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Security Perspectives in Integrity Verification for Shared Data in Cloud—A Survey 1

9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Pailkar, Harsh Murugan, Thangavel School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal India Department of Information Systems and Security College of Information Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

As businesses move their data and services to cloud computing environments, it is crucial to ensure the security and integrity of that shared data. In order to study the new security perspectives in the context of integrity verification for shared data in the cloud, this research article conducts an extensive examination of the existing solutions. A key component of cloud security, data integrity, is thoroughly examined with an emphasis on the difficulties it faces and the consequences it may have in cloud-based scenarios. Analyzing the distinctive qualities of shared data in the cloud reveals the complications that develop when organizations access and work together on data inside a shared ecosystem. This article addresses research gaps and limitations within the existing solutions by critically examining prior studies. It emphasizes the requirement for novel strategies and answers to deal with the complex data integrity problems of shared data in cloud. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data integrity

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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Discovering Personalized Characteristic Communities in Attributed Graphs 40

Discovering Personalized Characteristic Communities in Attri...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Niu, Yudong Li, Yuchen Karras, Panagiotis Wang, Yanhao Li, Zhao School of Computing and Information Systems Singapore Management University Singapore University of Copenhagen Department of Computer Science Copenhagen Denmark School of Data Science and Engineering East China Normal University Shanghai China Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350317152

What is the widest community in which a person exercises a strong impact? Although extensive attention has been devoted to searching communities containing given individuals, the problem of finding their unique communities of influence has barely been examined. In this paper, we study the novel problem of Characteristic cOmmunity Discovery (COD) in attributed graphs. Our goal is to identify the largest community, taking into account the query attribute, in which the query node has a significant impact. The key challenge of the COD problem is that it requires evaluating the influence of the query node over a large number of hierarchically structured communities. We first propose a novel compressed COD evaluation approach to accelerate the influence estimation by eliminating redundant computations for overlapping communities. Then, we further devise a local hierarchical reclustering method to alleviate the skewness of hierarchical communities generated by global clustering for a specific query attribute. Extensive experiments confirm the effectiveness and efficiency of our solutions to COD: they find characteristic communities better than existing community search methods by several quality measures and achieve up to 25 x speedups against well-crafted baselines. © 2024 IEEE.

关键词： attributed graph characteristic community community hierarchy influence

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Off-Road Autonomy Validation Using Scalable Digital Twin Simulations Within High-Performance computing Clusters

Off-Road Autonomy Validation Using Scalable Digital Twin Sim...

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2024 NDIA Michigan Chapter Ground Vehicle systems engineering and Technology Symposium, GVSETS 2024

作者： Samak, Tanmay Samak, Chinmay Krovi, Venkat Binz, Joey Luo, Feng Smereka, Jonathon Brudnak, Mark Gorsich, David Department of Automotive Engineering CU-ICAR Greenville United States School of Computing Clemson University ClemsonSC United States U.S. Army DEVCOM Ground Vehicle Systems Center DetroitMI United States

Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Variability analyses, by sequentially sweeping across the parameter space, struggle to comprehensively assess the performance of off-road autonomous systems within the imposed time constraints. This paper proposes leveraging scalable digital twin simulations within high-performance computing (HPC) clusters to address this challenge. By harnessing the computational power of HPC clusters, our approach aims to provide a scalable and efficient means to validate off-road autonomy algorithms, enabling rapid iteration and testing of autonomy algorithms under various conditions. We demonstrate the effectiveness of our framework through performance evaluations of the HPC cluster in terms of simulation parallelization and present the systematic variability analysis of a candidate off-road autonomy algorithm to identify potential vulnerabilities in the autonomy stack's perception, planning and control modules. © Rights reserved by the National Defense Industrial Association (NDIA) Michigan Chapter, authors and their respected organizations.

关键词： Cluster computing

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Power transmission network planning under uncertainties

Power transmission network planning under uncertainties

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Neven Vukadinović Ivica Pavić Ninoslav Holjevac Department of Energy and Power Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Republic of Croatia

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The optimization of load flow analysis is crucial for the efficient observation of the impact of new power plants into existing electricity power systems (EPS). The advent of integration of new energy sources has reduced construction timeline requirements to 4-5 years, creating a lag in the development of supporting electricity infrastructure. This paper explores the spatial and temporal dimensions of EPS development planning, illustrated through the example of two new connection requests to a transmission network, with emphasis on importance of load flow optimization and the potential failure analysis to meet the (N-1) contingency criterion, which ensures system reliability during outages. This paper underscores the necessity of strategic planning in EPS development to accommodate new generation units while maintaining system stability and reliability at the satisfactory levels.

关键词： Uncertainty Electricity Europe Power system stability Planning Power system reliability Complexity theory Reliability Artificial intelligence Optimization

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EmoSync: Facial Emotion Detection for Adaptive Music Playback 3

EmoSync: Facial Emotion Detection for Adaptive Music Playbac...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Devarajan, Devikanniga Rajkumar, K. GITAM Deemed to be University Computer Science and Engineering Nagadenahalli Doddabalpur Karnataka Bangalore561203 India SRM Institute of Science and Technology Department of Data Science and Business Systems School of Computing Tamil Nadu Kattankulathur603 203 India

ISBN: (纸本)9798350372120

This work investigates using Convolutional Neural Networks (CNN) and VGGNet architecture, for realtime emotion detection. Raw data from the FER-2013 dataset is transformed from CSV format into organized image directories, segmented by distinct emotions. The resulting model, equipped with convolutional, pooling, and dense layers, effectively classifies emotions from 48x48 grayscale images. In application, the model overlays detected emotions onto a webcam stream. An integrated music player module synchronizes music with the subject's emotions. Overall, this research explores a comprehensive interactive system focused on recognizing and responding to human emotions. Without further training data, our model, to the best of our understanding, achieves modern single-network accuracy of 74.57% on FER2013 © 2024 IEEE.

关键词： Python

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Fostering interdisciplinary data cultures through early career development: The RDA/US data share fellowship

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Data Science Journal 2021年第1期20卷 1-19页

作者： Kouper, Inna Scheidt, Lois A. Plale, Beth A. Department of Informatics School of Informatics Computing and Engineering Indiana University Bloomington United States School of Information Studies University of Wisconsin Milwaukee United States Department of Intelligent Systems and Engineering School of Informatics Computing and Engineering Indiana University Bloomington United States

Openness and interdisciplinarity in research and data are among the challenges that are frequently discussed in the context of changing scientific and scholarly practices. Gradually, the visions of open and widely shared data are being reconciled with complex realities that stem from the disciplinary differences in data cultures. In this paper we discuss interdisciplinarity through data as a way to create research environments that are more flexible and, as a result, more amenable to change. We report our findings from facilitating and evaluating a data-oriented early-career fellowship program that was administered as part of the Research Data Alliance (RDA), a global organization that aims to enable open sharing and re-use of data. We identify ways to foster interdisciplinary data cultures among the future researchers and professionals and propose recommendations for future programs. While the short-term early career programs cannot address the systemic factors that impact openness and interdisciplinarity, such as the systems of reward and recognition or the funding structures, they can introduce mechanisms that support diversity, learning, and leadership and, ultimately, contribute to a culture change. © 2021 The Author(s).

关键词： Data culture Data stewardship Early career training Open science

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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GENSIMO - A Generic Framework for Modelling Social Insurance systems 25th

GENSIMO - A Generic Framework for Modelling Social Insuranc...

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25th International Workshop on Multi-Agent-Based Simulation, MABS 2024

作者： de Haan, Fjalar J. Thompson, Jason School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Parkville Australia Melbourne Centre for Data Science The University of Melbourne Parkville Australia Department of Psychiatry Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Parkville Australia

ISBN: (纸本)9783031880162

We report on the design, development and prototype testing of gensimo (GENeric Social Insurance MOdeling), a free and open-source software framework for modelling and simulation of social insurance systems. We discuss the conceptual and software design considerations of the framework in general before demonstrating how it works in detail by setting it up to model a particular social insurance scheme. We then present how we used this prototype set up to model the impact of a policy intervention on the workload of the insurer. While gensimo is under active development and we are a while away from serious verification and validation, this prototype testing shows realistic results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Software testing

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Classification of Cacao Pods Ripeness Using Convolutional Neural Networks 16

Classification of Cacao Pods Ripeness Using Convolutional Ne...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Borromeo, Karl O. Bonotan, Melbert R. Llagas, Archie P. Juan, Joana Marie O. Sanchez, Philip Donald C. Berro, Nathan Kit A. Department of Computer Science College of Computing and Information Sciences Caraga State University Butuan City Philippines Department of Information Technology College of Computing and Information Sciences Caraga State University Butuan City Philippines Center for Resource Assessment Analytics and Emerging Technologies College of Engineering and Geo-Sciences Caraga State University Butuan City Philippines Department of Information Systems College of Computing and Information Sciences Caraga State University Butuan City Philippines

This paper presents methods for the development of Ripeness Identification of Cacao Pods using Convolutional Neural Network (CNN) and precision agriculture employed in a Mobile Application. This research explores if there is a correlation between the color dynamics of cacao pods and its physiological characteristics, and the possibility of using this correlation to develop Artificial Intelligence (AI) model for ripeness and variety identification, which conventional eye estimation resulted to unripe and overripe harvesting of Cacao Pods. Data analysis and correlation was developed, integrating detection and classification models based on the Single Shot Detection (SSD) MobileNet and Visual Geometry Group (VGG) 16 or VGG16 layers network architectures, respectively. The mobile application utilizes the TensorFlow template for object detection and incorporates image preprocessing techniques for enhanced accuracy. The successful integration of the model into a mobile application demonstrates its potential for practical applications in the cacao industry. © 2025 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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