Miracle-dz/UniFAR-FTUs huggingface dataset:
New Concepts for Presence and Availability in Ubiquitous and Mobile Computing - Enabling Selective Availability through Stream-Based Active Learning

Modern Computer-mediated Communication technologies like Instant Messaging (IM) systems enable spontaneous communication over distance. With the advances in Mobile and Ubiquitous Computing, these technologies move away from the desktop computers of our offices, and become more and more pervasive and interwoven with our daily lives. The introduction of these great possibilities to communicate from everywhere with everyone however comes at a cost: The cost of constantly being available to everybody, everywhere, leading to an increasing number of interruptions in our daily tasks. The challenge is, that current technology does not empower users to manage their availability in an adequate manner. Most IM clients for example, only support one single online status that needs to be managed manually by the user. In this work I am founding the concepts of Presence and Availability on a deep understanding of human privacy needs, derived from literature. Based on this foundation, I show how the selective and dynamic nature of privacy is not sufficiently reflected in current systems. Based on two user studies I reveal patterns for selective information disclosure and present an analysis of Selective Availability needs. With the collected study data, I further show that Selective Availability for nomadic users can be predicted based on sensors installed on the users’ laptop computer with a good accuracy through machine learning. As the personalised nature of the data requires new concepts for building an adaptive system, I introduce the LILOLE Framework. The LILOLE Framework outlines the concept of an adaptive system that relies on stream-based active learning to continuously learn and automatically adapt fine-grained personal availability preferences for individual users. The concept is validated through a proof-of-concept implementation and an evaluation based on real user data. In comparison to related work, the presented work is one of very few examples that goes beyond the pure analysis of the predictability, but provides a concept and an implementation of a real system as validation. My approach is novel by combining concepts from Data Stream Mining and Active Learning to predict availability, thus making it very flexible for different settings. This way I am able to address the selective and dynamic nature of availability preferences for nomadic users.
Parental Autonomy Support and Psychological Well-Being in Tibetan and Han Emerging Adults: A Serial Multiple Mediation Model

A growing body of research has explored well-being in diverse cultural contexts, and indicates that the definition and perception of well-being vary according to cultural context. Little is known, however, about whether intercultural differences in China (i.e., Tibetan and Han) lead to different perceptions of well-being and how social contexts and personal characteristics are associated with well-being in Tibetan and Han emerging adults. Using a self-determination framework, the current study examines the relationship between parental autonomy support (PAS) and psychological well-being (PWB) in Tibetan and Han emerging adults in China. Guided by implicit theory and self-regulatory theory, we propose a serial multiple mediation model of growth mindset and grit in the association between PAS and PWB. Propensity score matching was used to balance the two ethnic groups in terms of age, gender, socioeconomic status (SES), with a ratio of one to two. Finally, 59 Tibetan (71.2% girls) and 118 Han (69.5% girls) emerging adults aged from 18 to 25 years were included in the current study, and completed an online questionnaire survey. Findings suggest that (a) Tibetan emerging adults perceived higher levels of PWB than their peers from the Han ethnic group; (b) a serial multiple mediation model for the association between PAS and PWB was supported in Han emerging adults; (c) the indirect effects between PAS and PWB varied between Tibetan and Han emerging adults. Our findings suggest that PAS and grit contribute to PWB of emerging adults in both cultural contexts, whereas growth mindset may be beneficial for Han emerging adults only.
Race Strategies of Open Water Swimmers in the 5-km, 10-km, and 25-km Races of the 2017 FINA World Swimming Championships

Despite literature on the pacing strategies of endurance sports, there is an existing lack of knowledge about the swimmers’ tactical decisions in the open water races. The aims of the present research were (1) to compare the pacing profiles and tactical strategies of successful elite open water swimmers (men and women) in the 5-km, 10-km, and 25-km races and (2) to relate these pacing strategies to the end race results. Intermediate split times, positions and gaps with leaders of the first ten swimmers classified in the 2017 FINA World Swimming Championships races were collected from the public domain and were related to the finishing positions. Overall swimming velocities of the 5-km races were faster than the 10-km (δ 0.03 ± 0.03 m/s) and the 25-km (δ 0.14 ± 0.01 m/s) events with male swimmers achieving relatively faster mean velocities than females in the 5-km (δ 0.12 ± 0.01 m/s) compared to the 25-km (δ 0.08 ± 0.01 m/s) events. Medallist swimmers achieved moderate faster overall velocities than finalists in the 25-km races (0.01 ± 0.01 m/s) only. Inter-level differences were detected in selected splits for each race distance. Pacing profiles presented lap to lap velocity improvements in the 5-km and men’s 10-km races (from +0.02 ± 0.00 to +0.11 ± 0.01 m/s) but also mid-race decreases in the women’s 10-km and on the 25-km races. Successful swimmers were located in the leading positions of the 5-km races but at mid-group in the first part of the 10-km and 25-km races, with time gaps with leaders of 15–20 s. Faster lap swimming velocities, mid-race leading positions and shorter time-gaps were only related to the finishing positions in the last lap of the 10-km and in the three last laps of the 25-km events, but also in the first lap of the women’s 5-km race. Despite different mid-race positioning, successful open water swimmers typically presented negative pacing profiles, a consistent control of mid-race gaps with leaders (15–20 s maximum) and great spurts (4–6% faster than mean race velocities) at the end of races. Coaches and swimmers should be aware of the different race dynamics depending to the event distance in order to select optimal race strategies.
A Systematic Review and Meta-Analysis of the Prevalence of Methamphetamine Abuse in Iranian High School Students

Background and Aim: Abuse of drugs such as methamphetamine is one of the most important problems in high-school children and adolescents according to the World Health Organization (WHO), which has mentioned it as a concerning event in the world. Therefore, the purpose of this study was to estimate the prevalence of methamphetamine abuse in Iranian students using the meta-analysis method on studies conducted in Iran. Materials and Methods: To select the studies, a systematic search was performed on leading databases, including ISI web of science, PubMed, Scopus, Embase, PsycINFO and PROSPERO with no language limits from their inception to 31 Jan 2018. Furthermore, local databases, including SID, Magiran, and IRANDOC were searched systematically using both Persian and English languages from their inception to 31 Jan 2018. Results: We found a total of 828 potentially relevant studies, of which 30 met our criteria, and 7 articles (7452 students, 3063 females and 4389 males) were included. The pooled prevalence of methamphetamine use in Iranian students was 0.016% (95% CI: 0.06-0.041, P=0.00). The heterogeneity was low (I2 =44.41, d.f = 6), which shows that about 94.41 of the total observed variance was true variance between the studies. Conclusion: Although the prevalence of methamphetamine in high-school students is lower than in other drugs, a growing pattern in Iranian high-school students in recent years is a serious warning to authorities and families. Increasing families and students’ knowledge about the harmful effects of this drug can be an effective approach to reducing its prevalence in young people, especially high-school students.
A Comparative Study of Android Repackaged Apps Detection Techniques

Apps repackaging has become a serious problem which not only violates the copyrights of the original developers but also destroys the health of the Android ecosystem. A recent study shows that repackaged apps share a significant proportion of malware samples. Therefore, it is imperative to detect repackaged apps in various app markets. Although many detection technologies have been proposed, there lacks a systematic comparison among them. One reason is that many detection tools are not publicly available, and therefore little is known about their robustness and effectiveness. In this paper, we fill this gap by 1) analyzing these repackaging detection technologies; 2) implementing these detection techniques; 3) comparing them in terms of various metrics using real repackaged apps. The analysis and the experimental results reveal new insights, which shed light on the research of repackaged apps detection.
Survival and Behavior of Encapsulated Probiotics (Lactobacillus plantarum) in Calcium-Alginate-Soy Protein Isolate-Based Hydrogel Beads in Different Processing Conditions (pH and Temperature) and in Pasteurized Mango Juice

Hybrid alginate-soy protein isolate-based hydrogel beads were prepared and evaluated to enhance the survival of the encapsulated probiotics (Lactobacillus plantarum) during heat processing to incorporate in mango juice. The solutions of sodium alginate-soy protein isolate (SA-SPI) with probiotic cells were dropped into the gelation bath containing calcium chloride (3% w/v) solution to develop various types of hydrogel beads. The level of survival of probiotics in encapsulated beads under acidic conditions (pH 2, 3, and 6.5) and bile salt (0.5 and 1.0% w/v) was evaluated. The survival of the encapsulated probiotics to thermal processing was evaluated by treating the beads in saline solution (0.9% w/v) at 30, 50, 63, and 72°C. The encapsulated probiotic bacteria were found alive even after treatment at 72°C for 90 s. Most of the free cells did not survive at the temperature higher than 50°C and very low pH (pH 2 and 3). The survival of probiotic cells was found higher with the hybrid hydrogel beads containing alginate and soy protein isolate (1:8 w/w). Furthermore, mango juice fortified with encapsulated L. plantarum in hydrogel beads was subjected to thermal pasteurization at 72°C for 90 s.
How Stable Are Eclipse Application Framework Internal Interfaces?

Eclipse framework provides two interfaces: stable interfaces (APIs) and unstable interfaces (non-APIs). Despite the non-APIs being discouraged and unsupported, their usage is not uncommon. Previous studies showed that applications using relatively old non-APIs are more likely to be compatible with new releases compared to the ones that used newly introduced non-APIs; that the growth rate of non-APIs is nearly twice as much as that of APIs; and that the promotion of non-API to APIs happens at a slow pace since API providers have no assistance to identify public interface candidates. Motivated by these findings, our main aim was to empirically investigate the entire population (2,380K) of non-APIs to find the non-APIs that remain stable for a long period of time. We employ cross-project clone detection to identify whether non-APIs introduced in a given Eclipse release remain stable over successive releases. We provide a dataset of 327K stable non-API methods that can be used by both Eclipse interface providers as possible candidates of promotion. Instead of promoting non-APIs which are too fine-grained, we summarized the non-API methods groups in given classes that are stable together and present class-level non-APIs that possible candidates promotion. We have shown that it is possible to predict the stability of a non-API in subsequent Eclipse releases with a precision of $\ge 56$%, a recall of $\ge 96$% and an AUC of $\ge 92$% and an F-measure of $\ge 81$%. We have also shown that the metrics of length of a method and number of method parameters in a non-API method are very good predictors for the stability of the non-API in successive Eclipse releases. The results provided can help the API providers to estimate a priori how much work could be involved in performing the promotion.
Efficacy and Safety of Safflower Yellow in Early Diabetic Nephropathy: A Meta-Analysis

Background Diabetic nephropathy (DN) is a major cause of end-stage renal disease. In order to palliate renal function impairment and reduce kidney related mortality, it is crucial to treating DN patients at the early stage. This study aims to assess the efficacy and safety of conventional therapy combined with safflower yellow versus conventional therapy alone in early DN patients. Methods A meta-analysis of randomized controlled trials that compared safflower yellow plus conventional therapy with conventional therapy alone in early DN patients was conducted. Papers were searched using the electronic databases and reference lists. Two reviewers working independently extracted relevant data and carried out risk-of-bias assessments. Statistical analysis was undertaken in Review Manager 5.3. Results Fourteen trials (1,072 patients) were included in the meta-analysis. Conventional therapy combined with safflower yellow was associated with a higher effective rate (RD, 0.24; 95% CI, 0.17 to 0.30) and a greater decline in urinary albumin excretion rates (SMD, -1.34; 95% CI, -1.77 to -0.92), fasting blood glucose (MD, -0.57; 95% CI, -0.98 to -0.16), serum creatinine (MD, -12.36; 95% CI, -14.66 to -10.06), and blood urea nitrogen (SMD, -0.93; 95% CI, -1.13 to -0.73) in the subgroup with a follow-up time > 15 days. The incidence of adverse events did not differ significantly between these two regimens (RD, -0.01; 95% CI, -0.03 to 0.01). Findings were similar in the subgroup with a follow-up time < 15 days. Conclusions Conventional therapy combined with safflower yellow had a more beneficial effect than conventional therapy alone in early DN patients. There were significant differences in effective rate, urinary albumin excretion rates, fasting blood glucose, serum creatinine, and blood urea nitrogen between the two regimens and no significant difference in adverse events. More randomized controlled research using standardized protocols would be needed in the future to compare these two regimens.
The Effect of Mind-Body Therapies on Insomnia: A Systematic Review and Meta-Analysis

Background/Purpose Sleep plays an important role in individuals' health. The functions of the brain, the cardiovascular system, the immune system, and the metabolic system are closely associated with sleep. As a prevalent sleep disorder, insomnia has been closely concerned, and it is necessary to find effective therapies. In recent years, a growing body of studies has shown that mind-body therapies (MBTs) can improve sleep quality and ameliorate insomnia severity. However, a comprehensive and overall systematic review has not been conducted. In order to examine the effect of MBTs on insomnia, we conducted a systematic review and meta-analysis evaluating the effects of MBTs on sleep quality in healthy adults and clinical populations. Methods PubMed, EMBASE, the Cochrane Library, and review of references were searched up to July 2018. English language studies of all designs evaluating the effect of MBTs on sleep outcomes in adults with or without diseases were examined. To calculate the SMDs and 95% CIs, we used a fixed effect model when heterogeneity was negligible and a random effect model when heterogeneity was significant. Results 49 studies covering 4506 participants published between 2004 and 2018 were identified. Interventions included meditation, tai chi, qigong, and yoga which lasted 4 to 24 weeks. The MBTs resulted in statistically significant improvement in sleep quality and reduction on insomnia severity but no significant effects on sleep quantity indices, which were measured by sleep diary or objective measures. We analyzed the effects of tai chi and qigong separately as two different MBTs for the first time and found that qigong had a slight advantage over tai chi in the improvement of sleep quality. Subgroup analyses revealed that the effect of MBTs on sleep quality in healthy individuals was larger than clinical populations. The effect of MBTs might be influenced by the intervention duration but not the frequency. Conclusions MBTs can be effective in treating insomnia and improving sleep quality for healthy individuals and clinical patients. More high-quality and well-controlled RCTs are needed to make a better conclusion in further study.
COLOR: Correct Locator Recommender for Broken Test Scripts using Various Clues in Web Application

Test automation tools such as Selenium are commonly used for automating end-to-end tests, but when developers update the software, they often need to modify the test scripts accordingly. However, the costs of modifying these test scripts are a big obstacle to test automation because of the scripts’ fragility. In particular, locators in test scripts are prone to change. Some prior methods tried to repair broken locators by using structural clues, but these approaches usually cannot handle radical changes to page layouts. In this paper, we propose a novel approach called COLOR (correct locator recommender) to support repairing broken locators in accordance with software updates. COLOR uses various properties as clues obtained from screens (i.e., attributes, texts, images, and positions). We examined which properties are reliable for recommending locators by examining changes between two release versions of software, and the reliability is adopted as the weight of a property. Our experimental results obtained from four open source web applications show that COLOR can present the correct locator in first place with a 77% – 93% accuracy and is more robust against page layout changes than structure-based approaches.
A Splitting Strategy for Testing Concurrent Programs

Reachability testing is an important approach to testing concurrent programs. It generates and exercises every partially ordered synchronization sequence automatically and on-the-fly without constructing a static model and saving any test history. However, test sequences generated by existing reachability testing are ineffective in detecting concurrency faults involved in access anomalies, such as accessing shared variables without proper protections or synchronizations, incorrectly shrinking or shifting critical regions, etc. In this paper, we present a splitting strategy as well as a prototype tool called SplitRichTest for revealing such concurrency faults. SplitRichTest adopts the framework of conventional reachability testing while implementing our splitting strategy. By splitting relevant codes, SplitRichTest generates and exercises fine-grained synchronization sequences. The key ingredient of SplitRichTest is an efficient heuristic algorithm to select and sort candidate splitting points. We conducted an empirical study on representative concurrent Java programs and evaluated the effectiveness of SplitRichTest using mutation testing. Experimental results show that our splitting strategy facilitates generating more fine-grained synchronization sequences and significantly improves concurrency fault detection capability of reachability testing.
Want to Earn a Few Extra Bucks? A First Look at Money-Making Apps

Have you ever thought of earning profits from the apps that you are using on your mobile device? It is actually achievable thanks to many so-called money-making apps, which pay app users to complete tasks such as installing another app or clicking an advertisement. To the best of our knowledge, no existing studies have investigated the characteristics of moneymaking apps. To this end, we conduct the first exploratory study to understand the features and implications of money-making apps. We first propose a semi-automated approach aiming to harvest money-making apps from Google Play and alternative app markets. Then we create a taxonomy to classify them into five categories and perform an empirical study from different aspects. Our study reveals several interesting observations: (1) moneymaking apps have become the target of malicious developers, as we found many of them expose mobile users to serious privacy and security risks. Roughly 26% of the studied apps are potentially malicious. (2) these apps have attracted millions of users, however, many users complain that they are cheated by these apps. We also revealed that ranking fraud techniques are widely used in these apps to promote the ranking of apps inside app markets. (3) these apps usually spread inappropriate and malicious contents, while unsuspicious users could get infected. Our study demonstrates the emergency for detecting and regulating this kind of apps and protect mobile users.
Mining Specifications from Documentation using a Crowd

Temporal API specifications are useful for many software engineering tasks, such as test case generation. In practice, however, APIs are rarely formally specified, inspiring researchers to develop tools that infer or mine specifications automatically.Traditional specification miners infer likely temporal properties by statically analyzing the source code or by analyzing program runtime traces. These approaches are frequently confounded by the complexity of modern software and by the unavailability of representative and correct traces. Formally specifying software is traditionally an expert task. We hypothesize that human crowd intelligence provides a scalable and high-quality alternative to experts, without compromising on quality. In this work we present CrowdSpec, an approach to use collective intelligence of crowds to generate or improve automatically mined specifications. CrowdSpec uses the observation that APIs are often accompanied by natural language documentation, which is a more appropriate resource for humans to interpret and is a complementary source of information to what is used by most automated specification miners.
Identifying Redundancies in Fork-based Development

Fork-based development is popular and easy to use, but makes it difficult to maintain an overview of the whole community when the number of forks increases. This may lead to redundant development where multiple developers are solving the same problem in parallel without being aware of each other. Redundant development wastes effort for both maintainers and developers. In this paper, we designed an approach to identify redundant code changes in forks as early as possible by extracting clues indicating similarities between code changes, and building a machine learning model to predict redundancies. We evaluated the effectiveness from both the maintainer’s and the developer’s perspectives. The result shows that we achieve 57–83% precision for detecting duplicate code changes from maintainer’s perspective, and we could save developers’ effort of 1.9–3.0 commits on average. Also, we show that our approach significantly outperforms existing state-of-art.