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TASS: Threshold Adaptor Signature Scheme for Holographic IoT Consumer Devices in Blockchain Enabled Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Zengxiang Qin, Zhen Kumari, Saru Feng, Chaosheng Alenazi, Mohammed J.F Wu, Deyan Chen, Chien-Ming Xiong, Hu University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Sichuan Chengdu 610054 China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut India Sichuan Normal University School of Computer Science Sichuan Chengdu 610101 China King Saud University College of Computer and Information Sciences (CCIS) Department of Computer Engineering Riyadh 11451 Saudi Arabia Sichuan Institute of Computer Sciences China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing 250004 China

The integration of virtual medical assistants utilizing holographic projections has transformed holographic IoT consumer devices, fostering advanced healthcare interactions and attracting significant interest from industry and academia. Most federated learning (FL) systems in holographic IoT consumer devices are centrally managed, which exposes them to risks such as medical data breaches and single points of failure. Although blockchain technology provides an effective solution by enabling decentralized model training, it faces inherent limitations in terms of computational efficiency and security. Adapter signatures (AS) offload some of the computation on the blockchain to mitigate these problems through means such as key revelation. Despite their advantages, existing AS schemes suffer from bilateral design constraints, which increase the risks of key loss, single points of failure, and lack support for multi-party transaction scenarios. This paper proposes a novel threshold adapter signature scheme (TASS) based on elliptic curve cryptography (ECC) to address these challenges. TASS allows a group of entities to collaboratively generate partial signatures in a distributed manner, reducing the risks associated with key loss and single points of failure. Each participant holds only a share of the private key, ensuring that no single entity has complete control. This distributed approach enhances the system’s resilience against attacks and operational failures. Unlike traditional AS schemes that primarily support one-to-one transactions, TASS is designed to facilitate one-to-many and many-to-many transactions. Experimental evaluations demonstrate the superior performance of ECC-driven TASS in large-scale participants, providing a secure, efficient, and scalable foundation for integrating holographic interactions and blockchain technology within holographic IoT consumer devices. © 1975-2011 IEEE.

关键词： adaptor signature blockchain technology federated learning Holographic IoT consumer device privacy preserving threshold signature

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Federated Graph Learning via Constructing and Sharing Feature Spaces for Cross-Domain IoT

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IEEE Internet of Things Journal 2025年

作者： Chen, Jiale Zhuo, Shengda He, Jinchun Qiu, Wangjie Zhang, Qinnan Xiong, Zehui Zheng, Zhiming Tang, Yin Chen, Min Wang, Changdong Huang, Shuqiang Jinan University College of Cyber Security Guangzhou China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Zhongguancun Laboratory Beijing China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore Jinan University School of Management Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Computer Guangzhou China Jinan University College of Cyber Security of Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China

The Internet of Things (IoT) collects large volumes of diverse data, with graph data as a critical component, and extensively utilizes Federated Graph Learning (FGL) to process this data while preserving data security. However, the graph data collected by different IoT institutions are relatively independent due to various factors (e.g., data collection methods, geographical locations), and data access is restricted to local environments due to privacy constraints, IoT institutions typically possess heterogeneous feature spaces. Aggregating under these conditions could potentially contaminate local graph representations. Unfortunately, most existing FGL methods tend to overlook this issue. To address this challenge, we propose Federated Graph Learning via Constructing and Sharing Features (FedCSF), a novel FGL framework designed to build a globally consistent feature space and share it among IoT institutions. To construct and extract the feature space, we employ an uniform feature initialization across IoT institutions and design an encoder to extract both global and local feature relationships, thereby facilitating effective collaboration across data from different IoT institutions. Furthermore, we introduce an independent adaptive aggregation strategy to eliminate the integration of harmful knowledge, ensuring that the contributions from each IoT institution are effectively integrated into the global model. We theoretically analyze the convergence of FedCSF. To validate the effectiveness of FedCSF, we conducted extensive experiments under various settings (i.e., cross-datasets, and cross-domains), demonstrating its significant advantages of FedCSF in terms of performance, convergence speed, and practical adaptability © 2014 IEEE.

关键词： Federated learning

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Expression of Concern for: Emotion Identification and Detection from Multichannel EEG Signals Using artificial Neural Network

Expression of Concern for: Emotion Identification and Detect...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Cuddapah Anitha John E P K. Kathiresan N.J Krishnakumar. V. Karthik J. Sasidevi Department of Computer Science and Engineering Sree Vidyaniketan Engineering College Mohan Babu University Sri Sainath Nagar A Rangampeta Tirupati College of Management SRM Institute of Science and Technology Chennai TamilNadu India Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore TamilNadu India Department of Electronics and Communication Engineering Gopalan College of Engineering and Management Seetharamapalaya Basava Nagar Bangalore Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore TamilNadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Samayapuram Trichy TamilNadu

ISBN: (数字)9798350398267

ISBN: (纸本)9798350398274

Modified steady inclination affirmation considering multi-station EEG signals is shaping into a basic PC upheld technique for feeling issue finding in sensory system science and psychiatry as a problematic model affirmation task. In light of expansive space data, standard machine learning approaches call for arranging and eliminating various features from a lone or various channels. Along these lines, these strategies could acquaint a test with individuals who need point dominance. Then again, huge learning frameworks have been truly applied in various persistent created endeavors to see consolidates and sort different kinds of information. In this overview, standard signals are considered, and a direct yet strong pre-managing methodology is proposed to expand confirmation accuracy. In the interim, by truly learning compositional spatial-standard depiction of unpalatable EEG streams, a cross mix brain network that joins "Convolution Brain Organization (CNN)" and "Long Brain Organization (RNN)" has been used to portray human tendency states. The CNN module changes the chain-like EEG gathering into a 2D packaging improvement to mine the between channel relationship among genuinely lining EEG ***, harshness, and wrath are several the various sentiments that people experience reliably. Electroencephalography (EEG) data should in this way have a convincing inclination recognizing confirmation structure to unequivocally reflect feeling. in the present. In spite of the way that new assessment on this issue can offer great execution estimations, they are at this point lacking for the execution of a full inclination affirmation system.

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Renewable power distribution networks considering dynamic-differential pricing, carbon trading, and solid waste recycling: A fuzzy programming approach

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International Journal of Electrical Power and Energy Systems 2023年 144卷

作者： Tsao, Yu-Chung Dedimas Beyene, Tsehaye Gebeyehu, Sisay G. Kuo, Tsai-Chi Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Artificial Intelligence for Operations Management Research Center National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration Asia University Taichung Taiwan Institute of Innovation and Circular Economy Asia University Taichung Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Faculty of Mechanical and Industrial Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia

In the energy market, community of prosumers play a great role to enhance the sustainability of power distribution networks. They generate energy for their own consumption and sell the surplus energy to an electricity company. In this study, a power plant considered communities of prosumers and consumers with the objective to determine the capacity of distributed renewable energy generation units established by the power company itself, the power generation capacity of the prosumers, the dynamic-differential prices offered to the prosumers and consumers, and the optimal buy-back price offered to buy the surplus energy generated by the prosumers. According to the numerical analysis, the prosumers generated 89.82% of the energy demand of the community where the rest of the energy demand is covered by the power from the distributed renewable energy resource units. Both the buy-back energy price that the energy company offered to the prosumers and the net profit that the power company earned increased as the fixed price increased and price elasticity decreased. As a result of its participation in carbon trading by establishing distributed renewable generation units and collecting renewable energy from the prosumers, the power company increased its net profit by 1.13%. © 2022

关键词： Renewable energy resources

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Improving efficiency of the path optimization method for a gauge theory

arXiv

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arXiv 2022年

作者： Namekawa, Yusuke Kashiwa, Kouji Matsuda, Hidefumi Ohnishi, Akira Takase, Hayato Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan Department of Physics Faculty of Science Kyoto University Kyoto606-8502 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China

We investigate efficiency of a gauge-covariant neural network and an approximation of the Jacobian in optimizing the complexified integration path toward evading the sign problem in lattice field theories. For the construction of the complexified integration path, we employ the path optimization method. The 2-dimensional U(1) gauge theory with the complex gauge coupling constant is used as a laboratory to evaluate the efficiency. It is found that the gauge-covariant neural network, which is composed of the Stout-like smearing, can enhance the average phase factor, as the gauge-invariant input does. For the approximation of the Jacobian, we test the most drastic case in which we perfectly drop the Jacobian during the learning process. It reduces the numerical cost of the Jacobian calculation from O(N3) to O(1), where N means the number of degrees of freedom of the theory. The path optimization using this Jacobian approximation still enhances the average phase factor at expense of a slight increase of the statistical error. Copyright © 2022, The Authors. All rights reserved.

关键词： Efficiency

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Enhancing Security and Confidentiality using Trust Based Encryption (DHPKey) in Cloud Computing

Enhancing Security and Confidentiality using Trust Based Enc...

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International Conference on Computing and Networking technology (ICCNT)

作者： Kalaiyarasi R Mohanaprakash T A A.S Prakaash V. Divya Naveen P T. Sunitha Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Department of CSE Panimalar Engineeering College Chennai India Department of Mathematics Panimalar Engineeering College Chennai India Department of CSE Veltech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Department of CSE St.Joseph's College of Engineering Chennai India Department of CSE P. B College of Engineering Chennai India

Cloud computing has gained widespread adoption in enterprises due to its on-demand service, broad network access, and elasticity. However, security issues make it difficult to use these functions, especially when sending sensitive data through a cloud platform. A secure communication route is necessary to handle this. In order to improve security and lessen threats, the Diffie-Hellman key exchange method is suggested as a workable solution in this research. This technique is used in a cloud-based architecture that is specifically designed for engineering education to increase the security of user data. Multiple security goals, including secrecy, data integrity, and individualization, are successfully met by the design. Encryption, a technique to conceal private or sensitive information within seemingly ordinary data, is employed using the proposed novel method called DHPKey. It combines the benefits of public key techniques with the exchange of a private encryption key. Users can send text as a secret message and encrypt it using a key or password. Even if a hacker gains access to the encrypted text, decryption without the key is infeasible. The recipient, however, can decrypt the hidden text using the provided key. This approach ensures secure transmission of secret messages, safeguarding them from external interference. Publicly shared ciphertext remains incomprehensible to unauthorized users, ensuring that only the intended recipient can decrypt it. The system incorporates an online database for secure storage of all relevant information.

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Transfer Learning with a Fine-Tuned CNN Model for Classifying Augmented Natural Images 2nd

Transfer Learning with a Fine-Tuned CNN Model for Classifyin...

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2nd International Conference on Innovative Computing and Communication, ICICC 2019

作者： Ezzat, Dalia Hassanien, Aboul Ella Taha, Mohamed Hamed N. Bhattacharyya, Siddhartha Vaclav, Snasel Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt RCC Institute of Information Technology Kolkata India Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic

ISBN: (纸本)9789811512858

Convolutional neural network has proven to be highly efficient in image classification, but this approach has some disadvantages. Notably, a large number of images are required for training, and great time is need for training to achieve high degree of accuracy in the classification. Transfer learning with a pre-trained model can be used to overcome these problems. There are two approaches to transfer learning, feature extraction approach and fine-tuning approach. Based on the used dataset, both approaches were evaluated, and the two approaches yielded high accuracy with a little better result for fine-tuning approach. To overcome overfitting issues that may occur during transfer learning, data augmentation and dropout techniques were applied. The dataset studied contained 6899 images with 8 distinct unbalanced classes, but only 5600 images with balanced classes were analyzed. The classes include airplane, car, cat, dog, flower, fruit, motorbike and person. We used Google’s Inception-v3 model that is trained on ImageNet database and can classify images into 1000 object categories like pencil, Zebra and many animals. We retrained the Inception model to classify the used natural images using the Keras Library with Tensorflow as backend and achieved an overall accuracy of 99.7% for the test data. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Design to automate the detection and counting of tuberculosis(TB) bacilli

arXiv

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arXiv 2021年

作者： Samuel, Dinesh Jackson Baskaran, Rajesh Kanna Faculty of Technology Design and Environment Visual Artificial Intelligence Laboratory Oxford Brookes University United Kingdom School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Tuberculosis is a contagious disease which is one of the leading causes of death, globally. The general diagnosis methods for tuberculosis include microscopic examination, tuberculin skin test, culture method, enzyme linked immunosorbent assay (ELISA) and electronic nose system. World Health Organization (WHO) recommends standard microscopic examination for early diagnosis of tuberculosis. In microscopy, the technician examines field of views (FOVs) in sputum smear for presence of any TB bacilli and counts the number of TB bacilli per FOV to report the level of severity. This process is time consuming with an increased concentration for an experienced staff to examine a single sputum smear. The examination demands for skilled technicians in high-prevalence countries which may lead to overload, fatigue and diminishes the quality of microscopy. Thus, a computer assisted system is proposed and designed for the detection of tuberculosis bacilli to assist pathologists with increased sensitivity and specificity. The manual efforts in detecting and counting the number of TB bacilli is greatly minimized. The system obtains Ziehl-Neelsen stained microscopic images from conventional microscope at 100× magnification and passes the data to the detection system. Initially the segmentation of TB bacilli was done using RGB thresholding and Sauvola's adaptive thresholding algorithm. To eliminate the non-TB bacilli from coarse level segmentation, shape descriptors like area, perimeter, convex hull, major axis length and eccentricity are used to extract only the TB bacilli features. Finally, the TB bacilli are counted using the generated bounding boxes to report the level of severity. © 2021, CC BY-SA.

关键词： Tubes (components)

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Audio Segmentation Techniques and Applications Based on Deep Learning

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Scientific Programming 2022年第0期2022卷

作者： Aggarwal, Shruti Vasukidevi, G. Selvakanmani, S. Pant, Bhaskar Kaur, Kiranjeet Verma, Amit Binegde, Geleta Negasa Department of Computer Science and Engineering Thapar University Punjab Patiala147004 India Department of Science and Humanities R.M.K. College of Engineering and Technology R.S.M. Nagar Tamil Nadu Puduvoyal India Department of Artificial Intelligence and Data Science Velammal Institute of Technology Velammal Knowledge Park Tamil Nadu Chennai India Department of Computer Science and Engineering Graphic Era Deemed to be University Bell Road Clement Town Uttarakhand Dehradun248002 India University Center for Research and Development Chandigarh University Punjab Ajitgarh India Department of Computer Science College of Engineering and Technology Mettu University Metu Ethiopia

Audio processing has become an inseparable part of modern applications in domains ranging from health care to speech-controlled devices. In automated audio segmentation, deep learning plays a vital role. In this article, we are discussing audio segmentation based on deep learning. Audio segmentation divides the digital audio signal into a sequence of segments or frames and then classifies these into various classes such as speech recognition, music, or noise. Segmentation plays an important role in audio signal processing. The most important aspect is to secure a large amount of high-quality data when training a deep learning network. In this study, various application areas, citation records, documents published year-wise, and source-wise analysis are computed using Scopus and Web of science (WoS) databases. The analysis presented in this paper supports and establishes the significance of the deep learning techniques in audio segmentation. Copyright © 2022 Shruti Aggarwal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Speech recognition

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Anomaly Detection for Tower Crane Alarm Surge Based on Copula Function 2

Anomaly Detection for Tower Crane Alarm Surge Based on Copul...

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2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Shi, Hongyang Lu, Ming Wang, Hongjun Zhang, Yonghong Dong, Zengshou Shenyang Institute of computing technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Infrastructure & Cloud Service SSG Lenovo Beijing China R.C of Big Data and Artificial Intelligence Technology Shandong University Jinan China Beijing Zhongke Zhihe Digital Technology Co. Ltd Beijing China School of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi China

ISBN: (纸本)9783800757398

In recent years, tower crane safety accidents have occurred frequently, resulting in casualties and significant losses of construction site property. In order to reduce the probability of tower crane accidents, quality supervisor needs a method that can cause anomaly detection of alarm surges in a certain time period of the tower crane. Due to the lack of generalization capability of existing methods for new equipment and the large calculation overhead and weak interpretability, quality supervisor is often unable to effectively detect the risks brought about by the improper operation of special operators. This paper adopts the unsupervised anomaly detection method based on copula function, estimates the empirical copula function according to the empirical cumulative distribution of input data through nonparametric method, and then estimates the tail probability of joint distribution in all dimensions to evaluate the anomaly. In order to evaluate the effect of the model, this paper calculates the sudden increase point by comparing with each time series value and historical value, and uses it as an anomaly label. By comparing other different classes of anomaly detection algorithms horizontally, the copula function-based anomaly detection method for the scenario in this paper has better generalization and generalization ability, and the overall performance is better than other algorithms. In addition, compared with other methods, this method has the characteristics of low calculation consumption, scalability and high interpretability. For the anomaly detection of the total amount of tower crane alarm, this method has high application value. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Function evaluation

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