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Novel MITM attack scheme based on built-in negotiation for blockchain-based digital twins

Digital Communications and Networks 2025年第1期11卷 256-267页

作者： Xin Liu Rui Zhou Shohei Shimizu Rui Chong Qingguo Zhou Xiaokang Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China Faculty of Data Science Shiga UniversityHikone 522-8522Japan Beijing Zhongxin Xingkong Network Technology Co Beijing 100005China

As smart contracts,represented by Solidity,become deeply integrated into the manufacturing industry,blockchain-based Digital Twins(DT)has gained momentum in recent *** of the blockchain infrastructures in widespread use today are based on the Proof-of-Work(PoW)mechanism,and the process of creating blocks is known as“mining”.Mining becomes increasingly difficult as the blockchain grows in size and the number of on-chain business systems *** lower the threshold of participation in the mining process,“mining pools”have been *** can cooperate and share the mining rewards according to the hashrate they contributed to the *** is the most widely used communication protocol between miners and mining *** security is essential for the *** this paper,we propose two novel Man-In-The-Middle(MITM)attack schemes against Stratum,which allow attackers to steal miners'hashrate to any mining pool using hijacked TCP *** with existing attacks,our work is more secretive,more suitable for the real-world environment,and more *** Proof-of-Concept(PoC)shows that our schemes work perfectly on most mining softwares and ***,we present a lightweight AI-driven approach based on protocol-level feature analysis to detect Stratum MITM for blockchain-based *** detection model consists of three layers:feature extraction layer,vectorization layer,and detection *** prove that our detection approach can effectively detect Stratum MITM traffic with 98%*** work alerts the communities and provides possible mitigation against these more hidden and profitable attack schemes.

关键词： Digital twins MITM Blockchain Stratum Anomaly detection

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CLNet: A Federated Learning Approach for Privacy-aware Cognitive Load Evaluation while Walking Using Smart Shoes 10

CLNet: A Federated Learning Approach for Privacy-aware Cogni...

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10th IEEE Smart World Congress, SWC 2024

作者： Cao, Zehua Deng, Wenwu Ying, Qijun Long, Huajun Zhong, Yuchen Cai, Xiaohui University of Science and Technology of China School of Computer Science and Technology Hefei China University of Science and Technology of China School of Artificial Intelligence and Data Science Hefei China

ISBN: (纸本)9798331520861

Cognitive load while walking plays a role in safety, human-computer interaction, medicine, and other fields, and gait has been shown to have a robust connection with cognitive load. However, most studies on this issue only focus on controlled laboratory or clinical settings. Also, data privacy is not considered in the process. We propose a method based on smart shoes with multiple modalities and a federated learning approach that could evaluate cognitive load during walking in real-world scenarios. In our study, the classical N-back tasks are utilized, and the NASA-TLX questionnaire is used as ground truth. Our pipeline demonstrates utility in cognitive load evaluation while walking in the wild, with an accuracy of 9 9. 1 6 %. Our method provides a potential way to perform in-wild cognitive load evaluation in a user-friendly manner, which could be useful in various applications, especially in privacy-sensitive domains. © 2024 IEEE.

关键词： Federated learning

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NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

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SN Computer science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

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Computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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Human Stress Recognition by Correlating Vision and EEG data

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Computer Systems science & Engineering 2023年第6期45卷 2417-2433页

作者： S.Praveenkumar T.Karthick SRM Institute of Science and Technology Department of Data Science and Business SystemsSchool of ComputingKattankulathur603203TamilnaduIndia

Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday *** human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human *** the multimodal dataset DEAP(database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human *** combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when *** on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress *** the stress identification test,we utilized the DEAP dataset,which included video and EEG *** also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate *** the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG *** Level(FL)fusion that combines the features extracted from video and EEG *** use XGBoost as our classifier model to predict stress,and we put it into *** stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.

关键词： Mental stress physiological data XGBoost feature fusion DEAP video data EEG CNN HAR

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A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

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Computer Systems science & Engineering 2023年第7期46卷 337-356页

作者： N.Vasudevan T.Karthick Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathurTamilnadu603203India

Crop protection is a great obstacle to food safety,with crop diseases being one of the most serious *** diseases diminish the quality of crop *** detect disease spots on grape leaves,deep learning technology might be *** the other hand,the precision and efficiency of identification remain *** quantity of images of ill leaves taken from plants is often *** an uneven collection and few images,spotting disease is *** plant leaves dataset needs to be expanded to detect illness accurately.A novel hybrid technique employing segmentation,augmentation,and a capsule neural network(CapsNet)is used in this paper to tackle these *** proposed method involves three ***,a graph-based technique extracts leaf area from a plant *** second step expands the dataset using an Efficient Generative Adversarial Network ***,a CapsNet identifies the illness and *** proposed work has experimented on real-time grape leaf images which are captured using an SD1000 camera and PlantVillage grape leaf *** proposed method achieves an effective classification of accuracy for disease type and disease stages detection compared to other existing models.

关键词： Feature extraction neural network disease segmentation pattern analysis

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An automatic lung nodule detection and classification using an optimized convolutional neural network and enhanced k-means clustering

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16973-16984页

作者： Lydia, M. Dhasny Prakash, M. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

Lung cancer can be lethal if it is not found in the initial phases. Lung cancer, nevertheless, is challenging to identify early due to the dimensions and form of the nodules. Imaging specialists require the assistance of automated instruments for accurate interpretation. Therefore, in this paper, automatic lung nodule classification is proposed. In the proposed methodology, there are four phases, namely pre-processing, segmentation, classification, and severity analysis. As a first step, lung nodule images are collected from the dataset and pre-processed. After pre-processing, segmentation is carried out. For segmentation enhanced k-means clustering algorithm is applied. To identify the object as cancerous or benign, the segmented region is then fed into an optimized convolution neural network (OCNN). Here, adaptive sunflower optimization (ASFO) algorithm is used to pick the hyperparameters effectively to improve the effectiveness of the CNN classifier. Finally, from the segmented region, the severity of the patient is evaluated. The efficiency of the presented technique is analyzed based on various metrics and research work compared with different state-of-art-works. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

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Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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data-Driven Adaptive Predictive Control Method With Autotuned Weighting Factor for Nonlinear Systems Using Triangular Dynamic Linearization

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1880-1882页

作者： Zhong-Hua Pang Yumo Zhang Xueyuan Sun Shengnan Gao Guo-Ping Liu Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of TechnologyBeijing 100144China School of Data Science Capital University of Economics and BusinessBeijing 100070China Center for Control Science and Technology Southern University of Science and TechnologyShenzhen 518055China

Dear Editor,In this letter,a novel data-driven adaptive predictive control method is proposed using the triangular dynamic linearization *** proposed method only contains one time-varying parameter with explicit physical meaning,which can prevent severe deviation in parameter ***,a triangular dynamic linearization(TDL)data model is employed to predict future system outputs,and then to correct inaccurate predictive outputs,a feedback regulator is *** autotuned weighing factor is introduced to alleviate the computational burden in practical applications and further improve output tracking ***-loop stability conditions are derived by rigorous *** results are provided to demonstrate the efficacy of the proposed method.

关键词： estimation technique method

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