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2nd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2022

作者： Peter, Geno Stonier, Albert Alexander Sherine, Anli School of Engineering and Technology University of Technology Sarawak Crisd Malaysia Engineering College Department of Electrical and Electronics EngineeringKongu India School of Computing and Creative MediaUniversity of Technology Sarawak Malaysia

ISBN: (纸本)9781665437899

Smart Technologies are used to improve the quality of human life, especially the invention of the Internet. Thanks to the rapid growth of the Internet, more and more things can be solved and done in simple and faster ways compared to the old days. A relatively new field of e-voting has been discovered. There are many types and ways of voting, such as going to the polling station, online webpage and mobile application. Because of the rapid growth of the internet, the security of applications cannot be ignored. In this research we have developed an android application and provided a 3-step security before voting to prevent phishing attacks. This enables students to vote online using their own mobile phone from anywhere and anytime. The application is developed and deployed with Android Studio. This research follows the software development life cycle while developing the voting application. The outcome of this research is to develop a mobile app that is intuitive and easy for students to use, as well as serving as a functional application to let students vote with 3-step of security before voting. © 2022 IEEE.

关键词： Android (operating system)

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Image Caption Generation with Python: A Deep Learning-Based Approach

Image Caption Generation with Python: A Deep Learning-Based ...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Siram Chaitanya Krishna Painti Nagi Reddy P. Kirubanantham Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computing Technologies Faculty of Engineering and Technology School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Today, the day-to-day generation of such a large amount of content on social media and other websites makes it impossible to maintain each image manually, so the need for automatically identifying sensitive images and filtering them out becomes inevitable. This project proposes a sensitive image classification application using automatic captions in identifying and filtering inappropriate content. Thus, the information extracted from images by ResNet50 in the caption generation process is allowed to integrate such features with a standard model through Senti-Long Short-Term Memory networks (SLSTM) for better captioning. Deep learning models bring about drastically improved quality of captions along with contextual understanding. The captions are tested rigorously with training loss metrics, BLEU scores, and sentiment analysis through the VADER Sentiment Analyzer. This system efficiently categorizes images involving violence, negativity, or dangerous situations into the class of negative. A Flask-based web application was developed for implementing a scalable and user-friendly interface, ensuring efficient content moderation. This project has illustrated the possibility of integrating deep learning techniques with natural language processing to yield beneficial content moderation, further ensuring safe and controlled online environments.

关键词： Deep learning Measurement Training Sentiment analysis Filtering Social networking (online) Feature extraction Standards Image classification Testing

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Next-Generation Consumer Electronics Data Auditing Scheme Toward Cloud-Edge Distributed and Resilient Machine Learning

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 2244-2256页

作者： Li, Yi Shen, Jian Vijayakumar, Pandi Lai, Chin-Feng Sivaraman, Audithan Sharma, Pradip Kumar Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Tindivanam604001 India National Cheng Kung University Department of Engineering Science Tainan701 Taiwan Netaji Subhas University of Technology Department of Information Technology New Delhi110078 India University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

Distributed and resilient machine learning (DRML) endues next-generation consumer electronics with AI function. Intuitively, AI provides innovative, humanized, convenient applications based on the data extended by next-generation consumer electronics. Cloud-edge computing is an ideal undertaken architecture of DRML due to its distributed property. However, as one of the core elements driving AI applications, data could be lost or corrupted owing to damaged electronics, unstable communication, and even cloud providers' malicious behavior. It is essential to ensure the data integrity of next-generation electronics before AI applications. To this end, we proposed a privacy-protection distributed data auditing scheme for cloud-edge DRML. An efficient data integrity verification method that only uses algebraic operation is constructed. Then, Function Secret Share (FSS) extends the data integrity verification method to protect consumer privacy. Besides, a consensus for data auditing results is designed among the edge servers. Finally, we present an abundance of theoretical analyses and experimental findings to substantiate and validate the efficiency and effectiveness of our proposed scheme. © 1975-2011 IEEE.

关键词： Edge computing

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YOLOv4-Powered Traffic Intelligence: From Detection to Optimization

YOLOv4-Powered Traffic Intelligence: From Detection to Optim...

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Information technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Sinha Sanjiv Tharunn E M. Baskar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

The Traffic Analysis System utilizes machine learning for real-time traffic insights at a junction. It provides instant stats like vehicle count, density, and FPS, while summarizing average vehicle crossings, densities, stop times, and identifying peak minutes. Machine learning techniques include object detection, tracking, and lane segmentation. The system offers a user-friendly interface with graphical representations, aiding traffic management decisions. It not only ensures efficient traffic control but also allows for predictive analytics and potential integration with traffic light systems for adaptive signal adjustments. Continuous model training and future enhancements promise improved accuracy and adaptability to evolving traffic scenarios.

关键词： Training Accuracy Refining Machine learning Object detection Traffic control Real-time systems

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Towards Efficient and Diverse Generative Model for Unconditional Human Motion Synthesis 24

Towards Efficient and Diverse Generative Model for Unconditi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yu, Hua Liu, Weiming Bai, Jiapeng Gui, Xu Hou, Yaqing Ong, Yewsoon Zhang, Qiang Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education School of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science Zhejiang University Hangzhou China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China College of Computing and Data Science Nanyang Technological University Singapore Singapore Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798400706868

Recent generative methods have revolutionized the way of human motion synthesis, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DMs). These methods have gained significant attention in human motion fields. However, there are still challenges in unconditionally generating highly diverse human motions from a given distribution. To enhance the diversity of synthesized human motions, previous methods usually employ deep neural networks (DNNs) to train a transport map that transforms Gaussian noise distribution into real human motion distribution. According to Figalli's regularity theory, the optimal transport map computed by DNNs frequently exhibits discontinuities. This is due to the inherent limitation of DNNs in representing only continuous maps. Consequently, the generated human motions tend to heavily concentrate on densely populated regions of the data distribution, resulting in mode collapse or mode mixture. To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis. First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. Next, we employ convex optimization to match the noise distribution with the latent space distribution of human motions through the Optimal Transport (OT) map. Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion. Through qualitative and quantitative experiments, MOOT achieves state-of-the-art results surpassing the latest methods, validating its superiority in unconditional human motion generation. © 2024 ACM.

关键词： Generative adversarial networks

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Human-Computer Interaction: Innovations and Challenges in Virtual Reality 9

Human-Computer Interaction: Innovations and Challenges in Vi...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Anitha, Cuddapah Soundarraj, Prem Latha Geethanjali, N. Boopathy, C. Venkatesh, A. Narasima Krishna, I. Mohana School of Computing Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Department of Computer Science And Engineering Tirupati517102 India School of Business And Management Christ University Lavasa Maharashtra Pune412112 India Psna College of Engineering And Technology Department of Management Studies Tamil Nadu Dindigul India School of Management Studies Sri Krishna College of Engineering And Technology Coimbatore Kuniyamuthur India Hrm And General Management Isbr Business School Karnataka Bangalore India K L Business School Klef Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350365092

In an effort to shed light on the advances and difficulties that are shaping the area of Virtual Reality (VR), this research paper digs into the ever-evolving world of Human-Computer Interaction (HCI) within the context of VR. We have found important insights with theoretical and practical applications via a careful research technique comprising mathematical modelling, data collecting, and empirical analysis. Through our investigation of new technologies, we have shown the revolutionary potential of haptic feedback systems in VR settings. Our results, backed by a solid mathematical model, provide light on the measurable effect of haptic feedback and suggest it has the potential to radically alter user experiences in fields as diverse as gaming, instruction, and treatment. At the same time, we have overcome a number of obstacles inherent to virtual reality human-computer interaction, including motion sickness. Our mathematical model of motion sickness and its treatment lays the groundwork for creating VR experiences that are both enjoyable and safe for a wider range of users. This study highlights the ethical implications of VR HCI, highlighting the importance of responsible development and deployment in addition to advances and problems. To make sure that the advantages of this gamechanging technology are used in a responsible manner, we discuss issues like privacy, informed consent, and the possibility for addiction in VR. Our results, as we reach the end of our trip, are both a celebration of potential and a guide for where VR HCI is headed. They motivate more research into inclusive and human-centered design, personalized motion sickness prevention, and cutting-edge haptic feedback technologies. To further lead the development of VR HCI, we advocate for continuous multidisciplinary cooperation and the adoption of thorough ethical rules and regulations. © 2024 IEEE.

关键词： Virtual reality

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Chloroplast-mimicking nanoreactor for enhanced CO_(2) electrocatalysis

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Science Bulletin 2024年第18期69卷 2820-2824页

作者： Weiguang Maa Wenjun Fan Qinye Li Hefeng Zhang Kaixin Zhu Chenghua Sun Lianzhou Wang Xu Zong Clean Energy Center for Ship Marine Engineering CollegeDalian Maritime UniversityDalian 116026China State Key Laboratory of Catalysis Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian National Laboratory for Clean EnergyThe Collaborative Innovation Center of Chemistry for Energy Materials(iChEM)Dalian 116023China School of Science ComputingEngineering&TechnologySwinburne University of TechnologyHawthorn Vic 3122Australia Department Nanomaterials Centre School of Chemical Engineering and Australian Institute for Bioengineering and NanotechnologyThe University of QueenslandSt LuciaQLD 4072Australia

Producing fuels or chemicals via electrochemical carbon dioxide reduction reaction(CO_(2)RR)with renewable electricity has attracted great research interest due to its potential of alleviating the environmental and energy issues in a carbon–neutral manner[1].The CO_(2)RR is a proton-coupled electron transfer process with the simultaneous participation of multiple protons and electrons[2].

关键词： dioxide neutral carbon

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. Communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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Prediction of Cardiac Disease Using Quantum Enhanced Ensemble Learning Approach

Prediction of Cardiac Disease Using Quantum Enhanced Ensembl...

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Innovative Trends in Information technology (ICITIIT), International Conference on

作者： Jithu V Biju Adithya P Mallya Kirubanantham P Computing Technologies SRM Institute of Science & Technology Chennai India Department of Computing Technologies Faculty of Engineering and Technology Kattankulathur School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331536381

ISBN: (纸本)9798331536398

Our research introduces a state-of-the-art bagging ensemble model that leverages Quantum Support Vector Classifiers (QSVC) to assess the risk of heart disease. Testing involved rigorous experimentation on the Cleveland data set for which QSVC has demonstrated better performance compared with benchmark classifiers and other quantum-inspired models as well, achieving an excellent accuracy of 90.16%. The boosted accuracy shows the competence of quantum machine learning in serving as a potential upgrade of predictive analytics within the healthcare industry. In addition to its impressive performance metrics, the model incorporates SHAP (SHapley Additive exPlanations) analysis, which significantly enhances its interpretability. By identifying and elucidating the utmost need of various features in this entire route, SHAP analysis makes the persons who are into medical practicing get to know more about the core of this model. This transparency is crucial in medical applications, where understanding the factors influencing a diagnosis can lead to better key decisions and sentiments of patients. Thus, Implications of this research extend beyond mere performance statistics. The research shows that quantum machine learning, and specifically QSVC, can revolutionize healthcare by providing interpretable and accurate predictive models. Over time, the technology will keep improving, and ultimately it will improve patient care and the quality of healthcare.

关键词： Heart Analytical models Accuracy Cardiac disease Static VAr compensators Medical services Predictive models Vectors Ensemble learning Bagging

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Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning 38

Free-Rider and Conflict Aware Collaboration Formation for Cr...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Mengmeng Wu, Xiaohu Tang, Xiaoli He, Tiantian Ong, Yew-Soon Liu, Qiqi Lao, Qicheng Yu, Han Beijing University of Posts and Telecommunications China College of Computing and Data Science Nanyang Technological University Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore School of Engineering Westlake University China

Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. This necessitates FL-PT selection based on their data complementarity. In cross-silo FL, organizations that engage in business activities are key sources of FL-PTs. The resulting FL ecosystem has two features: (i) self-interest, and (ii) competition among FL-PTs. This requires the desirable FL-PT selection strategy to simultaneously mitigate the problems of free riders and conflicts of interest among competitors. To this end, we propose an optimal FL collaboration formation strategy -FedEgoists- which ensures that: (1) a FL-PT can benefit from FL if and only if it benefits the FL ecosystem, and (2) a FL-PT will not contribute to its competitors or their supporters. It provides an efficient clustering solution to group FL-PTs into coalitions, ensuring that within each coalition, FL-PTs share the same interest. We theoretically prove that the FL-PT coalitions formed are optimal since no coalitions can collaborate together to improve the utility of any of their members. Extensive experiments on widely adopted benchmark datasets demonstrate the effectiveness of FedEgoists compared to nine state-of-the-art baseline methods, and its ability to establish efficient collaborative networks in cross-silos FL with FL-PTs that engage in business activities. © 2024 Neural information processing systems foundation. All rights reserved.

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