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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Misha, Garima Hazela, Bramah Chaurasia, Brijesh Kumar Amity University Uttar Pradesh Computer Science and Engineering Amity School of Engineering and Technology Lucknow India Pranveer Sing Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350335095

Internet of Things (IoT) applications, such as e-healthcare departments have grown tremendously where devices gather patient data and instantly transmit it over a distance to servers. Despite its huge advantages, IoT in the healthcare sector has acquired little consideration, largely because of the threats of unauthorized access to private health data made possible by the weak wireless communication channel. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the Internet of medical things (IoMT) that is based on zero-knowledge proofs (ZKP). The research will include the comparison between computational delays in traditional protocols and ZKP protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP. © 2023 IEEE.

关键词： Authentication

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Automatic Area Measurement of Ring Cooler Corner Images Based on EU-Net 2

Automatic Area Measurement of Ring Cooler Corner Images Base...

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2nd International Conference on Artificial Intelligence, Human-computer Interaction and Robotics, AIHCIR 2023

作者： Wang, Wei Zhang, Dezheng Li, Qing Li, Jing Ma, Jinge Xi, Yaming School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Mathematics and Physics University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350360363

Aimed to mitigate the challenges associated with manual detection, such as elevated risks and limited accuracy, a machine vision-based automatic measurement method is proposed for determining the area of a ring cooler corner. Specifically, an enhanced lightweight model known as EU-Net (Efficient U-Net), derived from the classic semantic segmentation U-Net model, is developed to accurately segment the ring cooler corners within the images. Subsequently, the OpenCV toolkit is employed to conduct area statistics on the segmented masks. The results show that EU-Net achieves comparable segmentation effectiveness and area statistics results to the classic U-Net, while utilizing only 0.097% of the parameters present in the latter model. Our improved algorithm obtains 99.74% and 94.5% in pixel-wise accuracy and intersection over union. Moreover, it can process 60 frames per second, effectively meeting real-time operational demands. © 2023 IEEE.

关键词： computer vision

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Pairwise Alignment Improves Graph Domain Adaptation 41

Pairwise Alignment Improves Graph Domain Adaptation

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Shikun Zou, Deyu Zhao, Han Li, Pan Department of Electrical and Computer Engineering Georgia Institute of Technology GA United States School of Data Science University of Science and Technology of China Hefei China Department of Computer Science University of Illinois Urbana-Champaign Champaign United States

Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing. This work delves into Graph Domain Adaptation (GDA) to address the unique complexities of distribution shifts over graph data, where interconnected data points experience shifts in features, labels, and in particular, connecting patterns. We propose a novel, theoretically principled method, Pairwise Alignment (Pair-Align) to counter graph structure shift by mitigating conditional structure shift (CSS) and label shift (LS). Pair-Align uses edge weights to recalibrate the influence among neighboring nodes to handle CSS and adjusts the classification loss with label weights to handle LS. Our method demonstrates superior performance in real-world applications, including node classification with region shift in social networks, and the pileup mitigation task in particle colliding experiments. For the first application, we also curate the largest dataset by far for GDA studies. Our method shows strong performance in synthetic and other existing benchmark datasets. Copyright 2024 by the author(s)

关键词： Graph theory

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Tempered Image Detection Using ELA and Convolutional Neural Networks

Tempered Image Detection Using ELA and Convolutional Neural ...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Mishra, Anupama Kong, Kwok Tai Chui Hong Gupta, Brij B. Swami Rama Himalayan University Computer Science and Engineering Himalayan School of Science and Technology Dehradun India Hong Kong Asia University Taiwan Taiwan

ISBN: (纸本)9798350324136

Images are often manipulated to benefit one party, serving as crucial evidence. This manipulation, often used in fake news or misleading information, frequently involves image falsification. Detecting such falsification requires a robust model capable of processing vast image data efficiently. In today's data-rich era, Deep Learning, especially the use of Convolutional Neural Networks with Error Level Analysis, has achieved an impressive 87.75% accuracy and convergence in 10 epochs for detecting forged images. © 2024 IEEE.

关键词： CNN ELA Fake Image

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Anomalous Behavior Identification with Visual Federated Learning in Multi-UAVs Systems 29

Anomalous Behavior Identification with Visual Federated Lear...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Pengfei Yu, Xinrui Ye, Yefei Qi, Heng Yu, Shuo Yang, Leyou Zhang, Qiang Dalian University of Technology School of Computer Science and Technology Dalian China Ministry of Education Dalian116024 China Northeastern University School of Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350330717

Anomaly detection aims to identify data or behav-iors that are different from the usual patterns. In traditional anomaly detection settings, edge devices collect the data and send it to a centralized server for model training, which faces two critical issues: (1) it risks data exposure during transmission;(2) it demands a large amount of network bandwidth for data transfer. To tackle these problems, we propose a Visual Federated Learning algorithm (VFLA) for anomalous behavior identification in the multi-UAVs system. To the best of our knowledge, we are the first to merge federated learning with video-based anomaly detection. VFLA consists of two phases: The initial phase is training a pseudo-label generator. UAVs collect a dataset and manually annotate it. This labeled data is then used to train the pseudo-label generator on the server, which is subsequently distributed back to the UAVs. The second phase is the federated learning-based anomaly detection model training. UAVs leverage the pseudo-label generator to automatically annotate the collected video footage. These annotated videos are fed into an anomaly detection network for training. Once the local training is completed, UAVs upload their local models to a server for federated aggregation. The global model is then redistributed to the UAVs for additional training rounds, until reach the target accuracy. Finally, we simulate the federated learning anomaly detection algorithm on the Shanghai-tech dataset, it demonstrates an average accuracy boost of 5.6% compared to baselines. © 2023 IEEE.

关键词： Anomaly detection Distributed training Federated learning UAVs

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Latency-aware SDN-based Mobile Edge Computation Offloading in Industrial IoT 28

Latency-aware SDN-based Mobile Edge Computation Offloading i...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Hoseini, Seyyed Shayan Beyer, Tebbo Ghaderi, Ali Movahedi, Zeinab School of Computer Engineering Iran Uiniversity of Science and Technology Tehran Iran Kiel University Department of Computer Science Kiel Germany

ISBN: (纸本)9798350338195

Industrial Internet of things (IIoT) is a promising architecture for cyber-physical systems. Although it brings a vast number of different advantages, it enables some severe challenges as well, such as energy consumption and delay management. Due to producing a big amount of raw sensing data and the demands for processing them, various computation offloading methods over different infrastructures have been proposed. Mobile Edge Computing (MEC) is one of those infrastructures being able to give the required execution power at a much closer distance from the end devices. Software Defined Networking (SDN) is set to provide a programmable interface that can be used to manage a network of MECs in order to choose the optimal MEC for the received offloading requests. In this paper, we proposed a latency-aware SDN-based computation offloading method with specific communication, computation, and energy consumption models which aim at optimizing the overall response time. Results show that with having a delay threshold, a significant number of resources will be freed and as a result, overall response time will be decreased. © 2023 IEEE.

关键词： Computation offloading

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Understanding the Dynamics of Sparse Coding in Auto-Encoders for Cancer Detection

Understanding the Dynamics of Sparse Coding in Auto-Encoders...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Thiruvenkadam, T. Acharjya, Kalyan Goyal, Manish Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This summary outlines how sparse coding in vehicle encoders may be used to come across most cancers. It explains that by using learning sparse representations of raw records, you can benefit from a higher understanding of the underlying dynamics of cancer improvement. Furthermore, this summary outlines two of the most commonplace packages of vehicle encoders for cancer detection: function extraction and fitness tracking. This look gives insights into the capability of car encoders for efficient cancer analysis. For this reason, it should prove to be a vital element of most cancer detection and prevention. Sparse coding has been gaining interest inside the region of auto-encoder-based total methods for most cancer detection. With the increasing complexity of deep getting-to-know fashions being used for detection, the information and mastery of this research approach moderately encoded representations have turned out to be vital. Auto-encoders are used to encapsulate and encode inputs from high-dimensional facts into a lower-dimensional representation to keep the most crucial or crucial data from the facts. A selected type of vehicle encoder, a sparse automobile encoder, is designed to analyze sparsely encoded representations of records by introducing sparsity into the hidden layer of the automobile encoder via sure specialized regularizes. © 2024 IEEE.

关键词： automobile commonplace detection element interest

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Performance Design of High-Temperature Chloride Salts as Thermal Energy Storage Material

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Journal of Thermal science 2024年第2期33卷 479-490页

作者： ZHAO Le WANG Jingyao CUI Liu LI Baorang DU Xiaoze WU Hongwei Key Laboratory of Power Station Energy Transfer Conversion and System(North China Electric Power University) Ministry of EducationBeijing102206China School of Energy and Power Engineering Lanzhou University of TechnologyLanzhou730050China School of Physics Engineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK

The chloride salts have great potential used as high-temperature thermal energy storage(TES) medium for the concentrated solar power *** this study,LiCl,KCl and CaCl_(2) were selected as energy storage materials in order to further broaden the working temperature of ternary chloride salt and improve its energy storage *** new high-temperature energy storage ternary chloride composed of LiCl,KCl,and CaCl_(2) was developed based on the phase diagram generated by *** components of LiCl-KCl-CaCl_(2) with the mass ratio of 37.85%-53.38%-8.77%,30.90%-13.82%-55.28% and 1.78%-18.61%-79.61% were developed,of which the corresponding melting temperature(T_(m)) 340.93,433.57 and 626.85℃,respectively,was obtained *** that T_(m) of the third group of salts was too high,only the first two groups of salts were *** test showed that the actual melting point was only 0.46% and 1.64% different from the melting point predicted by *** thermal properties of the two ternary chloride salts were also *** solid and liquid-specific heat of ternary salts was determined by DSC using sapphire as the standard *** vapor pressure and decomposition temperature of ternary chloride salts were *** results showed that the vapor pressure of salt 1 was almost constant below 650℃ by ***,the TG results showed that the upper working temperature of salt 1 was 650℃ under the air *** addition,the ternary chloride salts after short-term cycling still exhibited excellent thermal properties,which revealed that these good thermal properties make them have broad application prospects in high-temperature thermal energy storage systems.

关键词： chloride salts thermal energy storages thermal properties thermal stability

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Lightweight Network Context Change Detection for Online Defense Against Heterogeneous IoT Attacks

Lightweight Network Context Change Detection for Online Defe...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Nitish, A. Hanumanthappa, J. Prakash, S. P. Shiva Krinkin, Kirill University of Mysore Department of Studies in Computer Science Mysuru India Jss Science and Technology University Department of Information Science and Engineering Mysuru India School of Computer Science Engineering Constructor University Bremen Germany

ISBN: (纸本)9798350313796

The heterogeneous resource-constrained IoT devices with their edge associations-called the edge-of-things (EoT), handle dynamic, time-critical, distributed workloads and are susceptible to proliferating time-bound, multivector Botnet attacks, resulting in unauthorized device or service discovery, service denial, and information leakage or theft-leading to degraded network availability. Several existing AI-based attack detection techniques analyze the traffic data, providing network-context-unaware statistical decisions that increase misclassifications and diminish EoT availability. However, addressing short-burst, dynamic, and latency-sensitive Botnet attacks requires a context change detection system of network topological parameters with low processing footprints, supplementing real-time threat assessment of IoT edge-constituting the proposed CCDS framework to differentiate between the threat and HetIoT (network) operational contexts. Additionally, the high-level CCDS framework parameters minimize referencing low-level network component logs for faster, reliable troubleshooting. It is also suitable for online and offline threat assessment and is deployable on resource-constrained EoT infrastructures. The proposed work results in an average detection rate increase of 9.6% on 101 and 102 samples and 14.3 % on 103 samples compared to the state-of-the-art for online (real-time) intrusion detection. © 2023 IEEE.

关键词： Botnet

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Enhancing Patient Well-Being in Healthcare Through the Integration of IoT and Neural Network

Enhancing Patient Well-Being in Healthcare Through the Integ...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Upreti, Kamal Divakaran, Prakash Lingareddy, Nagulapalli Deepika, Sheri School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science Ghaziabad India Himalayan University Itanagar India Vignan Institute of Technology and Science Department of CSE Deshmukhi India Sreyas Institute of Engineering and Technology Department of CSE Hyderabad India

ISBN: (纸本)9798350376470

This study analyses the revolutionary integration of Internet of Things (IoT) structures in healthcare through a complete examination of outstanding case research. The first case study focuses on real-time patient fitness monitoring in a clinic setting. The suggested device utilizes an Internet of Things-ready device that has many sensors, including oxygen, pressure, and temperature sensors. The issues of forecasting patient health in advance are handled with the deployment of machine learning models, notably Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM). The second case study analyses IoT's effect on patient-precise medication identification and remote fitness monitoring, uncovering issues associated with accessibility, pricing, and human interfaces. Proposed alternatives, which incorporates greater education, increased accessibility, and user-pleasant interfaces with robust technical assistance, have been evaluated with 30 patients over a three-month duration. The results reveal a great growth in impacted person health, along with heightened attention of periodic health monitoring. The results highlight how IoT technologies may transform healthcare procedures by offering pro-active solutions for patients' well-being. This study offers insightful information that may be used to solve practical issues, promote patient-centered solutions, and broaden the scope of the healthcare period. A significant step towards a patient-centered and technologically advanced healthcare environment, the successful outcomes validate the capacity for sustained innovation, cooperation, and improvement in the integration of IoT systems for optimal patient care. © 2024 IEEE.

关键词： Support vector machines

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