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15th Iberian Conference on Information systems and Technologies, CISTI 2020

作者： Cesconetto, Jonas Silva, Luis A. Valderi Leithardt, R.Q. Caceres, Maria N. Selva, Luis A. Garcia, Nuno M. Valderi Leithardt, R.Q. University of Vale Do Itajaí Laboratory of Embedded and Distributed Systems Brazil88302-901 Brazil University of Salamanca Department of Computer Science 37007 Spain Universidade da Beira Interior Departamento de Informática Portugal6201-001 Portugal

ISBN: (纸本)9789895465903

Intelligent environments work collaboratively, bringing more comfort to human beings. The intelligence of these environments comes from technological advances in sensors and communication. IoT is the model developed that allows a wide and intelligent communication between devices. Hardware reduction of IoT devices results in vulnerabilities. Thus, there are numerous concerns regarding the security of user information, since mobile devices are easily trackable over the Internet. Care must be taken regarding the information in user profiles. Mobile devices are protected by a permission-based mechanism, which limits third-party applications from accessing sensitive device resources. In this context, this work aims to present a proposal for materialization of application for the evolution of user profiles in intelligent environments. Having as parameters the parameters presented in the proposed taxonomy. The proposed solution is the development of two applications, one for Android devices, responsible for allowing or blocking some features of the device. And another in Cloud, responsible for imposing the parameters and privacy criteria, formalizing the profile control module (PRIPRO - PRIvacy PROfiles). © 2020 AISTI.

关键词： Taxonomies

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PreMatch: A Prediction-based Scheduler for Dataflow Asynchronous Processing on the Computing Continuum 25

PreMatch: A Prediction-based Scheduler for Dataflow Asynchro...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Narges Mehran Zahra Najafabadi Samani Samira Afzal Radu Prodan Frank Pallas Peter Dorfinger Intelligent Connectivity Group Salzburg Research Forschungsgesellschaft mbH Salzburg Salzburg Austria Faculty of Digital and Analytical Sciences Paris Lodron University of Salzburg Salzburg Salzburg Austria Distributed and Parallel Systems Group University of Innsbruck Innsbruck Tyrol Austria Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Overijssel Netherlands Institute of Information Technology Alpen-Adria-University of Klagenfurt Klagenfurt Carinthia Austria

ISBN: (纸本)9798400706295

The popularity of asynchronous data exchange patterns has recently increased, as evidenced by 23% of the communication between microservices in an Alibaba trace analysis. Such workloads necessitate methods for reducing dataflow processing and completion time by forecasting the future requirements of their microservices and (re-)scheduling them. Therefore, we investigate a prediction-based scheduling method of asynchronous dataflow processing applications by considering the stochastic changes due to dynamic user requirements. We present a microservice scaling and scheduling method named PreMatch combining a machine learning (ML) prediction strategy based on gradient boosting with ranking and game theory matching scheduling principles. Firstly, PreMatch predicts the number of microservice replicas, and then, the ranking method orders the microservice replicas and devices based on microservice and transmission times. Thereafter, the PreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis of the PreMatch method shows lower completion times on average 13% compared to a related prediction-based scheduling method.

关键词： microservice

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Human Position Tracking and Detection Using Geometric Active Contours 2

Human Position Tracking and Detection Using Geometric Active...

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2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020

作者： Arulananth, T.S. Manjula, Bandari Baskar, M. MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad Telangana 43 India Embedded Systems MLR Institute of Technology Hyderabad Telangana 43 India SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781728153742

Human position tracking and detection of human being is used as an initial step in biometric recognition. Continuous track a person will allow identifying people at any time. Its system consists of two parts person detection and another one is tracking. Active contours are a catch-all name for methods that found the curve that best separates objects in an image. This is known as segmentation. Segmentation is extraordinarily related to tracking. In 3D - it discusses about active surfaces. One way to divide segmentation methods - single object vs. multiple objects. Another way to divide segmentation methods: Edge-based - the object to be segmented should accept its boundary visible in the image, as some sort of outstanding edge. Region-based - the region of the object in the image should have a different actuarial in some sort of feature space, estimate to its surroundings. These functional are integrating with information on the shape of the area being extracted. © 2020 IEEE.

关键词： Tracking (position)

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Prediction of the final size for COVID-19 epidemic using machine learning:A case study of Egypt

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Infectious Disease Modelling 2020年第1期5卷 622-634页

作者： Lamiaa A.Amar Ashraf A.Taha Marwa Y.Mohamed Networks and Distributed Systems Department Informatics Research InstituteCity of Scientific Research and Technological ApplicationsSRTA-CITYEgypt Multimedia and Computer Graphics Department Informatics Research InstituteCity of Scientific Research and Technological ApplicationsSRTA-CITYEgypt Department of Mathematics and Computer Science Faculty of ScienceAlexandria UniversityEgypt

COVID-19 is spreading within the sort of an enormous epidemic for the *** epidemic infects a lot of individuals in *** World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory *** these days,Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this *** this paper displayed,the real database of COVID-19 for Egypt has been analysed from February 15,2020,to June 15,2020,and predicted with the number of patients that will be infected with COVID-19,and estimated the epidemic final *** regression analysis models have been applied for data analysis of COVID-19 of *** this study,we’ve been applied seven regression analysis-based models that are exponential polynomial,quadratic,thirddegree,fourth-degree,fifth-degree,sixth-degree,and logit growth respectively for the COVID-19 ***,the exponential,fourth-degree,fifth-degree,and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one *** addition,we have applied the well-known logit growth regression model and we obtained the following epidemiological insights:Firstly,the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September ***,the final total size for cases 1.6676Et05 *** action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.

关键词： COVID-19 Regression analysis model Epidemic model

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NPU Thermal Management

NPU Thermal Management

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作者： Amrouch, Hussam Zervakis, Georgios Salamin, Sami Kattan, Hammam Anagnostopoulos, Iraklis Henkel, Jorg in the Computer Science Electrical Engineering Faculty University of Stuttgart Stuttgart Germany Chair for Embedded Systems Karlsruhe Institute of Technology Karlsruhe Germany Department of Electrical and Computer Engineering Southern Illinois University CarbondaleIL United States

Neural processing units (NPUs) are becoming an integral part in all modern computing systems due to their substantial role in accelerating neural networks (NNs). The significant improvements in cost-energy-performance stem from the massive array of multiply accumulate (MAC) units that remarkably boosts the throughput of NN inference. In this work, we are the first to investigate the thermal challenges that NPUs bring, revealing how MAC arrays, which form the heart of any NPU, impose serious thermal bottlenecks to on-chip systems due to their excessive power densities. For the first time, we explore: 1) the effectiveness of precision scaling and frequency scaling (FS) in temperature reductions and 2) how advanced on-chip cooling using superlattice thin-film thermoelectric (TE) open doors for new tradeoffs between temperature, throughput, cooling cost, and inference accuracy in NPU chips. Our work unveils that hybrid thermal management, which composes different means to reduce the NPU temperature, is a key. To achieve that, we propose and implement PFS-TE technique that couples precision and FS together with superlattice TE cooling for effective NPU thermal management. Using commercial signoff tools, we obtain accurate power and timing analysis of MAC arrays after a full-chip design is performed based on 14-nm Intel FinFET technology. Then, multiphysics simulations using finite-element methods are carried out for accurate heat simulations in the presence and absence of on-chip cooling. Afterward, comprehensive design-space exploration is presented to demonstrate the Pareto frontier and the existing tradeoffs between temperature reductions, power overheads due to cooling, throughput, and inference accuracy. Using a wide range of NNs trained for image classification, experimental results demonstrate that our novel NPU thermal management increases the inference efficiency (TOPS/Joule) by $1.33\times $ , $1.87\times $ , and $2\times $ under different temperature constrain

关键词： Temperature control

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Theoretically Guaranteed Online Workload Dispatching for Deadline-Aware Multi-Server Jobs

arXiv

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arXiv 2021年

作者： Zhao, Hailiang Deng, Shuiguang Yin, Jianwei Dustdar, Schahram Zomaya, Albert Y. School of Computer Science and Technology Wuhan University of Technology Wuhan China The Massachusetts Institute of Technology United States China The Distributed Systems Group The Tu Wien Austria The Centre for Distributed and High-Performance Computing The University of Sydney Australia

Multi-server jobs are imperative in modern computing clusters. A multi-server job has multiple task components and each of the task components is responsible for processing a specific size of workloads. Efficient online workload dispatching is crucial but challenging to co-located heterogeneous multi-server jobs. The dispatching policy should decide (i) where to launch each task component instance of the arrived jobs and (ii) the size of workloads that each task component processes. Existing policies are explicit and effective when facing service locality and resource contention in both offline and online settings. However, when adding the deadline-aware constraint, the theoretical superiority of these policies could not be guaranteed. To fill the theoretical gap, in this paper, we design an α-competitive online workload dispatching policy for deadline-aware multi-server jobs based on the spatio-temporal resource mesh model. We formulate the problem as a social welfare maximization program and solve it online with several well designed pseudo functions. The social welfare is formulated as the sum of the utilities of jobs and the utility of the computing cluster. The proposed policy is rigorously proved to be α-competitive for some α ≥ 2. We also validate the theoretical superiority of it with simulations and the results show that it distinctly outperforms two handcrafted baseline policies on the social welfare. © 2021, CC BY-NC-ND.

关键词： Cluster computing

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Machine learning methods in Smartphone-Based Activity Recognition

Machine learning methods in Smartphone-Based Activity Recogn...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Istvan Pintye Laboratory of Parallel and Distributed Systems Institute for Computer Science and Control (SZTAKI) Hungary

ISBN: (数字)9781728173771

ISBN: (纸本)9781728173788

In this paper, we present a system for human physical Activity Recognition (AR) using smartphone with embedded sensors. This paper addresses the question whether there is a comfortable way to predict human activities based on collected data from smartphone embedded gyroscope and accelerometer. Computational background of this work based on self-learning machine learning methods. In order to train the machine learning algorithms, The University of California, Irvine (UCI) dataset was used and the different models were compared. After selecting the best model further modifications were suggested in order to improve the accuracy of the model. At the end 96.88% accuracy was reached.

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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Tracking Error Learning Control for precise mobile robot path tracking in outdoor environment

arXiv

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arXiv 2021年

作者： Kayacan, Erkan Chowdhary, Girish Senseable City Laboratory Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Coordinated Science Laboratory Distributed Autonomous Systems Laboratory University of Illinois at Urbana-Champaign United States

This paper presents a Tracking-Error Learning Control (TELC) algorithm for precise mobile robot path tracking in off-road terrain. In traditional tracking error-based control approaches, feedback and feedforward controllers are designed based on the nominal model which cannot capture the uncertainties, disturbances and changing working conditions so that they cannot ensure precise path tracking performance in the outdoor environment. In TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics and the plant-model mismatch problem is thus discarded. Therefore, the feedforward controller gradually eliminates the feedback controller from the control of the system once the mobile robot has been on-track. In addition to the proof of the stability, it is proven that the cost functions do not have local minima so that the coefficients in TELC algorithm guarantee that the global minimum is reached. The experimental results show that the TELC algorithm results in better path tracking performance than the traditional tracking error-based control method. The mobile robot controlled by TELC algorithm can track a target path precisely with less than 10 cm error in off-road terrain. © 2021, CC BY.

关键词： Learning algorithms

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Work-in-Progress: Using the PerFECt Framework to Design and Implement Blended Learning Activities to Introduce the Binary System in Primary School Students

Work-in-Progress: Using the PerFECt Framework to Design and ...

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9th International Conference on Interactive Collaborative and Blended Learning, ICBL 2020

作者： Moumoutzis, Nektarios Rigas, Nikolaos Apostolos Xanthaki, Chara Maragkoudakis, Yiannis Christodoulakis, Christina Paneva-Marinova, Desislava Pavlova, Lilia Laboratory of Distributed Multimedia Information Systems and Applications School of Electrical and Computer Engineering Technical University of Crete Chania Crete73100 Greece Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria Directorate of Secondary Education Chania Crete Greece Department of Computer Science University of Toronto Toronto Canada Laboratory of Telematics Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783030672089

This paper presents how the PerFECt framework is employed to enable innovative teaching and learning mathematics in schools using drama-based approaches. The topic addressed lies in the cross-section between mathematics and computer science: teaching and learning the binary system of arithmetic that constitutes the basis of modern computing. The work builds on previous research efforts targeting gamification of mathematics and is seen under new light offered by the Theatre in Mathematics (TIM) Methodology. The result is an engaging game to teach the binary system to children using their bodies and simple rules for interacting with each other. Following these simple rules the participants enact the operation of a calculator that is able to transform integers into their binary representations and compute the result of the four arithmetic operations (addition, subtraction, multiplication and division). The game can be combined with online activities using a simulator of the game, so that learners can explore it for themselves and discover how the arithmetic operations are done, even when they are not in the same physical space with their peers. This way, an effective blended learning approach is ultimately offered combining face-to-face and online activities. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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