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Cross-Cultural Study of a Location-Based Social Network Incentive Mechanism

Cross-Cultural Study of a Location-Based Social Network Ince...

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international conference on distributed computing in Sensor Systems (DCOSS)

作者： William O Souza Vinıcius F S Mota Thiago H Silva Dept. of Informatics Univer. Tecnológica Federal do Paraná Curitiba Brazil Dept. of Informatics Univer. Federal do Espírito Santo Vitória Brazil

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Having the proper incentive mechanism is paramount for the success of location-based social networks (LBSNs). With that in mind, we performed a cross-cultural study on the mechanism of incentive Mayorship, which Foursquare-Swarm employs to engage its users. The user who has the mayorship is the one who performed the highest number of check-ins in the last thirty days in a particular venue. We study how alternations and disputes for mayorship occur at the venues through automatic temporal monitoring. We collected data in two cities in different countries: Curitiba (Brazil) and Chicago (United States). We found, for instance, that renowned American food chains may have a more significant influence on the mayorship dispute in Curitiba. Also, hidden prestige associated with more affluent areas could be an extra motivator factor in Curitiba, but the same cannot be said for Chicago. This study shed some light on the mechanism of incentive mayorship in different venues, showing that local factors could play an important role. Our results can assist in improving user engagement on social web systems in different cultures.

关键词： Social networking (online) Urban areas Sensor systems distributed computing Monitoring

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A Novel Evaluation of Cryptocurrency Future Price Prediction to Manage Fluctuations in Market by using Hybrid Learning Methodology

A Novel Evaluation of Cryptocurrency Future Price Prediction...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), international conference on

作者： D. Pradeep G. Divya R Rajeshwari R. Deepa T. V. V. Satyanarayana Department of CSE M.Kumarasamy College of Engineering Karur Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Dept of MCA S.A. Engineering College Chennai Department of Computer Science Engineering R.M.K. Engineering College Chennai Department of ECE Mohan Babu University(Erstwhile SreeVidyanikethan Engineering College) Tirupati AP

In recognition of its optimistic vision on the probable future of cryptocurrencies, the value of cryptocurrency has risen dramatically during the previous eight decades. Cryptocurrency offers significant benefits over the established financial system, and investors have taken notice. Cryptocurrency is unique in that it facilitates decentralized financial services, making it immune to control by large financial institutions. With the rise of electronic currencies like Ethereum (ETH) and BTT, business may be conducted entirely online. One illustration of the distributed structure of physical currencies is the reality that all consumers who use virtual money have utilization of operations without the need for a third-party they can depend on. Like the market fluctuations of traditional stocks, cryptocurrencies fluctuations in price are extremely volatile and non stationary. Traders and academics alike have given increased focus to predictions of bitcoin prices due to the asset class' rising popularity. The use of deep learning in projecting the price of cryptocurrencies has become increasingly important. This research presented a new algorithm for predicting cryptocurrency prices called Hybrid Learning for Crypto Prediction (HLCP), and results from these models demonstrated that the proposed HLCP algorithm outperformed all other methods in this area. The HLCP model offered encouraging and precise predictions for all cryptocurrencies; hence it can be considered the most successful algorithm. The model was used to forecast the 6 month ending prices of cryptocurrency. To measure how well predictions match targets during training and testing, we used the HLCP correlation metric. The proposed method efficiency is evaluated with respect to the following metrics such as training and testing accuracy levels, training and testing loss ratio as well as different cryptocurrencies (ETH, BitCoin and BTT) prediction and actual price comparisons. These metrics clearly shows the effic

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Amalgamation of Fog computing and Software Defined networking in Healthcare 4.0: The Challenges, and a Way Forward 17

Amalgamation of Fog Computing and Software Defined Networkin...

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17th international Joint conference on e-Business and Telecommunications (ICE-B)

作者： Shah, Khushi Obaidat, Mohammad Modi, Preet Bhatia, Jitendra Tanwar, Sudeep Sadoun, Balqies Gujarat Technol Univ Vishwakarma Govt Engn Coll Ahmadabad Gujarat India Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates Univ Jordan KAIST Amman Jordan Univ Sci & Technol Beijing Beijing Peoples R China Nirma Univ Inst Technol Dept CSE Ahmadabad Gujarat India Al Balqa Appl Univ Coll Engn Al Salt Jordan

ISBN: (纸本)9789897584473

Rapid recent advances in automated data collection routines have led to a tsunami of health care oriented data stored in the distributed, heterogeneous, and a large databases. Healthcare is one of the pioneering fields that has started using Machine Learning (ML) and IoT (Internet of Things) for increasing life expectancies and decreasing death risks. Since then, major improvements are being witnessed for achieving real-time results and decreasing latency. This work attempts to provide an extensive and objective walkthrough in the direction of adoption of fog computing and software defined networking (SDN) framework for a huge data processing in healthcare domain. Both of these technologies, holds a great promise for the healthcare industry. In this paper, first of all, we survey various communication technologies involved in heathcare and also discuss the need of data processing and its security. Finally, we conclude with future research issues and challenges in this domain.

关键词： Healthcare Cloud computing Fog computing Software Defined networking

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Task Scheduling for ICN-Based computing First Network: A Deep Reinforcement Learning Approach

Task Scheduling for ICN-Based Computing First Network: A Dee...

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international conference on Computer and Communications (ICCC)

作者： Zhuang Zou Renchao Xie Yuzheng Ren F. Richard Yu Tao Huang State Key Lab. of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing P.R. China Depart. of Systems and Computer Eng. Carleton University Ottawa ON Canada

The computing first network (CFN) combines heterogeneous computing force information with network information and improves resource utilization and task execution efficiency through resource perception, service positioning, and task scheduling. However, since the heterogeneous computing force is difficult to express and perceive, and it is distributed on each node of the edge network, it is difficult to schedule tasks uniformly. Therefore, this paper proposes a CFN architecture based on information-centric network (ICN), which uses the naming mechanism of ICN to characterize the computing force and tasks, and the caching mechanism and routing and forwarding mechanism to improve the computing efficiency in the network. Under this network architecture, we study the scheduling problem of multi-user tasks in a period, model it as a multi-objective optimization model, and transform it into a Markov decision process (MDP) to enable control nodes to prioritize tasks and then schedule them to computing nodes for execution, which takes into account the task queue status and node resources. To solve the proposed problem, we use the deep reinforcement learning algorithm to approximate the optimal task scheduling solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

关键词： Deep learning Schedules Processor scheduling Computational modeling Force Reinforcement learning Transforms

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Data security and privacy in cloud computing focused on transportation sector with the aid of block chain approach 6

Data security and privacy in cloud computing focused on tran...

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6th IEEE international conference on Innovative Technologies in Intelligent System and Industrial Application, CITISIA 2021

作者： Gurung, Abhiyan Master of Information Technology/Networking/System Analyst Study Group Australia SydneyNSW Australia

ISBN: (纸本)9781665417846

Despite the exponential growth of cloud infrastructure over several years, data protection and reliable computing are still major obstacles for modern cloud computing applications. In order to address this issue, several researchers have done a lot of work on this and have suggested a variety of models, including data integrity checking and stable multi-party estimates. Vehicle ad-hoc networks (VANETs) have been diagnosed with several security issues, such as confidentiality, safe authorization/authentication, and system stability. Nevertheless, almost all these alternatives face challenges like over-computational complexity or absence of scalability. This paper explores the usage of blockchain technologies in order to strengthen this scenario. Blockchain is collaborative modern framework for distributed computation. Applying blockchain technologies to cloud infrastructure, leveraging the former encryption framework to boost the efficiency of data storage and decentralized computation, is an exciting research area. A system of message verification for the privacy and decentralization of knowledge utilizing blockchain technologies is proposed in this paper. This is where we add message authentication code (MAC) and public-private key for stable authentication. In this document, we follow consensus algorithms for computing blockchain structures such as proof of work (PoW) and Practical Byzantine Fault Tolerance (PBFT) in the proposed authorization method. Finally, it is shown that the suggested approach is secure from threats that require the impersonation of the internal intruder as well as the usual threats. © 2021 IEEE.

关键词： Intelligent systems

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Convolutional Neural Network Training with distributed K-FAC

Convolutional Neural Network Training with Distributed K-FAC

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international conference on High Performance computing, networking, Storage and Analysis (SC)

作者： Pauloski, J. Gregory Zhang, Zhao Huang, Lei Xu, Weijia Foster, Ian T. Texas Adv Comp Ctr Austin TX 78758 USA Univ Texas Austin Austin TX 78712 USA Univ Chicago Chicago IL 60637 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781728199986

Training neural networks with many processors can reduce time-to-solution;however, it is challenging to maintain convergence and efficiency at large scales. The Kronecker-factored Approximate Curvature (K-FAC) was recently proposed as an approximation of the Fisher Information Matrix that can be used in natural gradient optimizers. We investigate here a scalable K-FAC design and its applicability in convolutional neural network (CNN) training at scale. We study optimization techniques such as layer-wise distribution strategies, inverse-free second-order gradient evaluation, and dynamic K-FAC update decoupling to reduce training lime while preserving convergence. We use residual neural networks (ResNet) applied to the CIFAR-10 and ImageNet-I k datasets to evaluate the correctness and scalability of our K-FAC gradient preconditioner. With ResNet-50 on the ImageNet- lk dataset, our distributed K-FAC implementation converges to the 75.9% MLPerf baseline in 18-25% less time than does the classic stochastic gradient descent (SGI)) optimizer across scales on a GPI) cluster.

关键词： optimization methods neural networks scalability high performance computing

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A Comprehensive Performance Evaluation of Different Kubernetes CNI Plugins for Edge-based and Containerized Publish/Subscribe Applications 9

A Comprehensive Performance Evaluation of Different Kubernet...

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9th IEEE international conference on Cloud Engineering (IC2E)

作者： Kang, Zhuangwei An, Kyoungho Gokhale, Aniruddha Pazandak, Paul Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA Real Time Innovat Sunnyvale CA USA

ISBN: (纸本)9781665449700

The growing number of data- and latency-sensitive Internet of Things (IoT) applications is posing significant challenges for the edge and cloud deployment of publish/subscribe services, which are required by these applications. Two independently developed technologies show promise in addressing these challenges. First, Kubernetes (K8s) provides a de-facto standard for container orchestration that can manage and scale distributed applications in the cloud. Second, OMG's Data Distribution Service (DDS), a standardized real-time, data-centric and peer-topeer publish/subscribe middleware, is being used in thousands of critical systems around the world. However, the feasibility of running DDS applications within K8s for latency-sensitive edge computing, and specifically the performance overhead of K8s' network virtualization on DDS applications is not yet well-understood. To address this, in this paper we evaluate the performance overhead of several container network interface (CNI) plugins including Flannel, WeaveNet and Kube-Router installed on a hybrid (ARM+AMD) edge/cloud K8s cluster. The paper reports results from a comprehensive set of experiments conducted to measure and analyze the performance (throughput, latency, and CPU/memory usage) of containerized DDS applications from the perspectives of virtualization overhead, reliability (DDS Reliable and BestEffort QoS), transport mechanisms (UDP unicast and multicast), and security. The insights derived from this study provide concrete guidance to developers of DDS-based applications in choosing the right virtual network plugin and configurations when hosting their real-time IoT applications in real-world containerized environments.

关键词： Kubernetes Container networking Data Distribution Service Pub/Sub Performance Evaluation

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Incorporate Personas and Emotions into Dialog Generation

Incorporate Personas and Emotions into Dialog Generation

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Science and Technology Innovation (IAECST), international Academic Exchange conference on

作者： Yufeng Wu Chengyu Liu Wenjian Guo Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

Since the Turing test, creating believable open-domain dialogues has been a basic problem in the study of artificial intelligence. Achieving success with dialogue models can greatly improve natural human-computer interactions and open up new possibilities for applications such as chatbots for therapy. Incorporating emotional information into dialogue systems and giving them personas can enhance user satisfaction by fostering more natural communication and human-like conversations. Both of these study directions are comparatively well-established, with important accomplishments in them. On the other hand, various roles in real-life discussion scenarios portray emotions in different ways. In order to create more fulfilling and human-like discussions, we investigate in this study the integration of personas and emotions in dialogue creation systems. Through the combination of explicit and implicit persons, we tackle the problem of individualized and emotional conversation. While implicit personas are gleaned from linguistic style, colloquial idioms, and personality attributes, explicit personas are derived from user profiles. Explicit personas and emotional labels are incorporated into the dialogue agent, and implicit personas are captured through the use of latent variables in a pre-trained Transformer model. Our suggested method enhances the authenticity of discussions and user experience by producing responses that are consistent with personas and emotions, hence improving the quality of communication.

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FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning

FedHybrid: Hierarchical Hybrid Training for High-Performance...

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IEEE Communications Society conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Tao Chang Li Li Meihan Wu Wei Yu Xiaodong Wang National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China State Key Laboratory of Internet of Things for Smart City University of Macau China The 30th Research Institute of China Electronics Technology Group Corporation China

Federated Learning coordinates multiple devices to train a shared model while preserving data privacy. Despite its potential benefit, the increasing number of participating devices poses new challenges to the deployment in real-world cases. The highly limited amount of data located on each device coupled with significantly unbalanced data across different devices severely impede the performance of the shared model and the overall training progress at the same *** this paper, we propose FedHybrid, a hierarchical hybrid training framework for high-performance Federated Learning on a wide scale. Unlike the existing work that mainly focuses on the statistical challenge, FedHybrid establishes a hierarchical hybrid training framework that effectively utilizes the fragmented and unbalanced data located on the participating devices on a wide scale. Specifically, FedHybrid consists of the following two core components, a global coordinator deployed on the central server and a local coordinator deployed on each participating device. The global coordinator organizes the participating devices into different groups through jointly considering the system heterogeneity and unbalanced training data in order to accelerate the overall training progress while guaranteeing the model performance. Within each group, a novel device-to-device (D2D) sequential training procedure is coordinated by the local coordinator to effectively utilize the fragmented and unbalanced training data in order to intelligently update the local models. At the same time, we provide the theoretical analysis of FedHybrid and conduct extensive experiments to evaluate its effectiveness. The results show that FedHybrid effectively improves model accuracy up to 27% and accelerates the whole training process by 20% on average.

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A Study on the Difference between Social Network Analysis and Text Network Analysis

A Study on the Difference between Social Network Analysis an...

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Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing (SNPD-Winter), ACIS international Winter conference on

作者： Duk Jin Kim Woo Yeong Lee Do Hyung Kim Soongil University Seoul Korea

Recruitment corruption makes people demand fair and objective hiring procedures. The development of IT technology has promoted the emergence of recruitment system based on AI which make interview conduct without human intervention. As companies can reduce costs and efforts through the AI recruitment system, more and more companies are using the system. As the result, the research for figuring out the user intention on the system is needed. We designed the research model to find factors affecting the intention to use of AI-based recruitment system utilizing constructs of TAM and TOE. Also the moderating effect by the number of employees in the corporation will be estimated.

关键词： Social networking (online) Companies Artificial intelligence Interviews Recruitment Software engineering

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