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Sensitive information Protection Model Based on Bayesian Game

Computers, Materials & Continua 2022年第10期73卷 883-898页

作者： Yuzhen Liu Zhe Liu Xiaoliang Wang Qing Yang Guocai Zuo Frank Jiang School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan411201China College of computer and information engineering Hunan University of Technology and BusinessChangshaChina School of Computer Science and Information Engineering Guangzhou Maritime UniversityGuangzhouChina Hunan Software Vocational and Technical University XiangtanChina School of Engineering and IT University of New South WalesNSWAustralia

A game measurement model considering the attacker’s knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of *** quantified the sensitive level of information according to the user’s personalized sensitive information protection *** on the probability distribution of sensitive level and attacker’s knowledge background type,the strategy combination of service provider and attacker was analyzed,and a game-based sensitive information protection model was *** the combination of strategies under Bayesian equilibrium,the information entropy was used to measure the leakage of sensitive ***,in the paper the influence of the sensitive level of information and the attacker’s knowledge background on the strategy of both sides of the game was considered *** on,the leakage of the user’s sensitive information was ***,the feasibility of the model was described by experiments.

关键词： Sensitive information game theory Bayesian equilibrium sensitive level information entropy

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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Sustainability Forecasting for Deep Learning Packages 31

Sustainability Forecasting for Deep Learning Packages

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Han, Junxiao Wang, Yunkun Liu, Zhongxin Bao, Lingfeng Liu, Jiakun Lo, David Deng, Shuiguang School of Computer & Computing Science Hangzhou City University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (纸本)9798350330663

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, and the sustainability status is classified as 'sus-tainable' or 'dormant'. Relatedly, a DL package is considered as 'sustainable' if it has sustained activity in its last 12 months. Otherwise, it is deemed as 'dormant'. To this end, we propose an approach that begins with obtaining longitudinal features for each DL package in each month. Then, we develop a model to forecast the sustainability of DL packages by incorporating the longitudinal features, which can aptly predict sustainability with an accuracy of up to 0.81. Subsequently, an interpretable module is developed to interpret the determinants (i.e., important features) that impact the sustainability of DL packages. Finally, we generate sustainability trajectories for each DL package to better understand the monthly changes of their sustainability status. Our findings uncover that for most DL packages, fewer but more centralized developers and a balanced collaboration are more likely to help sustain the DL packages. Furthermore, although some DL packages are sustainable, their sustainability trajectories present statistically decreasing trends over time. Based on the findings, we shed light on the dynamic sustainability of DL packages, highlight future research directions, and provide practical suggestions to DL package maintainers, developers, users, and software engineering researchers. ©

关键词： Open source software

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A Powerful Load Balancing Algorithm for Cloud Performance Evaluation 5

A Powerful Load Balancing Algorithm for Cloud Performance Ev...

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5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Chandar, J. Praveen Chidambaranathan, C.M. Anbarasan, Anbarasa Kumar Karthikram, A. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Information Technology and Engineering Vellore Institute of Technology Vellore India School of Computers Madanapalle Institute of Technology & Science Department of Cyber Security Madanapalle India

ISBN: (纸本)9798350321425

The energy consumption of data centers plays a significant role in the cost estimation of maintenance, performance evaluation, and environmental pollution in cloud computing. Carbon emissions are one of the major drawback of data centers in this scenario. To manage the data center temperature as well as the necessity to reduce power consumption while satisfying customer demands. This is extremely beneficial to both the data center and the environment. The data center is a collection of servers from which consumers can benefit by using their services. In this context, cloud service providers are working hard to reduce energy usage and avoid environmental contamination. This research work describes the novel algorithm for scheduling virtual machines in servers in order to consume a minimal amount of energy. The weight approximation algorithm arranges servers and virtual machines in decreasing order depending on their configuration to allocate VMs with jobs to optimize power usage. The power consumption of the server is calculated before and after VM allocation in this algorithm. To avoid CRAC modifications, the critical threshold of each server is maintained in this instance. If it exceeds the server's critical threshold, the VM is moved to another ideal server to avoid migration. In this method of VM allocation, the ordered tasks are allocated to the server using round-robin fashion. The model is simulated using cloudsim tools, and the server's performance and energy consumption are better than the random selection algorithm. © 2023 IEEE.

关键词： Virtual machine

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Exploring the tidal effect of urban business district with large-scale human mobility data

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Frontiers of Computer science 2023年第3期17卷 77-90页

作者： Hongting NIU Ying SUN Hengshu ZHU Cong GENG Jiuchun YANG Hui XIONG Bo LANG School of Computer Science and Engineering Beihang UniversityBeijing 100191China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100086China Baidu Talent Intelligence Center Baidu Inc.Beijing 100085China Business School Imperial College LondonLondon SW72AZUK Artificial Intelligence Thrust The Hong Kong University of Science and TechnologyGuangzhou 510030China

Business districts are urban areas that have various functions for gathering people,such as work,consumption,leisure and *** to the dynamic nature of business activities,there exists significant tidal effect on the boundary and functionality of business ***,effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a ***,with the implicit and complex nature of business district evolution,it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business *** this end,we propose a data-driven and multi-dimensional framework for dynamic business district ***,we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time ***,we detect and forecast the functional changes in business *** results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business ***,the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business *** example,the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.

关键词： business district trajectory functionality detection tidal effect boundary detection visiting score

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Early Detection of Down Syndrome Through Ultrasound Imaging Using Deep Learning Strategies - A Review 2

Early Detection of Down Syndrome Through Ultrasound Imaging ...

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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Vidhyasagar, B.S. Madamanchi, Sree Chandana Kalimuthu, Sivakumar Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Build Environment and Information Technology SEGi University Damansara Faculty of Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350328202

Another name for Down syndrome is Trisomy 21, which is a complex chromosomal disorder that poses a substantial challenge in prenatal care due to its widespread prevalence. An additional copy of chromosome 21 is the defining feature of this disorder, leading to numerous physical and cognitive impairments. Typical physical traits of Down syndrome include a flattened facial structure, most notably in the nose bridge region, as well as upward-slanted almond-shaped eyes and a shortened neck. Apart from these features, individuals with Down syndrome also experience a range of intellectual disabilities, spanning from mild to severe. These challenges manifest in various areas, such as expressive communication, verbal memory, and attention. Furthermore, children with this disorder may exhibit behavioral issues that require special attention and care. Early and accurate detection of this condition is essential for informed decision-making by expectant parents. This review explores an overview of the data sets and results of recent studies that have used deep learning and machine learning methodologies to create effective models for the identification of Down syndrome. © 2024 IEEE.

关键词： Chromosomes

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A Heuristic-based Dynamic Scheduling and Routing Method for Industrial TSN Networks 10

A Heuristic-based Dynamic Scheduling and Routing Method for ...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Chen, Honglong Liu, Mindong Huang, Jing Zheng, Zhiling Huang, Weihong Xiao, Yufeng Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China Hunan Industry Polytechnic Information Center Changsha410125 China

ISBN: (纸本)9798350312461

In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve such challenges, a set of protocol standards developed by IEEE802.1 working group, namely Time-sensitive Networking (TSN), has been introduced into industrial networks. TSN can provide high real-time and reliability for data transmission, where the reliability is achieved by Frame duplication and Frame Elimination (FRER). In the realization process of FRER, it is necessary to determine the source node, destination node, and multiple disjoint paths to transmit redundant data. However, the transmission of these redundant traffic may result in the delay of other flows, and then affects the user experience. Therefore, it is very important to choose excellent redundant traffic paths to ensure reliability and reduce the impact on other flows. In the existing research, there are many dynamic scheduling and routing heuristics to determine the path, but they do not consider the influence of the location of the source node on the whole route scheduling. This paper proposes an improved dynamic scheduling and routing heuristic method, which takes the source node into account in the routing selection. In the flow test experiments of different magnitudes, it is found that the total delay of all flows is reduced by 1.4%-4.5% under the same magnitude of schedulability compared with Ant Colony Optimization. © 2023 IEEE.

关键词： Heuristic algorithms

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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach 5

Enhancing Lung Disease Identification with Multimodal Data F...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Grace Shalini, T. Shiny, G. Susan Saranya, R. Suresh Babu, P. Kavitha, R. Atheeswaran, Athiraja Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Sethu Institute of Technology Department of information technology Kariapatti India Velammal college of engineering and technology Department of Eee Madurai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Bannari Amman Institute Of Technology Department Of Computer Science And Engineering Erode Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331504403

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. Through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an F1-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine. © 2024 IEEE.

关键词： Lung cancer

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A GTFS data acquisition and processing framework and its application to train delay prediction

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International Journal of Transportation science and technology 2023年第1期12卷 201-216页

作者： Jianqing Wu Bo Du Zengyang Gong Qiang Wu Jun Shen Luping Zhou Chen Cai School of Computing and Information Technology University of WollongongNSW 2522Australia SMART Infrastructure Facility University of WollongongNSW 2522Australia School of Civil Mining and EnvironmentalUniversity of WollongongNSW 2522Australia Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China School of Electrical and Information Engineering University of SydneyNSW 2006Australia CSIRO’s Data61 NSW 2015Australia

With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Term Memory is developed to predict train delay with minor anomaly in Sydney as a case study. The contribution of this new framework will render great potential for broader applications and deeper research.

关键词： General transit feed specification Delay prediction Train delay Long short-term memory Data fusion

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Automatic Title Generation with Attention-Based LSTM 3rd

Automatic Title Generation with Attention-Based LSTM

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3rd International Conference on information technology, InCITe 2023

作者： Dhilsath Fathima, M. Seeni Syed Raviyathu Ammal, M. Singh, Prashant Kumar Shome, Sachi Kharsyienlieh, Manbha Hariharan, R. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Mohamed Sathak Engineering College Tamil Nadu Kilakarai India Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819959969

The objective of this article is to develop a generalised architecture that can generate headings for any type of document, including articles, book paragraphs, newspaper articles, and legal documents. The title of a document provides a concise overview of its content, allowing readers to quickly understand the main idea without focusing on the specifics. The more attractive the title is, the more the reader is bound to read it. Title generation will be helpful to the editors of newspapers or technical writers to easily find the central theme of the whole article. The Sequence-to-Sequence (Seq-to-Seq) model is implemented in this proposed work for generating the title for the project, article, and research articles. The Seq-to-Seq model utilizes an encoder-decoder recurrent neural network that has an attention layer and Long Short-Term Memory (LSTM). The main strength of the developed framework is the adoption of an attention layers to the encoder-decoder architecture, which assigns more weightage to background words. This proposed model achieves 90% prediction accuracy by including an attention layer into the LSTM. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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