Data Mining - Concepts and Techniques.

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

/pdf/social-media-and-fake-news-in-the-2016-election-1m4ttkb5mo.pdf

Social Media and Fake News in the 2016 Election

Context: Software development effort estimation (SDEE) is the process of predicting the effort required to develop a software system. In order to improve estimation accuracy, many researchers have proposed machine learning (ML) based SDEE models (ML models) since 1990s. However, there has been no attempt to analyze the empirical evidence on ML models in a systematic way. Objective: This research aims to systematically analyze ML models from four aspects: type of ML technique, estimation accuracy, model comparison, and estimation context. Method: We performed a systematic literature review of empirical studies on ML model published in the last two decades (1991-2010). Results: We have identified 84 primary studies relevant to the objective of this research. After investigating these studies, we found that eight types of ML techniques have been employed in SDEE models. Overall speaking, the estimation accuracy of these ML models is close to the acceptable level and is better than that of non-ML models. Furthermore, different ML models have different strengths and weaknesses and thus favor different estimation contexts. Conclusion: ML models are promising in the field of SDEE. However, the application of ML models in industry is still limited, so that more effort and incentives are needed to facilitate the application of ML models. To this end, based on the findings of this review, we provide recommendations for researchers as well as guidelines for practitioners.

http://romisatriawahono.net/lecture/rm/survey/software%20engineering/Software%20Cost%20Effort%20Estimation/Wen%20-%20software%20development%20effort%20estimation%20models%20-%202012.pdf

Systematic literature review of machine learning based software development effort estimation models

Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments. 

/pdf/ai-for-next-generation-computing-emerging-trends-and-future-34i92n14.pdf

AI for Next Generation Computing: Emerging Trends and Future Directions

Context Analogy-based Software development Effort Estimation (ASEE) techniques have gained considerable attention from the software engineering community. However, existing systematic map and review studies on software development effort prediction have not investigated in depth several issues of ASEE techniques, to the exception of comparisons with other types of estimation techniques. Objective The objective of this research is twofold: (1) to classify ASEE studies which primary goal is to propose new or modified ASEE techniques according to five criteria: research approach, contribution type, techniques used in combination with ASEE methods, and ASEE steps, as well as identifying publication channels and trends and (2) to analyze these studies from five perspectives: estimation accuracy, accuracy comparison, estimation context, impact of the techniques used in combination with ASEE methods, and ASEE tools. Method We performed a systematic mapping of studies for which the primary goal is to develop or to improve ASEE techniques published in the period 1990–2012, and reviewed them based on an automated search of four electronic databases. Results In total, we identified 65 studies published between 1990 and 2012, and classified them based on our predefined classification criteria. The mapping study revealed that most researchers focus on addressing problems related to the first step of an ASEE process, that is, feature and case subset selection. The results of our detailed analysis show that ASEE methods outperform the eight techniques with which they were compared, and tend to yield acceptable results especially when combining ASEE techniques with Fuzzy Logic (FL) or Genetic Algorithms (GA). Conclusion Based on the findings of this study, the use of other techniques such FL and GA in combination with an ASEE method is promising to generate more accurate estimates. However, the use of ASEE techniques by practitioners is still limited: developing more ASEE tools may facilitate the application of these techniques and then lead to increasing the use of ASEE techniques in industry.

http://romisatriawahono.net/lecture/rm/survey/software%20engineering/Software%20Cost%20Effort%20Estimation/Idri%20-%20Analogy%20based%20Software%20Effort%20Estimation%20-%202014.pdf

Analogy-based software development effort estimation: A systematic mapping and review

The key to the explosion of the Internet of Things and the ability to collect, analyze, and provide big data in the cloud is edge computing, which is a new computing paradigm in which data is processed from edges. Edge Computing has been attracting attention as one of the top 10 strategic technology trends in the past two years and has innovative potential. It provides shorter response times, lower bandwidth costs, and more robust data safety and privacy protection than cloud computing. In particular, artificial intelligence technologies are rapidly incorporating edge computing. In this paper, we introduce the concepts, backgrounds, and pros and cons of edge computing, explain how it operates and its structure hierarchically with artificial intelligence concepts, list examples of its applications in various fields, and finally suggest some improvements and discuss the challenges of its application in three representative technological fields. We intend to clarify various analyses and opinions regarding edge computing and artificial intelligence.

Understanding Edge Computing: Engineering Evolution With Artificial Intelligence

With the arrival of the fourth industrial revolution, new technologies that integrate emotional intelligence into existing IoT applications are being studied. Of these technologies, emotional analysis research for providing various music services has received increasing attention in recent years. In this paper, we propose an emotion-based automatic music classification method to classify music with high accuracy according to the emotional range of people. In particular, when the new (unlearned) songs are added to a music-related IoT application, it is necessary to build mechanisms to classify them automatically based on the emotion of humans. This point is one of the practical issues for developing the applications. A survey for collecting emotional data is conducted based on the emotional model. In addition, music features are derived by discussing with the working group in a small and medium-sized enterprise. Emotion classification is carried out using multiple regression analysis and support vector machine. The experimental results show that the proposed method identifies most of induced emotions felt by music listeners and accordingly classifies music successfully. In addition, comparative analysis is performed with different classification algorithms, such as random forest, deep neural network and K-nearest neighbor, as well as support vector machine.

https://www.mdpi.com/2079-9292/8/2/164/pdf

Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

Accurate software effort estimation has always been challenge for software engineering communities. To improve the estimation accuracy of software effort, many studies have focused on effort estimation methods without any consideration of data quality, although data quality is one of important factors to impact to the estimation accuracy. In this paper, we investigate the influence of outlier elimination upon the accuracy of software effort estimation through empirical studies applying two outlier elimination methods(Least trimmed square and K-means clustering) and three effort estimation methods( Least squares, Neural network and Bayesian network) associatively. The empirical studies are performed using two industry data sets(the ISBSG Release 9 and the Bank data set which consists of the project data performed in a bank in Korea) with or without outlier elimination.

An empirical analysis of software effort estimation with outlier elimination

Producing accurate and reliable software effort estimation has always been a challenge for both academic research and software industries. Regarding this issue, data quality is an important factor that impacts the estimation accuracy of effort estimation methods. To assess the impact of data quality, we investigated the effect of eliminating outliers on the estimation accuracy of commonly used software effort estimation methods. Based on three research questions, we associatively analyzed the influence of outlier elimination on the accuracy of software effort estimation by applying five methods of outlier elimination (Least trimmed squares, Cook's distance, K-means clustering, Box plot, and Mantel leverage metric) and two methods of effort estimation (Least squares regression and Estimation by analogy with the variation of the parameters). Empirical experiments were performed using industrial data sets (ISBSG Release 9, Bank and Stock data sets that are collected from financial companies, and a Desharnais data set in the PROMISE repository). In addition, the effect of the outlier elimination methods is evaluated by the statistical tests (the Friedman test and the Wilcoxon signed rank test). The experimental results derived from the evaluation criteria showed that there was no substantial difference between the software effort estimation results with and without outlier elimination. However, statistical analysis indicated that outlier elimination leads to a significant improvement in the estimation accuracy on the Stock data set (in case of some combinations of outlier elimination and effort estimation methods). In addition, although outlier elimination did not lead to a significant improvement in the estimation accuracy on the other data sets, our graphical analysis of errors showed that outlier elimination can improve the likelihood to produce more accurate effort estimates for new software project data to be estimated. Therefore, from a practical point of view, it is necessary to consider the outlier elimination and to conduct a detailed analysis of the effort estimation results to improve the accuracy of software effort estimation in software organizations.

On the value of outlier elimination on software effort estimation research

Context: Along with expert judgment, analogy-based estimation, and algorithmic methods (such as Function point analysis and COCOMO), Least Squares Regression (LSR) has been one of the most commonly studied software effort estimation methods. However, an effort estimation model using LSR, a single LSR model, is highly affected by the data distribution. Specifically, if the data set is scattered and the data do not sit closely on the single LSR model line (do not closely map to a linear structure) then the model usually shows poor performance. In order to overcome this drawback of the LSR model, a data partitioning-based approach can be considered as one of the solutions to alleviate the effect of data distribution. Even though clustering-based approaches have been introduced, they still have potential problems to provide accurate and stable effort estimates. Objective: In this paper, we propose a new data partitioning-based approach to achieve more accurate and stable effort estimates via LSR. This approach also provides an effort prediction interval that is useful to describe the uncertainty of the estimates. Method: Empirical experiments are performed to evaluate the performance of the proposed approach by comparing with the basic LSR approach and clustering-based approaches, based on industrial data sets (two subsets of the ISBSG (Release 9) data set and one industrial data set collected from a banking institution). Results: The experimental results show that the proposed approach not only improves the accuracy of effort estimation more significantly than that of other approaches, but it also achieves robust and stable results according to the degree of data partitioning. Conclusion: Compared with the other considered approaches, the proposed approach shows a superior performance by alleviating the effect of data distribution that is a major practical issue in software effort estimation.

Yeong-Seok Seo

Papers

Understanding Edge Computing: Engineering Evolution With Artificial Intelligence

Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

An empirical analysis of software effort estimation with outlier elimination

On the value of outlier elimination on software effort estimation research

AREION: Software effort estimation based on multiple regressions with adaptive recursive data partitioning