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11th international conference on distributed, Ambient and Pervasive Interactions, DAPI 2023, held as part of the 25th international conference on Human-Computer Interaction, HCII 2023

11th International Conference on Distributed, Ambient and Pe...

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11th international conference on distributed, Ambient and Pervasive Interactions, DAPI 2023, held as part of the 25th international conference on Human-Computer Interaction, HCII 2023

ISBN: (纸本)9783031346088

the proceedings contain 60 papers. the special focus in this conference is on distributed, Ambient and Pervasive Interactions. the topics include: A Systematic and Innovative Six-in-One Evaluation Framework to Drive the Development of Future Hidden Champions;untapped Potential of Participatory Design - Citizen Centered Social Data Utilization for Smart Cities;investigating the Psychological Impact of Emotion Visualization and Heart Rate Sharing in Online Communication;sustained Participation Motivation of Quantified Self for Personal Time Management;Evaluating Students Experiences in VR Case-Studies of Information Systems Problem-Based Teaching;assessing Lighting Experience Using Physiological Measures: A Review;advancing User Research in Naturalistic Gambling Environments through Behaviour Tracking. A Pilot Study;using the Experience Sampling Method to Find the Pattern of Individual Quality of Life Perceptions;mutual Recall Between Onomatopoeia and Motion Using Doll Play Corpus;Understanding Avoidance Behaviors of Users for Conversational AI;user-Developer Interaction in a Living Lab: A Case Study of an Exercise Support System for the Elderly;how Remote-Controlled Avatars Are Accepted in Hybrid Workplace;ethical Considerations of High Performance computing Access for Pervasive computing;a Taxonomy of Factors Influencing Data Quality;designing a Smart Standards Information Service: A Research Framework;Estimation of Water Consumption in a Family Home Using IOT Systems;development of Large-Scale Scientific Cyberinfrastructure and the Growing Opportunity to Democratize Access to Platforms and Data;Understanding Regional Characteristics through EC Data Analysis;human-Computer-Building Interaction: An Integrated Experience of the Digital and Physical Environments;living Labs Are the Silver Lining for Creating Sustainable Health and Care for the Future;towards an Interaction Design Framework for IoT Healthcare Systems.

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11th international conference on distributed, Ambient and Pervasive Interactions, DAPI 2023, held as part of the 25th international conference on Human-Computer Interaction, HCII 2023

11th International Conference on Distributed, Ambient and Pe...

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11th international conference on distributed, Ambient and Pervasive Interactions, DAPI 2023, held as part of the 25th international conference on Human-Computer Interaction, HCII 2023

ISBN: (纸本)9783031346675

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Research on Multi-domain Data Transmission and Presentation Algorithm Based on WiFi Network 11th

Research on Multi-domain Data Transmission and Presentation ...

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11th international conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Wu, Wanqing Yu, Baoguo Dong, He Yi, Qingwu He, Chenglong State Key Laboratory of Satellite Navigation System and Equipment Technology The 54Th Research Institute of CETC Shijiazhuang050081 China

ISBN: (纸本)9789819912599

Aiming at the perception and visualization of multi-domain data in indoor and outdoor environments, we proposed a multi-domain data transmission and presentation method based on WiFi networking. First, the multi-domain data sensed by distributed nodes is preprocessed to remove abnormal data and convert it into a standard format. then, the distributed nodes conduct WiFi networking, and transmit the multi-domain data to the fixed node, and the fixed node forwards the received data to the multi-domain data processing and display platform. Finally, the multi-domain data processing and display platform uses the graphing algorithm to visualize the multi-domain data. the experimental results show that the algorithm we proposed can effectively solve the problem of reliable transmission and mapping of multi-domain data in unknown environments. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data transfer

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Bifrost: A Secure, Scalable and Efficient File Sharing System Based on Dual Deduplication 11

Bifrost: A Secure, Scalable and Efficient File Sharing Syste...

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11th IEEE international conference on Cloud networking (CloudNet)

作者： Sehat, Hadi Lucani, Daniel E. Pagnin, Elena Aarhus Univ Dept Elect & Comp Engn Agile Cloud Lab DIGIT Aarhus Denmark Lund Univ Dept Elect & Informat Technol Lund Sweden

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

We consider the problem of sharing sensitive files across users while relying on a common, untrusted third-party, e.g., a Cloud Storage Provider (CSP). Although users can rely on a secure peer-to-peer (P2P) channel for file sharing, this introduces potential delay on the data transfer and requires the sender to remain connected while the transfer process occurs. Instead, users can upload information about the file on a common CSP and share only the essential information that enables the receiver to recover the original file. However, using encryption to protect the uploaded information from the untrusted cloud hinders deduplication capabilities, which is undesirable for CSPs. this paper introduces Bifrost, an innovative file sharing method inspired by recent results on dual deduplication. Bifrost achieves secure file sharing using an untrusted cloud, and simultaneously guarantees that (1) the CSP can efficiently deduplicate outsourced data;(2) the secure P2P channel is used only to transmit short, but crucial information;(3) users can check for data integrity, i.e., detect if the CSP alters the outsourced data;and (4) only the sender and the intended receiver can recover the sensitive file, i.e., the cloud or no malicious adversary can infer useful information about the shared file. We analyze compression and bandwidth performance using a proof-of-concept implementation. Our experiments show that secure file sharing can be achieved by sending as little as 640 bits on the P2P channel, irrespective of file size, while the CSP that aids the sharing can enjoy a deduplication rate of 86.9 %.

关键词： Cloud computing Data integrity Receivers Bandwidth Data transfer Peer-to-peer computing Encryption

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Towards Serverless Sky computing: An Investigation on Global Workload Distribution to Mitigate Carbon Intensity, Network Latency, and Cost 11

Towards Serverless Sky Computing: An Investigation on Global...

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11th IEEE international conference on Cloud Engineering (IC2E)

作者： Cordingly, Robert Kaur, Jasleen Dwivedi, Divyansh Lloyd, Wes Univ Washington Sch Engn & Technol Tacoma WA 98402 USA

ISBN: (纸本)9798350343946

the high demand for energy consumption and the resulting carbon footprint of the cloud pose significant sustainability challenges, as cloud data centers consume vast amounts of energy. the emergence of serverless cloud computing platforms has opened up new avenues for more sustainable cloud computing. Serverless Function-as-a-Service (FaaS) cloud computing platforms facilitate deploying applications as decoupled microservices to leverage automatic rapid scaling, high availability, fault tolerance, and on-demand pricing. the absence of always-on hosting costs associated with virtual machines enables serverless functions to be deployed with many different function configurations and cloud regions to achieve high performance, low network latency, and reduced costs. In this paper, we investigate the utility of a global sky computing platform where serverless resources are aggregated between up to 19 distinct cloud regions. We prototype a serverless load distribution system to distribute client requests across serverless aggregations to minimize performance objectives, including network latency, runtime, hosting costs, and carbon footprint. To evaluate our serverless distribution system's ability to meet performance objectives, we continuously executed large experiments across 19 regions around the world from November 2022 through March 2023. Our serverless load distribution approach using aggregated resources reduced the carbon intensity of a globally distributed serverless application by up to 99.8%, network latency by 65%, or hosting costs by 58% by optimizing function routing to deployments with optimal hardware configurations.

关键词： Sky computing Serverless computing Function-as-a-Service Green computing

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A Fog-Based IOV for distributed Learning in Autonomous Vehicles 1

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11th European-Alliance-for-Innovation (EAI) international conference on Mobile Networks and Management (MONAMI)

作者： Subedi, Pawan Yang, Beichen Hong, Xiaoyan Univ Alabama Comp Sci Tuscaloosa AL 35487 USA

ISBN: (数字)9783030947637

ISBN: (纸本)9783030947637;9783030947620

Internet of Vehicles (IoVs) consist of connected vehicles and connected autonomous vehicles. With fog computing built within the IoV, it becomes promising for federated learning to be used in vehicular environments. One important application of such a fog computing system is distributed deep learning for decision-making tasks in autonomous driving. In this paper, a distributed training system building on top of the Named-Data networking (NDN) architecture is introduced in order to combat the mobility challenges to the underlying network. the paper presents analyses on critical latency issues pertained to soliciting the worker CVs and collecting the partial updates. Further, the advantages of using NDN for the IoV are evaluated with comparisons to IP network through simulation. the results show promising performance gains for the evaluation cases.

关键词： Internet of Vehicles Fog computing Connected vehicles Federated learning Autonomous driving

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Virtual Network Embedding using a Federated DRL Approach 11

Virtual Network Embedding using a Federated DRL Approach

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11th IEEE international conference on Cloud networking (CloudNet)

作者： Chakraborty, Saurav Sivalingam, Krishna M. Indian Inst Technol Madras Dept Comp Sci & Engn Chennai India

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

this paper deals with allocating resources for virtual network requests in the underlying substrate of an infrastructure provider (INP). this is called the virtual network embedding problem. A client's virtual network request (VNR) is accepted if the INP's Virtual Network Embedding (VNE) agent successfully maps all the virtual nodes and links to suitable substrate nodes and links satisfying the CPU and bandwidth requirements of nodes and links. We consider a scenario where multiple INPs exist, catering to the VNR requests of their respective clients. In this paper, we have used a Federated Deep Reinforcement Learning (Federated DRL) approach, where the INPs individually train their agents based on DRL. During the training, the DRL agent learns to map the virtual nodes to substrate nodes. Subsequently, the INPs operators learn collaboratively by sharing only the respective trained model information, without revealing any detailed vital information to each other. the federated reinforcement learning model is implemented using TensorFlow;the VNRs arrival is modeled using SimPy package, and the network graphs for virtual and substrate networks are created using the NetworkX package. the proposed solution accepts around 5% more VNRs, accumulates around 7% more revenue, and performs around 10% better in terms of the revenue to embedding cost ratio compared to existing algorithms.

关键词： Training Cloud computing Network topology Federated learning Reinforcement learning Dynamic scheduling Topology

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Proceedings - 2024 IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

Proceedings - 2024 IEEE/ACM International Conference on Big ...

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11th IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

ISBN: (纸本)9798350367300

the proceedings contain 57 papers. the topics discussed include: machine learning-based big data analytics in smart cities: a survey of current trends and future research directions;attribute-based semantic type detection and data quality assessment;exploring the knowledge mismatch hypothesis: hallucination propensity in small models fine-tuned on data from larger models;an explanation technique for yield prediction in smart farming;optimizing domestic energy consumption: a comprehensive plug for enhanced monitoring and efficiency;an accurate salary estimation scheme by using bigdata technique;a novel transfer learning approach for detecting partial shading in photovoltaic systems;extracting health evidence information from biomedical literature using large language models;semantic communications for healthcare applications: opportunities and challenges;plant diseases recognition using machine learning algorithms;unusual invoice detection using a permutation based genetic algorithm;an efficient iris recognition technique through minimal preprocessing and custom CNN;a cloud-agnostic serverless architecture for distributed machine learning;age-friendly trip planning using ant colony optimization;and temporal dynamics and anomaly detection in transactional networks.

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Evaluation of Data Enrichment Methods for distributed Stream Processing Systems 11

Evaluation of Data Enrichment Methods for Distributed Stream...

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11th IEEE international conference on Cloud Engineering (IC2E)

作者： Scheinert, Dominik Casares, Fabian Geldenhuys, Morgan K. Styp-Rekowski, Kevin Kao, Odej Tech Univ Berlin Berlin Germany

ISBN: (纸本)9798350343946

Stream processing has become a critical component in the architecture of modern applications. With the exponential growth of data generation from sources such as the Internet of things, business intelligence, and telecommunications, real-time processing of unbounded data streams has become a necessity. DSP systems provide a solution to this challenge, offering high horizontal scalability, fault-tolerant execution, and the ability to process data streams from multiple sources in a single DSP job. Often enough though, data streams need to be enriched with extra information for correct processing, which introduces additional dependencies and potential bottlenecks. In this paper, we present an in-depth evaluation of data enrichment methods for DSP systems and identify the different use cases for stream processing in modern systems. Using a representative DSP system and conducting the evaluation in a realistic cloud environment, we found that outsourcing enrichment data to the DSP system can improve performance for specific use cases. However, this increased resource consumption highlights the need for stream processing solutions specifically designed for the performance-intensive workloads of cloud-based applications.

关键词： distributed Stream Processing Data Enrichment Data Analysis Resource Management Cloud computing

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Analysis of a Parallel and distributed BPSO Algorithm for EEG Classification: Impact on Energy, Time and Accuracy 11th

Analysis of a Parallel and Distributed BPSO Algorithm for EE...

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11th international Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Escobar, Juan Jose Lopez-Rodriguez, Jesus Garcia-Gil, Diego Morcillo-Jimenez, Roberto Prieto, Beatriz Ortiz, Andres Kimovski, Dragi Univ Granada CITIC Dept Software Engn Granada Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada Spain Univ Granada CITIC Dept Comp Engn Automat & Robot Granada Spain Univ Granada CITIC Dept Comp Sci & Artificial Intelligence Granada Spain Univ Malaga Dept Commun Engn Malaga Spain Univ Klagenfurt Inst Informat Technol Klagenfurt Austria

ISBN: (纸本)9783031646287;9783031646294

Today, the amount of data generated each year is growing exponentially, directly affecting the time required for its analysis. this problem worsens with high-dimensional datasets, such as those used in electroencephalography, so a good feature selection method and techniques that improve algorithms' efficiency are increasingly relevant. Consequently, computing time and energy consumption are reduced, which could be used to explore more solutions to the problem. However, it is also necessary to adapt the applications to take advantage of the hardware offered by high-performance computing systems. therefore, in this work, a parallel and distributed binary particle swarm optimization algorithm has been implemented, used as a feature selection method, and applied to two real electroencephalography datasets: the University of Essex dataset and the well-known BCI Competition IV 2a dataset. the proposed method has been analyzed in a multi-node computing cluster, not only in terms of classification accuracy, but also from the energy-time point of view to study its impact depending on different experimental conditions and datasets used.

关键词： Parallel and distributed computing Energy impact EEG classification KNN Particle swarm optimization Feature selection

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