Miracle-dz/UniFAR-FTUs huggingface dataset:
One Size Does Not Fit All: Comparing NMT Representations of Different Granularities

Recent work has shown that contextualized word representations derived from neural machine translation are a viable alternative to such from simple word predictions tasks. This is because the internal understanding that needs to be built in order to be able to translate from one language to another is much more comprehensive. Unfortunately, computational and memory limitations as of present prevent NMT models from using large word vocabularies, and thus alternatives such as subword units (BPE and morphological segmentations) and characters have been used. Here we study the impact of using different kinds of units on the quality of the resulting representations when used to model morphology, syntax, and semantics. We found that while representations derived from subwords are slightly better for modeling syntax, character-based representations are superior for modeling morphology and are also more robust to noisy input.
Pattern-Based Mining of Opinions in Q&A Websites

Informal documentation contained in resources such as Q&A websites (e.g., Stack Overflow) is a precious resource for developers, who can find there examples on how to use certain APIs, as well as opinions about pros and cons of such APIs. Automatically identifying and classifying such opinions can alleviate developers' burden in performing manual searches, and can be used to recommend APIs that are good from some points of view (e.g., performance), or highlight those less ideal from other perspectives (e.g., compatibility). We propose POME (Pattern-based Opinion MinEr), an approach that leverages natural language parsing and pattern-matching to classify Stack Overflow sentences referring to APIs according to seven aspects (e.g., performance, usability), and to determine their polarity (positive vs negative). The patterns have been inferred by manually analyzing 4,346 sentences from Stack Overflow linked to a total of 30 APIs. We evaluated POME by (i) comparing the pattern-matching approach with machine learners leveraging the patterns themselves as well as n-grams extracted from Stack Overflow posts; (ii) assessing the ability of POME to detect the polarity of sentences, as compared to sentiment-analysis tools; (iii) comparing POME with the state-of-the-art Stack Overflow opinion mining approach, Opiner, through a study involving 24 human evaluators. Our study shows that POME exhibits a higher precision than a state-of-the-art technique (Opiner), in terms of both opinion aspect identification and polarity assessment.
Categorical Classification of Factors Effecting Knowledge Management in Software Crowdsourcing: Hypothetical Framework

Within Software Crowdsourcing, Knowledge Management has a great significance in both academia and industry as a valuable tool used to manage knowledge from the crowd. The aim of this research is to ascertain which success are and which are failure factors of Knowledge Management in Software Crowdsourcing. Literature review techniques and Quantitative Research techniques were applied in order to establish the success and failure factors. By utilizing the literature review method a total of twelve success factors were established of which seven is supported. Eight failure factures were established out of which six are supported. Subsequent to the analysis, a framework is presented in which the factors are further linked to the implementation of Knowledge Management in Software Crowdsourcing. This research and its suggested framework will also prove useful for academics to further gain a comprehensive view of Knowledge Management factors in Software Crowdsourcing for use in future studies.
Net Cloud Thinning, Low-Level Cloud Diminishment, and Hadley Circulation Weakening of Precipitating Clouds with Tropical West Pacific SST Using MISR and Other Satellite and Reanalysis Data

Daily gridded Multi-Angle Imaging Spectroradiometer (MISR) satellite data are used in conjunction with CERES, TRMM, and ERA-Interim reanalysis data to investigate horizontal and vertical high cloud structure, top-of-atmosphere (TOA) net cloud forcing and albedo, and dynamics relationships against local SST and precipitation as a function of the mean Tropical West Pacific (TWP; 120°E to 155°W; 30°S–30°N) SST. As the TWP warms, the SST mode (~29.5 °C) is constant, but the area of the mode grows, indicating increased kurtosis of SSTs and decreased SST gradients overall. This is associated with weaker low-level convergence and mid-tropospheric ascent (ω500) over the highest SSTs as the TWP warms, but also a broader area of weak ascent away from the deepest convection, albeit stronger when compared to when the mean TWP is cooler. These associated dynamics changes are collocated with less anvil and thick cloud cover over the highest SSTs and similar thin cold cloud fraction when the TWP is warmer, but broadly more anvil and cirrus clouds over lower local SSTs (SST < 27 °C). For all TWP SST quintiles, anvil cloud fraction, defined as clouds with tops > 9 km and TOA albedos between 0.3–0.6, is closely associated with rain rate, making it an excellent proxy for precipitation; but for a given heavier rain rate, cirrus clouds are more abundant with increasing domain-mean TWP SST. Clouds locally over SSTs between 29–30 °C have a much less negative net cloud forcing, up to 25 W m−2 greater, when the TWP is warm versus cool. When the local rain rate increases, while the net cloud fraction with tops < 9 km decreases, mid-level clouds (4 km < Ztop < 9 km) modestly increase. In contrast, combined low-level and mid-level clouds decrease as the domain-wide SST increases (−10% deg−1). More cirrus clouds for heavily precipitating systems exert a stronger positive TOA effect when the TWP is warmer, and anvil clouds over a higher TWP SST are less reflective and have a weaker cooling effect. For all precipitating systems, total high cloud cover increases modestly with higher TWP SST quintiles, and anvil + cirrus clouds are more expansive, suggesting more detrainment when TWP SSTs are higher. Total-domain anvil cloud fraction scales mostly with domain-mean ω500, but cirrus clouds mostly increase with domain-mean SST, invoking an explanation other than circulation. The overall thinning and greater top-heaviness of clouds over the TWP with warming are possible TWP positive feedbacks not previously identified.
New results on information theoretic clustering

We study the problem of optimizing the clustering of a set of vectors when the quality of the clustering is measured by the Entropy or the Gini impurity measure. Our results contribute to the state of the art both in terms of best known approximation guarantees and inapproximability bounds: (i) we give the first polynomial time algorithm for Entropy impurity based clustering with approximation guarantee independent of the number of vectors and (ii) we show that the problem of clustering based on entropy impurity does not admit a PTAS. This also implies an inapproximability result in information theoretic clustering for probability distributions closing a problem left open in [Chaudhury and McGregor, COLT08] and [Ackermann et al., ECCC11]. We also report experiments with a new clustering method that was designed on top of the theoretical tools leading to the above results. These experiments suggest a practical applicability for our method, in particular, when the number of clusters is large.
Optimal Strategy of a GPS Position Time Series Analysis for Post-Glacial Rebound Investigation in Europe

We describe a comprehensive analysis of the 469 European Global Positioning System (GPS) vertical position time series. The assumptions we present should be employed to perform the post-glacial rebound (PGR)-oriented comparison. We prove that the proper treatment of either deterministic or stochastic components of the time series is indispensable to obtain reliable vertical velocities along with their uncertainties. The statistical significance of the vertical velocities is examined; due to their small vertical rates, 172 velocities from central and western Europe are found to fall below their uncertainties and excluded from analyses. The GPS vertical velocities reach the maximum values for Scandinavia with the maximal uplift equal to 11.0 mm/yr. Moreover, a comparison between the GPS-derived rates and the present-day motion predicted by the newest Glacial Isostatic Adjustment (GIA) ICE-6G_C (VM5a) model is provided. We prove that these rates agree at a 0.5 mm/yr level on average; the Sweden area with the most significant uplift observed agrees within 0.2 mm/yr. The largest discrepancies between GIA-predicted uplift and the GPS vertical rates are found for Svalbard; the difference is equal to 6.7 mm/yr and arises mainly from the present-day ice melting. The GPS-derived vertical rates estimated for the southern coast of the Baltic Sea are systematically underestimated by the GIA prediction by up to 2 mm/yr. The northern British Isles vertical rates are overestimated by the GIA model by about 0.5 mm/yr. The area of the Netherlands and the coastal area of Belgium are both subsiding faster than it is predicted by the GIA model of around 1 mm/yr. The inland part of Belgium, Luxemburg and the western part of Germany show strong positive velocities when compared to the GIA model. Most of these stations uplift of more than 1 mm/yr. It may be caused by present-day elastic deformation due to terrestrial hydrology, especially for Rhein basin, or non-tidal atmospheric loading, for Belgium and Luxembourg.
NOSOLAP: Moving from Data Warehouse Requirements to NoSQL Databases

Typical data warehouse systems are implemented either on a relational database or on a multi-dimensional database. While the former supports ROLAP operations the latter supports MOLAP. We explore a third alternative, that is, to implement a data warehouse on a NoSQL database. For this, we propose rules that help us move from information obtained from data warehouse requirements engineering stage to the logical model of NoSQL databases, giving rise to NOSOLAP (NOSql OLAP). We show the advantages of NOSOLAP over ROLAP and MOLAP. We illustrate our NOSOLAP approach by converting to the logical model of Cassandra and give an example.
Designing for Openness in Making: Lessons Learned from a Digital Project Week

Placing an emphasis on openness with regard to learning styles, idea generation and participation, maker culture can potentially appeal to many people. However, many makerspaces are struggling to attract representatively diverse groups of participants. In response to this observation, this qualitative study of a digital project week in a mid-size town in Germany explores constituents of openness with a focus on the gendered nature of computing. The degree to which makerspaces can or cannot transcend boundaries and impart openness for female participants and others remains vibrant, especially as gender is a construct that intersects with others demographic variables. We conducted several gender-related workshops and examined the local maker context through an intersectional lens, identifying four themes, which were consequential for openness in the context of the digital project week. Managing these themes, we suggest, can improve maker awareness of diversity issues but does not necessarily provide for a sustainable approach.
A Framework for Checking Regression Test Selection Tools

Regression test selection (RTS) reduces regression testing costs by re-running only tests that can change behavior due to code changes. Researchers and large software organizations recently developed and adopted several RTS tools to deal with the rapidly growing costs of regression testing. As RTS tools gain adoption, it becomes critical to check that they are correct and efficient. Unfortunately, checking RTS tools currently relies solely on limited tests that RTS tool developers manually write. We present RTSCheck, the first framework for checking RTS tools. RTSCheck feeds evolving programs (i.e., sequences of program revisions) to an RTS tool and checks the output against rules inspired by existing RTS test suites. Violations of these rules are likely due to deviations from expected RTS tool behavior, and indicative of bugs in the tool. RTSCheck uses three components to obtain evolving programs: (1) AutoEP automatically generates evolving programs and corresponding tests, (2) DefectsEP uses buggy and fixed program revisions from bug databases, and (3) EvoEP uses sequences of program revisions from actual open-source projects' histories. We used RTSCheck to check three recently developed RTS tools for Java: Clover, Ekstazi, and STARTS. RTSCheck discovered 27 bugs in these three tools.
On the Design of Estimators for Bandit Off-Policy Evaluation

Off-policy evaluation is the problem of estimating the value of a target policy using data collected under a different policy. We describe a framework for designing estimators for bandit offpolicy evaluation. Given a base estimator and a parametrized class of control variates, we seek a control variate in that class that reduces the risk of the base estimator. We derive the population risk as a function of the class parameters and we discuss some approaches for optimizing this function. We present our main results in the context of multi-armed bandits, and we decribe a simple design for contextual bandits that gives rise to an estimator that is shown to perform well in multi-class cost-sensitive classification datasets.
An Efficient Heuristic Method for Repairing Event Logs Independent of Process Models

Due to the big volume of data and complex execution, event logs of business processes inevitably contain various errors. In the field of process mining, if we derive process models from the event data without repairing, it is very likely that the resulting process is extremely different from what we expect. Current methods of repairing logs generally compare the log with an existing reference model to seek an optimal alignment, which requires that there should be a reliable reference model. Therefore, this paper presents an approach which only refers to the log itself to repair mistaken traces. We identify loop structures and frequent event sequences (sound conditions) between certain events. For each trace, basic trace and loop events are separated in advance. The basic trace is split into several parts to get repaired one by one according to sound conditions. Then loop events are added back and checked according to corresponding loop structure we discover. The repaired log should be as clean as possible and as similar to the original log as possible so that correctness and integrity of the original log are guaranteed. Experimental results based on different logs prove that our approach is effective and efficient.
A Systematic Strategy to Teaching of Exploratory Testing using Gamification

The Exploratory Testing (ET) is a growing approach in the industrial scenario, mainly in reason of the emerging utilization of agile practices in the software development process to satisfy the time to market. However, it is a subject little discussed in the academic context, for this reason, this work aims to use elements of gamification as a systematic strategy in the teaching of ET to keep a strong engagement of the students and stimulate a good performance, with that, obtaining as results, trained students to use this test approach in the industrial and academic context.
Towards Emotional Well-Being by Design: 17 Opportunities for Emotion Regulation for User-Centered Healthcare Design

Recent attention has been given to the role of emotion regulation in promoting well-being. Although various approaches have been established to design for emotion in design research and HCI, there is only little knowledge on how to support the process of regulating user emotions through design. The paper introduces a framework that delineates 17 emotion regulation strategies based on the theories of emotion regulation. The framework supports an understanding of emotion regulation strategies by enabling designers to compare differences and overlaps of the strategies. The paper describes the framework and its development process along with design examples. Implications and future research steps are discussed.
Stress Annotations from Older Adults - Exploring the Foundations for Mobile ML-Based Health Assistance

The number of new mobile and wearable technologies with built-in sensors for quantifying every aspect of our lives is increasing. Consequently, new data sources and opportunities arise for the development of machine learning (ML) models and their applications. In this paper, we report on a four weeks field study with 16 older adults, aged between 66 and 81 years (50% female), who were asked to provide stress-related experience samples in different modalities, including paper-based diaries and data collected with the help of a wearable (i.e., a Microsoft Band 2). We provide insights into participants' stress annotation behavior, report on a detailed analysis of the recorded data and the resulting implications regarding the annotation of stressful situations by older adults, discuss how mobile annotation technology can benefit from the synergies with traditional methods and argue why we believe that appropriate annotation techniques are the basis to benefit individually from future powerful machine learning models.
Read-Copy-Update as a Possible Locking Strategy in Scala

Ever since we have started approaching the practical limits of single-threaded CPU performance, concurrent programming has been in the focus of researchers and industry parties. Concurrent programming with shared mutable state is universally considered hard [Sutter and Larus 2005]. There are many reasons for this, starting from the numerous abstractions designed to solve specific problems that don’t work well together, the unpredictability of scheduling the concurrent modifications, to the complexity of reasoning about temporality of concurrent software. New approaches and concurrent programming models are being invented in hopes of reducing the complexity for software developers.
Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization

Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries. As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it. However, while the current state-of-the-art method suggested a new grouping method that was shown to improve the summary quality, its use of pairwise comparisons leads to polynomial runtime complexity that prohibits the application to large document collections. In this paper, we propose two alternative grouping techniques based on locality sensitive hashing, approximate nearest neighbor search and a fast clustering algorithm. They exhibit linear and log-linear runtime complexity, making them much more scalable. We report experimental results that confirm the improved runtime behavior while also showing that the quality of the summary concept maps remains comparable.
3D Car Tracking using Fused Data in Traffic Scenes for Autonomous Vehicle

Car tracking in a traffic environment is a crucial task for the autonomous vehicle. Through tracking, a self-driving car is capable of predicting each car’s motion and trajectory in the traffic scene, which is one of the key components for traffic scene understanding. Currently, 2D vision-based object tracking is still the most popular method, however, multiple sensory data (e.g. cameras, Lidar, Radar) can provide more information (geometric and color features) about surroundings and show significant advantages for tracking. We present a 3D car tracking method that combines more data from different sensors (cameras, Lidar, GPS/IMU) to track static and dynamic cars in a 3D bounding box. Fed by the images and 3D point cloud, a 3D car detector and the spatial transform module are firstly applied to estimate current location, dimensions, and orientation of each surrounding car in each frame in the 3D world coordinate system, followed by a 3D Kalman filter to predict the location, dimensions, orientation and velocity for each corresponding car in the next time. The predictions from Kalman filtering are used for re-identifying previously detected cars in the next frame using the Hungarian algorithm. We conduct experiments on the KITTI benchmark to evaluate tracking performance and the effectiveness of our method.
Improving Automated Program Repair with Retrospective Fault Localization

Although being recognized as a critical step in automated program repair, fault localization has been only loosely coupled into the fixing process in existing program repair approaches, in the sense that fault localization has limited interactions with other activities in fixing. We propose in this paper to deeply integrate fault localization into the fixing process to achieve more effective and efficient program repair. Our approach introduces a feedback loop in fixing between the activities for locating the fault causes and those for generating and evaluating candidate fixes. The feedback loop enables partial evaluation results of candidate fixes to be used to locate fault localization more accurately, and eventually leads to fixing processes with improved effectiveness and efficiency. We have implemented the approach into a tool, named RESTORE, based on the JAID program repair system. Experiments involving faults from the DEFECTS4J standard benchmark indicate that the integrated fault localization can boost automated program repair: RESTORE produced valid fixes to 63 faults and correct ones to 38 faults, outperforming any other state-of-the-art repair tool for Java while taking 36% less running time compared with JAID.
Polygraph: Accountable Byzantine Agreement

In this paper, we introduce Polygraph, the first accountable Byzantine consensus algorithm. If among $n$ users $t < n/3$ are malicious then it ensures consensus; otherwise (if $t\geq n/3)$, it eventually detects malicious users that cause disagreement. Polygraph is appealing for blockchain applications as it allows them to totally order blocks in a chain whenever possible, hence avoiding forks and double spending and, otherwise, to punish (e.g., via slashing) at least $n/3$ malicious users when a fork occurs. This problem is more difficult than perhaps it first appears. One could try identifying malicious senders by extending classic Byzantine consensus algorithms to piggyback signed messages. We show however that to achieve accountability the resulting algorithms would then need to exchange $\Omega(\kappa^{2}\cdot n^{5})$ bits, where $\kappa$ is the security parameter of the signature scheme. By contrast, Polygraph has communication complexity $O(\kappa\cdot n^{4})$. Finally, we implement Polygraph in a blockchain and compare it to the Red Belly Blockchain to show that it commits more than 10,000 Bitcoin-like transactions per second when deployed on 80 geodistributed machines.