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IEEE International Conference on Systems, Man and Cybernetics

作者： Yu Lu Pufan Cai Jingying Yu Shijie Shi Meng Li Huilin Ge Xianghua Fu College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Applied Sciences Shenzhen University Shenzhen China College of Automation Jiangsu University Science and Technology Zhenjiang China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The advancement of machine vision systems necessitates efficient and accurate signal reconstruction methods to enhance real-time perception and decision-making capabilities. This paper introduces a Generalized Backtracking Regularization Adaptive Matching Pursuit (GBRAMP) algorithm, designed to reconstruct signals within machine vision systems using compressed sensing techniques. The GBRAMP algorithm improves upon existing methods by incorporating regularization for enhanced atom selection and a backtracking approach to accurately estimate sparsity, addressing the limitations of traditional convex optimization, greedy, and Bayesian reconstruction algorithms. The paper provides a comparative analysis of the GBRAMP algorithm against other prominent reconstruction techniques. Experimental results validate the GBRAMP algorithm's improved performance in terms of both reconstruction accuracy and computational speed, making it a competitive solution for the next generation of machine vision systems.

关键词： Backtracking Adaptive systems Accuracy Machine vision Robot vision systems Matching pursuit algorithms Reconstruction algorithms Signal reconstruction Real-time systems Compressed sensing

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Deep Learning based Multilabel Hateful Speech Text Comments Recognition and Classification Model for Resource Scarce Ethiopian Language: The case of Afaan Oromo

Deep Learning based Multilabel Hateful Speech Text Comments ...

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2022 IEEE International Conference on Current Development in Engineering and technology, CCET 2022

作者： Defersha, Naol Bakala Abawajy, Jemal Kekeba, Kula College of Electrical and Mechanical Engineering Centre of Excellence for Hpc & Big Data Analytics Addis Ababa Science and Technology Software Engineering Department 16417 Ethiopia School of Information Technology Deakin University Faculty of Science Engineering and Built Environment GeelongVIC3220 Australia Centre of Excellence College of Electrical and Mechanical Engineering Addis Ababa Science and Technology Department of Software Engineering Technology 16417 Ethiopia

ISBN: (纸本)9781665454155

In response to the phenomenon of hate speech on popular social media such as Facebook, a number of researchers have investigated and developed different automated techniques in order to detect and moderate hate speech. Reliable hate speech detection tools ranging from traditional machine learning models to deep neural network models have been developed and deployed for resource rich languages such as English, French and Arabic. On the Other hand, the topic of building automated techniques for hate speech detection in Afaan Oromo is still the subject of very limited studies. In this study, we have used pretrained word embedding approaches such as word2vec and fast text, and assessed their effectiveness for hateful speech recognition and classification in Afaan Oromo text comments on Facebook. By using the proposed model, the effectiveness of deep learning algorithms like CNN and LSTM was also assessed. Moreover, the significance of one of the recent topic modeling approaches, i.e., BERTopic, has been investigated to in extracting topics that can help the categorization of Afaan Oromo hate speech. Afaan Oromo Multilabel Hateful Speech Detection and dataset (AOMLHSDD), which contains 30728 text comments, was created after we employed the BERTopic to extract relevant topics. Accordingly, Then, we have conducted different experiments to test the effectiveness of CNN and LSTM models by using word2vec and fast text. The result of our experiments has shown showed that LSTM has outperformed the CNN approach by achieving the accuracy of 98.32%. © 2022 IEEE.

关键词： Speech recognition

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Dynamic Bi-Elman Attention Networks (DBEAN): Dual-Directional Context-Aware Representation Learning for Enhanced Text Classification

arXiv

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arXiv 2025年

作者： Liao, Mengyao Lai, Zhenglin Xu, Dong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Text classification, a fundamental task in natural language processing (NLP), aims to categorize textual data into predefined labels. Traditional methods struggled with complex linguistic structures and semantic dependencies. The advent of deep learning, particularly recurrent neural networks (RNNs) and Transformer-based models, has significantly advanced the field by enabling nuanced feature extraction and context-aware predictions. Despite improvements, existing models exhibit limitations in balancing interpretability, computational efficiency, and long-range contextual understanding. This paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN), which integrates bidirectional temporal modelling with self-attention mechanisms. DBEAN dynamically assigns weights to critical segments of input, improving contextual representation while maintaining computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Elman neural networks

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

Towards Effective and Efficient Error Handling Code Fuzzing ...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

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.

关键词： Codes Computer bugs Fuzzing Software systems Software reliability Testing

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Online/Offline Learning to Enable Robust Beamforming: Limited Feedback Meets Deep Generative Models

arXiv

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arXiv 2024年

作者： Li, Ying Lin, Zhidi Li, Kai Zhang, Michael Minyi The Department of Statistics and Actuarial Science University of Hong Kong Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

Robust beamforming is a pivotal technique in massive multiple-input multiple-output (MIMO) systems as it mitigates interference among user equipment (UE). One current risk-neutral approach to robust beamforming is the stochastic weighted minimum mean square error method (WMMSE). However, this method necessitates statistical channel information, which is typically inaccessible, particularly in fifth-generation new radio frequency division duplex cellular systems with limited feedback. To tackle this challenge, we propose a novel approach that leverages a channel variational auto-encoder (CVAE) to simulate channel behaviors using limited feedback, eliminating the need for specific distribution assumptions present in existing methods. To seamlessly integrate model learning into practical wireless communication systems, this paper introduces two learning strategies to prepare the CVAE model for practical deployment. Firstly, motivated by the digital twin technology, we advocate employing a high-performance channel simulator to generate training data, enabling pretraining of the proposed CVAE while ensuring non-disruption to the practical wireless communication system. Moreover, we present an alternative online method for CVAE learning, where online training data is sourced based on channel estimations using Type II codebook. Numerical results demonstrate the effectiveness of these strategies, highlighting their exceptional performance in channel generation and robust beamforming applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks 3

Agricultural Water Conservation through AI-Enhanced Solar Io...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Maniraj, S.P. Monisha, J. Amutha, R. Kavitha, R. Shriidhar, P.J. Srinivasan, C. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur Tamilnadu Chennai India Tamil Nadu Chennai India Ultra College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Universityi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future. © 2025 IEEE.

关键词： Smart agriculture

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Personalized Care for Multiple Sclerosis with IoT Wearables and Deep Learning

Personalized Care for Multiple Sclerosis with IoT Wearables ...

引用

2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Vetriselvi, D. Sampathrajan, Rajeshkumar Deepa, A. Sujatha, V. Deepa, P. Pandi Murugan, S. Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Agni College of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

This research work presents a new method for improving individualized treatment for patients with Multiple Sclerosis (MS) by combining Internet of Things (IoT) wearables with deep learning (DL) methods, namely Long Short-Term Memory (LSTM) networks. MS is a long-term autoimmune disorder that affects the central nervous system. It is marked by unexpected episodes of worsening symptoms and periods of improvement, highlighting the need for tailored therapy. The widespread use of IoT devices presents unique possibilities for the ongoing monitoring of many physiological and activity measurements important to the development and symptoms of MS. Using LSTM networks, which are very proficient in analyzing temporal sequences, create a prediction model that may anticipate exacerbations of MS symptoms and customize therapies appropriately. Through the analysis of longitudinal data obtained from IoT devices, our model acquires knowledge about unique patterns of illness development and treatment response. This knowledge allows us to create customized care plans for individuals. These paths include proactive activities such as adjusting medication, making lifestyle changes, and seeking early medical advice. The goal is to reduce the worsening of symptoms, improve the quality of life, and optimize treatment results for individuals with MS. The proposed method shows potential for transforming the treatment of MS by offering proactive and patient-centered therapy adaptable to individual requirements and the dynamics of the illness. © 2024 IEEE.

关键词： Patient treatment

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A survey on copy-move image forgery detection based on deep-learning techniques

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Multimedia Tools and Applications 2024年 1-60页

作者： Samariya, Urmila Kamble, Shailesh D. Singh, Subhash Sonker, Rakesh Kumar Department of Information Technology Indira Gandhi Delhi Technical University for Women Delhi110006 India Department of Artificial Intelligence and Data Science Indira Gandhi Delhi Technical University for Women Delhi110006 India Department of Mechanical and Automation Engineering Indira Gandhi Delhi Technical University for Women Delhi110006 India Department of Physics Motilal Nehru College University of Delhi Delhi110021 India

The widespread use of digital images on social media and the internet has led to increased image forgery, which poses a significant challenge to their authenticity. It is crucial to validate the integrity of images in this digital era, where they are widely used for communication and information. This survey comprehensively reviews the existing digital image forgery detection techniques, focusing on copy-move forgery. Copy-move forgery is a common image tampering technique where a part of the image is duplicated and pasted elsewhere in the same image. Detecting such forgeries is crucial in various applications, including digital forensics, and image authentication. We discuss the different forgery detection methods, including block-based, keypoint-based, machine learning-based approaches, and various deep learning-based techniques, and a summary of benchmark datasets and evaluation measures is presented. The survey reveals that deep learning-based methods have shown promising results in detecting image forgeries, and the development of robust and efficient detection methods remains an active area of research. The study concludes with a discussion of future directions in image forgery detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Digital forensics

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection

Machine Learning is All You Need: A Simple Token-Based Appro...

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International Conference on Software Engineering (ICSE)

作者： Siyue Feng Wenqi Suo Yueming Wu Deqing Zou Yang Liu Hai Jin Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab Jinyinhu Laboratory Wuhan China School of Cyber Science and Engineering HUST Wuhan China Nanyang Technological University Singapore School of Computer Science and Technology HUST Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used bigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings.

关键词： Codes Scalability Computational modeling Cloning Machine learning Detectors Feature extraction

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Utilizing Automatic Speech Recognition for English Pronunciation Practice and Analyzing its Impact 12

Utilizing Automatic Speech Recognition for English Pronuncia...

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12th IEEE International Conference on Engineering Education, ICEED 2023

作者： Umezawa, Katsuyuki Nakazawa, Makoto Nakano, Michiko Hirasawa, Shigeichi Shonan Institute of Technology Department of Informatics Kanagawa Japan Junior College of Aizu Department of Industrial Information Science Fukushima Japan Waseda University Faculty of Education and Integrated Arts and Sciences Tokyo Japan Waseda University Data Science Center Tokyo Japan

ISBN: (纸本)9798350307429

The advancement of AI in recent years has been remarkable, along with the widespread use of speech recognition functions. In addition, an increasing number of people are self-studying in their fields of interest. In a world considered a global society, many people in countries where English is not their first language are learning English. Therefore, in this study, the authors focus on self-study English learning. The pronunciation is assumed to be correct if the speech recognition feature correctly identifies the pronounced English words and sentences. It has been recognized that feedback plays a crucial role in English pronunciation practice. If the speech recognition function can be employed to provide feedback, it would alleviate the burden on teachers and enable students to practice pronunciation independently. In this research, the authors propose practicing pronunciation using each browser's built-in speech recognition features, including Google Chrome, iOS, and Microsoft Edge, and evaluating if one's English pronunciation is correctly recognized. Through a 7-day experiment, the authors clarify the speech recognition function suitable for self-studying English pronunciation. Through this experiment, it was observed that the speech recognition function on iOS (version 15.0) outperforms compared to Google Chrome (version 107.0.5304.63) and Microsoft Edge (version 107.0.1418.24) in accurately understanding speech, even when the pronunciation is incorrect. However, it became apparent that such highly accurate speech recognition capabilities may not be suitable for self-study pronunciation practice. © 2023 IEEE.

关键词： Speech recognition

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