Mining developer communication data streams

This paper explores the concepts of modelling a software development project as a process that results in the creation of a continuous stream of data. In terms of the Jazz repository used in this research, one aspect of that stream of data would be developer communication. Such data can be used to create an evolving social network characterized by a range of metrics. This paper presents the application of data stream mining techniques to identify the most useful metrics for predicting build outcomes. Results are presented from applying the Hoeffding Tree classification method used in conjunction with the Adaptive Sliding Window (ADWIN) method for detecting concept drift. The results indicate that only a small number of the available metrics considered have any significance for predicting the outcome of a build

Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams

In this research, we apply ensembles of Fourier encoded spectra to capture and mine recurring concepts in a data stream environment. Previous research showed that compact versions of Decision Trees can be obtained by applying the Discrete Fourier Transform to accurately capture recurrent concepts in a data stream. However, in highly volatile environments where new concepts emerge often, the approach of encoding each concept in a separate spectrum is no longer viable due to memory overload and thus in this research we present an ensemble approach that addresses this problem. Our empirical results on real world data and synthetic data exhibiting varying degrees of recurrence reveal that the ensemble approach outperforms the single spectrum approach in terms of classification accuracy, memory and execution time

Follow-back Recommendations for Sports Bettors: A Twitter-based Approach

Social network based recommender systems are powered by a complex web of social discussions and user connections. Short text microblogs e.g. Twitter present powerful frameworks for information consumption, due to their real-time nature in content throughput as well as user connections. Therefore, users on such platforms consume the disseminated content to a greater or lesser extent based on their interests. Quantifying this degree of interest is a difficult task based on the amount of information that such platforms generate at any given time. Thus, the generation of personalized profiles based on the Degree of Interest (DoI) that users have towards certain topics in such short texts presents a research problem. We address this challenge by following a two-step process in generation of personalized sports betting related user profiles in tweets as a case study. We (i) compute the Degree of Interest in Sports Betting (DoiSB) of tweeters and (ii) affirm this DoiSB by correlating it with their friendship network. This is an integral process in the design of a short text based recommender systems for users to follow i.e follow-back recommendations as well as content-based recommendations relying on the interests of users on such platforms. In this paper, we described the DoiSB computation and follow-back recommendation process by building a vector representation model for tweets. We then use this model to profile users interested in sports betting. Experiments using real Twitter dataset geolocated to Kenya shows the effectiveness of our approach in the identification of tweeter\u27s DoiSBs as well as their correlation with their friendship network

TOPICAL EXPRESSIVITY IN SHORT TEXTS

With each passing minute, online data is growing exponentially. A bulk of such data is generated from short text social media platforms such as Twitter. Such platforms are fundamental in social media knowledge-based applications like recommender systems. Twitter, for example, provides rich real-time streaming information. Extracting knowledge from such short texts without automated support is not feasible due to Twitter\u27s platform streaming nature. Therefore, an automated method for comprehending patterns in such text is a need for many knowledge systems. This paper provides solutions to generate topics from Twitter data. We present several techniques related to topical modelling to identify topics of interest in short texts. Topic modelling is inherently problematic in shorter texts with very sparse vocabulary in addition to the informal language used in their dissemination. Such findings are informative in knowledge extraction for social media-based recommender systems as well as in understanding tweeters over time

SENTIPEDE: A Smart System for Sentiment-based Personality Detection from Short Texts

Personality distinctively characterises an individual and profoundly influences behaviours. Social media offer the virtual community an unprecedented opportunity to generate content and share aspects of their life which often reflect their personalities. The interest in using deep learning to infer traits from digital footprints has grown recently; however, very limited work has been presented which explores the sentiment information conveyed. The present study, therefore, used a computational approach to classify personality from social media by gauging public perceptions underlying factors encompassing traits. In the research reported in this paper, a Sentiment-based Personality Detection system was developed to infer trait from short texts based on the 'Big Five' personality dimensions. We exploited the spirit of Neural Network Language Model (NNLM) by using a uni ed model that combines a Recurrent Neural Network named Long Short-Term Memory (LSTM) with a Convolutional Neural Network (CNN). We performed sentiment classi cation by grouping short messages harvested online into three categories, namely positive, negative, and nonpartisan. This is followed by employing Global Vectors (GloVe) to build vectorial word representations. As such, this step aims to add external knowledge to short texts. Finally, we trained each variant of the models to compute prediction scores across the ve traits. Experimental study indicated the e ectiveness of our system. As part of our investigation, a case study was carried out to investigate the existing correlation of personality traits and opinion polarities which employed the proposed system. The results support the prior ndings of the tendency of persons with the same traits to express sentiments in similar ways

ISBS 2018 AUCKLAND CONFERENCE WORKSHOPS PROGRAMME

FORCEDECKS WORKSHOP - COMMERCIALISATION & FUNDING by Philip Graham Smith This workshop aims to improve your ability to attract and create commercialisation and funding opportunities. This workshop will challenge delegates to identify their real areas of expertise and consider ways in which they can attract and create funding opportunities. The aim is to help academics of all ages to focus their expertise, to manage their time more effectively and to explore new avenues to make their careers more rewarding, fulfilling and hopefully less stressful. Having been a former Head of Department and Associate Head of School (Business & Engagement), Dr Graham-Smith has been in the trenches and acknowledges the increasing demands and pressures of working in academia. The workshop will help delegates to strip back the various aspects of their roles, and to examine ways in which their teaching, research, consultancy and funding expectations can be managed successfully. Phil will be reflective on his own career and share experiences of working in academia, professional sport and private industry. SPRINZ WORKSHOP - ORAL PRESENTATION FEEDBACK by Joshua McGeown, Gillian Weir, Professor Mike McGuigan and SPRINZ PhD students This workshop aims to help you engaging your audience during your ISBS presentation. This workshop aims to provide delegates with tips and feedback as to how best present their research for the ISBS 2018 congress. This interactive workshop will help delegates to learn how to distill and communicate complex ideas, structure your narrative and how to best visualize your data. Participants are encouraged to bring their ISBS presentations to practice and receive constructive feedback. NZ HERALD WORKSHOP - HOW TO WORK WITH THE MEDIA TO AMPLIFY YOUR WORK by Dylan Cleaver, Editor at large with the New Zealand Herald This workshop will help delegates be able to interact with media to be able to amplify their work. Never before has there been so much attention given to the injury toll in elite sport, with the spotlight firmly centered on head injuries and the potential for long-term cognitive damage to those afflicted. With so much important research being done in the field of sports injury, it is important to know how to work with the media to highlight it. This workshop aims to give a brief overview of the fast-changing modern media landscape. It will offer advice as to how to establish contacts in the media and how to use those contacts wisely. It will demonstrate how to get your key messages across using simple language, without dumbing down the issue. It will traverse ethical issues and, finally, what to do when the message goes wrong. Attendees will use the lessons learnt from the examples, to workshop during the session how they can work with media to amplify their work. WORKSHOP - JAPAN COLLABORATION by Sayumi Iwamoto, Erika Ikeda, Ryu Nagahara, and Aaron Uthoff Do you want to share your experience with other researchers who are keen to conduct international research collaboration? The workshop will share experiences and key tips to enable successfully working together. “There are many positives with working with Japanese researchers, but the one that stands out the most to me is their willingness to share knowledge and lend a helping hand.” (Aaron Uthoff) AUT ENGINEERING WORKSHOP - AI CHALLENGES by Boris Bacic & Russell Pears from Auckland University of Technology Engineering School This workshop will help you to consider pushing your boundaries of biomechanics and sport science by embracing artificial intelligence (Dr Boris Bačić and Assoc. Prof Russel Pears, Auckland University of Technology, NZ). Pushing the boundaries of biomechanics and sport science also means embracing artificial intelligence (AI) to advance and augment ways in which sport is coached, played, promoted, broadcasted and commercialised. Technologies capable of capturing human motion enable the advancement of research and can create strategic differences in elite sport, which is reflected by their increasing presence in the growing market of sport gadgets, exergames and rehabilitation technologies. Data-driven machine-learning AI approaches have the potential to provide insights from data, find patterns in specific contexts, generate knowledge, validate expert’s common-sense rules, and offload support decisions and automate cognitive activities. The workshop will provide a theoretical introduction and a set of analytical and model-designing visual tools for getting started. For those interested in Matlab or other languages, code samples will be provided. The participants will be able to use free open source software alternatives as part of hands-on exercises in a supervised lab. SPRINGER WORKSHOP - WHAT MAKES A SUCCESSFUL PAPER – AN EDITOR’S PERSPECTIVE by Steve McMillan from Springer’s Sports Medicine journal This workshop will help delegates increase their likelihood of success in publishing in journals such as Sports Medicine. From a compelling cover letter to a concise conclusion, Sports Medicine’s Co-Editor in Chief, Steve McMillan, will provide an editor’s perspective on what makes a successful paper. Sports Medicine receives over 600 submissions a year and can publish only a quarter of these … How do the editors decide which manuscripts to send to peer review? Which manuscripts survive peer review? What details are essential to enable readers to best understand your research and allow for potential replication? What information is required from an ethical perspective? Why do word counts matter anyway?! This interactive workshop will guide you on how to produce an impressive manuscript and increase your chances of getting published in a reputable journal. NORAXON WORKSHOP - ELECTROMYOGRAPHY IN SPORTS PERFORMANCE by Coleman Bessert and Erin Feser from NORAXON. Noraxon USA (www.noraxon.com) will be hosting a workshop on electromyography (EMG) use in sports performance settings. “You will be able to develop a better understanding of how EMG fits into an athlete monitoring program or research investigation by learning what can, and cannot, be determined with EMG data and reporting. Participants will see hands-on use of precision EMG systems and biomechanics analysis software with practical, sport-specific examples.” Erin Feser , Director of Education for Noraxon USA

Precise guidance to dynamic test generation

Dynamic symbolic execution has been shown an effective technique for automated test input generation. However, its scalability is limited due to the combinatorial explosion of the path space. We propose to take advantage of data flow analysis to better perform dynamic symbolic execution in the context of generating test inputs for maximum structural coverage. In particular, we utilize the chaining mechanism to (1) extract precise guidance to direct dynamic symbolic execution towards exploring uncovered code elements and (2) meanwhile significantly optimize the path exploration process. Preliminary experiments conducted to evaluate the performance of the proposed approach have shown very encouraging results