Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing the Pedagogical Benefits of Adaptive Feedback for Compilation Errors by Novice Programmers

Can automated adaptive feedback for correcting erroneous programs help novice programmers learn to code better? In a large-scale experiment, we compare student performance when tutored by human tutors, and when receiving automated adaptive feedback. The automated feedback was designed using one of two well-known instructional principles: (i) presenting the correct solution for the immediate problem, or (ii) presenting generated examples or analogies that guide towards the correct solution. We report empirical results from a large-scale (N = 480,10,000+ person hour) experiment assessing the efficacy of these automated compilation-error feedback tools. Using the survival analysis on error rates of students measured over seven weeks, we found that automated feedback allows students to resolve errors in their code more efficiently than students receiving manual feedback. However, we also found that this advantage is primarily logistical and not conceptual; the performance benefit seen during lab assignments disappeared during exams wherein feedback of any kind was withdrawn. We further found that the performance advantage of automated feedback over human tutors increases with problem complexity, and that feedback via example and specific repair have distinct, non-overlapping relative advantages for different categories of programming errors. Our results offer a clear and granular delimitation of the pedagogical benefits of automated feedback in teaching programming to novices.
Hall-of-Apps: The Top Android Apps Metadata Archive

The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution chan-nel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of down-loads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://bit.ly/2uIkXs8. Dataset: https://bit.ly/2Wu9qsn
Recommending in changing times

Recommender systems today face major challenges in keeping up with dynamic customer preferences. Disruptions or sudden changes in the environment affect customer preferences drastically and render historical data ineffective for modeling. With businesses relying heavily on Machine Learning(ML) based recommender systems for catering to customer preferences, the accuracy of timely recommendations gains prime significance. To address these challenges, we propose a novel concept, LDT (Labeled Data Threshold), a newly defined parameter to determine the sufficiency of available labeled training data. Our proposed scheme, using LDT leads to a significant reduction ( 50X) in the training time for a model, thus enabling recommender systems to adapt quickly to disruptions. We illustrate the efficacy of our proposed scheme, by conducting extensive experimental analysis on six well known, structured data sets from various public domains.
Automatically Granted Permissions in Android apps: An Empirical Study on their Prevalence and on the Potential Threats for Privacy

Developers continuously update their Android apps to keep up with competitors in the market. Such constant updates do not bother end users, since by default the Android platform automatically pushes the most recent compatible release on the device, unless there are major changes in the list of requested permissions that users have to explicitly grant. The lack of explicit user's approval for each application update, however, may lead to significant risks for the end user, as the new release may include new subtle behaviors which may be privacy-invasive. The introduction of permission groups in the Android permission model makes this problem even worse: if a user gives a single permission within a group, the application can silently request further permissions in this group with each update-without having to ask the user. In this paper, we explain the threat that permission groups may pose for the privacy of Android users. We run an empirical study on 2,865,553 app releases, and we show that in a representative app store more than ~17% of apps request at least once in their lifetime new dangerous permissions that the operating system grants without any user's approval. Our analyses show that apps actually use over 56% of such automatically granted permissions, although most of their descriptions do not explicitly explain for what purposes. Finally, our manual inspection reveals clear abuses of apps that leak sensitive data such as user's accurate location, list of contacts, history of phone calls, and emails which are protected by permissions that the user never explicitly acknowledges.
OnRL: improving mobile video telephony via online reinforcement learning

Machine learning models, particularly reinforcement learning (RL), have demonstrated great potential in optimizing video streaming applications. However, the state-of-the-art solutions are limited to an "offline learning" paradigm, i.e., the RL models are trained in simulators and then are operated in real networks. As a result, they inevitably suffer from the simulation-to-reality gap, showing far less satisfactory performance under real conditions compared with simulated environment. In this work, we close the gap by proposing OnRL, an online RL framework for real-time mobile video telephony. OnRL puts many individual RL agents directly into the video telephony system, which make video bitrate decisions in real-time and evolve their models over time. OnRL then aggregates these agents to form a high-level RL model that can help each individual to react to unseen network conditions. Moreover, OnRL incorporates novel mechanisms to handle the adverse impacts of inherent video traffic dynamics, and to eliminate risks of quality degradation caused by the RL model's exploration attempts. We implement OnRL on a mainstream operational video telephony system, Alibaba Taobao-live. In a month-long evaluation with 543 hours of video sessions from 151 real-world mobile users, OnRL outperforms the prior algorithms significantly, reducing video stalling rate by 14.22% while maintaining similar video quality.
Contextual Meta-Bandit for Recommender Systems Selection

Recommendation systems operate in a highly stochastic and non-stationary environment. As the amount of user-specific information varies, the users’ interests themselves also change. This combination creates a dynamic setting where a single solution will rarely be optimal unless it can keep up with these transformations. One system may perform better than others depending on the situation at hand, thus making the choice of which system to deploy, even more difficult. We address these problems by using the Hierarchical Reinforcement Learning framework. Our proposed meta-bandit acts as a policy over options, where each option maps to a pre-trained, independent recommender system. This meta-bandit learns online and selects a recommender accordingly to the context, adjusting to the situation. We conducted experiments on real data and found that our approach manages to address the dynamics within the user’s changing interests. We also show that it outperforms any of the recommenders separately, as well as an ensemble of them.
Automatically Predicting Bug Severity Early in the Development Process

Bug severity is an important factor in prioritizing which bugs to fix first. The process of triaging bug reports and assigning a severity requires developer expertise and knowledge of the underlying software. Methods to automate the assignment of bug severity have been developed to reduce the developer cost, however, many of these methods require 70-90% of the project’s bug reports as training data and delay their use until later in the development process. Not being able to automatically predict a bug report’s severity early in a project can greatly reduce the benefits of automation. We have developed a new bug report severity prediction method that leverages how bug reports are written rather than what the bug reports contain. Our method allows for the prediction of bug severity at the beginning of the project by using an organization’s historical data, in the form of bug reports from past projects, to train the prediction classier. In validating our approach, we conducted over 1000 experiments on a dataset of five NASA robotic mission software projects. Our results demonstrate that our method was not only able to predict the severity of bugs earlier in development, but it was also able to outperform an existing keyword-based classifier for a majority of the NASA projects.Ccs Concepts• Software and its engineering → Software maintenance tools; Maintaining software; Software testing and debugging; • Computing methodologies → Machine learning.
Introduction to Bandits in Recommender Systems

The multi-armed bandit problem models an agent that simultaneously attempts to acquire new knowledge (exploration) and optimize his decisions based on existing knowledge (exploitation). The agent attempts to balance these competing tasks in order to maximize his total value over the period of time considered. There are many practical applications of bandit algorithms, including clinical trials, adaptive routing or portfolio design. Over the last decade there has been an increased interest in developing bandit algorithms to address specific issues in recommender systems, such as improved product recommendation, the cold start problem, or personalization. The aim of this tutorial is to provide a brief introduction to the bandit problem with an overview of the various applications of bandit algorithms in recommendation.
Polydora (Polychaeta: Spionidae) species from Taiwan

This report discusses 5 species of the genus Polydora (Polychaeta: Spionidae) from the shallow waters of Taiwan and off mainland China. These include P. cf. agassizi Claparede, 1869, P. cornuta Bosc, 1802, and 3 new species: P. fusca, P. triglanda, and P. villosa. Polydora cf. agassizi inhabits mud tubes on the surface of a horseshoe crab; P. cornuta and P. fusca inhabit mud tubes on soft bottoms while P. villosa bores into skeletons of living corals. Polydora triglanda is both a shell-borer and a tubedweller, and no morphological differences were found between individuals from the 2 habitats. Five species are described and illustrated, and a key is provided for their identification. Polydora species with black bands on the palps, median antenna on the caruncle, and needlelike spines on the posterior notopodia are reviewed, and their morphological characteristics are compared.
Using Public and Free Platform-as-a-Service (PaaS) based Lightweight Projects for Software Architecture Education

Background: Software architecture is a crucial while significantly challenging topic in the computer science curriculum. Although “learning by doing” is widely advocated to address this topic's abstract and fuzzy nature, educators are still facing various difficulties in practice, especially including students' vicious circle of inexperience and the mental model dilemma in experiential learning. Aims: To help break the aforementioned vicious circle and mental model dilemma, this work aims to investigate our educational strategy of using lightweight projects with public and free PaaS resources (1) to help students accumulate architectural experience from the early stage and (2) to facilitate strengthening students' fundamental architecture knowledge. Method: To collect more empirical evidence, we conducted action research on our educational strategy across three undergraduate-curriculum courses for two years. In particular, we employed the Technology Acceptance Model (TAM) to validate how well students received such an educational strategy. Results: The students involved in the relevant coursework generally gave positive feedback with respect to learning theoretical concepts and a set of architectural patterns. Meanwhile, we also observed diverse learning curves for utilizing PaaS resources. Conclusions: Although there still exist PaaS-related limits, our educational strategy can foster students' experiential learning and collaborative learning in fundamental architecture courses. We believe that there are also potentials to extend our work to broader and advanced courses of software architecture.
Experience: towards automated customer issue resolution in cellular networks

Cellular service carriers often employ reactive strategies to assist customers who experience non-outage related individual service degradation issues (e.g., service performance degradations that do not impact customers at scale and are likely caused by network provisioning issues for individual devices). Customers need to contact customer care to request assistance before these issues are resolved. This paper presents our experience with PACE (ProActive customer CarE), a novel, proactive system that monitors, troubleshoots and resolves individual service issues, without having to rely on customers to first contact customer care for assistance. PACE seeks to improve customer experience and care operation efficiency by automatically detecting individual (non-outage related) service issues, prioritizing repair actions by predicting customers who are likely to contact care to report their issues, and proactively triggering actions to resolve these issues. We develop three machine learning-based prediction models, and implement a fully automated system that integrates these prediction models and takes resolution actions for individual customers. We conduct a large-scale trace-driven evaluation using real-world data collected from a major cellular carrier in the US, and demonstrate that PACE is able to predict customers who are likely to contact care due to non-outage related individual service issues with high accuracy. We further deploy PACE into this cellular carrier network. Our field trial results show that PACE is effective in proactively resolving non-outage related individual customer service issues, improving customer experience, and reducing the need for customers to report their service issues.
Emotion GaRage Vol. II:: A Workshop on Affective In-Vehicle Display Design

Driver performance and behavior can be partially predicated based on one's emotional state. Through ascertaining the emotional state of passengers and employing various mitigation strategies, empathic cars can show potential in improving user experience and driving performance. Challenges remain in the implementation of such strategies, as individual differences play a large role in mediating the effect of affective intervention. Therefore, we propose a workshop that aims to bring together researchers and practitioners interested in affective interfaces and in-vehicle technologies as a forum for the development of targeted emotion intervention methods. During the workshop, we will focus on a common set of use cases and generate approaches that can suit different user groups. By the end of this short workshop, researchers will determine ideal intervention methods for prospective user groups. This will be achieved through the method of insight combination to generate and discuss ideas.
From the lab to production: A case study of session-based recommendations in the home-improvement domain

E-commerce applications rely heavily on session-based recommendation algorithms to improve the shopping experience of their customers. Recent progress in session-based recommendation algorithms shows great promise. However, translating that promise to real-world outcomes is a challenging task for several reasons, but mostly due to the large number and varying characteristics of the available models. In this paper, we discuss the approach and lessons learned from the process of identifying and deploying a successful session-based recommendation algorithm for a leading e-commerce application in the home-improvement domain. To this end, we initially evaluate fourteen session-based recommendation algorithms in an offline setting using eight different popular evaluation metrics on three datasets. The results indicate that offline evaluation does not provide enough insight to make an informed decision since there is no clear winning method on all metrics. Additionally, we observe that standard offline evaluation metrics fall short for this application. Specifically, they reward an algorithm only when it predicts the exact same item that the user clicked next or eventually purchased. In a practical scenario, however, there are near-identical products which, although they are assigned different identifiers, they should be considered as equally-good recommendations. To overcome these limitations, we perform an additional round of evaluation, where human experts provide both objective and subjective feedback for the recommendations of five algorithms that performed the best in the offline evaluation. We find that the experts’ opinion is oftentimes different from the offline evaluation results. Analysis of the feedback confirms that the performance of all models is significantly higher when we evaluate near-identical product recommendations as relevant. Finally, we run an A/B test with one of the models that performed the best in the human evaluation phase. The treatment model increased conversion rate by 15.6% and revenue per visit by 18.5% when compared with a leading third-party solution.
HPTLC quantification of triterpenoid compounds and evaluation of in-vitro potential of Trichodesma indicum L

Trichodesma indicum (Linn.) R.Br. is traditionally used folk medicine, having a lot of pharmacological bustles. The current study is focused on investigating the phytochemical and biological evaluation of T. indicum. Quantification of oleanolic acid, lupeol, and linolic acid is done by using a tertiary mobile phase consisting of toluene: ethyl acetate: formic acid (7: 2.5: 0.5 v/v). Oleanolic acid, lupeol, and linolic acid were resolved at Rf 0.46 ± 0.04, 0.55 ± 0.04 and 0.62 ± 0.05 respectively and it was quantified at its wavelength of maximum absorbance of 600nm after derivatization with anisaldehyde sulphuric acid reagent. The limits of detection and limit of quantification were found to be 662.4, 665.18 and 660.26ng per ml and 2007.4, 2015.7 and 2000.78 ng per ml for linolic acid lupeol, and oleanolic acid, respectively. The linear regression data for the calibration plot showed a good linear relationship between peak area and concentration in the range of 200-600ng/Spot. The developed HPTLC method is accurate, precise and has been successfully applied for the assay of this bioactive molecule in Trichodesma indicum. The pharmacological potential of the extract is dependent on the contents of active secondary metabolites present in the plants. The antioxidant potential was studied by using DPPH and 2-deoxyribose assay. Antioxidant potential was observed showing IC50 values (326.82±1.21μg/ml) and (449.62±2.01μg/ml) by DPPH and 2-deoxyribose method respectively. In vitro antidiabetic potential of the plant were also assessed by starch iodine assay and DNS method. IC50 was observed (86.57 ± 2.57μg/ml) in 3, 5 DNS assay and (222.86 ± 3.36 mg/ml) in starch iodine assay. Results are promising; this work forms a firm base for further research to explore the lead compound which is responsible for the medicinal value of this plant.
Toxicity, hypoglycemic and antioxidant potentials of Massularia acuminata Stem

Massularia acuminata is mostly employed in herbal decoctions for the treatment of diabetes, and as an aphrodisiac. Its toxicity index and acclaimed hypoglycemic potency have not been scientifically validated, which necessitates this study. High amount of saponins, alkaloids and cardiac glycosides were detected; extract and fractions produced cytotoxicity to brine shrimp (LC50, 1.26-21.54 μg/mL), and LD50 (111.8 mg/Kg) for intraperitoneal administration of extract using albino mice. Treatment of alloxaninduced diabetic rats with extract caused a significant reduction (P<0.05) in fasting blood glucose level (45.63%) in acute study and 61.73% reduction in prolong treatment (2 weeks); extract and fractions demonstrated moderate antioxidant effects (using 2,2-diphenyl-1-picrylhydrazyl radical and ferric reducing power assays) compared to ascorbic acid. These results suggest that M. acuminata stem is cytotoxic and possess good hypoglycemic effects. Its toxicity in herbal preparations is of essence.
Intra-Utterance Similarity Preserving Knowledge Distillation for Audio Tagging

Knowledge Distillation (KD) is a popular area of research for reducing the size of large models while still maintaining good performance. The outputs of larger teacher models are used to guide the training of smaller student models. Given the repetitive nature of acoustic events, we propose to leverage this information to regulate the KD training for Audio Tagging. This novel KD method, "Intra-Utterance Similarity Preserving KD" (IUSP), shows promising results for the audio tagging task. It is motivated by the previously published KD method: "Similarity Preserving KD" (SP). However, instead of preserving the pairwise similarities between inputs within a mini-batch, our method preserves the pairwise similarities between the frames of a single input utterance. Our proposed KD method, IUSP, shows consistent improvements over SP across student models of different sizes on the DCASE 2019 Task 5 dataset for audio tagging. There is a 27.1% to 122.4% percent increase in improvement of micro AUPRC over the baseline relative to SP's improvement of over the baseline.
An Experience of Game-Based Learning in Web Applications Development Courses

Preparing graduates for working in the software engineering industry is challenging and requires eﬀective learning frameworks and methodologies. More speciﬁcally, the challenge of teaching programming languages and paradigms is a very complex task that needs innovative educational tools. This paper presents a game-based educational tool named eLiza, developed and used to support the teaching and learning of programming languages and paradigms related to the development of web applications. eLiza was initially developed as a Moodle-based web application because Moodle is the educational eLearning platform used at the University of Valladolid, but as the use of mobile devices is constantly increasing, Android and iOS versions were created later in order to facilitate the access of the students to the games. This paper describes the main elements and the mechanics in playing eLiza. And it also describes an experience of its use in two engineering courses related to web programming applications development, oﬀered to students in two diﬀerent engineering study programs at the University of Valladolid, during the academic years 2017-2018 and 2018-2019. The great majority of the students, more than 75%, considered that the use of the eLiza game-based educational tool was positive to improve the teaching and learning process of the topics covered by the courses.