Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Reviewer Recommendation Help Developers?

Selecting reviewers for code changes is a critical step for an efficient code review process. Recent studies propose automated reviewer recommendation algorithms to support developers in this task. However, the evaluation of recommendation algorithms, when done apart from their target systems and users (i.e., code review tools and change authors), leaves out important aspects: perception of recommendations, influence of recommendations on human choices, and their effect on user experience. This study is the first to evaluate a reviewer recommender in vivo. We compare historical reviewers and recommendations for over 21,000 code reviews performed with a deployed recommender in a company environment and set out to measure the influence of recommendations on users’ choices, along with other performance metrics. Having found no evidence of influence, we turn to the users of the recommender. Through interviews and a survey we find that, though perceived as relevant, reviewer recommendations rarely provide additional value for the respondents. We confirm this finding with a larger study at another company. The confirmation of this finding brings up a case for more user-centric approaches to designing and evaluating the recommenders. Finally, we investigate information needs of developers during reviewer selection and discuss promising directions for the next generation of reviewer recommendation tools. Preprint: https://doi.org/10.5281/zenodo.1404814.
Supplement: Meta-analysis of Bayesian analyses

In this section, we first develop a posterior update rule given observed beliefs, which is motivated by the problem of conducting meta-analysis for a set of related posterior distributions. The notion of relatedness is here characterized as the exchangeability of the quantities targeted by the posteriors. The proposed update rule is given in Equation (3). We then show in Section 2 that this rule retains some basic theoretical properties of standard Bayesian inference.
Effectiveness of Score Normalisation in Multimodal Biometric Fusion

An important problem which can adversely influence the overall effectiveness of biometric recognition is the undesired variations in the biometric data. Thisis because such variations are reflected in the corresponding biometric scores.This paper investigates the effectiveness of different score normalisationtechniques for tackling this issue as well as enhancing the accuracy of multimodalbiometrics. The fusion process is accomplished at the score level.
Carrier Sense Multiple Access & Collision Avoidance- Visualisation Teaching Tool (CSMA/CA-VTT)

A Carrier-Sense Multiple-Access/Collision Avoidance (CSMA/CA) was a telecommunication protocol that first listened before sending a frame through a medium in order to avoid collision among heterogeneous sites across a network. To teach such a mechanism to students in the traditional format, especially to the ones from the Sub-Saharan region was a difficult to visualize such a concept. In view of that, Carrier-Sense Multiple- Access/Collision Avoidance - Visualisation Teaching Tool (CSMA/CA- VTT) was envisaged to demonstrate step-by-step the phenomenon of "listen-before-talk", the mechanism of Inter Frame Spacing (IFS) and Back off Timer (BOT) decremented generated random time to zero (0) to give one station high priority to send a frame. The CSMA/CA-VTT proved to be useful teaching aid in practice by showing sixty percent (60%) of the Regular group of twenty five (25) students that were taught using that tool, reported that they visualised the concept very high. In contrast, the Controlled group that were taught without using the tool, only four percent (4%) of students claimed that they were able to visualize the concept very high, whereas sixty eight percent (68%) of this same group regrettably lamented that their visualization to this concept was low. Such a result really suggested that the institutions especially in the sub-Saharan region did not have relevant telecommunications infrastructure that would be demonstrated before the learners. This showed that much of teaching-learning was done theoretically. It was against this background that alternative solutions of employing teaching tools like CSMA/CA-VTT were developed and used in learning institutions.
A systematic literature review of how mutation testing supports quality assurance processes

Mutation testing has been very actively investigated by researchers since the 1970s, and remarkable advances have been achieved in its concepts, theory, technology, and empirical evidence. While the most influential realisations have been summarised by existing literature reviews, we lack insight into how mutation testing is actually applied. Our goal is to identify and classify the main applications of mutation testing and analyse the level of replicability of empirical studies related to mutation testing. To this aim, this paper provides a systematic literature review on the application perspective of mutation testing based on a collection of 191 papers published between 1981 and 2015. In particular, we analysed in which quality assurance processes mutation testing is used, which mutation tools and which mutation operators are employed. Additionally, we also investigated how the inherent core problems of mutation testing, ie, the equivalent mutant problem and the high computational cost, are addressed during the actual usage. The results show that most studies use mutation testing as an assessment tool targeting unit tests, and many of the supporting techniques for making mutation testing applicable in practice are still underdeveloped. Based on our observations, we made 9 recommendations for future work, including an important suggestion on how to report mutation testing in testing experiments in an appropriate manner.
VerC3: A library for explicit state synthesis of concurrent systems

We propose an alternative, explicit state only, approach to concurrent system synthesis. In particular, the focus of this work is on the synthesis of distributed protocols. Given a correctness specification and a protocol skeleton (i.e. incomplete with holes), the goal is to synthesize the holes. At the heart of our technique is a dynamic programming based algorithm that prunes inferred failure candidates. The algorithm exploits the fact that typically only a few transitions are needed to reach an erroneous state in a faulty distributed protocol. Therefore, it is unlikely that every hole to be synthesized is contributing towards the error; thus, faulty protocol candidates where only a subset of holes were used can be used to infer failures of later candidates with a superset of holes. We evaluate the tool using a cache coherence protocol synthesis case study. Specifically, we study a directory based MSI protocol, assuming an unordered interconnect which gives rise to numerous race conditions which must be resolved via introducing transient states — a common cause of complexity and bugs in such protocols. In the case study, we therefore focus on synthesizing the transient state actions (we consider up to 12 holes out of possible 35). With the proposed candidate pruning optimization, we report up to 43x improvement over a naïve candidate enumeration scheme. We make available the tool and C++ library, VerC3.
Are Optimal Day-Ahead Markets Able to Face RES Uncertainty? : Evaluating Perfect Stochastic Energy Planning Models

Approximations made in day-ahead markets can result in suboptimal or even infeasible schedules for generating units and inaccurate predictions of actual costs and wind curtailment. Here we compare different optimal models of day-ahead markets based on unit commitment (UC) formulations, especially energy- vs. power-based UC; excluding or including startup and shutdown trajectories; and deterministic vs. “ideal” stochastic models to face wind uncertainty. The day-ahead hourly schedules are then evaluated against actual wind and load profiles using a (5-min) real-time dispatch model. We find that each simplification usually causes expected generation costs to increase by several percentage points, and results in significant understatement of expected wind curtailment and, in some cases, load interruptions
The use of Conditional Simulation for Drill Hole Spacing Evaluation and Decision-Making in Telégrafo Project, Northern Chile

The Telégrafo copper – gold deposit is located in the Centinela district of the Antofagasta region in northern Chile. Telégrafo is a porphyry copper deposit of Eocene – Oligocene age which has a total mineral resource in measured, indicated and inferred categories equal to 2.6 billion tonnes with an average grade of 0.38 per cent copper. Currently the project is in the prefeasibility study phase, including an infi ll drilling campaign to improve the knowledge of the deposit that should result in an improvement of the mineral resource classifi cation. In this context was decided to review the classifi cation criteria in order to optimise the drill hole density necessary to support the project. This paper presents an application of conditional simulation to evaluate the risk related to drill hole spacing, using a geostatistical approach that mimics the process of evaluation of resources from the perspective of geological modelling and grade estimation. The fi rst step of the approach is the generation of conditional simulations using the available samples to generate several plausible scenarios of the deposit in terms of copper grades and geological units. These scenarios are considered as the unknown reality of the deposit and sampled by synthetic drill holes in order to replicate the geological model process and copper resource estimation. In this way it is possible to establish differences between the possible scenarios and the generated resource estimation models for several simulated drill hole spacing alternatives. These differences or errors can then be quantifi ed in terms of estimated mean, metal content and tonnage over different production volumes. The resulting errors were useful in assisting the decision making process related to the design of drill hole spacing for each category of mineral resource in the Telégrafo project, and to redistribute the budget associated to the drill campaigns for the next stage of the project.
Enhancing Hadoop Performance Utilizing the Metadata of Related Jobs: A Survey

: Cloud computing influences Hadoop system for preparing BigData in parallel. Hadoop has certain confinements that could be abused to execute the job effectively. These restrictions are generally due to information locality in the cluster, employments and job planning, and asset distributions in Hadoop. Proficient asset designation remains a provocation in Cloud Computing MapReduce stages. Hadoop contains a few confinements that could be created to have a higher execution in executing jobs. These confinements are generally as a result of data locality in the cluster, job and job Scheduling , CPU execution time, or asset distributions in Hadoop. In this paper here is a study of how to overcome from these confinements.
Effect of Cooling on Solar Panel Performance

One of the main problems in using the photovoltaic system is the low energy conversion efficiency of photovoltaic cells and, furthermore, during the long operational period of solar cells, their energy conversion efficiency decreases even more due to increase in operating cell temperature over a certain limit. One way of improving the efficiency of photovoltaic system is to cool it during its operation period. This study compares the effects of cooling on the performance of photovoltaic system. Experiments are performed on the photovoltaic system without cooling initially to have a set of reference performance parameters for comparison. Air and water are the fluids used in cooling of the system. I-V tests and temperature tests, for all the cases, are performed for comparative analysis. The energy balance calculations showed that the experimental results are in conformity with the theoretical results. The results further showed that the cooling of photovoltaic system using water over the front surface enhances the performance even more as compared to air cooling of solar panel.
Investigating The Use of Smartphone Disorders in Learning by Students of Mechanical Engineering Expertise Program (MEEP)

The use of smartphones by MEEP students is expected to help the learning process. However, whether the smartphone can really help the learning process or even give a negative impact? This study is to describe the use of smartphones by MEEP students in learning. The type of research is Sequential Explanatory Design with Mixed Method Approach. The result of this research is the level of disruption of smartphone usage by MEEP students in general learning into the high category (mean count (3.32) > mean ideal (2.5)). It means that the use of smartphones by MEEP students has a negative impact on learning. Negative impacts that happened (as a medium for playing mobile games and social media) were more dominant than positive impacts. The use of smartphones by MEEP students cannot be stopped. However, students can be directed to use smartphones in a healthy way.
Classification of Mental Workload Levels Using EEG and a Hybrid Model of Stacked Denoising AutoEncoder

—Human-machine systems are increasingly being used in a variety of scenarios, so it is particularly important to determine the operator's mental workload (MW) levels in time. In this study, EEG was selected as the main indicator for MW levels. After we used Stacked Denoising AutoEncoder and K-nearest Neighbors algorithm to classify MW levels separately, we merged the two models by stacking. Finally, we proved its effectiveness by comparing this new model with the mainstream classifiers and the original two models.
Application of Blind Deblurring Algorithm for Face Biometric

Face recognition is one of the most important abilities which we use in public security and for identity verification for physical and logical access. It is well known that many image-based face recognition algorithms perform well, when constrained (frontal, well illuminated, high-resolution, sharp, and full) face images are acquired. However, their performance degrades signi? cantly when the test images contain variations that are not present in the training images. Face recognition in constrained acquisition conditions is one of the most challenging problems that have been actively researched in recent years. There are many factors low resolution, poor illumination, pose variation, occlusion and relative motion between the sensor and objects in the scene to substantially degrade performance more than the other quality. The work described in this paper is interested in Motion Blur.
A Rare Case Report of Sirenomelia Following Intracytoplasmic Sperm Injection Embryo Transfer

Sirenomelia is a very rare developmental abnormality which is characterized by fusion of lower limb to form a single limb. This condition is often associated with internal organ abnormality and is considered incompatible with life. Sirenomelia is sporadically reported from across the world, but no case associated with artificial reproductive technology (ART) has not been reported. We report a case of sirenomelia in a 29-year old woman who conceived by ART, which to our knowledge is the first reported case in ART. The defect was detected early during first trimester and the pregnancy was terminated.
Hierarchical Classification of Kelps utilizing Deep Residual Features

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, like kelps. This paper presents an automatic hierarchical classification method to classify kelps from images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned image representations extracted from deep residual networks. These powerful and generic features outperform the traditional off-the-shelf CNN features, which have already shown superior performance over the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the common parallel multi-class classifications by a significant margin. Experimental results are provided to illustrate the efficient applicability of the proposed method to study the change in kelp cover over time for annually repeated AUV surveys.
Leveraging Discourse Information Effectively for Authorship Attribution

We explore techniques to maximize the effectiveness of discourse information in the task of authorship attribution. We present a novel method to embed discourse features in a Convolutional Neural Network text classifier, which achieves a state-of-the-art result by a significant margin. We empirically investigate several featurization methods to understand the conditions under which discourse features contribute non-trivial performance gains, and analyze discourse embeddings.
A convenient category for higher-order probability theory

Higher-order probabilistic programming languages allow programmers to write sophisticated models in machine learning and statistics in a succinct and structured way, but step outside the standard measure-theoretic formalization of probability theory. Programs may use both higher-order functions and continuous distributions, or even define a probability distribution on functions. But standard probability theory does not handle higher-order functions well: the category of measurable spaces is not cartesian closed. Here we introduce quasi-Borel spaces. We show that these spaces: form a new formalization of probability theory replacing measurable spaces; form a cartesian closed category and so support higher-order functions; form a well-pointed category and so support good proof principles for equational reasoning; and support continuous probability distributions. We demonstrate the use of quasi-Borel spaces for higher-order functions and probability by: showing that a well-known construction of probability theory involving random functions gains a cleaner expression; and generalizing de Finetti's theorem, that is a crucial theorem in probability theory, to quasi-Borel spaces.
An SDN hybrid architecture for vehicular networks: Application to Intelligent Transport System

Vehicular networks are one of the cornerstone of an Intelligent Transportation System (ITS). They are expected to provide ubiquitous network connectivity to moving vehicles while supporting various ITS services, some with very stringent requirements in terms of latency and reliability. Two vehicular networking technologies are envisioned to jointly support the full range of ITS services : DSRC (Dedicated Short Range Communication) for direct vehicle to vehicle/Road Side Units (RSU) communications and cellular technologies. To the best of our knowledge, approaches from the literature usually divide ITS services on each of these networks according to their requirements and one single network is in charge of supporting the each service. Those that consider both network technologies to offer multi-path routing, load balancing or path splitting for a better quality of experience of ITS services assume obviously separately controlled networks. Under the umbrella of SDN (Software Defined Networking), we propose in this paper a hybrid network architecture that enables the joint control of the networks providing connectivity to multi-homed vehicles and, also, explore the opportunities brought by such an architecture. We show through some use cases, that in addition to the flexibility and fine-grained programmability brought by SDN, it opens the way towards the development of effective network control algorithms that are the key towards the successful support of ITS services and especially those with stringent QoS. We also show how these algorithms could also benefit from information related to the environment or context in which vehicles evolve (traffic density, planned trajectory, .), which could be easily collected by data providers and made available via the cloud.
Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game

Interpersonal relations are fickle, with close friendships often dissolving into enmity. In this work, we explore linguistic cues that presage such transitions by studying dyadic interactions in an online strategy game where players form alliances and break those alliances through betrayal. We characterize friendships that are unlikely to last and examine temporal patterns that foretell betrayal. We reveal that subtle signs of imminent betrayal are encoded in the conversational patterns of the dyad, even if the victim is not aware of the relationship's fate. In particular, we find that lasting friendships exhibit a form of balance that manifests itself through language. In contrast, sudden changes in the balance of certain conversational attributes---such as positive sentiment, politeness, or focus on future planning---signal impending betrayal.
Learning Communities in the Presence of Errors

We study the problem of learning communities in the presence of modeling errors and give robust recovery algorithms for the Stochastic Block Model (SBM). This model, which is also known as the Planted Partition Model, is widely used for community detection and graph partitioning in various fields, including machine learning, statistics, and social sciences. Many algorithms exist for learning communities in the Stochastic Block Model, but they do not work well in the presence of errors. In this paper, we initiate the study of robust algorithms for partial recovery in SBM with modeling errors or noise. We consider graphs generated according to the Stochastic Block Model and then modified by an adversary. We allow two types of adversarial errors, Feige---Kilian or monotone errors, and edge outlier errors. Mossel, Neeman and Sly (STOC 2015) posed an open question about whether an almost exact recovery is possible when the adversary is allowed to add $o(n)$ edges. Our work answers this question affirmatively even in the case of $k>2$ communities. We then show that our algorithms work not only when the instances come from SBM, but also work when the instances come from any distribution of graphs that is $\epsilon m$ close to SBM in the Kullback---Leibler divergence. This result also works in the presence of adversarial errors. Finally, we present almost tight lower bounds for two communities.
What If We Considered Awareness for Sustainable Knowledge Management? Towards a Model for Self Regulated Knowledge Management Systems Based on Acceptance Models of Technologies and Awareness

We propose, in this paper, a model of continuous use of corporate collaborative KMS. Companies do not always have the guaranty that their KMS will be continuously used. This statement can constitute an important obstacle for knowledge management processes. Our work is based on the analysis of classical models for initial and continuous use of technologies. We also analyse the regulation concept and explain how it is valuable to support a continuous use of KMS. We observed that awareness may be a regulation means that allows taking this problem into account. Awareness is a concept, which has been profusely used to improve user experience in collaborative environments. It is an important element for regulation of activity. In our model, we assume that one can integrate awareness in information systems to positively influence beliefs about them. The final objective of our work is to refine some concepts to fit the particularities of collaborative KMS and to propose an awareness regulation process using the traces of the usersâ interactions with the systems.