Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Issue Recommendation for Open Source Communities

In open source software development, a major challenge is the prioritization of new requirements as well as the identification of responsible developers for their implementation. Unlike conventional industrial software development, where requirements engineers have to explicitly define who implements what, in the context of open source development, developers (contributors) usually decide on their own which requirements to implement next. Contributors have to deal with a huge number of requirements where the recognition of the most relevant ones often becomes a crucial task with a high impact on the success of a software project. This fact defines our major motivation for the development of a prioritization tool for the ECLIPSE community which recommends relevant requirements (issues/bugs) to open source developers. Our tool uses real-world data from ECLIPSE in order to build a prediction model. We trained and tested our tool with different classifiers such as Naive Bayes (representing our baseline), Decision Tree, and Random Forest. The evaluation results indicate that the Random Forest classifier correctly predicts issues with a precision of 0.88 (F1-score 0.68). CCS CONCEPTS• Information systems → Recommender systems; • Humancentered computing → Open source software; • Computing methodologies → Machine learning approaches; Natural language processing; • Software and its engineering → Requirements analysis.
Discussion-skill Analytics with Acoustic, Linguistic and Psychophysiological Data

In this paper, we propose a system for improving the discussion skills of participants in a meeting by automatically evaluating statements in the meeting and effectively feeding back the results of the evaluation to them. To evaluate the skills automatically, the system uses both acoustic features and linguistic features of statements. It evaluates the way a person speaks, such as their “voice size,” on the basis of the acoustic features, and it also evaluates the contents of a statement, such as the “consistency of context,” on the basis of linguistic features. These features can be obtained from meeting minutes. Since it is difficult to evaluate the semantic contents of statements such as the “consistency of context,” we build a machine learning model that uses the features of minutes such as speaker attributes and the relationship of statements. In addition, we argue that participants’ heart rate (HR) data can be used to effectively evaluate their cognitive performance, specifically the performance in a discussion that consists of several Q&A segments (question-and-answer pairs). We collect HR data during a discussion in real time and generate machine-learning models for evaluation. We confirmed that the proposed method is effective for evaluating the discussion skills of meeting participants.
Rethinking Strategies of Hackathons to Increase Team's Creativity: Findings of a Qualitative Research

Hackathons are events that have become increasingly common around the world. This kind of event, described as a programming marathon, is based on problem-solving that can go beyond the technological boundary. This paper presents the findings of an international hackathon to aid its organizers to rethink their strategies to improve the development of the team’s creativity to solve the challenge proposed. The paper summarizes qualitative research based on interviews and observations which point out that the organizers should consider strategies to improve knowledge application, resolving conflicts, individual learning, and experienced emotions, during pre-hackathon as well as post-hackathon events. Our findings could leverage the innovation, creativity, and knowledge sharing and creation within hackathons.
Multivariate Time Series Forecasting with Deep Learning Proceedings in Energy Consumption

We propose to study the dynamic behavior of indoor temperature and energy consumption in a cold room during demand response periods. Demand response is a method that consists of smoothing demand over time, seeking to reduce or even stop consumption during periods of high demand in order to shift it to periods of lower demand. Such a system can therefore be tackled as the study of a time-series, where each behavioral parameter is a time-varying parameter. Different network topologies are considered, as well as existing approaches for solving multi-step ahead prediction problems. The predictive performance of short-term predictors is also examined with regard to prediction horizon. The performance of the predictors are evaluated using measured data from real scale buildings, showing promising results for the development of accurate prediction tools.
Of the Utmost Importance: Resource Prioritization in HTTP/3 over QUIC

Not even five years after the standardization of HTTP/2, work is already well underway on HTTP/3. This latest version is necessary to make optimal use of that other new and exiting protocol: QUIC. However, some of QUIC’s unique characteristics make it challenging to keep HTTP/3’s functionalities on par with those of HTTP/2. Especially the efforts on adapting the prioritization system, which governs how multiple resources can be multiplexed on a single QUIC connection, have led to some difficult to answer questions. This paper aims to help answer some of those questions by being the first to provide experimental evaluations and result comparisons for 11 different possible HTTP/3 prioritization approaches in a variety of simulation settings. We present some non-trivial insights, discuss advantages and disadvantages of various approaches, and provide results-backed actionable advice to the standardization working group. We also help foster further experimentation by contributing our complete HTTP/3 implementation, results dataset and custom visualizations to the community.
Extracting Ego-Centric Social Networks from Linked Open Data

Linked Open Data (LOD) refers to freely available data on the WWW that are typically represented using Resource Description Framework (RDF). LOD is an invaluable source of rich and structured information, and enables a wide range of new applications, such as Social Network Analysis (SNA). In this paper, we address the extraction of social networks from LOD using SPARQL language, and we present various patterns to extract ego-centric networks. We also present two case studies: i) influence networks of intellectuals, and ii) co-acting networks, to demonstrate the applicability and usefulness of the approach. CCS CONCEPTS • Information systems → Data extraction and integration; Social networks; Resource Description Framework (RDF).
Evolutionary Fuzzy Logic-based Model Design in Predicting Coronary Heart Disease and Its Progression

Various data-driven models are often involved in epidemiological studies, wherein the availability of data is constantly increasing. Accurate and, at the same time, interpretable models are preferable from the practical point of view. Finding simple and compact dependences between predictors and outcome variables makes it easier to understand necessary interventions and preventive measures. In this study, we applied a Fuzzy Logic-based model, which meets these requirements, to predict the coronary heart disease (CHD) progression during a 30-year follow-up. The Fuzzy Logic-based model was automatically designed with an ad hoc Genetic Algorithm using the data from the Kuopio Ischemic Heart Disease Risk Factor (KIHD) Study, a Finnish cohort of 2682 men who were middle-aged at baseline in 1980s. Using cross-validation, we found out that the sample from the KIHD study is heterogeneous and after filtering out 10% of outliers, the predictive accuracy increased from 65% to 73%. The generated rule bases include 19 fuzzy rules on average with maximum 7 variables in one rule from the initial set of 638 predictor variables. The selected predictors of CHD progression are informative and diverse representing physical aspects, behavior, and socioeconomics. The Fuzzy Logic-based model creates a comprehensive set of predictors that enables us to better understand the complexity of illnesses and their progression. Moreover, the Fuzzy Logic-based model has potential to provide tools to analyse and deal with heterogeneity in large cohorts.
Knowledge Requirements for Sustainable Smart Service Design

Recent research have addressed the topic of smart services from distinct angles, covering both technical and business aspects. However, a holistic approach in development processes of such services have yet to be fully covered. Therefore, this paper proposes an elicitation of requirements process as the initial step of a smart service design approach. The process takes information and knowledge needs as its core element for development, also considering customer centricity, service lifecycle, and sustainability concerns. A text mining tool was used to discover the unknown knowledge requirements from different text-data sources presented in a case ecosystem. After a co-occurrence analysis performed by our text mining software, we extracted the most relevant natural linguistic elements, which are expressed as knowledge requirements. The proposed elicitation process aims to lay the foundations for further propositions with a holistic point of view. Future research could aim the application of other technologies and methods for service design, as well as a broader approach in business processes and interdisciplinary cooperation.
A Smart Socks System for Running Gait Analysis

Running gait analysis is an often used tool for running performance improvement and injury prevention due to an incorrect running style. The typical gait analysis methods are unavailable to amateur runners outside of special clinics due to their relatively high cost. Smart socks are a relatively cheap gait analysis method that can be used by amateur runners and professional athletes for running performance improvement. This paper presents a smart socks system for feet plantar pressure measurement during running, as well as methods for characterisation of the acquired plantar pressure measurement for running gait analysis. The validation of the smart socks with a Pedar insole system is described, and the measurement analysis methods are demonstrated by practical running tests. The validation tests demonstrated good temporal and pressure sensing characteristics of the system, while the simplicity of the developed gait analysis methods was demonstrated in the practical tests.
Exploring Differences in the Impact of Users’ Traces on Arabic and English Facebook Search

This paper proposes an approach on Facebook search in Arabic and English, which exploits several users’ traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (signals) can play a vital role in improving Arabic and English Facebook search. Firstly, we identify polarities (positive or negative) carried by the textual signals (e.g. comments) and non-textual ones (e.g. the reactions love and sad) for a given Facebook posts. Therefore, the polarity of each comment expressed in Arabic or in English on a given Facebook post, is estimated on the basis of a neural sentiment model. Secondly, we group signals according to their complementarity using attributes (features) selection algorithms. Thirdly, we apply learning to rank (LTR) algorithms to re-rank Facebook search results based on the selected groups of signals. Finally, experiments are carried out on 13,500 Facebook posts, collected from 45 topics, for each of the two languages. Experiments results reveal that Random Forests was the most effective LTR approach for this task, and for the both languages. However, the best appropriate features selection algorithms are ReliefFAttributeEval and InfoGainAttributeEval for Arabic and English Facebook search task, respectively.
Debugging Flaky Tests on Web Applications

Testing web applications is a challenging practice because it involves managing asynchronous requests between clients and servers, the integration of heterogeneous technologies, and concurrent accesses to the resources. Therefore, rerunning the test cases of these applications under the same conditions is difficult as one test case can be executed in many different ways according to several environmental factors like memory, screen size or network. Moreover, some of these test cases could be flaky, i.e., due to environmental factors the test outcome can vary even though the application did not change. Understanding which factors are the root cause of flakiness is very important for web developers to both prevent and fix flakiness. This paper introduces a technique to locate the root causes of flakiness based on a characterization of the different environmental factors that are not controlled during the testing of web applications. The root cause of flakiness is located by a spectrum-based localization technique that analyses the execution of the same flaky test under different environmental factors that can trigger the flakiness. The technique is illustrated on an educational web platform named FullTeaching.
Generating Objective Summaries of Sports Matches Using Social Media

Social media has become a platform where users post their messages about a wide range of topics, making it a useful source of information to summarize events such as sports matches. Previous summaries of sports matches generated using social media tended to be biased towards one of the teams, due to a high proportion of the posts used being from fans of the teams involved. This is problematic because in general people desire summaries that are free from bias and objective. To remedy this problem and generate higher quality summaries, we propose two approaches. The first is a function maximization method which measures the objectivity of each post based on its constituent words. The second is a neural network-based approach, where we use an encoder-decoder architecture. Then, we compare them with an existing approach and show promising results that indicate the effectiveness of our methods.CCS CONCEPTS • Information systems → Social networks; • Computing methodologies → Natural language generation; Information extraction.
Identifying Different Types of Social Ties in Events from Publicly Available Social Media Data

Tie strength is an essential concept in identifying different kind of social ties - strong ties and weak ties. Most present studies that evaluated tie strength from social media were carried out in a controlled environment and used private/closed social media data. Even though social media has become a very important way of networking in professional events, access to such private social media data in those events is almost impossible. There is very limited research on how to facilitate networking between event participants and especially on how to automate this networking aspect in events using social media. Tie strength evaluated using social media will be key in automating this process of networking. To create such tie strength based event participant recommendation systems and tools in the future, first, we need to understand how to evaluate tie strength using publicly available social media data. The purpose of this study is to evaluate tie strength from publicly available social media data in the context of a professional event. Our case study environment is community managers’ online discussions in social media (Twitter and Facebook) about the CMAD2016 event in Finland. In this work, we analyzed social media data from that event to evaluate tie strength and compared the social media analysis-based findings with the individuals’ perceptions of the actual tie strengths of the event participants using a questionnaire. We present our findings and conclude with directions for future work.
Consumers' Willingness to Purchase High Animal-welfare Beef Products in Japan: Exploratory Research based on the Theory of Planned Behavior

Agricultural industry needs to face both increasing demand from a growing population and transform in order to enhance its sustainability. Animal welfare, an aspect of this transformation, is still an unfamiliar concept for consumers in Japan, although this is expected to catch up with the global trend. Researchers have been working around the world to explore consumer behavior in markets, but few such studies have been performed in Japan. This study aimed to explore consumer behavior concerning high animal-welfare products in Japan, using the Theory of Planned Behavior (TPB). An online questionnaire was used to identify consumer characteristics and perceived attributes of high animal-welfare products among 620 consumers. We found that awareness of animal welfare was still low among Japanese consumers, and was not related to demographic characteristics. Two components out of three which are considered in TPB, attitude and social norm, were likely related to consumers’ willingness to purchase high animal-welfare products. Consumers’ empathy with, and psychological responses to, farmers and animals are suggested to be related to their willingness to purchase.
Semi-supervised text classification with deep convolutional neural network using feature fusion approach

Supervised learning algorithms employ labeled training data for classification purposes while obtaining labeled data for large datasets is costly and time consuming. Semi-supervised learning algorithms, on the contrary, use a small set of labeled data and a large set of unlabeled data to improve predication performance and thus may be a good alternative to supervised learning algorithms for large text datasets. Although many semi-supervised learning algorithms have been proposed in the data science literature, most of these algorithms are not feasible for discrete and unstructured text data.This paper aims to improve classification accuracy of semi-supervised learning algorithms applied to text data. To achieve this goal, a novel design for convolutional neural network is employed in a co-training semi-supervised learning algorithm which adds augmented data as the second input of the convolutional neural network to predict labels of text data. we also propose a novel approach for partitioning the dataset into independent views via topic modeling to train independent classifiers. In so doing, neighbour classifiers are found and confident predictions of unlabeled data are fused into labeled data. The prediction accuracy of the combined algorithm is then compared to the state-of-the-art supervised and semi-supervised learning algorithms. Our findings show that the proposed combined algorithm outperforms the supervised and semi-supervised algorithms in terms of prediction accuracy. CCS CONCEPTS• Information systems → Content analysis and feature selection.
A Graph-Theoretic Embedding-Based Approach for Rumor Detection in Twitter

In this paper, we present a graph-theoretic embedding-based approach to model user-generated contents in online social media for rumor detection. Starting with a small set of seed rumor words of four different lexical categories, we generate a words co-occurrence graph and apply centrality-based analysis to identify prominent rumor characterizing words. Thereafter, word embedding is applied to represent each category of seed words as numeric vectors and to train three different classification models for rumor detection. The performance of the proposed approach is empirically evaluated over two versions of a benchmark dataset. The proposed approach is also compared with one of the state-of-the-art methods for rumor detection and performs significantly better. CCS CONCEPTS • Information systems $\rightarrow$ Data analytics; • Human-centered computing $\rightarrow$ Social network analysis; • Computing methodologies $\rightarrow$ Supervised learning by classification.
Visualization Techniques for Network Analysis and Link Analysis Algorithms

Military applications require big distributed, disparate, multi-sourced and real-time data that have extremely high rates, high volumes, and diverse types. Warfighters need deep models including big data analytics, network analysis, link analysis, deep learning, machine learning, and artificial intelligence to transform big data into smart data. Explainable deep models will play a more essential role for future warfighters to understand, interpret, and therefore appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners when facing complex threats. In this paper, we show how visualization is used in two typical deep models with two use cases: network analysis, which addresses how to display and present big data both in the exploratory and discovery process, and link analysis, which addresses how to display and present the smart data generated from these processes. By using various visualization tools such as D3, Tableau, and lexical link analysis, we derive useful information from discovering big networks to discovering big data patterns and anomalies. These visualizations become intepretable and explainable deep models that can be readily used by warfighters and decision makers to achieve the sense making and decision making superiority.
Interdependent Multi-layer Spatial Temporal-based Caching in Heterogeneous Mobile Edge and Fog Networks

Applications and services hosted in the mobile edge/fog networks today (e.g., augmented reality, self-driving, and various cognitive applications) may suffer from limited network coverage and localized congestion due to dynamic mobility of users and surge of traffic demand. Mobile opportunistic caching at the edges is expected to be an effective solution for bringing content closer and improve the quality of service for mobile users. To fully exploit the edge/fog resources, the most popular contents should be identified and cached. Emerging research has shown significant importance of predicting content traffic patterns related to users’ mobility over time and locations which is a complex question and still not well-understood. This paper tackles this challenge by proposing K-order Markov chain-based fully-distributed multi-layer complex analytics and heuristics to predict the future trends of content traffic. More specifically, we propose the multilayer real-time predictive analytics based on historical temporal information (frequency, recency, betweenness) and spatial information (dynamic clustering, similarity, tie-strength) of the contents and the mobility patterns of contents’ subscribers. This enables better responsiveness to the rising of newly high popular contents and fading out of older contents over time and locations. We extensively evaluate our proposal against benchmark (TLRU) and competitive protocols (SocialCache, OCPCP, LocationCache) across a range of metrics over two vastly different complex temporal network topologies: random networks and scale-free networks (i.e. real connectivity Infocom traces) and use Foursquare dataset as a realistic content request patterns. We show that our caching framework consistently outperforms the state-of-the-art algorithms in the face of dynamically changing topologies and content workloads as well as dynamic resource availability.