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Russian Court Decisions Data Analysis Using distributed computing and Machine Learning to Improve Lawmaking and Law Enforcement 8

Russian Court Decisions Data Analysis Using Distributed Comp...

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8th international Young Scientists conference on Computational Science (YSC)

作者： Metsker, Oleg Trofimov, Egor Petrov, Max Butakov, Nikolay ITMO Univ St Petersburg Russia All Russian State Univ Justice Moscow Russia

this article describes the study results of semi-structured data processing and analysis of the Russian court decisions (almost 30 million) using distributed cluster-computing framework and machine learning. Spark was used for data processing and decisions trees were used for analysis. the results of the automation of data collection and structuring of court decisions are presented. the methods for extracting and structuring knowledge from semi-structured data for the field ofjustice, taking into account the specifics of the Russian Federation legislation, have been developed. On the example of the fire safety law, the machine learning method for identify the effectiveness of changes in the law and predictions of the consequences of changing the law is demonstrated. It is also shown an association on the impact of lawmaking on law enforcement. the regularities in law enforcement change associate by changes in the law. the connections of law enforcement with economic and social indicators between the regions are identified. the judicial interpretations of the observations are also described in this article what proves the compliance of the results. (C) 2019 the Authors. Published by Elsevier Ltd. this is an open access article under the CC BY-NC -ND license (https://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 8th international Young Scientist conference on Computational Science.

关键词： Law Modelling Court decisions analysis Lawmaking modelling law modelling Enforcement modelling Enforcement analysis Russian law data Big data distributed computing Machine learning Artificial intelligence

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Enabling Real-time Communications and Services in Heterogeneous Networks of Drones and Vehicles 15

Enabling Real-time Communications and Services in Heterogene...

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15th international conference on Wireless and Mobile computing, networking and Communications (WiMob)

作者： Radenkovic, Milena Vu San Ha Huynh John, Robert Manzoni, Pietro Univ Nottingham Nottingham NG8 1BB England Univ Politecn Valencia Camino Vera S-N E-46022 Valencia Spain

ISBN: (纸本)9781728133164

this paper describes the deployment of the Mobile Opportunistic Disconnection-Tolerant Networks and Systems platform in heterogeneous real-world scenarios. Our aim is to prototype and test fully-distributed multi-layer complex DTN forwarding and processing algorithms in different real-time dynamic topologies with varying mobility, network resources and data workload (publish-subscribe) patterns. In this work, we describe two different real-world contexts of mobile heterogeneous sensing and query in disconnection-prone environments: 1) networked drones and vehicles for agricultural monitoring applications and 2) vehicles and drone communications for smart city applications in urban environments. We discuss the performance characteristics of different mobile disconnection prone communication protocols and dynamic architectures across a range of criteria.

关键词： Disconnection tolerant networking mobile ad hoc networks prototypes wireless sensor networks

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Exploiting Data Entropy for Neural Network Compression 8

Exploiting Data Entropy for Neural Network Compression

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8th IEEE international conference on Big Data (Big Data)

作者： Chen, Tse-Wen Liu, Pangfeng Wu, Jan-Jan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Acad Sinica Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9781728162515

Convolutional neural networks (CNN) achieves tremendous success in computer vision. However, due to the increasing number of parameters and the limitation of hardware/software resources, model compression has become an important issue, so we should reduce the size of CNN's and improve the train and inference speed. this paper focuses on channel pruning, a model compression technique that evaluates the importance of channels within a convolution layer, and prune away the less important ones. In this paper, we propose a mutual information metric to prune the network. By measuring the entropy of feature maps, we can estimate how much information goes through each channel during the label recognition and prune away those that have the least information. We compute the mutual information between feature maps and labels, which is the only information relevant to the label classification. We also propose a weighted mutual information metric that further improves the accuracy. We observe from our experiments that the weighted mutual information metric achieves better accuracy than the classic L1-norm metric [1] and the original entropy metric [2]. We also discover that the classic L1-norm pruning metric can be improved by computing the L1-norm of output filter weights (denoted as output L1) instead of input filter weights (denoted as input L1). We test our channel pruning algorithms on the SVHN, the CIFAR-10, and the CIFAR-100 datasets using Simplenet [3]. When we prune away 70% parameters for all convolution layers, our weighted mutual information method has 1.52%, 13.24%, and 7.90% higher accuracy than the output L1 metric on these three datasets. In the global pruning experiment, our weighted mutual information metric has about 2% higher accuracy than the output L1 metric when we removed 55% of parameters from the SVHN dataset. On the CIFAR-100 dataset, our metric is 1.5% more accurate than the output L1 metric when there are only 53% of the parameters remain. the only e

关键词： Model Compression Filter Pruning Entropy CNN Machine Learning

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Intelligent system of forest area recognition for tasks of geographically distributed economic systems 8

Intelligent system of forest area recognition for tasks of g...

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8th international Scientific conference on computing in Physics and Technology, CPT 2020

作者： Kuzmenko, A.A. Kondrashov, D.. Sazonova, A.S. Filippova, L.B. Filippov, R.. Bryansk State Technical University Bryansk Russia

For a long period, our country has been in the process of radical transformations of the state economic system, associated with the final transition to a market system of management, the development of local self-government and the independence of economic entities. In the new conditions of the emerging market, the issues of ensuring the sustainable development of territorial economic systems and sectors of the economy, which are the source and guarantor of social stability, employment, a high level and quality of life of the population of the regions, come to the fore. the paper deals with an intelligent system for recognizing the dynamics of changes in forest areas based on automatic pattern recognition methods. the existing methods of processing graphical information, classification and clustering methods that are of value within the framework of the problems being solved are considered, and several original algorithms are proposed. LTP and FFT algorithms were selected as feature extractors of which the simplest and most productive option is LTP. Histogram equalization algorithms, median and Gaussian filters to eliminate noise and remove small image details are chosen to pre-process the image. Euclidean and Mahalanobis distances were used as separability measures. Naive Bayes classifier is proposed to use for classification. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： Intelligent systems

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A Microservices Oriented Edge computing Framework for LVC Simulation in the IoT Era 19

A Microservices Oriented Edge Computing Framework for LVC Si...

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11th international conference on Computer Modeling and Simulation (ICCMS) / 8th international conference on Intelligent computing and Applications (ICICA)

作者： Sun, Wenxin Zhai, Yanlong Bao, Tianhong Mudassar, Muhammad Shen, Jun Yang, Kai Beijing Inst Technol Sch Comp Sci Beijing Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia Beijing Simulat Ctr Sci & Technol Special Syst Simulat Lab Beijing Peoples R China

ISBN: (纸本)9781450366199

Modeling and simulation are essential methods to better understand a complex system in the real world. Many simulation systems have strong demand for involving physical devices or equipment in the system to improve the fidelity. But most of the current cloud based simulation systems are designed for equipment with adequate resources and suffering from high latency for their centralized communication. Edge computing is getting much attention as a new paradigm for distributed systems involving heterogeneous devices and requiring real-time communication. However, edge computing is still in its initial stage and there is no consensus on the edge framework and implementation. To the best of our knowledge, no detailed studies have explored the edge-side simulation and modeling. In this paper, we study and design an edge-side simulation framework, which exploits light-weight microservices architecture to build scalable and real-time LVC (Live, Virtual and Constructive) simulations. Specifically, the application model is designed to illustrate how LVC simulation integrates with edge computing and cloud computing. the autonomous negotiation service and time synchronization services are discussed in details. though it is a preliminary edge computing framework for LVC simulation, no similar research has been conducted for modeling and simulation using edge computing.

关键词： Edge-side simulation microservices LVC simulation IoT

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Efficient Routing for Overlay Networks in a Smart Grid Context 8

Efficient Routing for Overlay Networks in a Smart Grid Conte...

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8th international conference on Smart Cities and Green ICT Systems (SMARTGREENS)

作者： Orda, Lasse Jensen, Tue Vissing Gehrke, Oliver Bindner, Henrik W. Tech Univ Denmark Dept Elect Engn Lyngby Denmark

ISBN: (纸本)9789897583735

In the modern smart grid, prosumers and communities, organized in energy collectives, are showing an increased desire to trade energy directly using different forms of decentralized market models. To support local autonomy and distributed services, decentralized networking, using peer-to-peer (P2P) networks, can be used to facilitate dynamic discovery and establish communication between peers. When using a modern overlay network based on a distributed Hash Table (DHT), the performance of the overlay network can be improved by using the physical properties found in the smart grid context. this paper presents an improved network routing model that takes advantage of these properties, to add a location-aware heuristic to the search algorithms of a standard overlay network. Concepts from complex networks are utilized to improve the average search path and provide a more efficient design over previous solutions. A proof of concept shows that the design results in a more efficient routing model, when used in a smart grid context, compared to a standard uniformly distributed network model.

关键词： Smart Grid Overlay Network Peer-to-Peer (P2P) distributed Hash Table (DHT) Routing Table Location-aware Routing

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the next generation neutrino telescope: IceCube-Gen2 38

The next generation neutrino telescope: IceCube-Gen2

引用

38th international Cosmic Ray conference, ICRC 2023

作者： Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Audehm, J. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Becker Tjus, J. Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Bishop, A. Blaufuss, E. Blot, S. Bohmer, M. Bontempo, F. Book, J.Y. Borowka, J. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bouma, S. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, D. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Cataldo, M. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, D. Choi, S. Clark, B.A. Clark, R. Classen, L. Coleman, A. Collin, G.H. Conrad, J.M. Cowen, D.F. Dasgupta, B. Dave, P. Deaconu, C. De Clercq, C. De Kockere, S. DeLaunay, J.J. Delgado, D. Deng, S. Deoskar, K. Desai, A. Desiati, P. de Vries, K.D. de Wasseige, G. DeYoung, T. Diaz, A. Díaz-Vélez, J.C. Dittmer, M. Domi, A. Dujmovic, H. DuVernois, M.A. Ehrhardt, T. Eller, P. Ellinger, E. El Mentawi, S. Elsässer, D. Engel, R. Erpenbeck, H. Evans, J. Evans, J.J. Evenson, P.A. Fan, K.L. Fang, K. Farrag, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, L. Flaggs, B. Fox, D. Franckowiak, A. Fritz, A. Fujii, T. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, L. Gernhaeuser, R. Ghadimi, A. Giri, P. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Goswami, S. Grant, D. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Guevel, D. Günther, C. Gutjahr, P. Haack, C. Haji Azim, T. Hallgren, A. Halliday, R. Hallmann, S. Halve, L. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haugen, J. Haungs, A. Heinen, D. Helbing, K. Hellrung, J. Hendricks, B. Henningsen, F. Henrichs, J. Heuermann, L. Heyer, N. Hickford, S. Hidvegi, A. Hignight, J. Hill, C. III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Dept. of Physics University of Chicago ChicagoIL60637 United States Enrico Fermi Institute University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio Sta

the IceCube Neutrino Observatory, a cubic-kilometer-scale neutrino detector at the geographic South Pole, has reached a number of milestones in the field of neutrino astrophysics: the discovery of a high-energy astrophysical neutrino flux, the temporal and directional correlation of neutrinos with a flaring blazar, and a steady emission of neutrinos from the direction of an active galaxy of a Seyfert II type and the Milky Way. the next generation neutrino telescope, IceCube-Gen2, currently under development, will consist of three essential components: an array of about 10,000 optical sensors, embedded within approximately 8 cubic kilometers of ice, for detecting neutrinos with energies of TeV and above, with a sensitivity five times greater than that of IceCube;a surface array with scintillation panels and radio antennas targeting air showers;and buried radio antennas distributed over an area of more than 400 square kilometers to significantly enhance the sensitivity of detecting neutrino sources beyond EeV. this contribution describes the design and status of IceCube-Gen2 and discusses the expected sensitivity from the simulations of the optical, surface, and radio components. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Telescopes

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Emotion Estimation Using Body Expression Types Based on LMA and Sensitivity Analysis 8

Emotion Estimation Using Body Expression Types Based on LMA ...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and Pattern Recognition (icIVPR) / international conference on Activity and Behavior computing (ABC)

作者： Ono, Yuki Aoyagi, Saizo Yamazaki, Yoichi Yamamoto, Michiya Nagata, Noriko Kwansei Gakuin Univ Sch Sci & Technol Sanda Hyogo 6691337 Japan Toyo Univ Fac Informat Networking Innovat & Design Kita Ku Tokyo 1150053 Japan

ISBN: (纸本)9781728107882

the authors have been working on methods of estimating emotions from spontaneous body movements during a collaborative fabrication task. In this study, the authors propose a novel method of grouping emotional expressions on the basis of body movements to create type-specific emotion estimation models. We assume that there are typical expression types that are idiosyncratic characteristics for each person. We propose a method of grouping people by expression types using body movements only, based on Laban Movement Analysis (LMA) and the concept of sensitivity analysis. We perform an experiment to measure body movements and emotions during a fabrication task. Finally, we evaluate the accuracy of estimation and type-specific estimation models. the accuracy was, on average, 67.92%, which was 18% higher than that of person-independent models and superior to existing studies.

关键词： emotion recognition affective computing

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distributed System Based on Deep Learning for Vehicular Re-routing and Congestion Avoidance 8th

Distributed System Based on Deep Learning for Vehicular Re-r...

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8th international conference on Software Process Improvement (CIMPS)

作者： Perez-Murueta, Pedro Gomez-Espinosa, Alfonso Cardenas, Cesar Gonzalez-Mendoza, Miguel Tecnol Monterrey Escuela Ingn & Ciencias Av Epigmenio Gonzalez 500 Santiago De Queretaro 76130 Queretaro Mexico Tecnol Monterrey Escuela Ingn & Ciencias Av Gen Ramon Corona 2514 Zapopan 45138 Jalisco Mexico Tecnol Monterrey Escuela Ingn & Ciencias Av Lago Guadalupe KM 3-5 Atizapan De Zaragoza 52926 Estado De Mexic Mexico

ISBN: (纸本)9783030335472;9783030335465

the excessive growth of the population in large cities has created great demands on their transport systems. the congestion generated by public and private transport is the most important cause of air pollution, noise levels and economic losses caused by the time used in transfers, among others. Over the years, various approaches have been developed to alleviate traffic congestion. However, none of these solutions has been very effective. A better approach is to make transportation systems more efficient. To this end, Intelligent Transportation Systems (ITS) are currently being developed. One of the objectives of ITS is to detect congested areas and redirect vehicles away from them. this work proposes a predictive congestion avoidance by re-routing system that uses a mechanism based on Deep Learning that combines real-time and historical data to characterize future traffic conditions. the model uses the information obtained from the previous step to determine the zones with possible congestion and redirects the vehicles that are about to cross them. Alternative routes are generated using the Entropy-Balanced kSP algorithm (EBkSP). the results obtained from simulations in a synthetic scenario have shown that the proposal is capable of reducing the Average Travel Time (ATT) by up to 7%, benefiting a maximum of 56% of the vehicles.

关键词： Traffic congestion detection Traffic prediction Congestion avoidance Intelligent transportation systems Deep learning

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Comparisons and appropriate solutions to prevent data threats of cloud computing, applied in green environment 8

Comparisons and appropriate solutions to prevent data threat...

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8th international conference on Modeling Simulation and Applied Optimization, ICMSAO 2019

作者： Alsadoon, Ghossoon AMA International University Salmabad Bahrain

ISBN: (纸本)9781538676844

Cloud computing security or online storage is an E-business that provides mass storage solutions in return for a periodic set fee. Such services enable individuals to store or upload content over a secure, established network of remote servers hosted on the internet to store, manage, and process data, as an alternative to a local server or a personal computer, to avoid the risk of potential loss of vital information without the need to manually or periodically back up data. Due to the shared nature of the network, the main priority is the privacy and protection of organization data utilizing cloud computing. With an increase of individuals and organizations using cloud services, appropriate cloud security is mandatory to avoid potential in an organization. the researcher conducted online survey and distributed for deferent academic staff and university students to check the legacy of the privacy and find the appropriate solutions using statistic tools such as correlation measurement. One of the key challenges in cloud computing is how to protect stored information from hacking and maintain a high quality of performance in the cloud computing environment. Several different topics are raised in discussions about cloud computing, such as data, privacy, infected applications, and security. this paper discusses different approaches to protecting information and preventing unauthorized access to data, application of cloud computing to keep data safe data and the make an environment green. © 2019 IEEE.

关键词： Cloud security

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