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SSRN 2025年

作者： Hamza, Muhammad Muhammad, Waseem Akbar, Muhammad Azeem Smolander, Kari Mikkonen, Tommi Department of Software Engineering LUT University Lahti15210 Finland Faculty of Information Technology and Communication Sciences Tampere University Tampere33100 Finland Faculty of Information Technology University of Jyväskylä Jyväskylä40014 Finland University of Jyvaskyla Finland

Context: Serverless computing, also referred to as Function-as-a-Service (FaaS), has revolutionized application development by abstracting the management of underlying infrastructure. Therefore, the software industry frequently adopts open-source serverless frameworks such as OpenFaaS and Apache OpenWhisk for greater portability and flexibility. However, this adaptability often introduces a unique set of issues during the development of ***: To this end, this study aims to analyze issues and their underlying causes from 12 open-source serverless ***: We conducted an empirical investigation on 3,550 closed issues from 12 opensources serverless ***: Our analysis led to the development of a comprehensive taxonomy of 263 types of issues and 158 types of their underlying causes that developers encounter when developing applications with these frameworks. The findings revealed that Development Workflow, Release Workflow and Containerization, System configuration and Interoperability are the leading categories of issues, whereas Improper Development and Execution Practices, Missing Components and Critical Features, and Mismanagement of Versioning and Variables are the most prevalent causes of the issues. Conclusion: Finally, we propose future research directions that can help researchers and practitioners overcome these issues and devise new solutions based on the findings of this study. © 2025, The Authors. All rights reserved.

关键词： Application programs

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A Multivocal Review of MLOps Practices, Challenges and Open Issues

arXiv

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arXiv 2024年

作者： Eken, Beyza Pallewatta, Samodha Tran, Nguyen Khoi Tosun, Ayse Babar, Muhammad Ali Faculty of Computer and Information Sciences Sakarya University Turkey CREST - The Centre for Research on Engineering Software Technology The University of Adelaide Australia Faculty of Computer and Informatics Engineering Istanbul Technical University Turkey

With the increasing trend of Machine Learning (ML) enabled software applications, the paradigm of ML Operations (MLOps) has gained tremendous attention of researchers and practitioners. MLOps encompasses the practices and technologies for streamlining the resources and monitoring needs of operationalizing ML models. software development practitioners need access to the detailed and easily understandable knowledge of MLOps workflows, practices, challenges and solutions to effectively and efficiently support the adoption of MLOps. Whilst the academic and industry literature on the MLOps has been growing rapidly, there have been relatively a few attempts at systematically synthesizing and analyzing the vast amount of existing literature of MLOps for improving ease of access and understanding. We conducted a Multivocal Literature Review (MLR) of 150 relevant academic studies and 48 gray literature to provide a comprehensive body of knowledge on MLOps. Through this MLR, we identified the emerging MLOps practices, adoption challenges and solutions related to various areas, including development and operation of complex pipelines, managing production at scale, managing artifacts, and ensuring quality, security, governance, and ethical aspects. We also report the socio-technical aspect of MLOps relating to diverse roles involved and collaboration practices across them through the MLOps lifecycle. We assert that this MLR provides valuable insights to researchers and practitioners seeking to navigate the rapidly evolving landscape of MLOps. We also identify the open issues that need to be addressed in order to advance the current state-of-the-art of MLOps. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

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Computers, Materials & Continua 2022年第9期72卷 4615-4630页

作者： Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Faculty of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource *** all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords ***,automatic weapons detection is a vital requirement now a *** current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–*** time datasets,from local surveillance department’s test sessions are used for model training and *** consist of local environment images and videos from different type and resolution cameras that minimize the *** research also contributes in the making of Efficient-Net that is experimented and results in a positive *** results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research ***-Net algorithm gives better results than existing *** using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

关键词： Detection algorithms machine learning machine vision video surveillance

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Classifying Psychiatric Patients Using Machine Learning

Classifying Psychiatric Patients Using Machine Learning

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information technology (ACIT)

作者： Hmmam M. Alrjoob Raed Alazaidah Radwan Batyha Hayel Khafajeh Esraa Abu Elsoud Ala'a Saeb Al-Sherideh Ali Elrashidi Muhyeeddin Alqaraleh Waleed Amer Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan Department Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyper Security Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty on Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection.

关键词： Machine learning algorithms Correlation Psychology Feature extraction Classification algorithms Bayes methods information technology Random forests Bagging Logistics

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RASNet: A Reinforcement Assistant Network for Frame Selection in Video-based Posture Recognition

RASNet: A Reinforcement Assistant Network for Frame Selectio...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ruotong Hu Xianzhi Wang Xiaojun Chang Yeqi Hu Xiaowei Xin Xiangqian Ding Baoqi Guo Faculty of Information Science and Engineering Ocean University of China Qingdao China Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia NEWSTAR Software & Consulting CO.LTD Qingdao China

Most existing video-based posture recognition methods treat frames equally using unified or random sampling strategies, thus losing the temporal relationship information among frames. To address this problem, we propose a lightweight framework, namely RASNet, to adaptively select informative frames for recognition. Specifically, we design a video-suited exploration environment to guide the agent in learning the selection strategy. We introduce the reparametrization method to convert the discrete action space into a continuous space, making the agent robust and random. For the reward part, we design a multi-factor function to reward the agent keeping a balance between frame usage and accuracy. Extensive experiments on three large-scale datasets prove the effectiveness of RASNet, e.g., achieving 85.9% accuracy with fewer 1.15 frames than other state-of-the-art methods on Kinetics 600.

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A Systemic Big Data Framework for the Charging Pile Business 22

A Systemic Big Data Framework for the Charging Pile Business

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Proceedings of the 7th International Conference on information Systems engineering

作者： Licheng Xu Shuai Xie Department of Software Engineering Universiti Malaya Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Malaysia

ISBN: (纸本)9781450397889

There are some problems in the electric vehicle (EV) charging pile industry, such as the unreasonable location of charging station construction, low utilization rate of charging piles, and imprecise marketing strategies, which may elicit a negative response from EV users, cause colossal waste of resources and hinder the development of EVs. Based on the relevant charging pile data to be analyzed, this paper combines big data-related technologies to propose a big data framework for analyzing the charging pile data to solve the common charging service problems from a systemic perspective. The purpose of the framework is to provide decision-making reference information for charging pile operators, charging pile application operators and charging service marketers to improve the charging pile business more effectively. The framework is demonstrated by functional structure and technical structure.

关键词： Charging pile data Big Data framework Decision-making reference Charging business improvement

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A co-optimization pso for fuzzy rule-based classifier design problem based on enlarged hedge algebras

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2021年第4期65卷 290-301页

作者： Nguyen, Du Duc Pham, Phong Dinh Software Engineering Department Faculty of Information Technology University of Transport and Communications 3 Cau Giay street Hanoi100000 Viet Nam Computer Science Department Faculty of Information Technology University of Transport and Communications 3 Cau Giay street Hanoi100000 Viet Nam

Fuzzy Rule-Based Classifier (FRBC) design problem has been widely studied due to many practical applications. Hedge Algebras based Classifier Design Methods (HACDMs) are the outstanding and effective approaches because these approaches based on a mathematical formal formalism allowing the fuzzy sets based computational semantics generated from their inherent qualitative semantics of linguistic terms. HACDMs include two phase optimization process. The first phase is to optimize the semantic parameter values by applying an optimization algorithm. Then, in the second phase, the optimal fuzzy rule based system for FRBC is extracted based on the optimal semantic parameter values provided by the first phase. The performance of FRBC design methods depends on the quality of the applied optimization algorithms. This paper presents our proposed co-optimization Particle Swarm Optimization (PSO) algorithm for designing FRBC with trapezoidal fuzzy sets based computational semantics generated by Enlarged Hedge Algebras (EHAs). The results of experiments executed over 23 real world datasets have shown that Enlarged Hedge Algebras based classifier with our proposed co-optimization PSO algorithm outperforms the existing classifiers which are designed based on Enlarged Hedge Algebras methodology with two phase optimization process and the existing fuzzy set theory based classifiers. © 2021 Budapest University of technology and Economics. All rights reserved.

关键词： Semantics

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education

Enhancing Early Childhood Learning using Image Processing fo...

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Advancements in Computing (ICAC), International Conference on

作者： R.G.V Saranga A.H.N. Jayasinghe U.A.S. Divyantha S.W.L.P de Silva N.H.P. Ravi Supunya Swarnakantha P. K. Suriya Kumari Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children.

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Multispectral Texture Benchmark 27

Multispectral Texture Benchmark

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27th International Conference on Knowledge Based and Intelligent information and engineering Sytems, KES 2023

作者： Kríž, Pavel Haindl, Michal Faculty of Information Technology Czech Technical University Department of Software Engineering Thákurova 9 Prague160 00 Czech Republic Institute of Information Theory and Automation Czech Academy of Sciences Pattern Recognition Department Pod Vodárenskou veží4 Prague182 08 Czech Republic

Dozens of textural features have been published, but their realistic validation for efficient recognition applications still needs to be discovered. Textural features are derived using various approaches. We present a benchmark that can be used to evaluate these features and categorize them based on their information efficiency. We propose how the features can be benchmarked and explain different ways of measuring their properties and performance. Most textural feature-extracting algorithms are only based on information extraction from monospectral images (gray-level). Apart from native multispectral algorithms, we generalize some of these originally monospectral features for hyperspectral textures in our illustrating examples. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： benchmark multispectral features representation Textural features

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Engine Fault Diagnosis Method Based on Convolution Neural Network and BiLSTM

Engine Fault Diagnosis Method Based on Convolution Neural Ne...

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2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Feng, Donghua Li, Yahong Wu, Sa Software and Communication School Tianjin Sino-German University of Applied Sciences Tianjin China School of Information Engineering Tianjin College Beijing University of Science and Technology Tianjin China University of Newcastle Upon Tyne Faculty of Science Agricultuer and Engineering Newcastle upon Tyne United Kingdom

ISBN: (纸本)9781665457033

Aiming at the complex characteristics of engine fault and fuzzy boundary of fault area, a method of engine fault diagnosis based on convolution neural network and bidirectional LSTM is proposed. To explore the causes of common engine faults, a convolutional neural network feature extraction model is established, the modified linear unit is used to improve the over fitting problem of the model, and the SoftMax classifier is used to lock the fault area, and the long short-term is established Memory rural network model, the introduction of remaining life tag, using min max method to complete data preprocessing, adding a dropout layer in the bidirectional LSTM layer and full connection layer, removing the output value of hidden layer, obtaining the final fault diagnosis results, and using root mean square error and asymmetric score function to evaluate the reliability of the method. The simulation results show that the fault diagnosis has fast convergence rate, high diagnosis accuracy and strong applicability, which provides valuable technical reference for engine maintenance and maintenance. © 2023 IEEE.

关键词： Feature extraction

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