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8th World conference on Information Systems and technologies, WorldCIST 2020

8th World Conference on Information Systems and Technologies...

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8th World conference on Information Systems and technologies, WorldCIST 2020

ISBN: (纸本)9783030456900

the proceedings contain 135 papers. the special focus in this conference is on Information Systems and technologies. the topics include: Social care services for older adults: Paper registration versus a web-based platform registration;enabling green building’s comfort using information and communication technologies: A systematic review of the literature;Exploring the innovative aspects of CV distributed ledgers based on blockchain;eFish – An innovative fresh fish evaluation system;a virtual reality approach to automatic blood sample generation;Building information modeling academic assessment: international architecture workshop UPC-UAM;radar system for the reconstruction of 3D Objects: A preliminary study;Achieving stronger compaction for DCT-Based Steganography: A region-growing approach;teaching computer programming as well-defined domain for beginners with protoboard;machine learning and data networks: Perspectives, feasibility, and opportunities;underground channel model for visible light wireless communication based on neural networks;mpCUBIC: A CUBIC-like congestion control algorithm for multipath TCP;context-Aware mobile applications in fog infrastructure: A literature review;characterizing the cost of introducing secure programming patterns and practices in ethereum;analyzing IoT-Based botnet malware activity with distributed low interaction honeypots;Evolution of HTTPS usage by Portuguese municipalities;augmented reality to enhance visitors’ experience at archaeological sites;Design and performance analysis for intelligent F-PMIPv6 mobility support for smart manufacturing;development of trustworthy self-adaptive framework for wireless sensor networks;traffic Flow Prediction Using Public Transport and Weather Data: A Medium Sized City Case Study;filtering users accounts for enhancing the results of social media mining tasks;from reinforcement learning towards artificial general intelligence;preface.

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A Load-Considered Handover Control Scheme for distributed Mobility Management (DMM) Using Software Defined Network (SDN) in the Vehicular Environment

A Load-Considered Handover Control Scheme for Distributed Mo...

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8th IEEE international conference on Communications and Electronics (IEEE ICCE)

作者： Duy-Tuan Dao Huang, Chung-Ming Chiang, Meng-Shu Van-Tuan Nguyen Univ Danang Univ Sci & Technol Fac Elect & Telecommun Engn Da Nang Vietnam Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan Far East Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781728154718

Supporting mobile devices and smart city applications are the most crucial goals in the ongoing development of SC cellular systems. distributed Mobility Management (DMM) can be used for levelling the vehicular mobility architecture to resolve issues, e.g., non-optimal path and scalability that exist in the future mobile Internet. there is the network situation that it may have multiple target destinations of the next network to the moving vehicle, which is called as Mobile Node (MN), can handover in vehicular networks. therefore, it requires to make a suitable handover decision based on both the networking loading and MN's received signal strength. Although there are several studies applying Software Defined Network (SDN) to DMM for improving the performance of existed DMM-based schemes, the current network-based handover approaches still endure the high handover latency and high signaling cost. In this paper, a Load-Considered Handover Control Scheme for distributed Mobility Management (DMM) using Software Defined Network (SDN) over the vehicular environment, which is called as LCSDN-DMM, that can support the handover decision and let MN be able to complete the handover preparation before MN disconnecting from the currently serving Mobility Anchor and Access Router (MAAR) to the next suitable MAAR, was proposed. LC-SDN-DMM also can support selecting the suitable next anchor router through the assistance of the SDN Controller. the simulation analysis has proved that the proposed LC-SDN-DMM approach has better handover performance than the conventional DMM approach regarding to throughput, handover delay and packets loss.

关键词： distributed Mobility Management (DMM) Handover Mobility Management Software Defined Network (SDN) Vehicular Network

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Concerto: Leveraging Ensembles for Timely, Accurate Model Training Over Voluminous Datasets 7

Concerto: Leveraging Ensembles for Timely, Accurate Model Tr...

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7th IEEE/ACM international conference on Big Data computing, applications and technologies (BDCAT)

作者： Budgaga, Walid Malensek, Matthew Pallickara, Sangmi Lee Pallickara, Shrideep Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA Univ San Francisco San Francisco CA USA

ISBN: (纸本)9780738123967

As data volumes increase, there is a pressing need to make sense of the data in a timely fashion. Voluminous datasets are often multidimensional with individual data points representing a vector of features. Data scientists fit models to the data using all features or a subset thereof and then use these models to inform their understanding of phenomena or make predictions. the performance of these analytical models is assessed based on their accuracy and ability to generalize on unseen data. Several frameworks exist for drawing insights from voluminous datasets, but have limited scalability (which leads to prolonged training times), poor resource utilization, narrow applicability across problem domains, and insufficient support for combining diverse model fitting algorithms. In this study, we describe our methodology for scalable supervised learning over voluminous datasets. the methodology explores the effect of controlled partitioning of the feature space, as well as how analytical models can be combined to preserve accuracy. Rather than build a single, all-encompassing model, we enable practitioners to construct an ensemble of models that are trained independently in parallel over different portions of the data space. this can provide faster training times and increased prediction accuracy overall;our empirical benchmarks demonstrate the suitability of our approach using real-world data.

关键词： distributed ensemble learning voluminous datasets parallel model training

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26th international European conference on parallel and distributed computing, Euro-Par 2020

26th International European Conference on Parallel and Distr...

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26th international European conference on parallel and distributed computing, Euro-Par 2020

ISBN: (纸本)9783030576745

the proceedings contain 39 papers. the special focus in this conference is on parallel and distributed computing. the topics include: A makespan lower bound for the tiled cholesky factorization based on ALAP schedule;preface;skipping non-essential instructions makes data-dependence profiling faster;Optimal GPU-CPU offloading strategies for deep neural network training;improving mapping for sparse direct solvers: A trade-off between data locality and load balancing;modelling standard and randomized slimmed folded clos networks;ompMemOpt: Optimized memory movement for heterogeneous computing;Accelerating deep learning inference with cross-layer data reuse on GPUs;distributed fine-grained traffic speed prediction for large-scale transportation networks based on automatic LSTM customization and sharing;Optimizing FFT-based convolution on ARMv8 multi-core CPUs;maximizing I/O bandwidth for reverse time migration on heterogeneous large-scale systems;TorqueDB: distributed querying of time-series data from edge-local storage;data-centric distributed computing on networks of mobile devices;a toolchain to verify the parallelization of OmpSs-2 applications;WPSP: A multi-correlated weighted policy for VM selection and migration for cloud computing;LCP-aware parallel string sorting;Mobile RAM and shape formation by programmable particles;approximation algorithm for estimating distances in distributed virtual environments;on the power of randomization in distributed algorithms in dynamic networks with adaptive adversaries;3D coded SUMMA: Communication-efficient and robust parallel matrix multiplication;managing failures in task-based parallel workflows in distributed computing environments;accelerating nested data parallelism: Preserving regularity;using dynamic broadcasts to improve task-based runtime performances;a compression-based design for higher throughput in a lock-free hash map;NV-PhTM: An efficient phase-based transactional system for non-volatile memory.

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Proceedings of the 11th Workshop on parallel Programming and Run-Time Management Techniques for Many-Core Architectures / 9th Workshop on Design Tools and Architectures for Multicore Embedded computing Platforms, PARMA-DITAM 2020

Proceedings of the 11th Workshop on Parallel Programming and...

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8th international Symposium on Highly-Efficient Accelerators and Reconfigurable technologies, HEART 2017

ISBN: (纸本)9781450375450

the proceedings contain 5 papers. the topics discussed include: sparse matrix-dense matrix multiplication on heterogeneous CPU+FPGA embedded system;run-time power modeling in embedded GPUs with dynamic voltage and frequency scaling;fault-tolerant online scheduling algorithms for CubeSats;an OpenMP parallel genetic algorithm for design space exploration of heterogeneous multi-processor embedded systems;and automated precision tuning in activity classification systems: a case study.

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parallel Generalized Suffix Tree Construction for Genomic Data 7th

Parallel Generalized Suffix Tree Construction for Genomic Da...

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7th international conference on Algorithms for Computational Biology (AlCoB) / 8th international conference on Algorithms for Computational Biology (AlCoB)

作者： Al Aziz, Md Momin thulasiraman, Parimala Mohammed, Noman Univ Manitoba Comp Sci Winnipeg MB Canada

ISBN: (纸本)9783030422660;9783030422653

After a decade of digitization and technological advancements, we have an abundance of usable genomic data, which provide unique insights into our well-being. However, such datasets are large in volume, and retrieving meaningful information from them is often challenging. Hence, different indexing techniques and data structures have been proposed to handle such a massive scale of data. We utilize one such technique: Generalized Suffix Tree (GST). In this paper, we introduce an efficient parallel generalized suffix tree construction algorithm that is scalable for arbitrary genomic datasets. Our construction mechanism employs shared and distributed memory architecture collectively while not posing any fixed, prior memory requirement as it uses external memory (disks). Our experimental results show that our proposed architecture offers around 4-times speedup with respect to the sequential algorithm with only 16 parallel processors. the experiments on different datasets and parameters also exhibit the scalability of the execution time. In addition, we utilize different string queries and demonstrate their execution time on such tree structure, illustrating the efficacy and usability of GST for genomic data.

关键词： parallel generalized suffix tree Genomic data indexing parallel computation on genomic data GST construction on disks

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parallel Computation for Sparse Network Component Analysis 4th

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4th international conference on Advanced Machine Learning technologies and applications, AMLTA 2019

作者： Elsayad, Dina Hamad, Safwat Shedeed, Howida A. Tolba, M.F. Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9783030141172

the Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network goal is determining the topological order of genes interactions. Moreover, the regulatory network is a vital for understanding genes influence on each other. However, the main challenge confronting gene regulatory network algorithms is the massive data size. Where, the algorithm runtime is relative to the data size. this paper presents a parallel computation for Sparse Network Component Analysis (PSparseNCA) with application on gene regulatory network. PSparseNCA is a parallel version of SparseNCA. PSparseNCA enhanced the computation of SparseNCA using a distributed computing model. Where, the workload is distributed among P processing nodes, PSparseNCA is more efficient than SparseNCA. It achieved a better performance and its speedup reached 12.33. In addition, PsparseNCA complexity is O(NM2/P) instead of O(NM2) for SparseNCA. © 2020, Springer Nature Switzerland AG.

关键词： Gene expression

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A distributed Multithreaded Evolutionary computing Frame Work using Differential Evolution Algorithm

A Distributed Multithreaded Evolutionary Computing Frame Wor...

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international conference on Inventive Computation technologies (ICICT)

作者： S Raghul G Jeyakumar Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9781728185019

ISBN: (纸本)9781728185026

Differential Evolution (DE), the evolution algorithm (EA) in the repository of evolutionary computing (EC), is known for its simplicity and adaptability to high performance computing paradigms. the distributed algorithmic frameworks of DE are the natural extension of DE, as DE is highly suitable for parallel and distributed computing. this paper proposes a study on implementing an enhanced distributed differential algorithm framework (named as mtdDE - multithreaded distributed DE) with multi-threaded islands in a distributed framework, to attain maximum level of data parallelism. the main objective of mtdDE is to reduce the computation time and to improve the solution quality for the targeted optimization problems. this paper presents this study in two phases. the Phase I implements the traditional distributed DE (dDE) framework and compares its performance with a state-of-the-art distributed DE, on a set of 8 benchmarking functions. In Phase II, the proposed mtdDE was implemented and its performance was compared with the dDE on a set of 4 benchmarking problems. the performance metrics considered were - the objective function values (ofv), the mean objective function values (mov), the execution time (et) and the average execution time (aet). the results acquired for the performance metrics of the dDE and mtdDE were studied, and the study revealed that the mtdDE outperforms the dDE in terms of both the solution accuracy and the computation time. the detailed discussion about the algorithmic structure, the experimental setup, the results obtained and the comparative study were presented in this paper.

关键词： Measurement Computational modeling Instruction sets Benchmark testing parallel processing Linear programming Optimization

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distributed Architecture of an Intrusion Detection System Based on Cloud computing and Big Data Techniques 8th

Distributed Architecture of an Intrusion Detection System Ba...

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8th international conference on Sciences of Electronics, technologies of Information and Telecommunication, SETIT 2018

作者： Ben Fekih, Rim Jemili, Farah University of Sousse Sousse Tunisia University of Sousse Sousse Tunisia

ISBN: (纸本)9783030210045

An efficient intrusion detection system (IDS) requires more than just a good machine learning (ML) classifier. However, current IDSs offer a limited perspective in handling alerts’ databases. these databases must be local and structured in order to be referenced by these IDSs, offering an obsolete approach to solve advanced attacks and intrusions on distributed systems. With the emergence of big data, cyber-attacks have become a concerning issue worldwide. In situations where data security is paramount, swiftness becomes an obligation in processing and analytic operations. In that aspect, cloud-computing services can deal efficiently with big data issues. they offer storage and distributed analysis as the tools to be featured in our paper. To handle a large scale of alert data, we propose a new distributed IDS model that solves data storage problems, combines multiple heterogeneous sources of alert data, and makes data treatment much faster than local IDSs. For this purpose, this paper presents an approach of IDS using Databricks as a Cloud environment and Spark as a big data analysis tool. © 2020, Springer Nature Switzerland AG.

关键词： Intrusion detection

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Artificial Intelligence at the Edge in the Blockchain of things 8th

Artificial Intelligence at the Edge in the Blockchain of Thi...

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8th EAI international conference on Mobile Communication and Healthcare, MobiHealth 2019

作者： Nguyen Gia, Tuan Nawaz, Anum Peña Querata, Jorge Tenhunen, Hannu Westerlund, Tomi Group Department of Future Technologies University of Turku Turku Finland Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9783030492885

Traditional cloud-centric architectures for Internet-of-things applications are being replaced by distributed approaches. the Edge and Fog computing paradigms crystallize the concept of moving computation towards the edge of the network, closer to where the data originates. this has important benefits in terms of energy efficiency, network load optimization and latency control. the combination of these paradigms with embedded artificial intelligence in edge devices, or Edge AI, enables further improvements. In turn, the development of blockchain technology and distributed architectures for peer-to-peer communication and trade allows for higher levels of security. this can have a significant impact on data-sensitive and mission-critical applications in the IoT. In this paper, we discuss the potential of an Edge AI capable system architecture for the Blockchain of things. We show how this architecture can be utilized in health monitoring applications. Furthermore, by analyzing raw data directly at the edge layer, we inherently avoid the possibility of breaches of sensitive information, as raw data is never stored nor transferred outside of the local network. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.

关键词： Blockchain

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