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Secure and Lightweight Signcryption Scheme With Group Equality Test for Heterogeneous WBAN

IEEE Internet of Things Journal 2025年第13期12卷 25314-25332页

作者： Zhang, Qijia Zhang, Jianhong Yang, Nan Tian, Youliang Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang050025 China North China University of Technology College of Information Sciences and Technology Beiing100144 China

Wireless body area network (WBAN) integrates body sensors to collect and upload various health indicators, which facilitates timely remote healthcare. To realize the real-time monitoring of the patient’s health status while ensuring the security of sensitive data, signcryption schemes with equality test provide a feasible solution. Nevertheless, most of the existing schemes suffer from heavy computational burdens and are vulnerable to some inherent threats, such as offline message recovery attack and known session temporary key attack. Additionally, some of them cannot provide the essential security properties. To cope with these problems, this paper constructs a secure and lightweight signcryption scheme with group equality test, which is suitable for the heterogeneous environment. To formally define the resistance to offline message recovery attack and known session temporary key attack, we first propose two corresponding security models. Then, we introduce a group mechanism to the equality test to support the aforementioned security properties. Besides, we adopt the online/offline construction to avert the time-consuming operations in the real-time data processing phase. Furthermore, our scheme supports message aggregation to reduce the computational overhead of handling multiple messages. Formal security proof shows that our scheme satisfies confidentiality and unforgeability. Compared to several recent schemes, the experimental results demonstrate that our scheme enjoys the best performance in terms of both computational overhead and communication cost. With implementation, our scheme is proved to be feasible for lightweight devices in real-world applications. © 2014 IEEE.

关键词： Electronic health record

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Modelling Mobile-X Architecture for Offloading in Mobile Edge Computing

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Intelligent Automation & Soft Computing 2023年第4期36卷 617-632页

作者： G.Pandiyan E.Sasikala Department of Data Science and Business System SRM Institute of Science and TechnologyChennaiIndia

Mobile Edge Computing(MEC)assists clouds to handle enormous tasks from mobile devices in close *** edge servers are not allocated efﬁciently according to the dynamic nature of the *** leads to processing delay,and the tasks are dropped due to time *** researchersﬁnd it difﬁcult and complex to determine the ofﬂoading decision because of uncertain load dynamic condition over the edge *** challenge relies on the ofﬂoad-ing decision on selection of edge nodes for ofﬂoading in a centralized *** study focuses on minimizing task-processing time while simultaneously increasing the success rate of service provided by edge ***,a task-ofﬂoading problem needs to be formulated based on the communication and *** ofﬂoading decision problem is solved by deep analysis on taskﬂow in the network and feedback from the devices on edge *** signiﬁcance of the model is improved with the modelling of Deep Mobile-X architecture and bi-directional Long Short Term Memory(b-LSTM).The simulation is done in the Edgecloudsim environment,and the outcomes show the signiﬁcance of the proposed *** processing time of the anticipated model is 6.6 *** following perfor-mance metrics,improved server utilization,the ratio of the dropped task,and number of ofﬂoading tasks are evaluated and compared with existing learning *** proposed model shows a better trade-off compared to existing approaches.

关键词： Mobile edge computing cloud ofﬂoading delay task drop reinforcement learning mobile-X architecture

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Social Media User Behavior Mining and Sentiment Analysis based on Computer big data 4

Social Media User Behavior Mining and Sentiment Analysis bas...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Zhang, Qiang Hou, Yutong School of Artificial Intelligence and Big Data Chongqing Metropolitan College of Science and Technology Chongqing China

ISBN: (纸本)9798350352931

Faced with the rapid development of social networks and the enormous business opportunities they contain, data mining and analysis based on social networks has become an inevitable trend. By utilizing various technologies such as Visual C++6.0 and Pajek, data collection and analysis were achieved. The user ID (Identification) was added to the Sina server and relevant user information was requested through the API (Application Programming Interface). Through the data analysis module, the stored data was processed appropriately for different research objects, and network analysis tools can be used to visually display certain results. Using this model, sentiment classification was performed on Weibo samples with existing labels, and the accuracy, recall and F1 value of manual labeling were evaluated. The positive accuracy was 0.85;recall was 0.82;F1 value was 0.84. This article helps to improve user experience and explore more user value. © 2024 IEEE.

关键词： data accuracy

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Homomorphic Encryption-Based Privacy Protection data Processing Strategies in Fog Computing 2

Homomorphic Encryption-Based Privacy Protection Data Process...

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2nd International Conference on big data and Privacy Computing, BDPC 2024

作者： Song, Shiyang Tang, Jinhai Wang, Haozhe Yuan, Dongxu Zhang, Zhiyuan Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9798350307597

With the development of Intelligent Transportation Systems (ITS), real-time processing and privacy protection for traffic data become particularly important. In this research, we explore how to process traffic data efficiently and securely in fog computing environments, especially in wireless in-vehicle networks. First, we propose a homomorphic encryption-based strategy that performs computation on ciphertexts, eliminating the need for frequent decryption and thus achieving higher data processing efficiency. Then, we provide an in-depth discussion on data integrity and real-time performance, and propose a novel data validation method to ensure data integrity and real-time updates in fog computing environments. In addition, we designed a series of simulation experiments using virtual data to verify the effectiveness of our approach. The experimental results show that our strategy not only greatly improves the data processing speed, but also effectively guarantees the privacy and integrity of the data. Overall, this study provides a new and efficient method for traffic data processing in fog computing environments, which is an important reference value for future traffic data processing. © 2024 IEEE.

关键词： Differential privacy

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On the convergence of tracking differentiator with multiple stochastic disturbances

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science China(Information sciences) 2024年第2期67卷 157-169页

作者： Zehao WU Huacheng ZHOU Baozhu GUO Feiqi DENG School of Mathematics and Big Data Foshan University School of Mathematics and Statistics Central South University Academy of Mathematics and Systems Science Chinese Academy of Sciences Systems Engineering Institute South China University of Technology

This paper investigates the convergence, noise-tolerance, and filtering performance of a tracking differentiator in the presence of multiple stochastic disturbances for the first time. We consider a general case wherein the input signal is corrupted by additive colored noise, and the tracking differentiator is disturbed by additive colored noise and white noise. The tracking differentiator is shown to track the input signal and its generalized derivatives in the mean square sense. Further, the almost sure convergence can be achieved when the stochastic noise affecting the input signal is vanishing. Herein, numerical simulations are performed to validate the theoretical results.

关键词： tracking differentiator convergence noise-tolerance filtering performance multiple stochastic disturbances

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Sparse representation scheme with enhanced medium pixel intensity for face recognition

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CAAI Transactions on Intelligence technology 2024年第1期9卷 116-127页

作者： Xuexue Zhang Yongjun Zhang Zewei Wang Wei Long Weihao Gao Bob Zhang State Key Laboratory of Public Big Data Institute for Artificial IntelligenceCollege of Computer Science and TechnologyGuizhou UniversityGuiyangGuizhouChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test *** has been widely used in various image classification *** in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for *** deformable images such as human faces,pixels at the same location of different images of the same subject usually have different ***,extracting features and correctly classifying such deformable objects is very ***,the lighting,attitude and occlusion cause more *** the problems and challenges listed above,a novel image representation and classification algorithm is ***,the authors’algorithm generates virtual samples by a non-linear variation *** method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable *** combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the ***,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion *** weighting coefficients in the score fusion scheme are set entirely ***,the algorithm classifies the samples based on the final *** experimental results show that our method performs better classification than conventional sparse representation algorithms.

关键词： computer vision face recognition image classification image representation

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

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Computer Modeling in Engineering & sciences 2024年第1期138卷 827-843页

作者： Tao Yin Changgen Peng Weijie Tan Dequan Xu Hanlin Tang State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China Guizhou Big Data Academy Guizhou UniversityGuiyang550025China Key Laboratory of Advanced Manufacturing Technology Ministry of EducationGuizhou UniversityGuiyang550025China College of Computer Science and Technology Guizhou UniversityGuiyang550025China ChinaDataPay Company Guiyang550025China

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie *** traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a *** address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data *** algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the *** determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate *** homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting *** tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging *** assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

关键词： Rate setting Tweedie distribution generalized linear models federated learning homomorphic encryption

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Implementation and Evaluation of Student Microexpression Recognition Method Based on Neural Network Model in Blended Learning 6

Implementation and Evaluation of Student Microexpression Rec...

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6th International Workshop on Artificial Intelligence and Education, WAIE 2024

作者： Peng, Yuying Zhou, Hui Liu, Tong Du, Menghan Dalian Neusoft University of Information Department of Software and Big Data Technology Dalian China

ISBN: (纸本)9798350378894

In the era of artificial intelligence, facial expression recognition is a research hotspot, and student micro expression recognition is a prerequisite for teachers to understand student states and emotions. Facial micro expressions typically contain various difficult to recognize types, and different convolutional neural networks are suitable for different types of facial expression recognition tasks. However, in complex situations, when multiple types of facial micro expressions coexist, the recognition results may be inaccurate. This article proposes a student micro expression recognition method based on neural network models and stacking methods. Experiments have shown that compared with traditional CNN neural network models, this method significantly improves the accuracy of student learning state recognition, can solve the tracking and feedback problems of student learning states in blended learning, adjust instructional design and digital evaluation system in real time, and better assist students in effective learning. © 2024 IEEE.

关键词： Contrastive Learning

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Thrackles, Superthrackles and the Hanani-Tutte Theorem

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Journal of Graph Algorithms and Applications 2024年第1期28卷 95-127页

作者： Dehkordi, Hooman R. Farr, Graham Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia

A thrackle is a drawing of a graph in which any two vertex-disjoint edges cross exactly once and incident edges do not cross. A graph that has a thrackle drawing is thracklable. In the past three decades, mathematicians investigated a number of variations of thrackles by relaxing or changing the required number of crossings on edges or restricting the placement of vertices on the surface (e.g., restricting to outerdrawings, in which vertices are restricted to a boundary curve of the surface). A graph is superthracklable if it can be drawn so that any two edges cross exactly once. We provide a simple proof for the following: a graph can be drawn on the plane so that every two edges cross an odd number of times if and only if it is superthracklable on the plane. A graph is outersuperthracklable if it has a drawing on a disc in which every vertex is on the boundary of the disc and every pair of edges cross exactly once. We introduce some natural generalisations of outersuperthracklable graphs and we show that these classes of graphs are equivalent. Lastly, using the Hanani-Tutte characterisation of planar graphs we show that for any surface Σ, there is a relationship between the class of graphs that are not embed-dable on Σ and the class of graphs that are not superthracklable with respect to Σ. More specifically, we show how to construct, from any forbidden minor G for embed-dability in Σ, two infinite families of graphs that are not superthracklable with respect to Σ. This sheds further light on the relationship between some of Archdeacon and Stor’s forbidden configurations for superthrackles and Kuratowski’s forbidden minors for planarity. © 2024, Brown University. All rights reserved.

关键词： Graphic methods

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