of Mathematics and Computer Science Swansea University United Kingdom

This paper systematically explores the advancements in adaptive trip route planning and travel time estimation (TTE) through Artificial Intelligence (AI). With the increasing complexity of urban transportation systems, traditional navigation methods often struggle to accommodate dynamic user preferences, real-time traffic conditions, and scalability requirements. This study explores the contributions of established AI techniques, including Machine Learning (ML), Reinforcement Learning (RL), and Graph Neural Networks (GNNs), alongside emerging methodologies like Meta-Learning, Explainable AI (XAI), Generative AI, and Federated Learning. In addition to highlighting these innovations, the paper identifies critical challenges such as ethical concerns, computational scalability, and effective data integration—that must be addressed to advance the field. The paper concludes with recommendations for leveraging AI to build efficient, transparent, and sustainable navigation systems. © 2025, CC BY.

关键词： Federated learning

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection 31

Towards Effective and Efficient Error Handling Code Fuzzing ...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin Jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China

ISBN: (纸本)9798350330663

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL-F I are effective that contribute significantly to its overall performance. © 2024 IEEE.

关键词： Errors

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