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A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

Computers, Materials & Continua 2023年第10期77卷 115-137页

作者： Bishrul Haq Sher Muhammad Daudpota ali shariq imran Zenun Kastrati Waheed Noor Department of Computer Science Sukkur IBA UniversitySukkur65200Pakistan Department of Computer Science Norwegian University of Science and Technology(NTNU)Gjøvik2815Norway Department of Informatics Linnaeus UniversityVäxjö35195Sweden Department of Computer Science&IT University of BalochistanQuetta87300Pakistan

The Internet has become one of the significant sources for sharing information and expressing users’opinions about products and their interests with the associated *** is essential to learn about product reviews;however,to react to such reviews,extracting aspects of the entity to which these reviews belong is equally ***-based Sentiment Analysis(ABSA)refers to aspects extracted from an opinionated *** literature proposes different approaches for ABSA;however,most research is focused on supervised approaches,which require labeled datasets with manual sentiment polarity labeling and aspect *** study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect ***,the study deals with two main sub-tasks in ABSA,named Aspect Category Detection(ACD)and Aspect Term Extraction(ATE).In the first sub-task,aspects categories are extracted using topic modeling and filtered by an oracle further,and it is fed to zero-shot learning as the prompts and the augmented *** predicted categories are the input to find similar phrases curated with extracting meaningful phrases(e.g.,Nouns,Proper Nouns,NER(Named Entity Recognition)entities)to detect the aspect *** study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment(MAMS)dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories.

关键词： Natural language processing sentiment analysis aspect-based sentiment analysis topic-modeling POS tagging zero-shot learning

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Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

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Computers, Materials & Continua 2022年第2期70卷 2171-2190页

作者： Altaf Hussain Khan Muhammad Hayat Ullah Amin Ullah ali shariq imran Mi Young Lee Seungmin Rho Muhammad Sajjad Department of Software Sejong University143-747SeoulKorea Digital Image Processing Lab Islamia College Peshawar25000Pakistan Color Lab Department of Computer ScienceNorwegian University of Science and Technology(NTNU)2815GjovikNorway CORIS Institute Oregon State University97331OregonUSA

Digital surveillance systems are ubiquitous and continuously generate massive amounts of data,and manual monitoring is required in order to recognise human activities in public *** surveillance systems that can automatically identify normal and abnormal activities are highly desirable,as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are *** paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition *** proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step *** the first step,the salient frames are selected from the online video stream using a frame differencing method.A lightweight 3D convolutional neural network(3DCNN)architecture is applied to extract spatio-temporal features from the salient frames in the second ***,the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the *** proposed system performs well compared to state-of-theart violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance *** experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98%with an F1-score of 0.97 on the Hockey Fight dataset,and an accuracy of 99%with an F1-score of 0.98 on the Violent Crowd dataset.

关键词： Camera prioritisation surveillance networks convolutional neural network computer vision deep learning resource-constrained device violent activity recognition

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Recommender System for Configuration Management Process of Entrepreneurial Software Designing Firms

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Computers, Materials & Continua 2021年第5期67卷 2373-2391页

作者： Muhammad Wajeeh Uz Zaman Yaser Hafeez shariq Hussain Haris Anwaar Shunkun Yang Sadia ali Aaqif Afzaal Abbasi Oh-Young Song University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Electrical Electronics and Telecommunication EngineeringUniversity of Engineering and TechnologyLahore54000Pakistan School of Reliability and Systems Engineering Beihang UniversityBeijing100191China Department of Software Sejong UniversitySeoul05006South Korea

The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers *** problem is addressed by the software development industry by software product line(SPL)practices that employ feature ***,optimal feature selection based on user requirements is a challenging ***,there is a requirement to resolve the challenges of software development,to increase satisfaction and maintain high product quality,for massive customer needs within limited *** this work,we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and prior experiences of similar developers and *** proposed system recommends features with their estimated cost concerning new software requirements,from all over the globe according to similar developers’and clients’needs and *** system guides and facilitates the development team by suggesting a list of features,code snippets,libraries,cheat sheets of programming languages,and coding references from a cloud-based knowledge management ***,a list of features is suggested to the client according to their needs and *** experimental results revealed that the proposed recommender system is feasible and effective,providing better recommendations to developers and *** provides proper and reasonably well-estimated costs to perform development tasks effectively as well as increase the client’s satisfaction *** results indicate that there is an increase in productivity,performance,and quality of products and a reduction in effort,complexity,and system ***,our proposed system facilitates developers and clients during development by providing better recommendations in terms of solutions and anticipated ***,the increase in productivity and satisfaction level maximizes the benefits and usability of SPL in the

关键词： Feature selection recommender system software reuse configuration management

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Mining Software Repository for Cleaning Bugs Using Data Mining Technique

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Computers, Materials & Continua 2021年第10期69卷 873-893页

作者： Nasir Mahmood Yaser Hafeez Khalid Iqbal shariq Hussain Muhammad Aqib Muhammad Jamal Oh-Young Song University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Mathematics and Statistics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Sejong UniversitySeoul05006Korea

Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and ***,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in ***,there is a need for a repository mining technique for relevant and bug-free data *** paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality *** predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation ***,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure *** evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial *** evaluation showed that the results of our proposal are *** and developers can utilize these rules for defect prediction during early software development.

关键词： Fault prediction association rule data mining frequent pattern mining

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