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An Unsupervised Graph Embedding Method Based on Dynamic Graph Attention Networks and Infomax for Link Prediction

An Unsupervised Graph Embedding Method Based on Dynamic Grap...

引用

IEEE international conference on Data Science in Cyberspace (DSC)

作者： Jingzhu Lu Feifan Song Jingchen Wu Xu Wu Key Lab of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Key Lab of Trustworthy Distributed Computing and Service Ministry of Education School of Economics and Finance Changchun Finance College Jilin China Key Lab of Trustworthy Distributed Computing and Service Ministry of Education School of Computer Science Beijing University of Posts and Telecommunications Beijing China

Due to the lack of labeled data in many real-world scenarios, Graph Neural Network models that require a large volume of labeled data are not effective, and research based on unsupervised learning frameworks has become essential. To solve the problem of sparsity in labeled graph data, this paper proposes an unsupervised graph representation learning method based on graph infomax and dynamic graph attention mechanism(IMDGA). this method conducts unsupervised learning based on the idea of contrastive learning and optimizes the encoder by calculating the maximum mutual information between the graph's local information and global information. At the same time, we use a dynamic Graph Attention Network as an encoder model to prevent the Graph Convolutional Neural network's inability to adapt to neighbor nodes with different weights. Finally, link prediction experiments on three public data sets of cora, citeseer, and pumbed show that the proposed model has achieved superior results compared with other graph representation learning, showing its effectiveness and competitiveness.

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SSDAEE: Semi-Supervised Data-Augmented Event Extraction for Transfer Learning

SSDAEE: Semi-Supervised Data-Augmented Event Extraction for ...

引用

IEEE international conference on Data Science in Cyberspace (DSC)

作者： Yueyue Shen Feifan Song Jingchen Wu Xu Wu Key Lab of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Key Lab of Trustworthy Distributed Computing and Service Ministry of Education School of Economics and Finance Changchun Finance College Jilin China Key Lab of Trustworthy Distributed Computing and Service Ministry of Education School of Computer Science Beijing University of Posts and Telecommunications Beijing China

Although the general event extraction model has achieved good results in the standard data sets, there is a gap in the extraction results in low-resource environments. the general event extraction model still has room for improvement in the performance of special fields. therefore, this paper proposes a semi-supervised reading comprehension event extraction model adapted to transfer learning, based on improved UDA for data enhancement, called SSDAEE, which can increase the accuracy of reading comprehension event type recognition. SSDAEE has a good performance on the public opinion data set. It can not only enhance a small number of supervised data in low-resource fields with unsupervised data but also improve the generalization ability of the model. Experiments show that when only 5% unsupervised data is used, the F1 score of SSDAEE on the public opinion data set is increased by 9% compared with the fully supervised model, and by 2% compared with other semi-supervised models.

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Visionize: Text To Image Generator

Visionize: Text To Image Generator

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Himanshu Kothari Nishtha Raut Bhagyashree Kalambe Amey Salvi Namita Pulgam Department of Computer Engineering Ramrao Adik Institute of Technology D Y Patil Deemed to be University Navi Mumbai India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Visionize is presented as an innovative platform that introduces a cutting-edge text-to-image model with the potential to redefine communication and idea expression within the digital domain. this tool seamlessly translates textual descriptions into captivating images, thereby extending the boundaries of creativity and enhancing communication clarity. the streamlined work-flow facilitates the visualization of ideas without necessitating specialized design skills or software expertise. It serves as a solution applicable across various domains, including the creation of engaging social media posts, presentations, and the illustration of complex concepts. Its implementation represents a significant advancement in transforming textual input into visually compelling outputs.

关键词： Visualization Social networking (online) Scalability Semantics Text to image Virtual environments Process control Streaming media Software Research and development

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Data Encryption in 6G Networks: A Zero- Knowledge Proof Model

Data Encryption in 6G Networks: A Zero- Knowledge Proof Mode...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Aryan Khamesra P. Selvaraj Isha Singh Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

this work presents an innovative algorithm demonstrating the effectiveness of zero-knowledge proofs (ZKPs) in network security. By integrating Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) for key generation, the algorithm showcases their applicability in enhancing security measures within 6G networks. It highlights the utility of ZKPs in bolstering data privacy and security by enabling entities to validate knowledge without compromising sensitive information. the algorithm shows its capability to ensure robust communication security through comprehensive simulations, thereby laying the groundwork for dependable next-generation communication infrastructures.

关键词： 6G mobile communication Data privacy Analytical models Privacy Network security Data models Encryption Security Standards Next generation networking

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the Influence of Technophilia, Social Media Marketing and EV Infrastructure on the Continuing Adoption of Electric Vehicles in Malaysia via Technology Continuance theory (TCT) Lens: Cognitive Interview Analysis

The Influence of Technophilia, Social Media Marketing and EV...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Nour A.M. Mohammed K. S. Savita Hafizan Mat Som M H Hasan Computer and Information Sciences Department Universiti Teknologi PETRONAS Perak Malaysia

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

this paper explores the factors influencing the continued adoption of Electric Vehicles (EVs) in Malaysia, employing the Technology Continuance theory (TCT) as a conceptual framework. through a Pre-Test phase utilizing Cognitive Interview-based analysis, feedback from 7 participants is leveraged to refine the questionnaire for the pilot study. Currently ongoing, the pilot study aims to collect data to scrutinize the factors impacting EV adoption in Malaysia. the research will further expound on the TCT framework within the paper and utilize insights from the interviews to refine the questionnaire for the pilot study. the findings are expected to contribute to a deeper understanding of EV continuance adoption and offer practical strategies for sustainable transportation in Malaysia.

关键词： Automation Social networking (online) Transportation Electric vehicles Interviews

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Resource Allocation in Cloud computing Using Genetic Algorithm and Neural Network

Resource Allocation in Cloud Computing Using Genetic Algorit...

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IEEE international conference on Smart Cloud (SmartCloud)

作者： Mahdi Manavi Yunpeng Zhang Guoning Chen Department of Computer Science University of Houston Houston USA Department of Information Science Technology University of Houston Houston USA

Cloud computing is one of the most used distributed systems for data processing and data storage. Due to the continuous increase in the size of the data processed by cloud computing, scheduling multiple tasks to maintain efficiency while reducing idle becomes more and more challenging. Efficient cloud-based scheduling is also highly sought by modern transportation systems to improve their security. In this paper, we propose a hybrid algorithm that leverages genetic algorithms and neural networks to improve scheduling. Our method classifies tasks with the Neural Network Task Classification (N2TC) and sends the selected tasks to the Genetic Algorithm Task Assignment (GATA) to allocate resources. It is fairness aware to prevent starvation and considers the execution time, response time, cost, and system efficiency. Evaluations show that our approach outperforms the state-of-the-art method by 3.2 % at execution time, 13.3 % in costs, and 12.1 % at response time.

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Calculation of the Maximum Hosting Capacity of distributed Photovoltaics in Distribution Networks Considering Flexible Interconnections

Calculation of the Maximum Hosting Capacity of Distributed P...

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IEEE international conference on Power and Power and Renewable Energy (ICPRE)

作者： Tong Sun Yi Song Kai Yuan Ruosong Hou Jiakun An Wenguang Jin Shenxi Zhang Haozhong Cheng College of Smart Energy Shanghai Jiao Tong University Shanghai China State Grid Economic and Technological Research Institute Co. Ltd. Beijing China Economic and Technological Research Institute of State Grid Hebei Electric Power Co. Ltd Hebei China Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai Jiao Tong University Shanghai China

Large-scale access to distributed photovoltaics leads to power imbalance and frequent voltage fluctuations in distribution station areas, increasing the difficulty of operation control of distribution networks. To solve these challenges while improving the utilization rate of distributed photovoltaics, this study proposes a method for calculating the maximum hosting capacity (HC) of distributed photovoltaics in distribution networks, considering the flexible interconnection of low-voltage distribution station areas. First, two types of networking modes of the flexible interconnection of distribution station areas are analyzed and mathematical models are developed for them. then, an optimization model of the maximum HC of distributed photovoltaics in a distribution network that considers the flexible interconnection of distribution station areas is established, and the increasingly tight linear-cut algorithm is used to solve it, which aims to narrow the gap introduced by the convex relaxation to get the optimal solution. Finally, the accuracy of the model and algorithm is checked in the modified IEEE 33-node distribution network.

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IoT-based Office Buildings Energy Management with distributed Edge computing Capability

IoT-based Office Buildings Energy Management with Distribute...

引用

Technology and Energy Management (ICTEM), international conference on

作者： Meisam Nesary Moghadam Mehdi Abapour Greater Tehran Electricity Distribution Company Tehran Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Building Energy Management systems (BEMS) are becoming very popular, for providing net Zero Energy Buildings (nZEBs). Attachment of these systems through IoT technologies, enables the building owners as well as utility companies to control energy's usage, generation, and storage in a more smart and real time manner, which is the foundational concept for Internet of Energy (IoE). High number of buildings especially in metropolitan areas imposes high amount of computation resources and increased communication and computation delays to the central cloud servers. In this paper, we first describe the details of a real IoT-based large scale deployment of office buildings energy management system in Greater Tehran Electricity Distribution Company (GTEDC), and then we propose an edge computing solution to reduce the complexity and delay of data dissemination in the system expansion phase.

关键词： Cloud computing Urban areas Companies Control systems Real-time systems Delays Servers

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distributed and Biometric Signature-Based Identity Proofing System for the Maritime Sector

Distributed and Biometric Signature-Based Identity Proofing ...

引用

international conference on Computer Science and Engineering (UBMK)

作者： Taylan Akbas Ahmet Koltuksuz Cagatay Yucel Dept. of Computer Engineering School of Eng. Yasar University Izmir Turkiye Dept. of Computing and Informatics Bournemouth University UK

the maritime sector is an industry that faces significant and various challenges related to cyber security and data management, such as fraud and user authentication. therefore, there is a need for a secure solution that can effectively manage data transactions while resolving digital identity. A biometric signature application in blockchain for fighting fraud and fake identities may provide a solution in the maritime sector. this research proposes a biometric signature and an IPFS network-blockchain framework to address these challenges. this paper also discusses the proposed framework's cyber security challenges that threaten behavioral biometric security.

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QoE-Driven Multimedia Streaming Leveraging distributed Network Function Virtualization at Edge

QoE-Driven Multimedia Streaming Leveraging Distributed Netwo...

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Information and Network Technologies (ICINT), international conference on

作者： Reza Shokri Kalan Digiturk beIN MEDIA GROUP Istanbul Turkiye

Driven by the limitation of legacy network communications, recent years have seen a paradigm shift in network architecture, from the conventional TCP/IP towards Software Defined networking (SDN). the main feature of SDN is to give more flexibility to the network by pushing control from forwarding devices into scalable centralized orchestration. this paradigm enables using forwarding storage and processing capacity for caching content at the network level. the objective of this study is to address the Quality of Experience (QoE) problems in video applications. To this end, we propose a network-assisted QoE-aware bitrate adaptation algorithm that works in cooperation with MPEG-DASH’s proposed SAND technology and virtualized in-network caching function (denotes vDANE). In the proposed framework, client quality adaptation can be assisted by vDANE according to network bandwidth and availability of the appropriate segment/bitrate stored in vDANEs hosted by OpenFlow switches. Simulation results indicate that the proposed method can improve QoE parameters compared with other solutions.

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