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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Mass composition of Telescope Array’s surface detectors events using deep learning 37

Mass composition of Telescope Array’s surface detectors eve...

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37th international Cosmic Ray conference, ICRC 2021

作者： Kharuk, I. Kalashev, O. Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. thomas, S.B. thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Institute for Nuclear Research The Russian Academy of Sciences 60th October Anniversary Prospect 7a Moscow117312 Russia Moscow Institute of Physics and Technology Institutsky lane 9 Moscow region Dolgoprudny141700 Russia Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université

We report on an improvement of deep learning techniques used for identifying primary particles of atmospheric air showers. the progress was achieved by using two neural networks. the first works as a classifier for individual events, while the second predicts fractions of elements in an ensemble of events based on the inference of the first network. For a fixed hadronic model, this approach yields an accuracy of 90% in identifying fractions of elements in an ensemble of events. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-ND 4.0).

关键词： Cosmology

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6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 and 4th international Workshop on Visual Performance Analysis, VPA 2017 and Workshop on Extreme-Scale Programming Tools, ESPT 2018 and 5th international Workshop on Visual Performance Analysis, VPA 2018

6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 a...

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6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 and 4th international Workshop on Visual Performance Analysis, VPA 2017 and Workshop on Extreme-Scale Programming Tools, ESPT 2018 and 5th international Workshop on Visual Performance Analysis, VPA 2018

ISBN: (纸本)9783030178710

the proceedings contain 17 papers. the special focus in this conference is on Extreme-Scale Programming Tools. the topics include: Gotcha: An Function-Wrapping Interface for HPC Tools;Projecting Performance Data over Simulation Geometry Using SOSflow and ALPINE;Visualizing, Measuring, and Tuning Adaptive MPI Parameters;visual Analytics Challenges in Analyzing Calling Context Trees;paScal Viewer: A Tool for the Visualization of Parallel Scalability Trends;using Deep learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges;visualizing Multidimensional Health Status of Data Centers;generic Library Interception for Improved Performance Measurement and Insight;improved Accuracy for Automated Communication Pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning;Moya—A JIT Compiler for HPC;polyhedral Optimization of TensorFlow Computation Graphs;CAASCADE: A System for Static Analysis of HPC Software application Portfolios;visual Comparison of Trace Files in Vampir;understanding the Scalability of Molecular Simulation Using Empirical Performance Modeling;Advanced Event-Sampling Support for PAPI.

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Intrusion Detection Using machine learning and Deep learning Techniques

Intrusion Detection Using Machine Learning and Deep Learning...

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international conference on computer Science and Engineering (UBMK)

作者： Sinan Çalışır Remzi Atay Meltem Kurt Pehlivanoğlu Nevcihan Duru Bilgisayar Mühendisliği Bölümü Kocaeli Üniversitesi Kocaeli Türkiye

Unlike traditional Denial of Service (DoS) attacks, application layer DoS attacks are nearly undetectable at the network layer. CIC DoS is one of the intrusion detection dataset which includes application layer DoS attacks. therefore in this study, we handle this dataset to detect application based DoS attacks by using Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting machine (LGBM), Gradient Boosting, Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN) and Support Vector machine (SVM) algorithms. the experimental results show that the performance of the LGBM based model is better than the other algorithms.

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the application of Deep learning in Vessel Extraction with Fundus Image 4

The Application of Deep Learning in Vessel Extraction with F...

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2019 4th international conference on Intelligent Computing and Signal Processing, ICSP 2019

作者： Yang, Zuman Wu, Jiaxing Jiang, Kagyu Computer Science and Technology Liaoning University No.66 Chongshan Road Shenyang HuangGu District China

Fundus image is a very prevalent Biomedical data, so it is very important to choose a method to extract blood vessel from fundus images. Extracting vessel from the fundus image can be used for personal identity verification and assist doctor to diagnose diseases [1] such as diabetes. However, traditional extracted methods such as manually annotation area waste of time and energy. there is some another method [2] based on the image recognition, extracting the feature with the filter and segment with mathematical morphology method. But in the fact of the application, this method has low stability and accuracy. Traditional machine learning methods such as SVM [3] are also used for vessel extraction, but this method can only apply to dealing with one-dimensional data while the images showed up as matrix form. So, we have to stretch a matrix line by line or column by column into one-dimensional vectors [4-6]. However, by doing so, we will be face that some important information can't be reflected because the position relation of rows and rows in the original matrix will be lost. Deep learning can process image data naturally and extract features automatically with convolution, and it is also popular for its good learning ability and high accuracy. U-net [7] is a convolutional network based on deep learning and widely used in biological image extraction, which constructs a U-shaped network from input image and output image. In this paper, building a U-Net convolutional network, which takes fundus image as input and vessel image as output, to extract vessels in fundus image. We established a training set with 20 fundus images and a test set with 20 fundus images. Finally, the accuracy rate of our U-net model on the training set can reach 99 percent, while the accuracy rate on the test set was stable above 99.5 percent. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Matrix algebra

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application of Random Forests for Air quality estimation in India by adopting terrain features

Application of Random Forests for Air quality estimation in ...

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international conference on computer, Communication and Signal Processing (ICCCSP)

作者： Sumneet Kaur Bamrah Saiharshith K R Gayathri K S Sri Venkateswara College of Engineering Chennai India

ISBN: (数字)9781728165097

ISBN: (纸本)9781728165103

India houses a population of more than a billion. the worlds most polluted cities are in our country. Pollution is present in various forms. Air pollution is on the rise. Excessive vehicular emissions, stubble burning, fire and smoke emissions are some of the major causes. the overall quality of air impacts the health, growth and development of the people. Pollutants present in the air impact the air quality. In order to estimate the quality of air, an Air Quality Index (AQI) is computed. It is done using the concentration levels of the pollutants. Climate also plays a significant role where parameters such as temperature, Wind Speed (WS), Wind Direction (WD), Solar Radiation (SR), Barometric Pressure (BP) affect the quality of air. Earlier works considered only pollutant data to generate models for AQI estimation using machine learning approaches. A further study in the domain showed that terrain features also influence AQI. thus, the proposed system derives terrain features for any given location. In addition, it considers a combination of pollutant and meteorological parameters to generate the air quality estimation model. Since AQI is a continuous value, regression approach is preferred for modeling. Analysis is performed in three stages, firstly only with pollutant information, secondly with pollutant and climate information and thirdly with pollutant, climate and terrain information. Various regression techniques are applied on the given dataset. the results showcase the consistent performance of an ensemble technique of Random Forests with an overall accuracy of 81%.

关键词： Atmospheric modeling Meteorology Estimation Air pollution Urban areas Monitoring Gases

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Small Dataset Modeling and application of Plant Medicine Extraction 4th

Small Dataset Modeling and Application of Plant Medicine Ext...

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4th international conference on Cognitive Systems and Information Processing (ICCSIP)

作者： Liu, Boyan Chen, Juan Dong, Cuiying Beijing Univ Chem Technol North Third Ring Rd 15 Beijing Peoples R China

ISBN: (纸本)9789811379864;9789811379857

Intelligent modeling is an effective method to build prediction model of the plant medicine with ultrasonic extraction. However, there are obstacles when obtaining lots of data by the plant medicine extraction experiments, and small dataset will result in a model with low accuracy and poor generalization ability, which has a great influence on it. this paper proposes a novel virtual sample generation (VSG) approach based on Response Surface Methodology (RSM) and Extreme learning machine (ELM) algorithm, selecting through Cuckoo Search (CS) algorithm. the new prediction model is constructed based on ELM with the virtual sample dataset generated by this method and the original small sample dataset. the performance of the model is verified via the case of extracting the active ingredients, liquiritin, from liquorice by dual-frequency ultrasound. the experiment results show that the model established by the method proposed in this paper can significantly reduce the prediction error and improve the accuracy of the model, which provides a certain theoretical basis and reference for the industrialization of the active ingredients extraction of plant medicine.

关键词： Small dataset modeling Virtual sample generation Plant medicine extraction

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17th international conference on Service-Oriented Computing, ICSOC 2019

17th International Conference on Service-Oriented Computing,...

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17th international conference on Service-Oriented Computing, ICSOC 2019

ISBN: (纸本)9783030337018

the proceedings contain 42 papers. the special focus in this conference is on Service-Oriented Computing. the topics include: Personal service ecosystem (Pse) and its evolution pattern analysis;adaptive mobile business process monitoring service with enhanced nfv mano;satp: Sentiment augmented topic popularity prediction on social media;a hierarchical optimizer for recommendation system based on shortest path algorithm;preface;An Empirical Study of GraphQL Schemas;N2TM: A New Node to Trust Matrix Method for Spam Worker Defense in Crowdsourcing Environments;qoS Value Prediction Using a Combination of Filtering Method and Neural Network Regression;harmonia: A Continuous Service Monitoring Framework Using DevOps and Service Mesh in a Complementary Manner;ESDA: An Energy-Saving Data Analytics Fog Service Platform;Leveraging AI in Service Automation Modeling: From Classical AI through Deep learning to Combination Models;a Wearable machine learning Solution for Internet Traffic Classification in Satellite Communications;FAME: An Influencer Model for Service-Oriented Environments;latency-Aware Deployment of IoT Services in a Cloud-Edge Environment;trusted Data Integration in Service Environments: A Systematic Mapping;CSI2: Cloud Server Idleness Identification by Advanced machine learning in theories and Practice;Automating SLA-Driven API Development with SLA4OAI;an Energy Efficient and Interference Aware Virtual machine Consolidation Algorithm Using Workload Classification;thread-Level CPU and Memory Usage Control of Custom Code in Multi-tenant SaaS;optimized application Deployment in the Fog;toward Cost Efficient Cloud Bursting;optimized Renewable Energy Use in Green Cloud Data Centers;Operating Enterprise AI as a Service;towards Automated Patch Management in a Hybrid Cloud;constraint-Aware Drone-as-a-Service Composition.

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Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks 4

Non-Volatile Memory Array Based Quantization- and Noise-Resi...

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4th IEEE international conference on Rebooting Computing (ICRC)

作者： Ma, Wen Chiu, Pi-Feng Choi, Won Ho Qin, Minghai Bedau, Daniel Lueker-Boden, Martin Western Digital Res Milpitas CA 95035 USA Western Digital Res San Jose CA USA

ISBN: (纸本)9781728152219

In cloud and edge computing models, it is important that compute devices at the edge be as power efficient as possible. Long short-term memory (LSTM) neural networks have been widely used for natural language processing, time series prediction and many other sequential data tasks. thus, for these applications there is increasing need for low-power accelerators for LSTM model inference at the edge. In order to reduce power dissipation due to data transfers within inference devices, there has been significant interest in accelerating vector-matrix multiplication (VMM) operations using non-volatile memory (NVM) weight arrays. In NVM array-based hardware, reduced bit-widths also significantly increases the power efficiency. In this paper, we focus on the application of quantization-aware training algorithm to LSTM models, and the benefits these models bring in terms of resilience against both quantization error and analog device noise. We have shown that only 4-bit NVM weights and 4-bit ADC/DACs are needed to produce equivalent LSTM network performance as floating-point baseline. Reasonable levels of ADC quantization noise and weight noise can be naturally tolerated within our NVM-based quantized LSTM network. Benchmark analysis of our proposed LSTM accelerator for inference has shown at least 2.4x better computing efficiency and 40x higher area efficiency than traditional digital approaches (GPU, FPGA, and ASIC). Some other novel approaches based on NVM promise to deliver higher computing efficiency (up to x4.7) but require larger arrays with potential higher error rates.

关键词： quantization noise LSTM non-volatile memory machine learning hardware

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6th international Symposium on Security in Computing and Communications, SSCC 2018

6th International Symposium on Security in Computing and Com...

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6th international Symposium on Security in Computing and Communications, SSCC 2018

ISBN: (纸本)9789811358258

the proceedings contain 55 papers. the special focus in this conference is on Security in Computing and Communications. the topics include: Workload distribution for supporting anonymous communications in automotive network;policy-based network and security management in federated service infrastructures with permissioned blockchains;statistical comparison of opinion spam detectors in social media with imbalanced datasets;a participatory privacy protection framework for smart-phone application default settings;designing a user-experience-first, privacy-respectful, high-security mutual-multifactor authentication solution;investigating deep learning for collective anomaly detection - An experimental study;BVD - A blockchain based vehicle database system;HGKA: Hierarchical dynamic group key agreement protocol for machine type communication in lte networks;Security threats against LTE networks: A survey;Network anomaly detection using artificial neural networks optimised with PSO-DE hybrid;privacy-preserving searchable encryption scheme over encrypted data supporting dynamic update;effective hardware trojan detection using front-end compressive sensing;enhanced session initiation protocols for emergency healthcare applications;wearable device forensic: Probable case studies and proposed methodology;accessing data in healthcare application;diversity and progress controlled gravitational search algorithm for balancing load in cloud;on minimality attack for privacy-preserving data publishing;probabilistic real-time intrusion detection system for docker containers;Identification of bugs and vulnerabilities in TLS implementation for windows operating system using state machine learning;security improvement of common-key cryptographic communication by mixture of fake plain-texts;SPIC - SRAM PUF intergrated chip based software licensing model;survey on prevention, mitigation and containment of ransomware attacks;deepMal4J: Java malware detection employing deep learning;KarmaNe

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application of fuzzy logic approach for the determination of the integral index of the implicit impact of the higher education system on regional development 8

Application of fuzzy logic approach for the determination of...

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8th international conference on Monitoring, Modeling and Management of Emergent Economy: Experimental Economics and machine learning for Prediction of Emergent Economy Dynamics, M3E2-EEMLPEED 2019

作者： Sotula, Oksana Denysenko, Viktoriya Bohdan Khmelnytsky National University of Cherkasy 81 Shevchenko Blvd. Cherkasy18031 Ukraine

In this paper the theoretical and methodological aspects of theinfluence of the higher education system on the socio-economic development ofthe regions of Ukraine are considered. On the basis of fuzzy logic approach, wehave calculated the integral index of the implicit impact (IIII = I4) of the highereducation system on regional development. Regions are grouped by this indicatorover time. the integral indicator allowed us to identify regions with thecorresponding I4 of the higher education system at the regional level and comparethem with other regions of Ukraine (identification of inter-regional imbalance).the analysis shows that there is no strategy for embedding the university in thelocal economy and society. It is impossible to state unequivocally that the mostdeveloped regions have the greatest influence on the system of higher education,and vice versa. An important continuation of the study should be an assessmentof the situation in each region of the country separately. Such analytics shouldhelp develop differentiated directions for the development of regional highereducation systems. © 2019 CEUR-WS. All rights reserved.

关键词： computer circuits

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