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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Akhilesh Latoria S. K. Joshi A. Priya M. Kalyan Chakravarthi Radha Raman Chandan Bhasker Pant School of Information Technology Auro University Surat Gujarat Uttaranchal University Dehradun Uttarakhand India School of Architecture KoneruLakshmaiah Educational Foundation Guntur Andhra Pradesh India School of Electronics Engineering VIT-AP University Amaravathi India Department of Computer Science School of Management Sciences (SMS) Khushipur Bachhaon Varanasi UP Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

Due to the rising use of the internet for communication, commercial financial transactions, and other purposes, cloud computing is described as a crucial feature that has risen lately. Through network systems, this entails providing a flexible growth of different services. The fog is often used as a concept to describe different *** often trust cloud service providers to store enormous amounts of data and information with the intention of accessing it later. This paper is mainly concerned with doing a critical examination of the usage of supervised learning and deep learning in cloud applications, as well as their effects on production *** businesses are concentrating on using technology to increase returns on investments, assemble vast quantities of data, and archive it securely so that it may be utilised for organisational decision-making. Notwithstanding, they are more concerned with the cost and security of storing the information. so in order to accommodate the customers, the cloud services have used a wide range of approaches, including human brains, supervised learning, advanced analytics, and others. This helps to increase the users’ credibility and help them use cloud storage to achieve their *** order to understand the overall efficacy of employing neural networks and advanced learning processes connected to cloud technology, this research is capable of using both secondary and primary information. The efficacy will be assessed by the researchers using correlation matrix and other non-parametric approaches. The conclusion is reached after thorough data examination and interpretation.

关键词： Deep learning Cloud computing Correlation Statistical analysis Supervised learning Production Syntactics

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Wind Comfort Analysis Using an Immersed Boundary Method for Early-Stage Building Design

SSRN

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SSRN 2024年

作者： Mitkov, Radostin Hunger, Franziska Pantusheva, Mariya Naserentin, Vasilis Gösta, Alexander Mark, Andreas Petrova-Antonova, Dessislava Logg, Anders GATE Institute Sofia University "St. Kliment Ohridski" Sofia1113 Bulgaria Fraunhofer-Chalmers Centre for Industrial Mathematics Computational Engineering and Design Chalmers Science Park GothenburgSE-412 88 Sweden Department of Mathematical Sciences Chalmers University of Technology GothenburgSE-412 969 Sweden Department of Electrical and Computer Engineering Aristotle University of Thessaloniki ThessalonikiGR- 54124 Greece RISE - Research Institutes of Sweden Architecture and spatial planning Drottning Kristinas Väg 61 StockholmSE-114 28 Sweden Liljewall Arkitekter Odinsplatsen 1 GothenburgSE-411 02 Sweden

This paper presents a thorough exploration of wind comfort dynamics in urban environments through the application of computational fluid dynamics simulations, with a specific emphasis on the Immersed Boundary Method. The study validates the Immersed Boundary Octree Flow solver, IBOFlow, against experimental data and then simulates the flow in a real-world case study of an area in Mölndal, Sweden. The research examines the impact of varying the number of simulated wind directions on the wind comfort maps, revealing that employing 16 wind directions is a reliable balance between computational efficiency and precision. Further, the investigation delves into the implications of using a simplified building geometry as available in early-stage design, demonstrating that computational simulations can provide reliable information about the pedestrian wind comfort early in the design process. While underscoring the significance of early-stage CFD simulations in informing design decisions, the paper concludes by advocating for a holistic approach. It emphasizes the relevance of simulation testing in later design iterations, particularly as architectural details continue to be refined. © 2024, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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iLGaCo: Incremental learning of gait covariate factors

arXiv

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arXiv 2020年

作者： Mu, Zihao Castro, Francisco M. Marín-Jiménez, Manuel J. Guil, Nicolás Li, Yan-Ran Yu, Shiqi College of Computer Science and Software Engineering Shenzhen University China Department of Computer Architecture University of Málaga Spain Department of Computing and Numerical Analysis University of Córdoba Spain China Department of Computer Science and Engineering Southern University of Science and Technology China

Gait is a popular biometric pattern used for identifying people based on their way of walking. Traditionally, gait recognition approaches based on deep learning are trained using the whole training dataset. In fact, if new data (classes, view-points, walking conditions, etc.) need to be included, it is necessary to re-train again the model with old and new data samples. In this paper, we propose iLGaCo, the first incremental learning approach of covariate factors for gait recognition, where the deep model can be updated with new information without re-training it from scratch by using the whole dataset. Instead, our approach performs a shorter training process with the new data and a small subset of previous samples. This way, our model learns new information while retaining previous knowledge. We evaluate iLGaCo on CASIA-B dataset in two incremental ways: adding new view-points and adding new walking conditions. In both cases, our results are close to the classical ‘training-from-scratch’ approach, obtaining a marginal drop in accuracy ranging from 0.2% to 1.2%, what shows the efficacy of our approach. In addition, the comparison of iLGaCo with other incremental learning methods, such as LwF and iCarl, shows a significant improvement in accuracy, between 6% and 15% depending on the experiment. Copyright © 2020, The Authors. All rights reserved.

关键词： Gait analysis

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Rubik: A hierarchical architecture for efficient graph learning

arXiv

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arXiv 2020年

作者： Chen, Xiaobing Wang, Yuke Xie, Xinfeng Hu, Xing Basak, Abanti Liang, Ling Yan, Mingyu Deng, Lei Ding, Yufei Du, Zidong Chen, Yunji Xie, Yuan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Computer Science University of California Santa Barbara United States Department of Electrical and Computer Engineering University of California Santa Barbara United States

Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs. However, learning from graphs is non-trivial because of its mixed computation model involving both graph analytics and neural network computing. To this end, we decompose the GCN learning into two hierarchical paradigms: graph-level and node-level computing. Such a hierarchical paradigm facilitates the software and hardware accelerations for GCN learning. We propose a lightweight graph reordering methodology, incorporated with a GCN accelerator architecture that equips a customized cache design to fully utilize the graph-level data reuse. We also propose a mapping methodology aware of data reuse and task-level parallelism to handle various graphs inputs effectively. Results show that Rubik accelerator design improves energy efficiency by 26.3x to 1375.2x than GPU platforms across different datasets and GCN models. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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A Machine Vision-Based Person Detection Under Low-Illuminance Conditions Using High Dynamic Range Imagery for Visual Surveillance System

A Machine Vision-Based Person Detection Under Low-Illuminanc...

引用

Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Julie Ann B. Susa Sammy V. Militante Aimee G. Acoba Ryan Soriente Evangelista Luisito Lolong Lacatan Charlemagne G. Laviňa Bartolome T. Tanguilig Dept. of Computer Engineering Southern Luzon State University Lucban Quezon Philippines College of Engineering and Architecture University of Antique Antique Philippines Electronics Eng'g Technology Department Technological University of the Philippines Manila Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Computing and Engineering Pamantasan ng Cabuyao Laguna Philippines

The most difficult component of any computer vision application objects recognition and tracking. Video surveillance is a major study subject in computer vision in a dynamic environment, especially for security purposes. Video surveillance technology is critical in preventing crime, terrorism, and other threats. Several existing technologies can accurately monitor a person's mobility in interior environments. As well as regulated outdoor settings, object recognition and tracking at night, on the other hand, remain significant challenges for visual surveillance. The objects are usually farther away, inconspicuous, and have low brightness against the background. In this paper, detecting objects specifically people under low lighting conditions with the help of deep learning algorithms was used. For applications like in-car cameras and surveillance systems that operate in low light, these algorithms might improve picture identification performance.

关键词： Deep learning Training Visualization computer vision Heuristic algorithms Computational modeling Lighting

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GPU-based Parallel Heuristics for Capacited Vehicle Routing Problem

GPU-based Parallel Heuristics for Capacited Vehicle Routing ...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Pramod Yelmewad Basavaraj Talawar Systems Parallelization & Architecture Research at NITK (SPARK) Department of Computer Science and Engineering National Institute of Technology Karnataka Mangalore India

ISBN: (数字)9781728168289

ISBN: (纸本)9781728168296

This paper presents the novel GPU-based parallel strategy for the heuristic algorithms to solve the large-scale Capacited Vehicle Routing Problem (CVRP). A combination of five improvement heuristic approaches has been used to improve the constructed feasible solution. It is noticed that a large amount of CPU time is spent in the solution improvement phase while improving a feasible solution. We aim to discover an independent part of the improvement heuristic approaches and make it run over the GPU platform simultaneously. The proposed parallel version has been tested on large-scale instances of up to 20000 customers. The parallel version offers speedup up to 176.12 × compared to the corresponding sequential version.

关键词： Heuristic algorithms Instruction sets Graphics processing units Data structures Vehicle routing Kernel Transportation

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FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech

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computers in Biology and Medicine 2024年 173卷 108280-108280页

作者： Tuncer, Turker Dogan, Sengul Baygin, Mehmet Barua, Prabal Datta Palmer, Elizabeth Emma March, Sonja Ciaccio, Edward J. Tan, Ru-San Acharya, U. Rajendra Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey Department of Computer Engineering Faculty of Engineering and Architecture Erzurum Technical University Erzurum Turkey University of Southern Queensland Australia Centre of Clinical Genetics Sydney Children's Hospitals Network Randwick2031 Australia School of Women's and Children's Health University of New South Wales Randwick2031 Australia School of Psychology and Counselling and Centre for Health Research University of Southern Queensland Springfield Australia Department of Medicine Columbia University Irving Medical Center United States Department of Cardiology National Heart Centre Singapore Singapore Duke-NUS Medical School Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Centre for Health Research University of Southern Queensland Australia

Background: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. Materials and methods: In this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor;(2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector;(3) classification via support vector machine and k-nearest neighbors;and (4) outcome determination using combinational majority voting to select the most favorable results. Results: To validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79%

关键词： Feature extraction

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A new computational approach to the fractional-order Liouville equation arising from mechanics of water waves and meteorological forecasts

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Journal of Ocean Engineering and Science 2022年

作者： Yue, Xiao-Guang Zhang, Zeying Akbulut, Arzu Kaabar, Mohammed K.A. Kaplan, Melike Wuchang Institute of Technology Wuhan China Department of Computer Science and Engineering European University Cyprus Nicosia 1516 Cyprus CIICESI-ESTG Politécnico do Porto Felgueiras 4610-156 Portugal Mathematics-Computer Department Art-Science Faculty Eskişehir Osmangazi University Eskişehir Turkey Institute of Mathematical Sciences Faculty of Science University of Malaya Kuala Lumpur 50603 Malaysia Department of Mathematics and Statistics Washington State University Pullman 99163 WA United States Jabalia Camp United Nations Relief and Works Agency (UNRWA) Palestinian Refugee Camp Gaza Strip Jabalya Palestine Department of Computer Engineering Faculty of Engineering and Architecture Kastamonu University Kastamonu Turkey

The current analysis employs the improved F-expansion, modified extended tanh, exponential rational function, and (g′)−expansion procedures to find a divergent collection of the fractional Liouville equation's exact solutions in the context of beta-derivative. Also, we have given the graphical representations of the obtained results. These plots are useful in describing the dynamic characteristics of the solutions. The investigated equation is very essential in studying the mechanics of water waves and atmospheric predictability. Therefore, our techniques provide a great help in investigating various nonlinear models formulated in the contexts of fractional derivatives arising from oceanography and mathematical physics. © 2022

关键词： Beta-derivative Exact solutions Fractional differential equations Symbolic computation

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A Novel Method for Wind Power Plants’ Repowering Based on Multi-Criteria Decision-Making

SSRN

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SSRN 2023年

作者： Gil García, Isabel C. Fernández-Guillamón, Ana García-Cascales, M. Socorro Molina-García, Ángel c/ Coruña km 38500 Collado Villalba Madrid28400 Spain Dept. of Applied Mechanics and Projects Engineering - E.T.S. de Ingenieros Industriales & Renewable Energy Research Institute Universidad de Castilla-La Mancha C/ De la Investigación S/N Albacete02006 Spain Department of Electronics Computer Architecture and Project Engineering Department of Automatics Electrical Engineering and Electronic Technology Universidad Politécnica de Cartagena Campus Muralla del Mar Murcia Cartagena30202 Spain

Due to the climate change, over the last decades there have been changes in the rhythm of nature. However, there is an increasing social consciousness regarding decarbonization, and governments include new policies and economic support measures for renewables. The participation of the onshore and offshore wind sectors play a central role in this scenario, expecting that more than 6000 GW will be globally installed in 2050. However, the useful life of many wind power plants is coming to their end, and thus, some countries will face in the coming years the decision of repowering, decommissioning, installing new turbines, or a combination of them. In general, such areas with the greatest wind potential are already occupied by relatively old wind turbines. Subsequently, a complex group of factors influences on the best solution decision-making: climatic, technological, environmental, social or economic. This paper proposes an optimized methodology based on multi-criteria decision-making to estimate the most appropriate solution for extending the useful-life of wind power plants. The methodology is divided into three stages: (i) data, (ii) alternatives and (iii) optimization. It was assessed with a Spanish onshore wind power plant connected to the grid. From the initial data, 11 alternatives were designed based on 10 selection factors. The results provide an optimal solution, combining both repowering and installation of new wind turbines. It would allow us to reduce the LCOE of the new wind plants installed in the European Union by more than 50%. © 2023, The Authors. All rights reserved.

关键词： Wind turbines

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