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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer Science Georgia State UniversityAtlantaGA 30303USA College of Information Science and Technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

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Robust Tracking of Dynamic Visual Object with Complex Environmental Factors 24

Robust Tracking of Dynamic Visual Object with Complex Enviro...

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2024 7th International Conference on computer Information science and Artificial Intelligence, CISAI 2024

作者： Zhang, Jiahe Wang, Bingxiang Niu, Jinyuan School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China School of Mechanical Engineering Xi'an Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9798400707254

Aiming at a series of problems caused by complex environment changes, such as target tracking instability, target retracking deviation after occluding or disappearing and high similarity interference, a target tracking algorithm based on TLD framework is proposed in this paper. By using CLAHE(Contrast Limited Adaptive Histogram Equalization) image enhancement technology to preprocess the image, the color contrast of different pixels can be enlarged to increase the image recognition ability. To ensure the accuracy and real-time performance of the model, Kalman filter is used to predict the possible position of the target when it is blocked or disappeared, and detect and track the target immediately after it reappears. Then, the Median-Flow optical flow tracker with CSRT assisted TLD algorithm makes use of its dual reliability mechanism of channel and space. As the result, the target tracking task can be fulfilled effectively and stably. Experimental results show that the proposed algorithm can achieve high tracking accuracy and robustness under complex environment. © 2024 Copyright held by the owner/author(s).

关键词： Kalman filters

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NegCPARBP: Enhancing Privacy Protection for Cross-Project Aging-Related Bug Prediction Based on Negative Database

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IEEE Transactions on Emerging Topics in Computing 2025年第02期13卷 283-298页

作者： Zhao, Dongdong Liu, Zhihui Zhang, Fengji Liu, Lei Keung, Jacky Wai Yu, xiao Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China City University of Hong Kong Department of Computer Science Hong Kong Xi'an Jiaotong University School of Electronic Science and Engineering Xi'an China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China

The emergence of Aging-Related Bugs (ARBs) poses a significant challenge to software systems, resulting in performance degradation and increased error rates in resourceintensive systems. Consequently, numerous ARB prediction methods have been developed to mitigate these issues. However, in scenarios where training data is limited, the effectiveness of ARB prediction is often suboptimal. To address this problem, CrossProject Aging-Related Bug Prediction (CPARBP) is proposed, which utilizes data from other projects (i.e., source projects) to train a model aimed at predicting potential ARBs in a target project. However, the use of source-project data raises privacy concerns and discourages companies from sharing their data. Therefore, we propose a method called Cross-Project AgingRelated Bug Prediction based on Negative Database (NegCPARBP) for privacy protection. NegCPARBP first converts the feature vector of a software file into a binary string. Secondly, the corresponding Negative DataBase (NDB) is generated based on this binary string, containing data that is significantly more expressive from the original feature vector. Furthermore, to ensure more accurate prediction of ARB-prone and ARB-free files based on privacy-protected data (i.e., maintain the data utility), we propose a novel negative database generation algorithm that captures more information about important features, using information gain as a measure. Finally, NegCPARBP extracts a new feature vector from the NDB to represent the original feature vector, facilitating data sharing and ARB prediction objectives. Experimental results on Linux, MySQL, and NetBSD datasets demonstrate that NegCPARBP achieves a high defense against attacks (privacy protection performance reaching 0.97) and better data utility compared to existing privacy protection methods. © 2025 IEEE.

关键词： Differential privacy

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Honeycomb-like biochar framework coupled with Fe_(3)O_(4)/FeS nanoparticles as efficient heterogeneous Fenton catalyst for phenol degradation

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Journal of Environmental sciences 2024年第2期136卷 390-399页

作者： Aihua Cheng Yi He xiaohe Liu Chi He Department of Environmental Engineering College of Geology and EnvironmentXi’an University of Science and TechnologyXi’an 710054China State Key Laboratory of Multiphase Flow in Power Engineering School of Energy and Power EngineeringXi’an Jiaotong UniversityXi’an 710049China National Engineering Laboratory for VOCs Pollution Control Material and Technology University of Chinese Academy of SciencesBeijing 101408China

Achieving an efficient and stable heterogeneous Fenton reaction over a wide pH range is of great significance for wastewater ***,a pollen-derived biochar catalyst with a unique honeycomb-like structure,coupled with the dispersion of magnetic Fe_(3)O_(4)/FeS(Fe/S)nanoparticles,was synthesized by simple impregnation precursor,followed by *** prepared Fe/S-biochar catalyst demonstrated outstanding phenol degradation efficiency across a wide pH range,with 98%of which eliminated even under neutral conditions(pH 7.0).The high catalytic activity was due to the multilevel porous structure of pollenderived biochar provided enough active sites and allowed for better electron transfer,then increases oxidation ability to promote the ***,the acid microenvironment formed by SO_(4)^(2-)group from Fe/S composite extended the pH range for Fenton reaction,and S^(2-)facilitated the conversion of≡Fe^(3+)to≡Fe^(2+),resulting in remarkable degradation ***,biochar can effectively promote cycling stability by limiting Fe *** work may provide a general strategy for designing 3D framework biochar-based Fe/S catalysts with excellent performance for heterogeneous Fenton reactions.

关键词： Biomass Biochar Fenton reaction Fe_(3)O_(4)/FeS(Fe/S) Phenol degradation

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Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

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computer Modeling in engineering & sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang Yao Zhou Guangchun Luo Peng Chen Dezhong Peng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer Science Sichuan UniversityChengdu610065China National Innovation Center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

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computer Modeling in engineering & sciences 2023年第1期134卷 637-655页

作者： Zhaoxi Ma Qin Zhao Tianyou Cang Zongjian Li Yiyun Zhu xinhong Hei Department of Civil Engineering and Architecture Xi’an University of TechnologyXi’an710048China State Key Laboratory of Rail Transit Engineering Informatization(FSDI) Xi’an710043China Department of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China

To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable *** one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction *** cutting waste increases construction costs and generates a considerable amount of CO_(2)*** study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable *** the proposed algorithm,the integer linear programming algorithm was applied to solve the *** was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data *** proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction *** the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation *** the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.

关键词： Building construction rebar work cutting stock problem optimization algorithm integer linear programming

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Voiceprint recognition model based on Ecapa-Tdnn and ResNet 5

Voiceprint recognition model based on Ecapa-Tdnn and ResNet

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5th International Conference on Telecommunications, Optics, and computer science, TOCS 2024

作者： Jia, Chengjie Fei, Rong Li, xiaomeng School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710004 China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510691667

In recent years, with the continuous progress of deep learning neural networks and the construction of large data sets, new breakthroughs in voiceprint verification technology for voice calls have been rapidly achieved. Deep learning-based neural networks, such as X-Vectors, ResNets, Ecapa-Tdnn, etc., are able to utilize their own network characteristics to extract rich voiceprint features, thus further improving the accuracy of voiceprint recognition, but there is still room for improvement in the performance and parameter count of the traditional models. In this paper, we propose the Ecapa2Tdnn model based on EcapaTdnn and ResNet, which has the advantage of possessing a deeper network architecture while performing better in terms of temporal modeling capability. This indicates that the model can effectively handle long time series of speech data, thus improving the performance of the voiceprint recognition system. The experimental results show that under the same experimental environment, compared with the equal error rate (EER) of 11.31% of the EcapaTdnn model, the Ecapa2Tdnn model proposed in this paper has only an EER of 10.27%, which is 1.04% lower than the former EER, and the number of parameters is still one-fourth of the original model. The model improves accuracy while reducing the total number of parameters, which is expected to be widely used in the field of voiceprint recognition. © 2025 SPIE.

关键词： Deep neural networks

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Collective orientation of CNT coated magnetic microchains for effective microwave absorption in S and C band

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Journal of Materials science & technology 2024年第29期196卷 215-223页

作者： You Wu Yixuan Han Jinwen Hu Na He Mukun He Hua Guo Haihong Xu Zhongwu Liu Yali Zhang Wenbo Ju School of Physics and Optoelectronics South China University of TechnologyGuangzhou 510640China Shaanxi Key Laboratory of Macromolecular Science and Technology School of Chemistry and Chemical EngineeringNorthwestern Polytechnical UniversityXi’an 710072China School of Materials Science and Engineering South China University of TechnologyGuangzhou 510640China

The control of undesired electromagnetic radiation in S-and C-band spectra requires novel microwave absorbing materials(MAMs)having high microwave attenuation capability together with optimal impedance *** are conformally coated onto the surface of one-dimensional FeCo-based magnetic microchains via electrostatic self-assembly,and then the magnetic inclusions are collectively oriented in matrices by applying an external magnetic *** proper incorporation of CNTs with magnetic microchains demonstrates a feasible pathway for effectively absorbing microwaves in the S and C *** consisting of oriented microchains have anisotropic complex permittivity,of which the real part ranges from 6.1 to 30.4 at 2 *** the electric field is parallel to microchains,the 5-mm-thick MAM has an effective absorbing bandwidth(EAB)in the range of 2.3 to 2.9 GHz,and reduces the radar cross section to be lower than-15.9 dB m^(2) from the vertical to the glancing *** the magnetic field is parallel to microchains,the MAM adsorbs C-band microwaves with an EAB of 1.5 GHz,and achieves maximal reflection loss of-46.4 *** collective orientation of shape-anisotropic magnetic materials,in addition to the composition and microstructure,is a new variable for the design of effective MAMs.

关键词： Radar absorbing material Magnetic microchain CNT Electrostatic self-assembly Collective orientation

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Revealing the Trends in the Academic Landscape of the Health Care System Using Contextual Topic Modeling

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Data Intelligence 2023年第4期5卷 923-946页

作者： Muhammad Inaam ul haq Qianmu Li School of computer science and Engineering Nanjing University of Science and Technology

The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of *** to the rising debates on the healthcare systems in relation to diseases,treatments,interventions,medication,and clinical practice guidelines,the world is currently discussing the healthcare industry,technology perspectives,and healthcare *** gain a comprehensive understanding of the healthcare systems research paradigm,we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of *** research work discovered 60 contextual topics among them fteen topics are the hottest which include smart medical monitoring systems,causes,and effects of stress and anxiety,and healthcare cost estimation and twelve topics are the ***,thirty-three topics are showing in-significant *** further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific *** current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary *** further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based *** also utilized corpus tuning,the mean pooling technique,and the hugging face *** method gives a higher coherence score as compared to the state-of-the-art models(LSA,LDA,and Ber Topic).

关键词： Contextual topic modeling health care Bert content analysis health care system

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