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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Nour Mahmoud Bahbouh Abdulelah Alhussain Hassan Hakami Yaser Mohammed Aljohani Adnan Ahmed Abi Sen Ahmad Alkhodre Aeshah Alsughayyir Department of Information and Communication Sciences Granada University Granada Spain Saudi Electronic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Computing and Information Technology Islamic University Al-Madinah Saudi Arabia Faculty of Computer and Information Systems Islamic University Al-Madinah Saudi Arabia College of Computer Science and Engineering Taibah University Al-Madinah Saudi Arabia

Smart cities promise a lot of well-being to their users in all areas of life through millions of applications and services. Smart services rely heavily on collecting data and the preferences of users. But on the dark side, the users' information is posed to threat and penetration during transmission or stored far in the clouds. Depending on encryption only is not sufficient if the attacker has strong resources or if the attacker is the service provider (SP) itself. In addition, changing data before sending is not a practical solution in many systems because of the adverse impact on the quality of a main service. This research presented a new idea to address the issue of protection. The proposed method enhanced the privacy and security of users' data without affecting the accuracy of service. The core of the suggested solution relies on a knowledge base of services that are managed by experts. Also, the solution depends on fog nodes to measure the level of security and privacy of users' queries without delay. Moreover, the fog nodes manage contact with SPs. Finally, the proposed method divided the SP into two, one for user queries and the other for user data. The simulation and analytical discussion on a practical case in smart cities demonstrated the superiority of the proposed approach over previous methods in the level of protection by maintaining the quality of services, and the resistance to attacks.

关键词： Resistance Privacy Data privacy Machine learning algorithms Smart cities Knowledge based systems Quality of service

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Towards A Taxonomy for Ranking Knowledge in Internet of Everything

Towards A Taxonomy for Ranking Knowledge in Internet of Ever...

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International Conference on computer Supported Cooperative Work in Design

作者： Viviane Cunha Farias da Costa Luiz Oliveira Jano de Souza Systems Engineering and Computer Science Program/COPPE Federal University of Rio de Janeiro Brazil Rio de Janeiro Brazil Systems Engineering and Computer Science Program/COPPE IFRJ Campus Niterói Federal Institute of Rio de Janeiro Federal University of Rio de Janeiro Brazil Rio de Janeiro Brazil

The paradigm of the Internet of Everything (IoE) adds value to Internet of Things (IoT) in connections among people, processes, data, and things. However, knowledge creation process in IoE context still requires concerns to what extent connected sensors and actuators collaborates in value created and outcomes of IoE applications. This study focuses on the requirement and design approach for identification of critical knowledge within IoE domain though a knowledge ranking approach. Existing solutions for semantic definitions and taxonomies in IoE are reviewed for this purpose. In addition, this work proposes the use of a knowledge-based taxonomy as the main driver for ranking knowledge in smart sensors as IoE enablers with a qualitative approach of its dimensions. To the best of our knowledge, this is the first work that comprehensively integrate a taxonomy concerning ranking knowledge in IoE applications.

关键词： Actuators Conferences Taxonomy Semantics Knowledge based systems Collaborative work Internet of Things

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GNN-Based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection

GNN-Based Point Cloud Maps Feature Extraction and Residual F...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wei-Hsiang Liao Chieh-Chih Wang Wen-Chieh Lin Graduate Degree Program of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, taking the environment information (HD maps) into account could be beneficial to predetermine where targets are more or less likely to appear. Compared with semantic maps, HD maps formed by point clouds provide much richer information from surrounding static objects and scenes. In this work, we construct a GNN-based feature extraction of point cloud maps to increase the receptive fields of learning map features. Our work is based on PVRCNN, the state-of-the-art LiDAR object detection method. With point-wise and voxel-wise features obtained from PVRCNN, residual feature fusion is proposed to fuse the features from PVRCNN and the map features from GNN. Our approach is evaluated on NuScenes dataset. It achieves a 24.78% average precision improvement for long-range objects at 40–50 meters, the farthest areas with ground truth annotation. Our approach also has a 4.22% reduction of false positives in the entire sensing areas.

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Games in Technology Forecasting & Foresight: A Rapid Review

Games in Technology Forecasting & Foresight: A Rapid Review

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IEEE International Conference on systems, Man and Cybernetics

作者： Rafael Machado Andrade Aline Lima de Souza Carlos Eduardo Barbosa Alan Lyra Herbert Salazar Yuri Lima Matheus Argôlo Jano Moreira de Souza Program of Systems and Computer Engineering (PESC) COPPE Universidade Federal do Rio de Janeiro (UFRJ) Horácio Macedo St. Rio de Janeiro RJ Brazil Centro de Análises de Sistemas Navais Marinha do Brasil Praça Barão de Ladário - Ilha das Cobras Rio de Janeiro RJ Brazil

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Future-related studies are key in identifying and predicting emerging technologies, fostering innovation, and driving scientific progress. Games have increasingly served as a tool to engage researchers in Technology Forecasting and Foresight activities, presenting a gamified approach to gather, analyze, and synthesize information to allow future foresight. In this work, we use the Rapid Review methodology to analyze the scientific literature and explore the use of games as tools for Technology Forecasting and Foresight. We analyzed and categorized the articles by theme and type of study, identifying the areas where games can be most beneficial to guide professionals performing Technology Forecasting and Foresight, helping companies and governments better understand the future. The empirical evidence found provides a comprehensive understanding of how games can enhance foresight activities and contribute to the success of Technology Forecasting and Foresight initiatives. Our analysis also provides insights for future research about gamified strategies in Foresight.

关键词： Technological innovation Reviews Databases Decision making Games Strategic planning Technology forecasting Market research Teamwork Sustainable development

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Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan The Department of Computer Science National Chengchi University Taipei11529 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity. © 2024, CC BY.

关键词： Audiovisual

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Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Chengchi University Taipei11529 Taiwan Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Institute of Information Science Academia Sinica Taipei11529 Taiwan

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

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Software Project Management Effectiveness Evaluation Tool 20

Software Project Management Effectiveness Evaluation Tool

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Proceedings of the 9th International Conference on Software and Information engineering

作者： Mohamed Ellithey Barghoth Akram Salah Manal A Ismail Master in Software Engineering Program Egyptian E-Learning University Giza Egypt Cairo University Giza Egypt Department of Computer Engineering and Systems Helwan University Helwan Egypt

ISBN: (纸本)9781450377218

Project management is important for the success of a software project. Enhancing software project management effectiveness measurements should lead the project manager to advance practices that contribute to the successful development of software projects. This study aims to enhance project management effectiveness measurements in the direction of increase the likelihood of software project success. An enhanced evaluation model of software project management effectiveness has been proposed in this study. Correspondingly, the model fed into a developed software project management effectiveness evaluation tool. This measurement tool helps software development managers to evaluate, monitor and improve project management effectiveness in software projects. The developed software project management effectiveness evaluation tool has been validated by a feedback survey. Feedback survey participants have been demonstrated the importance of the developed project management effectiveness measurement tool.

关键词： Software project management model Measurements of software project management effectiveness Software project management effectiveness Software project success

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Digital Nomads during the COVID-19 Pandemic: Evidence from Narratives on Reddit discussions

Digital Nomads during the COVID-19 Pandemic: Evidence from N...

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International Conference on computer Supported Cooperative Work in Design

作者： Marcos Antonio De Almeida Antó nio Correia Jano Moreira De Souza Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Tr&#x00E1 s-os-Montes e Alto Douro Vila Real Portugal Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil NCE/UFRJ T&#x00E9 rcio Pacitti Institute of Computer Applications and Research Rio de Janeiro Brazil

In this paper, we report on new findings about the results of an empirical study which aims to investigate how the COVID-19 pandemic has been shaping nomadic work practices and also challenging the lifestyles of digital nomads (DN). To do this, we collected textual data from posts in a Reddit community. We argue that, in order to understand how to design technical solutions for the so-called ‘new normal’ working conditions, one way to approach this is to understand how digital nomads are being impacted in their work practices and routines, and also how they are seeing the future of their technology-mediated work-life space. Finally, we show how evidence collected from DNs about their experiences and difficulties perceived during the pandemic period can inform CSCW researchers worldwide about future design-oriented strands.

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Queue Management Priority-Based Traffic engineering Method

Queue Management Priority-Based Traffic Engineering Method

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International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Oleksandr Lemeshko Oleksandra Yeremenko Anatoliy Persikov Maryna Yevdokymenko Amal Mersni Serhii Harkusha V.V. Popovskyy Department of Infocommunication Engineering Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Engineering Computer Sciences and Engineering Program International University of Sarajevo Sarajevo Bosnia and Herzegovina Department of Document Studies and Information Activities in Economic Systems Poltava University of Economics and Trade Poltava Ukraine

The work proposes the improvement of queue management priority-based Traffic engineering method. It is based on the interaction prediction principle to coordinate decisions at various levels. The lower level of calculations is responsible, firstly, for the distribution and aggregation of packet flows between the macro queues and sub-queues organized on the router interface (congestion management task), and, secondly, for the balanced distribution of interface bandwidth among sub-queues, weighted relative to their priorities (resource allocation task). The upper level of the method's calculations is accountable for distributing the interface's bandwidth between macro queues by performing the iterative procedure. The numerical research results of the proposed two-level queue management method confirmed its effectiveness in ensuring high scalability, balanced priority- based distribution of packet flows and interface bandwidth between the macro queues and sub-queues organized on it. The method demonstrated high coordination procedure convergence and the final quality of centralized calculations.

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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