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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Shreya Gupta Gargi Bhardwaj Megha Arora Ritu Rani Poonam Bansal Ravinder Kumar Computer Science Engineering with Specialization in Artificial Intelligence Indira Gandhi Delhi Technical University For Women New Delhi India Center of Excellence- Artificial Intelligence Indira Gandhi Delhi Technical University for Women Kashmere Gate Delhi India Skill department of CS/IT Shri Vishwakarma Skill University Palwal

Employee attrition is a major issue among all employee-related issues in the current setting, notwithstanding changes in the external environment. Attrition is defined as a gradual decrease in the number of employees due to seclusion, death, renunciation, and resignations. This research employs logistic regression to forecast whether or not a firm employee will leave. We evaluate job involvement, average monthly hours worked, years employed, gender, education level, and job satisfaction, among other things, as our features. K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and decision trees are more methods for solving this issue. The dataset was divided, with 80% of it being used to train the algorithm and 20% of it being used to test it, yielding a Logistic Regression (LR) accuracy of 88.0952%.

关键词： Support vector machines Radio frequency Industries Machine learning algorithms Computational modeling Education Forestry

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software Testing Issues Improvement in Quality Assurance

Software Testing Issues Improvement in Quality Assurance

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Mohammed Salahat Raed A. Said Khalid Hamid Usama Haseeb Elsaid Abdel Maguid Abdel Ghani Abedallah Abualkishik Muhammad Waseem Iqbal Mohammad Inairat College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Faculty of Management Canadian University Dubai Dubai UAE Department of Computer Science Superior University Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Software Engineering Superior University Lahore Pakistan Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates VICE CHANCELLOR Skyline University College Dubai

Quality assurance ensures that the project is carried out following the agreed-upon supplies, principles, and then purposes. The goal of type and its calibration investigation remains to better comprehend the multifaceted course of software analysis. Current software rehearses have some errors such as analysis techniques, user arrogances, and logistic culture. Altogether of these subjects, including such rushed testing, way to quickly times, totally inadequate supporting documents, and so on others must be addressed together. Describe a solution to the significant trouble of product testing in quality managing. We will discuss about the solutions of testing problems in quality management. propose a solution to the glitches mentioned above.

关键词： software testing Quality assurance Philosophical considerations Process control Documentation Production software

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Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

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FUTURE GENERATION computer SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 134卷 414-429页

作者： Ejarque, Jorge Badia, Rosa M. Albertin, Loic Aloisio, Giovanni Baglione, Enrico Becerra, Yolanda Boschert, Stefan Berlin, Julian R. D'Anca, Alessandro Elia, Donatello Exertier, Francois Fiore, Sandro Flich, Jose Folch, Arnau Gibbons, Steven J. Koldunov, Nikolay Lordan, Francesc Lorito, Stefano Lovholt, Finn Macias, Jorge Marozzo, Fabrizio Michelini, Alberto Monterrubio-Velasco, Marisol Pienkowska, Marta de la Puente, Josep Queralt, Anna Quintana-Orti, Enrique S. Rodriguez, Juan E. Romano, Fabrizio Rossi, Riccardo Rybicki, Jedrzej Kupczyk, Miroslaw Selva, Jacopo Talia, Domenico Tonini, Roberto Trunfio, Paolo Volpe, Manuela Barcelona Supercomputing Center (BSC) Spain Atos BDS R&D HPC & AI Software France Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) Italy Istituto Nazionale di Geofisica e Vulcanologia (INGV) Italy Universitat Politècnica de Catalunya (UPC) Spain Siemens AG Germany Department of Information Engineering and Computer Science University of Trento Italy Universitat Politècnica de València (UPV) Spain Consejo Superior Investigaciones Cientificas (CSIC) Spain Norwegian Geotechnical Institute (NGI) Norway Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung Germany

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows;and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project. (C) 2022 Elsevier B.V. All rights reserved.

关键词： High performance computing Distributed computing Parallel programming HPC-DA-AI convergence Workflow development Workflow orchestration

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SCALING-UP DEEP LEARNING PREDICTIONS OF HYDROGRAPHY FROM IFSAR DATA IN ALASKA

SCALING-UP DEEP LEARNING PREDICTIONS OF HYDROGRAPHY FROM IFS...

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2022 Free and Open Source software for Geospatial, FOSS4G 2022

作者： Stanislawski, L.V. Shavers, E.J. Duffy, A.J. Thiem, P. Jaroenchai, N. Wang, S. Jiang, Z. Kronenfeld, B.J. Buttenfield, B.P. U.S. Geological Survey Center of Excellence for Geospatial Information Science 1400 Independence Road RollaMO65401 United States University of Missouri Science & Technology RollaMO United States Department of Geography and Geographic Information Science University of Illinois at Urbana Urbana-Champaign United States Computer & Information Science & Engineering University of Florida United States Department of Geology and Geography Eastern Illinois University United States Department of Geography University of Colorado-Boulder United States

The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution elevation data. However, deriving hydrography through flow-routing methods is a complex process that needs to be tailored to different geographic conditions, which can lead to varying solutions. To address this problem, this paper evaluates automated deep learning and its transferability to extract hydrography from interferometric synthetic aperture radar (IfSAR) elevation data spanning a range of geographic conditions in Alaska. © Copyright:

关键词： Surface waters

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Reinforcement-Learning Based Method for Accelerating Functional Coverage Closure of Traffic Light Controller Dynamic Digital Design

Reinforcement-Learning Based Method for Accelerating Functio...

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International Conference on computer Theory and Applications (ICCTA)

作者： Youstina M. Halim Khaled A. Ismail Mohamed A. Abd El Ghany Sameh A. Ibrahim Youssef M. Halim Mixel Egypt. Electronics and Communications Engineering Dept. Faculty of Engineering Ain Shams University Cairo Egypt GSI Helmholtz Center for Heavy-Ion Research GmbH (GSI) Germany Electronics Department German University in Cairo Egypt. Integrated Electronic Systems Lab TU Darmstadt Germany Electronics and Communications Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Engineering and Software Systems Dept. Faculty of Engineering Ain Shams University Cairo Egypt

Functional verification of digital designs is an increasingly complex and time-consuming endeavor. One of the major challenges in functional verification is achieving functional coverage closure in a timely manner. Verification experts have moved from using directed stimulus, towards constrained random stimulus and finally, towards using machine learning to direct stimulus. In this work, reinforcement learning (RL) was used, incorporating three different kinds of RL agents to direct stimulus generation for a traffic light controller system. Two of the agents are based on Q-learning (DQN and SARSA), and one is based on Monte-Carlo method (CEM). The maximum reduction in simulation time needed to achieve a targeted percent of coverage was 97%. The average reduction of simulation time across all testcases and agents used was 70%. The solution is highly reusable, the architecture was integrated with different agents and testcases.

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FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures

FedMonitor: An Implementation Architecture for Monitoring of...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization computer architecture Quality of service Production software Real-time systems Stakeholders Monitoring

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Lightglue in Cross-Spectral Image Registration

SSRN

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SSRN 2024年

作者： Velesaca Lara, Henry O. Rivadeneira, Rafael E. Sappa, Angel D. ESPOL Polytechnic University Escuela Superior Politécnica del Litoral Campus Gustavo Galindo Km. 30.5 Via Perimetral Guayaquil Ecuador Software Engineering Department University of Granada C. Periodista Daniel Saucedo Aranda Granada18014 Spain Computer Vision Center UAB Edificio O Campus Bellaterra Barcelona08193 Spain

Cross-spectral imaging is essential for applications ranging from environmental surveillance to advanced medical diagnostics, due to its ability to exploit the distinctive properties of various spectral bands. However, before any implementation the information from the different sources need to be represented in the same reference system, which is a particular challenging problem since the heterogeneours nature of the information. A precise alignment is required to achieve a unified representation of scenes. The current work explores the adaptation of "LightGlue," a deep neural network initially developed for matching local features within visible images, to the task of cross-spectral image registration, in particular when thermal and visible spectrum images are considered. The evaluation is based on a newly developed benchmark dataset comprising both thermal and visible spectrum scenarios. It demonstrates that "LightGlue" efficiently registers cross-spectral images with high accuracy. The results highlight the adaptability of "LightGlue" to complex imaging conditions and its potential to significantly enhance cross-spectral imaging techniques. © 2024, The Authors. All rights reserved.

关键词： Image registration

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Unsupervised software repositories mining and its application to code search

Unsupervised software repositories mining and its applicatio...

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作者： Hu, Gang Peng, Min Zhang, Yihan Xie, Qianqian Gao, Wang Yuan, Mengting Department of National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Computing National University of Singapore Singapore

software repositories are crucial resources for many software tasks, including code retrieval and annotation. Programming forums provide questions and answers (Q&A) from software developers, containing abundant code-description posts for exchanging knowledge about programming issues. However, most posts provide personal opinions of users that are often not adequately confirmed or outdated. Mining software repositories in such open and unrestricted forums is challenging. Since the posts can be arbitrary and noisy, it is difficult to get unified labels for supervised noise elimination. Different from existing mining approaches, this paper proposes Code-Description Mining Framework (CodeMF), an unsupervised framework to eliminate noisy posts and extract high quality software repositories from programming forums. CodeMF treats all social features of the posts as discrete-time signals for kernel principal component analysis and further performs wavelet transform feature fusion to find the delicate changes (noises in temporal signals). We conduct comprehensive experiments on StackOverflow. Experimental results demonstrate that CodeMF can effectively reduce running time and improve precision via mining high-quality software repositories for various programming languages, especially for the large-scale codebases. To further illustrate the effect of CodeMF applied in software tasks, we introduce it to improve the performance of query-expansion code search. Meanwhile, for SQL and C# programs, compared to the state-of-the-art query-expansion method QECK, the improvement of QECKCodeMF is 2% and 6% on Recall@10, and 4% and 14% on mean reciprocal rank, respectively. © 2019 John Wiley & Sons, Ltd.

关键词： Knowledge management

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Joint route guidance and demand management using generalized MFDs ⁎

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IFAC-PapersOnLine 2020年第2期53卷 15023-15028页

作者： Charalambos Menelaou Stelios Timotheou Panayiotis Kolios Christos G. Panayiotou KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering University of Cyprus

In this work we propose a Model Predictive Control (MPC) framework that combines multi-region route guidance with demand management at a macroscopic level. While route guidance is employed to control all vehicular routes, demand management is introduced to control the flows’ departure times. In effect a portion of the demand may be instructed to wait at their origin before commercing their journey (i.e., delayed departure) and thus ensure that, when vehicles do enter the network, they will travel at free-flow conditions. We show that the resulting problem is a nonlinear optimization problem that is solved by a novel convex relaxation with tight lower bounds on the optimal solution. Extensive simulations are conducted to evaluate the performance of the proposed MPC convex optimization problem indicating the substantial performance improvements in the network utilization.

关键词： Demand Management Route Guidance Modelling Control Optimization of Transportation Systems

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

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Procedia computer Science 2023年 220卷 470-477页

作者： Rima Kaafarani Leila Ismail Oussama Zahwe ICCS-Lab Computer Science Department American University of Culture and Education 1507 Beirut Lebanon Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.

关键词： Blockchain Decision-Map Health Insurance Frauds Detection Hyperledger Fabric Neo Smart Contract Smart Healthcare

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