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25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022

25th International Computer Symposium on New Trends in Compu...

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25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022

ISBN: (纸本)9789811995811

The proceedings contain 59 papers. The special focus in this conference is on New Trends in Computer Technologies and Applications. The topics include: Prediction of Middle-Aged Unhealthy Facial Skin Using VGG19 and Support Vector machine Models;real-Time Intentional Eye Blink Detection Using Rhombus Identification;difficulty-Aware Mixup for Replay-based Continual learning;vision-Based Lightweight Facial Respiration and Heart Rate Measurement Technology;GAN-Based Criminal Suspect Face Generator;priority algorithms with Advice for Disjoint Path Allocation Problems (Extended Abstract);automatic Summarization of Critical Threat Intelligence Using Transfer learning;anti-screenshot Watermarking Algorithm About Archives Image Based on Deep learning Model;detecting Android Malware by Combining System Call Sequence Relationships with Local Feature Calculation;a Detector Using Variant Stacked Denoising Autoencoders with Logistic Regression for Malicious JavaScript with Obfuscations;a Lightweight and Robust Authentication and Key Agreements with Physically Unclonable Function in Manufacturing Environments;image Compression and Meaningful Ciphertext Encryption Based on Histogram Shift Embedding;a Robust Two Factor Authentication Scheme with Fine Grained Biometrics Verification;applying an IoT Analytics Framework in East asia Area;Android Malware Classifier Combining Permissions and API Features to Face Model Drifting;design and Implementation of the Optimized computing Architecture for Matrix Decomposition algorithms;on Two Variants of Induced Matchings;Design and Implementation of the CNN Accelator Based on Multi-streaming SIMD Mechanisms;Design and Implementation of the Link-List DMA Controller for High Bandwidth Data Streaming;STEM Education Meets HPC;Research on KNN-Based GNSS Coordinate Classification for Epidemic Management;An Improved Light Weight Countermeasure Scheme to Efficiently Mitigate TCP Attacks in SDN;the Effectiveness of Block-Based Programming learning on th

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Backpropagation Artificial Neural Network Enhancement using Beale-Powell Approach Technique 1

Backpropagation Artificial Neural Network Enhancement using ...

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1st Lekantara Annual conference on Engineering and Information Technology, LiTE 2021

作者： Singarimbun, Roy Nuary Putra, Ondra Eka Ginantra, N.L.W.S.R. Dewi, Mariana Puspa Universitas Battuta Medan Indonesia Universitas Putra Indonesia YPTK Padang Indonesia Institut Bisnis Dan Teknologi Indonesia Denpasar Indonesia Institut Teknologi Dan Bisnis Asia Malang Malang Indonesia

machine learning algorithms can study existing data to perform specific tasks. One of the well-known machine learning algorithms is the backpropagation algorithm, but this algorithm often provides poor convergence speed in the training process and a long training time. The purpose of this study is to optimize the standard backpropagation algorithm using the Beale-Powell conjugate gradient algorithm so that the training time needed to achieve convergence is not too long, which later can be used as a reference and information for solving predictive problems. The Beale-Powell conjugate gradient algorithm can solve unlimited optimization problems and is much more efficient than gradient descent-based algorithms such as standard backpropagation. The research data used for the analysis were formal education participation data in Indonesia. To be trained and tested using the 7-10-1 architecture. The results showed that the Beale-Powell Conjugate Gradient algorithm could more quickly perform the training and convergence process. However, the MSE value of testing and performance is still superior to the backpropagation algorithm. So it can be concluded that for the prediction case of Formal Education Participation in Indonesia, the Conjugate Gradient Beale-Powell algorithm is good enough to optimize the performance of backpropagation standards seen from the convergence speed and training performance. © Published under licence by IOP Publishing Ltd.

关键词： learning algorithms

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8th International conference on Computers, Management and Mathematical Sciences, ICCM 2022

8th International Conference on Computers, Management and Ma...

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8th International conference on Computers, Management and Mathematical Sciences, ICCM 2022

ISBN: (纸本)9783031251931

The proceedings contain 28 papers. The special focus in this conference is on Computers, Management and Mathematical Sciences. The topics include: Simulation of GA-Based Harmonics Elimination in CHMLI Using DTC for Dynamic Load;Discussing the Future Perspective of machine learning and Artificial Intelligence in COVID-19 Vaccination: A Review;embedded Platform-Based Heart-Lung Sound Separation Using Variational Mode Decomposition;MIMO: Modulation Schemes for Visible Light Communication in Indoor Scenarios;image Resampling Forensics: A Review on Techniques for Image Authentication;the Impact of Perceived Justice and Customer Trust on Customer Loyalty;performance Analysis of Wind Diesel Generation with Three-Phase Fault;C-RPI: Cluster-Based Rendezvous Point Identification and Mobile Sink-Based Data Collection in LR-WPAN;Effect of Weather on the COVID-19 Pandemic in North East India;sentiment Analysis Using Social Media and Product Reviews;a Comparative Study on the Performance of Soft computing Models in the Prediction of Orthopaedic Disease in the Environment of Internet of Things;Maximization of Active Power Delivery in WECS Using BDFRG;Identification and Detection of Credit Card Frauds Using CNN;different Degradation Modes of Field-Deployed Photovoltaic Modules: A Literature Review;impact of Security Attacks on Spectrum Allocation in Cognitive Radio Networks;human Activity Detection Using Attention-Based Deep Network;software Vulnerability Classification Using learning Techniques;service Recovery: Past, Present, and Future;an In-depth Accessibility Analysis of Top Online Shopping Websites;a Stable Model for learningand Memorization in Over-Parameterized Networks;analysis of Depth Sensing and Lane Detection algorithms for Advanced Driver Assistance Systems;implementation of E-Commerce Application with Analytics.

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The CityLearn Challenge 2022: Overview, Results, and Lessons Learned 36

The CityLearn Challenge 2022: Overview, Results, and Lessons...

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36th Annual conference on Neural Information Processing Systems - Competition Track, NeurIPS 2022

作者： Nweye, Kingsley Nagy, Zoltan Mohanty, Sharada Chakraborty, Dipam Sankaranarayanan, Siva Hong, Tianzhen Dey, Sourav Henze, Gregor Drgona, Jan Lin, Fangquan Jiang, Wei Zhang, Hanwei Wang, Li Yi, Zhongkai Zhang, Jihai Yang, Cheng Motoki, Matthew Khongnawang, Sorapong Ibrahim, Michael Zhumabekov, Abilmansur May, Daniel Yang, Zhihu Song, Xiaozhuang Zhang, Han Dong, Xiaoning Zheng, Shun Bian, Jiang The University of Texas at Austin AustinTX United States AI Crowd Electric Power Research Institute Palo AltoCA United States Lawrence Berkeley National Laboratory BerkeleyCA United States University of Colorado Boulder BoulderCO United States Pacific Northwest National Laboratory RichlandWA United States Alibaba Group Hangzhou China University of Washington SeattleWA United States University of Hawaii at Manoa HonoluluHI United States Computer Engineering Department Faculty of Engineering Cairo University Giza Egypt University of Alberta Edmonton Canada Zuoyebang Education Technology Beijing China Southern University of Science and Technology Shenzhen China Tsinghua University Beijing China Microsoft Research Asia Beijing China

The shift to renewable power sources and building electrification to decarbonize existing and emerging building stock present unique challenges for the power grid. Building loads and flexible resources e.g. batteries must be adequately managed simultaneously to unlock the full flexibility potential and reduce costs for all stakeholders. Simple control algorithms based on expert knowledge e.g. rule-based control (RBC), as well as, advanced control algorithms e.g. model predictive control (MPC) and reinforcement learning control (RLC) can be utilized to intelligently manage flexible resources. The CityLearn Challenge is an opportunity to compete in investigating the potential of artificial intelligence (AI) and distributed control systems to tackle multiple problems within the built-environment. The CityLearn Challenge 2022 is the third of its kind with the overall objective of crowd-sourcing generalizable control policies that improve energy, cost and environmental objectives by taking advantage of batteries for load shifting in a CityLearn digital twin of a real-world grid-interactive neighborhood. Highlighted here are the uniqueness of this third edition, baseline and top solutions, and lessons learned for future editions. © 2023 K. Nweye et al.

关键词： Model predictive control

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Cross-Model Operator Batching for Neural Network Architecture Search 17th

Cross-Model Operator Batching for Neural Network Architectur...

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17th International conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Ye, Lingling Zhang, Chi Li, Mingxia Han, Zhenhua Tan, Haisheng Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Microsoft Res Asia MSRA Shanghai Peoples R China

ISBN: (纸本)9783031192135;9783031192142

Recently, automated machine learning (AutoML) and neural architecture search (NAS), regarded as promising techniques to design deep learning (DL) models automatically, have received increasing attention from both industry and academia. NAS will generate a large number of candidate models, which typically consist of numerous common substructures, providing a vast opportunity for cross-model optimization (e.g., operator batching) to improve training efficiency. However, most of the existing AutoML frameworks do not make use of operator batching and we also lack an efficient batching strategy. In this work, we propose a heuristic scheme named DPBat to guide the operator batching among multiple models in NAS. For most models, the operator batching of DPBat can be finished in just a few seconds, which is negligible compared to the subsequent training. We adopt Microsoft's open source AutoML framework NNI to implement DPBat to real NAS scenarios. Extensive experiments show that DPBat is highly effective in improving training efficiency and reducing the overhead of operator batching, with a throughput 3.7x higher than the standard practice of running each job without batching.

关键词： AutoML NAS Operator batching

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International conference on computing, Communication, Electrical and Biomedical Systems, ICCCEBS 2021

International Conference on Computing, Communication, Electr...

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International conference on computing, Communication, Electrical and Biomedical Systems, ICCCEBS 2021

ISBN: (纸本)9783030861643

The proceedings contain 55 papers. The special focus in this conference is on computing, Communication, Electrical and Biomedical Systems. The topics include: Selection of Routing Protocol-Based QoS Improvement for Mobile Ad Hoc Network;Mediating Effect of Quality Enhancement Between ICT-Related Factors and Student Satisfaction in Open and Distance Education;a Review on Deep learning Technique on Basis of Different Types of Crop Diseases;Malicious Firmware Injection Detection on Wireless Networks Using Deep learning TF-IDF Normalization (MFI-IDF);Hybrid Blind Watermarking Using RDWT-DCT in Singular Value Decomposition Domain;a Brief Analysis of Border Gateway Protocol for Internet Controlling and Malicious Attacks;Importance of Information and Communication Technology (ICT) in Higher Education Paper;EYE-TM: An Automatic Teller machine for the Visually Impaired;Online OPD Management with Rating and Review System to Hospitals and Doctors;Wield NodeMCU IOT to Shelter Coal Mining;digital Certificate-Based User Authentication to Access Networks and Hosts;food Detection and Nutritional Recognition System Using Neural Networks;Analysis and Prediction of COVID-19 Cases Using machine learning algorithms;ethereum-Based P2P Lending System (Dao App): Qualitative Review of a Possible Replacement for Lending Practices;Artificial Intelligence to Protect Cyber Security Attack on Cloud E-learning Tools (AIPCE);Enhancing Social Divergence Using Genetic algorithms and Linked Open Data for Improving Immunity System Against COVID-19;Predictive Water Quality Modeling Using ARIMA and VAR for Locations of Krishna River, Andhra Pradesh, India;multimodal Biometric Person Identification System Based on Speech and Keystroke Dynamics;mining of Customer Review Feedback Using Sentiment Analysis for Smart Phone Product;preface.

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Analysis of N-Way K-Shot Malware Detection Using Few-Shot learning 1

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International conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Chui, Kwok Tai Gupta, Brij B. Lee, Lap-Kei Torres-Ruiz, Miguel Kowloon Hong Kong Department of Computer Science and Information Engineering International Center for AI and Cyber Security Research and Innovations Asia University Taichung41354 Taiwan Lebanese American University Beirut1102 Lebanon Department of Electronic Engineering and Computer Science School of Science and Technology Hong Kong Metropolitan University Kowloon Hong Kong Instituto Politécnico Nacional Centro de Investigacion en Computacion UPALM-Zacatenco Mexico City07320 Mexico

ISBN: (数字)9783031220180

ISBN: (纸本)9783031220173

Solving machine learning problems with small-scale training datasets becomes an emergent research area to fill the opposite end of big data applications. Attention is drawn to malware detection using few-shot learning, which is typically formulated as N-way K-shot problems. The aims are to reduce the effort in data collection, learn the rare cases, reduce the model complexity, and increase the accuracy of the detection model. The performance of the malware detection model is analyzed with the variation in the number of ways and the number of shots. This facilitates the understanding on the design and formulation of three algorithms namely relation network, prototypical network, and relation network for N-way K-shot problems. Two benchmark datasets are selected for the performance evaluation. Results reveal the general characteristics of the performance of malware detection model with fixed ways and varying shots, and varying ways and fixed shots based on the trends of results of 30 scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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F3FAD: Fast 3D Facial Avatar Digitization With XR Cloud Service 24

F3FAD: Fast 3D Facial Avatar Digitization With XR Cloud Serv...

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24th IEEE International conference on High Performance computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Yang, Qing Zhang, Hua Beijing Engineering Research Center of Mixed Reality and Advanced Display Beijing Institute of Technology China Intel China Research Center Ltd. China Intel Asia-Pacific Research and Development Ltd. China

ISBN: (纸本)9798350319934

As 3D Facial Avatar Digitization has become an increasingly important and popular feature for Extended Reality (XR), a high-fidelity and specially customized face avatar can greatly improve immersive experience for users who have been engaged in the virtual space. In this paper, we propose Fast 3D Facial Avatar Digitization (F3FAD), a novel end-to-end deep learning method for digitizing highly detailed 3D faces from a single image. There are four advantages of the F3FAD. Unlike previous works, F3FAD can infer both 3D mesh surface and color texture for overall human head model from a single view. There would be no need for sophisticated 3D scanning devices, multi-view stereo algorithms, or tedious capture procedures of hand-hold devices. It merely takes a single picture to digitize an entire face in the 3D world. Next, face attributes of the avatar can be freely manipulated according to user's preference. Thirdly, the avatar can be physically driven by multiple modalities of message inputs. At last, we have designed a novel AI computing framework to intelligently arrange the workload of F3FAD pipeline on a server-client system. In addition, we demonstrate the F3FAD technology with a speech-driven talking avatar, to showcase the advantages of our technology and system design in the real-world scenario. © 2022 IEEE.

关键词： Deep learning

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9th International conference on Future Data and Security Engineering, FDSE 2022

9th International Conference on Future Data and Security Eng...

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9th International conference on Future Data and Security Engineering, FDSE 2022

ISBN: (纸本)9789811980688

The proceedings contain 57 papers. The special focus in this conference is on Future Data and Security Engineering. The topics include: Preface;towards a Foundation of Web3;covid-19 Detection Based on Lung Lesion Signs in Big Data Processing Environment;predicting Loan Repayment Using a Hybrid of Genetic algorithms, Logistic Regression, and Artificial Neural Networks;performance Evaluation of Regular Decomposition and Benchmark Clustering Methods;authentication of Luxury Products – Identifying Key Requirements from a Seller and Consumer Perspective;Analysis of a New Practical SPN-Based Scheme in the Luby-Rackoff Model;authorization and Access Control for Different Database Models: Requirements and Current State of the Art;a Hierarchical Deterministic Wallet Using Ed25519 Digital Signature Scheme;a Secure Framework for Internet of Medical Things Security Based System Using Lightweight Cryptography Enabled Blockchain;signature algorithms on Non-commutative Algebras Over Finite Fields of Characteristic Two;design a Smart Lock System Using Fingerprint and Password for Enhancing Security;in-Memory computing Architectures for Big Data and machine learning Applications;towards an Attention-Based Threat Detection System for IoT Networks;security and Privacy Issues and Solutions in Federated learning for Digital Healthcare;pPATE: A Pragmatic Private Aggregation of Teacher Ensembles Framework by Sparse Vector Technique Based Differential Privacy, Paillier Cryptosystem and Human-in-the-loop;vietnamese Text’s Writing Styles Based Authorship Identification Model;application of machine learning in Malware Detection;text Classification Models and Topic Models: An Overall Picture and a Case Study in Vietnamese;deep Hybrid Models for Forecasting Stock Midprices from the High-Frequency Limit Order Book;an Image Denoising Model Based on Nonlinear Partial Diferential Equation Using Deep learning;recommendations in E-Commerce Systems Based on Deep Matrix Factorization;AttendanceKit: A s

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International conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2022

International Conference on Applications in Electronics Perv...

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International conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2022

ISBN: (纸本)9783031303326

The proceedings contain 52 papers. The special focus in this conference is on Applications in Electronics Pervading Industry, Environment and Society. The topics include: Analysis, Design and Synthesis of an Execution Tracing Unit (ETU) Based on AUTOSAR Run-Time Interface (ARTI);a Side Channel Attack Methodology Applied to Code-Based Post Quantum Cryptography;A 2 GHz Wide Tuning Range LC-Tank Digitally Controlled Oscillator in 28 nm CMOS Technology;Affordance Segmentation Using RGB-D Sensors for Application in Portable Embedded Systems;ML-Based Classifier for Precision Agriculture on Embedded Systems;deep Reinforcement learning for Automated Car Parking;machine learning Techniques for Anomaly-Based Detection System on CSE-CIC-IDS2018 Dataset;AI-Based Sound Event Detection on IoT Nodes: Requirements Evaluation;contextual Bandits algorithms for Reconfigurable Hardware Accelerators;efficient Uploading *** Datasets into a Non-Relational Database Management System;low-Cost Lithium-Ion Battery Characterization Setup Based on Auxiliary Batteries;model-Based Vital Control Architecture for Highly Automated Train Operations;exposure of the Human Head to 5G Electromagnetic Radiations: Modeling and Analysis;a Short-Range Free-Space Optical Communication System for Space-Assembled Microsatellites;A Low-Area, Low-Power, Wide Tuning Range Digitally Controlled Oscillator for Power Management Systems in 28 nm CMOS Technology;sorting of Live/dead Escherichia Coli by Means of Dielectrophoresis for Rapid Antimicrobial Susceptibility Testing;soluble Mandrel Technology to Produce Parts in Composite Material for Formula 1;A Reconfigurable 2D-Convolution Accelerator for DNNs Quantized with Mixed-Precision;diagnostic Analytics for Pixelated Particle Detectors: A Case Study;developing a Toolchain for Synthetic Driving Scenario Datasets;microcontroller Based Edge computing for Pipe Leakage Detection;ticketing Systems for Smart Public Transportation: Tools at the User Side;Debris Detection

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