Popularity-Aware and Diverse Web APIs Recommendation Based on Correlation Graph

Shengqi, Wu; Shen, Shigen; Xu, Xiaolong; Chen, Ying; Zhou, Xiaokang; Liu, Dongning; Xue, Xiao; Qi, Lianyong

doi:10.1109/tcss.2022.3168595

Cited by 66 publications

(29 citation statements)

References 35 publications

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“…Another future direction is to identify critical stakeholders 41 in the Google Play Store and prioritize or group 42,43 them based on their severe deceptive review contributions to provide remedies in either blocking, limiting, or restricting them to enhance end‐user trust in the software applications 20 . Another possible future direction is to employ the extended proposed approach in the Internet of Medical Things (IoMTs) to identify the malware that seriously threatens medical software assets, such as abusing critical information, sensitive patient data, and so forth 44–48 . In the future, the best‐performing M&DL algorithm will be selected to develop a FakeSE tool that automatically identifies the end‐user reviews as fake or real.…”

Section: Discussionmentioning

confidence: 99%

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Khan,

Ullah,

Khan

et al. 2023

Concurrency and Computation

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SummaryEnd‐user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end‐user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high‐star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end‐users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end‐user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end‐users and professionals use to report fake reviews by critically analyzing the end‐user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd‐users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end‐users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end‐user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f‐measures. The accuracy of manually identifying fake reviews by the crowd‐users is 44.4%. In contrast, the M&DL classifiers obtained an average accuracy of 96%. The experimental results obtained with various M&DL classifiers are encouraging. It is the first step towards identifying fake reviews in the app store by studying its implications in software and requirements engineering.

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Section: Discussionmentioning

confidence: 99%

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Khan,

Ullah,

Khan

et al. 2023

Concurrency and Computation

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“…However, it was not until the 1980s that digital animation started to gain traction in the mainstream, driven primarily by advancements in computer technology and software. 9,10 The introduction of computer generated imagery (CGI) opened a new realm of possibilities for animators. The first significant use of CGI in films was in the 1982 movie "Tron."…”

Section: Historical Overview Of Animationmentioning

confidence: 99%

“…The advent of computers led to a paradigm shift in animation, marking the transition from traditional to digital. The 1960s and 1970s saw the first experiments with computer animation, notably in works like Ivan Sutherland's “Sketchpad” and John Whitney's “Catalog.” However, it was not until the 1980s that digital animation started to gain traction in the mainstream, driven primarily by advancements in computer technology and software 9,10 . The introduction of computer generated imagery (CGI) opened a new realm of possibilities for animators.…”

Section: Historical Overview Of Animationmentioning

confidence: 99%

Evolution and innovations in animation: A comprehensive review and future directions

Wang,

Zhong

2023

Concurrency and Computation

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SummaryIn this article, we offer an in‐depth exploration of the history, advancements, and innovations in the field of animation. From the roots of traditional hand‐drawn animation to the contemporary realm of computer‐generated imagery, the article outlines the progression of techniques and technologies that have revolutionized the industry. This study meticulously examines the transformative impact of emerging trends such as virtual reality, augmented reality, and artificial intelligence on the development and application of animation. A significant focus is placed on the societal implications of animation, investigating its pervasive influence in fields as diverse as education, advertising, and entertainment. The use of animation as a pedagogical tool, a marketing strategy, and a medium for storytelling has not only redefined these sectors but has also reshaped audience expectations and experiences. In forecasting the future of animation, the article identifies and analyzes potential technologies and trends poised to push the boundaries of the field. This includes examining challenges such as the ethical considerations of AI and the technical constraints of VR and AR. By providing a comprehensive overview of past developments and future possibilities, this review contributes to the ongoing discourse on the continually evolving landscape of animation.

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“…Individual treatment effect prediction. Predicting individual treatment effects of actions plays a critical role in many domains [20][21][22][23]. Synthetic Minority Oversampling TEchnique (SMOTE) technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy [23].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning based Analysis of the Effect of Team Competition on College Students’ Academic Performance

Zhang,

Wu,

et al. 2023

Preprint

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In the field of Mobile Edge Computing (MEC), machine learning techniques present a promising avenue for intelligent integration and processing of data from MEC terminals. Our study delves into the intersection of Machine Learning with MEC terminal data, exploring the complexity of team competition mechanisms based on social identity and competition theory. This exploration aims to enhance student participation and enthusiasm within university classrooms. However, despite of its potential benefit, there are still many unresolved issues: What type of students and teams benefit more from team competition? In what teaching context is team competition more effective? Which competition design methods better increase student academic performance? To answer these questions, we first de-sign a randomized field experiment among freshmen enrolled in college English course. Then, we collected data using mobile devices and analyzed the observational data to predict the individual treatment effect of academic performance in college English through linear and nonlinear machine learning models. Finally, by carefully investigating features of teams and individual student, we reduce the prediction error by up to 30%. In addition, through interpreting the predictive models, we discover some valuable insights regarding the practice of team competition in college classrooms.

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Popularity-Aware and Diverse Web APIs Recommendation Based on Correlation Graph

Cited by 66 publications

References 35 publications

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end‐user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Evolution and innovations in animation: A comprehensive review and future directions

Machine Learning based Analysis of the Effect of Team Competition on College Students’ Academic Performance

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