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Joint Delay and Energy Optimization for WPT-MEC System Based on Immune Algorithm 19th

Joint Delay and Energy Optimization for WPT-MEC System Bas...

19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Sun, Lu Li, Dianju Lu, Hao Wan, Liangtian Zheng, Jianbo Wang, Xianpeng Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China City University of Macau Macau China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Software Dalian University of Technology Dalian116620 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (纸本)9783031651250

In some production workshops, because the space is too small to be suitable for the layout of the equipment charging device, the joint application of mobile edge computing (MEC) and wireless power transfer (WPT) can solve such problems. However, this scenario has a double near-far effect that is unfair to terminal devices far away from edge servers and energy transfer stations, so this paper considers relay collaboration among devices when computing offloading. This paper uses the frequency division multiple access (FDMA) technology to enable multiple terminals to perform tasks offloading simultaneously. In this paper, the total communication delay and total energy consumption of the system are optimized through effective computing resources scheduling and reasonable tasks allocation, which is a weighting and minimizing problem of normalized system delay and energy consumption rate, and also a NP-hard problem. This paper improves the immune algorithm (IA) in the scenario of multi-server and multi-terminal to obtain the Q-IADE algorithm. The improved Q-IADE algorithm not only has the characteristics of wide application, but also further improves the global search ability, which can better solve the problems we raised. Finally, the simulation results show that the proposed Q-IADE algorithm has strong global search ability and stable convergence effect, and the algorithm performance is superior to the other three comparison algorithms, especially when relay offloading can be performed. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Energy utilization

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Dogodo: IoT Based Mobile Application to Provide Essential Health Services to Dogs 3

Dogodo: IoT Based Mobile Application to Provide Essential He...

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3rd International Conference on Advancements in computing, ICAC 2021

作者： Thilakarathne, L.V.I.S. Salay, M.S. Wijethilaka, M. G. R Chethana Fernando, T.S. Sriyaratna, Disni Rupasinghe, Shalini Sri Lanka Institute of Information Technology Faculty of Computing Department of Infotmation Tchnology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Voice of dogs can be heard by people who listen to them. The more you listen, the more you learn about the dogs. This study proposes a platform to identify and observe dogs' behavior and their activities by using the newest technologies. The proposed system will mainly cover the relevant areas that are supposed to be covered to full fill the pet owners' expectations by providing necessary services such as internal health, voice recognition, and emotion translations and external issues such as skin diseases, breeding patterns, and breeding outcomes. Our priority is to emphasize necessary services in the mobile application and provide fluid services with fewer interruptions. Primarily research will offer an IoT device and a mobile application that covers the dog's internal health and external health consecutively. The study revealed current developments and the challenges and possibilities related to the system. © 2021 IEEE.

关键词： Speech recognition

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Neighboring Information Exploitation for Anomaly Detection in Intelligent IoT 8th

Neighboring Information Exploitation for Anomaly Detection i...

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8th International Conference on Future Data and Security engineering , FDSE 2021

作者： Dang, Thien-Binh Le, Duc-Tai Kim, Moonseong Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Department of IT Convergence Software Seoul Theological University Bucheon Korea Republic of College of Computing and Informatics Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9783030913861

The identification of anomalies has become increasingly important for the security of sensory data gathering in the intelligent Internet of Things (iIoT). The current approaches might not be applied to the general cases of anomalies, i.e., both long-term and short-term anomalies, as well as not be suitable with real-time applications such as natural disaster monitoring and early warning systems. To address this challenge, this paper proposes a comprehensive approach, named DWT-PCA Anomaly Detection (DAD) to detect both long- and short-term anomalies. DAD bases on the combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) to improve the system performance. In particular, we first utilize the DWT to extract approximation coefficients and detail coefficients from the input data which are capable to highlight long-term and short-term anomalies, respectively. We then exploit the spatial-temporal correlation of neighboring sensors by applying PCA on these coefficients to obtain a high detection accuracy. Numerical experiments based on the real dataset of Intel Berkeley Research reveal that the proposed scheme obtains higher accuracy and a lower false-positive rate on detecting three typical anomalies: drift, noise, and outlier, comparing to existing schemes. © 2021, Springer Nature Switzerland AG.

关键词： Anomaly detection

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Machine learning for Autopsy reports Forensic using Text Classification Techniques 2

Machine learning for Autopsy reports Forensic using Text Cla...

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2nd International Conference on computing and Information Technology, ICCIT 2022

作者： Shahid, Muhammad Rehman Munir, Asim Ifraheem, Layeba Aldabbas, Hamza Wadood, Abdul Alwada'n, Tariq University of Faisalabad Departmental of Computational Sciences Faislabad Pakistan International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan Al-Balqa Applied University Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Salt Jordan School of Computing Engineering Digital Technologies Teesside University Middlesbrough United Kingdom

ISBN: (纸本)9781665436052

A forensic autopsy is a surgical process in which experts collect a deceased body's internal and external information. These death certificates are the source of timely warnings of an increase in disease activity. It's only helpful if accurate and quantitative data is available. Therefore, the Classification of plain text medical autopsy reports reduces the time consumption and irregularities. The motive is to design an automatic text classification system that classifies plain text autopsy reports. Therefore, a methodology proposes using different Automatic Text Classification Techniques (ATC). This technique has embedded Feature Extraction, Feature Representation, and Feature Reduction techniques. These techniques use for the construction of classification models that classify the text of autopsy reports. Data sets collected from these types will be helpful in future experiments. Finally, the performance of the classifier measures by using different Evaluation parameters. These Evaluation Measures are Precision, Recall, Accuracy, and F-measure. © 2022 IEEE.

关键词： Support vector machines

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Effective Entry-Wise Flow for Molecule Generation

Effective Entry-Wise Flow for Molecule Generation

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International Conference on Data engineering

作者： Qifan Zhang Junjie Yao Yuquan Yang Yizhou Shi Wei Gao Xiaoling Wang School of Computer Science and Technology East China Normal University Software Engineering Institute East China Normal University Shanghai Key Laboratory of Trustworthy Computing Department of Chemistry University of Massachusetts Boston University of Michigan Ann Arbor.

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts.

关键词： Training Couplings Materials science and technology Interpolation Data engineering Drug discovery Task analysis

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Metaverse: A Vision, Architectural Elements, and Future Directions for Scalable and Realtime Virtual Worlds

arXiv

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arXiv 2023年

作者： Ismail, Leila Buyya, Rajkumar Lab School of Computing and Information Systems The University of Melbourne Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

With the emergence of Cloud computing, Internet of Things-enabled Human-Computer Interfaces, Generative Artificial Intelligence, and high-accurate Machine and Deep-learning recognition and predictive models, along with the Post Covid-19 proliferation of social networking, and remote communications, the Metaverse gained a lot of popularity. Metaverse has the prospective to extend the physical world using virtual and augmented reality so the users can interact seamlessly with the real and virtual worlds using avatars and holograms. It has the potential to impact people in the way they interact on social media, collaborate in their work, perform marketing and business, teach, learn, and even access personalized healthcare. Several works in the literature examine Metaverse in terms of hardware wearable devices, and virtual reality gaming applications. However, the requirements of realizing the Metaverse in realtime and at a large-scale need yet to be examined for the technology to be usable. To address this limitation, this paper presents the temporal evolution of Metaverse definitions and captures its evolving requirements. Consequently, we provide insights into Metaverse requirements. In addition to enabling technologies, we lay out architectural elements for scalable, reliable, and efficient Metaverse systems, and a classification of existing Metaverse applications along with proposing required future research *** Codes ***, *** © 2023, CC BY.

关键词： Energy efficiency

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Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

Expediting In-Network Federated Learning by Voting-Based Con...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Xiaoxin Su Yipeng Zhou Laizhong Cui Song Guo College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKSAR

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic.

关键词： Training Data privacy Quantization (signal) Federated learning Computational modeling Memory management Collaboration

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INNSE: Invertible neural network-based DNA image storage with self-correction encoding

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Computational and Structural Biotechnology Journal 2025年 27卷 2492-2502页

作者： Zheng, Yanfen Zhang, Xiaokang Sun, Lijun Cao, Ben Wang, Bin Zhang, Qiang Zheng, Pan School of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Liaoning Dalian116622 China Department of Accounting and Information Systems University of Canterbury Upper Riccarton Christchurch8140 New Zealand

DNA storage has become an alternative to silicon-based storage of media. However, when used to store multimodal data such as images, it fails to take full advantage of the characteristics of high correlation and variability present in such data, and currently remains inferior in terms of encoding density and time complexity. It also has the shortcoming of weak performance in terms of error correction methods to address insertion and deletion errors caused by error crosstalk. Therefore, we proposed an invertible neural network-based DNA image storage self-correction encoding method (INNSE) that uses the invertibility of network to perform up-down sampling processing on image and video data, thereby greatly reducing the number of DNA sequences required for data storage. A codebook generator was then designed to encode low-dimensional vectors into DNA sequences that meet various constraints. Finally, a self-correction method was constructed to achieve DNA sequence error correction at the base level without adding any redundant information. Our experimental results showed that, compared to the Yin-Yang algorithm, the encoding density of INNSE was increased by 3×, encoding time complexity was reduced by 95.26%. Under a simulated error rate of 2%, PSNR and SSIM were raised by 80.88% and 63.82%, respectively, proving that INNSE has significant advantages in encoding performance and error correction ability. © 2025

关键词： Network coding

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Innovative Urban Planning for Harnessing Blockchain and Edge Artificial Intelligence for Smart City Solutions

Innovative Urban Planning for Harnessing Blockchain and Edge...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： K. E. Purushothaman N. Ragavendran S. P. Ramesh V. Ganesh Karthikeyan G. Uma Maheswari R. Saravanakumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Science & Engineering Galgotias University Greater Noida Uttar Pradesh India School of Computing (SoC) SASTRA Deemed to be University Thanjavur Tamil Nadu India RMK College of Engineering and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This research explores the potential of blockchain and edge AI to revolutionize smart city planning and management. By incorporating these emerging technologies, cities can address current urban challenges, including traffic congestion, energy management, and data security. This research study highlights the benefits of blockchain, such as simplified processes, reduced costs, enhanced security, and transparency. Additionally, this study reviews the applications of edge AI in smart transportation, focusing on vehicle detection, counting, and identification algorithms. The potential of blockchain technology in smart energy trading, vehicle transfer, and encrypted communication is also discussed. Overall, this research study provides valuable insights for researchers to explore new application areas for blockchain and edge AI in the context of smart cities.

关键词： Smart cities Vehicle detection Scalability Urban planning Edge AI Blockchains Smart transportation Energy management Sustainable development Traffic congestion

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software Development Practice: How Organisation Dynamics Inhibit the Utilization of Process Tools in Small software Companies 10

Software Development Practice: How Organisation Dynamics Inh...

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10th International Conference on software and Information engineering, ICSIE 2021

作者： Tuape, Micheal Hasheela-Mufeti, Victoria Iiyambo, Petrus Kayanda, Anna Kasirinen, Jussi Department of Software Engineering Lappeenranta-Lahti University of Technology Finland Department of Computing Mathematical and Statistical Sciences University of Namibia Namibia Information Systems Department College of Business Education Dar-es Salaam Tanzania United Republic of

ISBN: (纸本)9781450384315

Low-quality software products are synonymous with small software companies, yet thousands of process tools and methods are available, although unfortunately, they remain unutilized. The utilization of these process tools are said to be very challenging to small software companies with the difficulty of adaptability tagged to the context in which the companies operate since the process tools are not designed to take care of the operational context of the small software companies. This survey study was undertaken to empirically investigate the implication of the organizational dynamics on using process tools in SSCs. A total of 115 respondents from Tanzania, Namibia, and Ghana were reached through a structured questionnaire. Our findings reveal that the ad-hoc behaviors and attitude breed a culture that inhibits procedural and process-intensive practices synonymous with most software engineering process tools. This finding is interesting because this area has been underestimated while looking at software practice, yet it is significant in understanding practice in relation to software process tools, significantly how small software companies can be helped to adopt the different tools and also provide a mechanism to help the designers of process tools make tools that are usable in the context of the practice of SSCs. © 2021 ACM.

关键词： Surveys

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