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4th International Conference on Future Networks and Distributed systems, ICFNDS 2020

作者： Fomin, Vladimir Aleksandrov, Igor Gallyamov, Denis Kirichek, Ruslan Dept. of Information Systems Herzen State Pedagogical University of Russia Saint Petersburg Russia Dept. of Software Engineering and Computer Science Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781450388863

The Internet of Things (IoT) algorithms process huge amounts of heterogeneous data in real-time. One of the most computationally intensive tasks using cloud technologies is the task of clustering and classifying data. The authors propose to develop an approach to data classification within the "Query by Similarity"paradigm, which uses the technology of data indexing based on Metric Access Methods (MAM). To improve the performance of data indexing, this paper proposes a similar nearest neighbor search method combining a multiple vantage point tree (MVP) and improved algorithms for processing the priority queue of nodes. The following two algorithms for processing the priority queue of nodes were developed: 1) algorithm for all kinds of points-queries, which makes it possible to take into account parent nodes of all higher levels;2) algorithm for grouped based on clustering of points-queries by reusing previously obtained search results. Experimental results confirm the effectiveness of the proposed approaches and algorithms. © 2020 ACM.

关键词： Nearest neighbor search

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A toolkit to generate social navigation datasets

arXiv

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arXiv 2020年

作者： Baghel, Rishabh Kapoor, Aditya Bachiller, Pilar Jorvekar, Ronit R. Rodriguez-Criado, Daniel Manso, Luis J. Dept. of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Goa India Robotics and Artificial Vision Laboratory University of Extremadura Spain Dept. of Computer Engineering Pune Institute of Computer Technology India Dept. of Computer Science College of Engineering and Physical Sciences Aston University United Kingdom

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used. Copyright © 2020, The Authors. All rights reserved.

关键词： Navigation

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Single-channel noise spectral estimation based on compensated speech presence probability 8

Single-channel noise spectral estimation based on compensate...

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8th IEEE Global Conference on Consumer Electronics, GCCE 2019

作者： Kawanaka, Masaki Une, Masakazu Miyazaki, Ryoichi National Institute of Technology Tokuyama College Dept. of Computer Science and Electronic Engineering Yamaguchi Japan Graduate School of Systems and Information Engineering University of Tsukuba Dept. of Computer Science Ibaraki Japan

ISBN: (纸本)9781728135755

Recently, various noise spectrum estimation techniques have been proposed. The problem of the conventional techniques has the underestimation of the noise spectrum due to the excessive guess of the speech presence probability. Therefore, we propose a highly accurate noise spectrum estimation method to solve this problem by applying a sigmoid or softplus functions to the speech presence probability. From the evaluation experiment, we confirm that the proposed methods can achieve higher noise estimation accuracy than conventional techniques. © 2019 IEEE.

关键词： Probability

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Security of Enhancement of Adder in Stream Cipher System 1

Security of Enhancement of Adder in Stream Cipher System

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1st International Conference information Technology to Enhance E-Learning and other Application, IT-ELA 2020

作者： Hayder, Israa M. Younis, Hussain A. Abed, Issa Ahmed Younis, Hameed A.K. Qurna Technique Institute Stu Dept. of Computer Systems Techniques Basrah Iraq Universiti Sains Malaysi 11800 Usm College of Education for Women School of Computer Sciences Penang Malaysia Southern Technical University Engineering Technical College Basrah Iraq University of Basrah College of Computer Science and Information Technology Basrah Iraq

ISBN: (纸本)9781728182339

The strength of the encryption system depends on the power of the key generator that produces a pseudorandom stream. The generator generates a pseudorandom sequence so passes several tests. The non-linear flow coding system that relies on the Adder function has several unwanted vulnerabilities. It leads to failure to face randomness tests and relative success in the correlation attack. In this paper, the Adder function was developed by adding a third offset register to improve its ability to bypass randomness tests. In addition to increasing the non-linear complexity of the generated chain, it is resistant to breaking the code using a link attack. Many sequences were applied to the improved Adder system, and good results were obtained. © 2020 IEEE.

关键词： Adder generator Correlation attack Non-linear-feedback shift registers Stream cipher

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LETHE: A Digital Intervention for Cognitive Decline*

LETHE: A Digital Intervention for Cognitive Decline*

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vasileios S. Loukas Thomas Kassiotis Ignacio Lamata Martinez Lefteris Koumakis Jeroen Bruinsma Roberto Pasciuti Monica Balatresi Ville Tenhunen Adam Fiakkas Lelia Ataliani Georgia S. Karanasiou Manolis Tsiknakis Hannes Hilberger Markus Bödenler Bianca Schnalzer Simone Huber Mattia Pirani Matteo Colombo Sten Hanke Dimitrios I. Fotiadis Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Biomedical Research Institute FORTH-BRI Ioannina Greece Computational BioMedicine Laboratory Institute of Computer Science FORTH-ICS Heraklion Greece EGI Foundation Amsterdam The Netherlands Dept. of Health Promotion Care and Public Health Research Institute Maastricht University Maastricht The Netherlands ExtraRed Innovations Pisa Italy Infotrend Innovations Co. Ltd Nicosia Cyprus eHealth Institute FH Joanneum University of Applied Sciences Graz Austria Innovation2Grow I2G Bologna Italy

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform *** Relevance — Patient Management systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

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Learning Personal Conscientiousness from Footprints in E-Learning systems

Learning Personal Conscientiousness from Footprints in E-Lea...

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IEEE International Conference on Data Mining (ICDM)

作者： Lo Pang-Yun Ting Shan-Yun Teng Kun-Ta Chuang Ee-Peng Lim Dept. of Computer Science and Information Engineering National Cheng Kung University Taiwan School of Information Systems Singapore Management University Singapore

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Personality inference has received widespread attention for its potential to infer psychological well being, job satisfaction, romantic relationship success, and professional performance. In this research, we focus on Conscientiousness, one of the well studied Big Five personality traits, which determines if a person is self-disciplined, organized, and hard-working. Research has shown that Conscientiousness is related to a person's academic and workplace success. For an expert to evaluate a person's Conscientiousness, long-term observation of the person's behavior at work place or at home is usually required. To reduce this evaluation effort as well as to cope with the increasing trend of human behavior turning digital, there is a need to conduct the evaluation using digital traces of human behavior. In this paper, we propose a novel framework, called HAPE, to automatically infer an individual's Conscientiousness scores using his/her behavioral data in an E-learning system. We first determine how users learn in the E-learning system, and design a novel Pattern Relational Graph Embedding method to learn the representations of users, their learning actions, and learning situations. The interaction between users, learning actions and situations characterizes the learning style of a user. Through experimental studies on real data, we demonstrate that HAPE framework outperforms the baseline methods in the Conscientiousness inference task.

关键词： Electronic learning Employment Psychology Turning Market research Robustness Task analysis

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The Impact and Potential of Block chain Applications Based on IoT

The Impact and Potential of Block chain Applications Based o...

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Simulation, Automation & Smart Manufacturing (SASM), International Conference on

作者： Sandeep Srivastava Anurag Aeron Thulasi Bikku Manisha Kaur Varsha Satish Rupraoji Billewar GL Bajaj of Institute of Technology &#x0026 Management Greater Noida India Dept. of Computer Science and Engineering DIT University Dehradun India Dept. of Computer Science and Engineering Vignan&#x2019 s Nirula Institute of Technology and Science for Women Guntur Andhra Pradesh India Department of Computer Science and Engineering CT Group of Institute (CTIEMT) Shahpur Jalandhar India I.K.Gujral Punjab Technical University Jalandhar India Information Technology / Systems Vivekananda Institute of Management Studies and Research Chembur (E) Mumbai Maharashtra India

ISBN: (数字)9781665403573

ISBN: (纸本)9781665447768

The Block chain is a course of action of recording information to such an extent that makes it irritating or hard to alter, slash, or swindle the system. In blockchain decentralized amend the proceedings that could be used to indicate, join up also send wide scope of agreement to various social affairs in association. The scope of block chain applications stretches out to open and public administrations including wellbeing, financial, automotive, Internet of Things (IoT) and risk management. This paper presents a thorough outline on blockchain innovation. Lately, block chain innovation has become extremely stylish and infiltrated various areas, generally because of the crypto currencies determination of rotator for the particular sort of suspension needs some fundamental information.

关键词： Technological innovation Education Blockchains Recording Cryptocurrency Internet of Things Risk management

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Cooperative Convolutional Neural Network Deployment over Mobile Networks

Cooperative Convolutional Neural Network Deployment over Mob...

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IEEE International Conference on Communications (ICC)

作者： Chia-Chun Hsu Chung-Kai Yang Jian-Jhih Kuo Wen-Tsuen Chen Jang-Ping Sheu Dept. of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science & Information Engineering National Chung Cheng University Chiayi Taiwan

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Inference acceleration has drawn much attention to cope with the real-time requirement of artificial intelligence (AI) applications. To this end, model partition for Deep Neural Networks (DNN) has been proposed to utilize the parallel and distributed computing units. However, the previous works focus on the load balancing among servers but may overlook the interplay between the computing and communication. This issue makes the existing approaches less efficient especially in mobile edge networks at which smart devices usually with limited computing capacity have to offload the tasks via limited bandwidth capacity to nearby servers. In this paper, therefore, we innovate a new system and formulate a new optimization problem, CONVENE, to minimize the completion time of inference for the smart devices with one or more antennas. To explore the intrinsic properties, we first study CONVENE with Single Antenna and derive an algorithm termed THREAD-SA to foster the optimum solution. Then, an extension, THREAD, is proposed to subtly utilize multiple antennas to further reduce completion time. Simulation results manifest that our algorithm outperforms others by 100%.

关键词： Servers Smart devices Computational modeling Antennas Data models Prefetching Data transfer

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Dynamic Resilient Graph Games for State-Dependent Jamming Attacks Analysis on Multi-Agent systems

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IFAC-PapersOnLine 2020年第2期53卷 3421-3426页

作者： Yurid Nugraha Ahmet Cetinkaya Tomohisa Hayakawa Hideaki Ishii Quanyan Zhu Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo 101-8430 Japan Dept. of Computer Science Tokyo Insitute of Technology Yokohama 226-8502 Japan Dept. of Electrical and Computer Engineering New York University Brooklyn NY 11201 USA

A cybersecurity problem for a multi-agent consensus problem is investigated through a dynamic game formulation. Specifically, we consider a game repeatedly played between a jamming attacker and a defender. The attacker attempts to jam the links between a number of agents to delay their consensus. On the other hand, the defender tries to maintain the connection between agents by attempting to recover some of the jammed links with the goal of achieving faster consensus. In each game, the players decide which links to attack/recover and for how long to continue doing so based on a Lyapunov-like function representing the largest difference between the states of the agents. We analyze the subgame perfect equilibrium of the game and obtain an upper bound of the consensus time that is influenced by the strategies of the players. The results are illustrated with a numerical example.

关键词： Multi-agent systems Consensus problem Game theory Cybersecurity

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Challenges and Opportunities in Open Data Collaboration – a focus group study

Challenges and Opportunities in Open Data Collaboration – a...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Per Runeson Thomas Olsson Dept. of Computer Science Lund University Lund Sweden Software and Systems Engineering Lab RISE Research Institutes of Sweden Lund Sweden

ISBN: (数字)9781728195322

ISBN: (纸本)9781728195339

Data-driven software is becoming prevalent, especially with the advent of machine learning and artificial intelligence. With data-driven systems come both challenges - to keep collecting and maintaining high quality data - and opportunities - open innovation by sharing data with others. We propose Open Data Collaboration (ODC) to describe pecuniary and non-pecuniary sharing of open data, similar to Open Source software (OSS) and in contrast to Open Government Data (OGD), where public authorities share data. To understand challenges and opportunities with ODC, we organized five focus groups with in total 27 practitioners from 22 companies, public organizations, and research institutes. In the discussions, we observed a general interest in the subject, both from private companies and public authorities. We also noticed similarities in attitudes to open innovation practices, i.e. initial resistance which gradually turned into interest. While several of the participants were experienced in open source software, no had shared data openly. Based on the findings, we identify challenges which we set out to continue addressing in future research.

关键词： Organizations Technological innovation Ecosystems Conferences software Collaboration Government

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