nils-herrmann/clean_scirepeval huggingface dataset:
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair

Code comprehension is critical in software maintenance. Towards providing tools and approaches to support maintenance tasks, researchers have investigated various research lines related to how software code can be described in an abstract form. So far, studies on change pattern mining, code clone detection, or semantic patch inference have mainly adopted text-, tokenand tree-based representations as the basis for computing similarity among code fragments. Although, in general, existing techniques form clusters of “similar” code, our experience in patch mining has revealed that clusters of patches formed by such techniques do not usually carry explainable semantics that can be associated to bug-fixing patterns. In this paper, we propose a novel, automated approach for mining semantically-relevant fix patterns based on an iterative, three-fold, clustering strategy. Our technique, FixMiner, leverages different tree representations for each round of clustering: the Abstract syntax tree, the edit actions tree, and the code context tree. We have evaluated FixMiner on thousands of software patches collected from open source projects. Preliminary results show that we are able to mine accurate patterns, efficiently exploiting change information in AST diff trees. Eventually, FixMiner yields patterns which can be associated to the semantics of the bugs that the associated patches address. We further leverage the mined patterns to implement an automated program repair pipeline with which we are able to correctly fix 25 bugs from the Defects4J benchmark. Beyond this quantitative performance, we show that the mined fix patterns are sufficiently relevant to produce patches with a high probability of correctness: 80% of FixMiner’s A. Koyuncu, K. Liu, T. F. Bissyandé, D. Kim,  Kim, and Y. Le Traon SnT, University of Luxembourg E-mail: {firstname.lastname}@uni.lu M. Monperrus KTH Royal Institute of Technology E-mail: martin.monperrus@csc.kth.se ar X iv :1 81 0. 01 79 1v 1 [ cs .S E ] 3 O ct 2 01 8 2 Anil Koyuncu et al. generated plausible patches are correct, while the closest related works, namely HDRepair and SimFix, achieve respectively 26% and 70% of correctness.
Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks

We propose a novel approach for constructing effective treatment policies when the observed data is biased and lacks counterfactual information. Learning in settings where the observed data does not contain all possible outcomes for all treatments is difficult since the observed data is typically biased due to existing clinical guidelines. This is an important problem in the medical domain as collecting unbiased data is expensive and so learning from the wealth of existing biased data is a worthwhile task. Our approach separates the problem into two stages: first we reduce the bias by learning a representation map using a novel auto-encoder network – this allows us to control the trade-off between the bias-reduction and the information loss – and then we construct effective treatment policies on the transformed data using a novel feedforward network. Separation of the problem into these two stages creates an algorithm that can be adapted to the problem at hand – the bias-reduction step can be performed as a preprocessing step for other algorithms. We compare our algorithm against state-of-art algorithms on two semi-synthetic datasets and demonstrate that our algorithm achieves a significant improvement in performance.
Markov Logic Networks with Numerical Constraints

Markov logic networks (MLNs) have proven to be useful tools for reasoning about uncertainty in complex knowledge bases. In this paper, we extend MLNs with numerical constraints and present an efficient implementation in terms of a cutting plane method. This extension is useful for reasoning over uncertain temporal data. To show the applicability of this extension, we enrich log-linear description logics (DLs) with concrete domains (datatypes). Thereby, allowing to reason over weighted DLs with datatypes. Moreover, we use the resulting formalism to reason about temporal assertions in DBpedia, thus illustrating its practical use.
Soft Manipulators and Grippers: A Review

Soft robotics is a growing area of research which utilizes the compliance and adaptability of soft structures to develop highly adaptive robotics for soft interactions. One area in which soft robotics has the ability to make significant impact is in the development of soft grippers and manipulators. With an increased requirement for automation, robotics systems are required to perform task in unstructured and not well defined environments; conditions which conventional rigid robotics are not best suited. This requires a paradigm shift in the methods and materials used to develop robots such that they can adapt to and work safely in human environments. One solution to this is soft robotics, which enables soft interactions with the surroundings while maintaining the ability to apply significant force. This review paper assesses the current materials and methods, actuation methods and sensors which are used in the development of soft manipulators. The achievements and shortcomings of recent technology in these key areas are evaluated, and this paper concludes with a discussion on the potential impacts of soft manipulators on industry and society.
Impact of Culture on Human Resource Management Practices : A 10-Country Comparison

Le ModeÁ le de Culture Fit explique la manieÁ re dont l'environnement socioculturel influence la culture interne au travail et les pratiques de la direction des ressources humaines. Ce modeÁ le a eÂ teÂ testeÂ sur 2003 salarieÂ s d'entreprises priveÂ es dans 10 pays. Les participants ont rempli un questionnaire de 57 items, destineÂ aÁ mesurer les perceptions de la direction sur 4 dimensions socioculturelles, 6 dimensions de culture interne au travail, et les pratiques HRM (Management des Ressources Humaines) dans 3 zones territoiriales. Une analyse pondeÂ reÂ e par reÂ gressions multiples, au niveau individuel, a montreÂ que les directeurs qui caracteÂ risaient leurs environnement socio-culturel de facË on fataliste, supposaient aussi que les employeÂ s n'eÂ taient pas malleÂ ables par nature. Ces directeurs ne pratiquaient pas l'enrichissement des postes et donnaient tout pouvoir au controÃ le et aÁ la reÂ muneÂ ration en fonction des performances. Les directeurs qui appreÂ ciaient une grande loyauteÂ des APPLIED PSYCHOLOGY: AN INTERNATIONAL REVIEW, 2000, 49 (1), 192±221
Data mining techniques and applications — A decade review

Data mining is also known as Knowledge Discovery in Database (KDD). It is also defined as the process which includes extracting the interesting, interpretable and useful information from the raw data. There are different sources that generate raw data in very large amount. This is the main reason the applications of data mining are increasing rapidly. This paper reviews data mining techniques and its applications such as educational data mining (EDM), finance, commerce, life sciences and medical etc. We group existing approaches to determine how the data mining can be used in different fields. Our categorization specifically focuses on the research that has been published over the period 2007–2017. With this categorization, we present an easy and concise view of different models adapted in the data mining.
Deep Learning for Stock Market Prediction Using Event Embedding and Technical Indicators

Recently, ability to handle tremendous amounts of information using increased computational capabilities has improved prediction of stock market behavior. Complex machine learning algorithms such as deep learning methods can analyze and detect complex data patterns. The recent prediction models use two types of inputs as (i) numerical information such as historical prices and technical indicators, and (ii) textual information including news contents or headlines. However, the use of textual data involves text representation construction. Traditional methods like word embedding may not be suitable for representing the semantics of financial news due to problems of word sparsity in datasets. In this paper, we aim to improve stock market predictions using a deep learning approach with event embedding vectors extracted from news headlines, historical price data, and a set of technical indicators as input. Our prediction model consists of Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) architectures. We use accuracy and annualized return based on trading simulation as performance metrics, and then perform experiments on three datasets obtained from different news sources namely Reuters, Reddit, and Intrinio. Results show that enhancing text representation vectors and considering both numerical and textual information as input to a deep neural network can improve prediction performance.
A Multi-Modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling

While deep convolutional neural networks have shown a remarkable success in image classification, the problems of inter-class similarities, intra-class variances, the effective combination of multi-modal data, and the spatial variability in images of objects remain to be major challenges. To address these problems, this paper proposes a novel framework to learn a discriminative and spatially invariant classification model for object and indoor scene recognition using multi-modal RGB-D imagery. This is achieved through three postulates: 1) spatial invariance <inline-formula><tex-math notation="LaTeX">$-$</tex-math><alternatives> <inline-graphic xlink:href="asif-ieq1-2747134.gif"/></alternatives></inline-formula>this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations, rotations, and scale changes, 2) high discriminative capability<inline-formula> <tex-math notation="LaTeX">$-$</tex-math><alternatives><inline-graphic xlink:href="asif-ieq2-2747134.gif"/> </alternatives></inline-formula>this is achieved by introducing Fisher encoding within the CNN architecture to learn features which have small inter-class similarities and large intra-class compactness, and 3) multi-modal hierarchical fusion<inline-formula><tex-math notation="LaTeX">$-$</tex-math><alternatives> <inline-graphic xlink:href="asif-ieq3-2747134.gif"/></alternatives></inline-formula>this is achieved through the regularization of semantic segmentation to a multi-modal CNN architecture, where class probabilities are estimated at different hierarchical levels (i.e., image- and pixel-levels), and fused into a Conditional Random Field (CRF)-based inference hypothesis, the optimization of which produces consistent class labels in RGB-D images. Extensive experimental evaluations on RGB-D object and scene datasets, and live video streams (acquired from Kinect) show that our framework produces superior object and scene classification results compared to the state-of-the-art methods.
WhirlingFuzzwork: a taint-analysis-based API in-memory fuzzing framework

Fuzz testing is widely used as an automatic solution for discovering vulnerabilities in binary programs that process files. Restricted by their high blindness and low code path coverage, fuzzing tests typically provide quite low efficiencies. In this paper, a novel API in-memory fuzz testing technique for eliminating the blindness of existing techniques is discussed. This technique employs dynamic taint analyses to locate the routines and instructions that belong to the target binary executables, and it consists of parsing and processing the input data. Within the testing phase, binary instrumentation is used to construct loops around such routines, in which the contained taint memory values are mutated in each loop. According to experiments using the prototype tool, this technique could effectively detect defects such as stack overflows. Comparedwith traditional fuzzing tools, this API in-memory fuzzing eliminated the bottleneck of interrupting execution paths and gained a greater than 95% enhancement in execution speed. Communicated by V. Loia. B Baojiang Cui cuibj@bupt.edu.cn Fuwei Wang fuwei.wfw@alibaba-inc.com Xiaofeng Chen xfchen@xidian.edu.cn 1 Beijing University of Posts and Telecommunications and National Engineering Laboratory for Mobile Network Security, Beijing, China 2 Security Department, Alibaba Group, Hangzhou, China 3 China Information Technology Security Evaluation Center, Beijing, China 4 School of Telecommunications Engineering, Xidian University, Xi’an, China
Unified Pragmatic Models for Generating and Following Instructions

We show that explicit pragmatic inference aids in correctly generating and following natural language instructions for complex, sequential tasks. Our pragmatics-enabled models reason about why speakers produce certain instructions, and about how listeners will react upon hearing them. Like previous pragmatic models, we use learned base listener and speaker models to build a pragmatic speaker that uses the base listener to simulate the interpretation of candidate descriptions, and a pragmatic listener that reasons counterfactually about alternative descriptions. We extend these models to tasks with sequential structure. Evaluation of language generation and interpretation shows that pragmatic inference improves state-of-the-art listener models (at correctly interpreting human instructions) and speaker models (at producing instructions correctly interpreted by humans) in diverse settings.
Substitute Teacher Networks: Learning with Almost No Supervision

Learning through experience is time-consuming, inefficient and often bad for your cortisol levels. To address this problem, a number of recently proposed teacherstudent methods have demonstrated the benefits of private tuition, in which a single model learns from an ensemble of more experienced tutors. Unfortunately, the cost of such supervision restricts good representations to a privileged minority. Unsupervised learning can be used to lower tuition fees, but runs the risk of producing networks that require extracurriculum learning to strengthen their CVs and create their own LinkedIn profiles1. Inspired by the logo on a promotional stress ball at a local recruitment fair, we make the following three contributions. First, we propose a novel almost no supervision training algorithm that is effective, yet highly scalable in the number of student networks being supervised, ensuring that education remains affordable. Second, we demonstrate our approach on a typical use case: learning to bake, developing a method that tastily surpasses the current state of the art. Finally, we provide a rigorous quantitive analysis of our method, proving that we have access to a calculator2. Our work calls into question the long-held dogma that life is the best teacher. Give a student a fish and you feed them for a day, teach a student to gatecrash seminars and you feed them until the day they move to Google.
Bearing Fault Diagnosis Using Fully-Connected Winner-Take-All Autoencoder

Intelligent fault diagnosis of bearings has been a heated research topic in the prognosis and health management of rotary machinery systems, due to the increasing amount of available data collected by sensors. This has given rise to more and more business desire to apply data-driven methods for health monitoring of machines. In recent years, various deep learning algorithms have been adapted to this field, including multi-layer perceptrons, autoencoders, convolutional neural networks, and so on. Among these methods, autoencoder is of particular interest for us because of its simple structure and its ability to learn useful features from data in an unsupervised fashion. Previous studies have exploited the use of autoencoders, such as denoising autoencoder, sparsity aotoencoder, and so on, either with one layer or with several layers stacked together, and they have achieved success to certain extent. In this paper, a bearing fault diagnosis method based on fully-connected winner-take-all autoencoder is proposed. The model explicitly imposes lifetime sparsity on the encoded features by keeping only  $k$ % largest activations of each neuron across all samples in a mini-batch. A soft voting method is implemented to aggregate prediction results of signal segments sliced by a sliding window to increase accuracy and stability. A simulated data set is generated by adding white Gaussian noise to original signals to test the diagnosis performance under noisy environment. To evaluate the performance of the proposed method, we compare our methods with some state-of-the-art bearing fault diagnosis methods. The experiments result show that, with a simple two-layer network, the proposed method is not only capable of diagnosing with high precision under normal conditions, but also has better robustness to noise than some deeper and more complex models.
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving

Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN are perceived as the most impactful direction of machine learning in the last decade. This paper focuses on the application of GAN in autonomous driving including topics such as advanced data augmentation, loss function learning, semi-supervised learning, etc. We formalize and review key applications of adversarial techniques and discuss challenges and open problems to be addressed.
Effects of Annotations and Homework on Learning Achievement: An Empirical Study of Scratch Programming Pedagogy

In Taiwan elementary schools, Scratch programming has been taught for more than four years. Previous studies have shown that personal annotations is a useful learning method that improve learning performance. An annotation-based Scratch programming (ASP) system provides for the creation, share, and review of annotations and homework solutions in the interface of Scratch programming. In addition, we combine the ASP system with the problem solving-based teaching approach in Scratch programming pedagogy, which boosts cognition development and enhances learning achievements. This study is aimed at exploring the effects of annotations and homework on learning achievement. A quasi-experimental method was used with elementary school students in a Scratch programming course over a 4-month period. The experimental results revealed that students’ thoughts and solutions in solving homework assignments have a significant influence on learning achievement. We further investigated that only making annotations in solving homework activities, among all other variables (the quantity of annotations, the quantity of one’s own annotations reviewed, the quantity of peers’ annotations reviewed, the quantity of one’s own homework solutions reviewed, and the quantity of peers’ homework solutions reviewed), can significantly predict learning achievements.
BOYS WILL BE BOYS : GENDER , OVERCONFIDENCE , AND COMMON STOCK INVESTMENT

Theoretical models predict that overcondent investors trade excessively. We test this prediction by partitioning investors on gender. Psychological research demonstrates that, in areas such as nance, men are more overcondent than women. Thus, theory predicts that men will trade more excessively than women. Using account data for over 35,000 households from a large discount brokerage, we analyze the common stock investments of men and women from February 1991 through January 1997. We document that men trade 45 percent more than women. Trading reduces men’s net returns by 2.65 percentage points a year as opposed to 1.72 percentage points for women.
IOT based Energy Meter Monitoring using ARM Cortex M4 with Android Application

Internet of Things (IOT) aims at interfacing different gadgets to the internet web – encouraging human-machine and machine-machine connections offering superior security, console and effectiveness. The concept of IOT is utilized in this model, remote monitoring of energy meter which is intended to overcome the issues in existing Automatic Meter Reading (AMR) system. It spares tremendous human work. A controller integrated with electronic energy meter assist in distant correspondence from the developed android application. This application enables monitoring of bill generation at consumer premises without human intervention and also in visualizing live data consumption and sight energy expended points of interest on daily/monthly basis. In addition, it gives authority to power organizations to seize lenient customers who have extraordinary dues for remote disconnection of the power supply. So IOT based remote AMR framework is more viable methodology than tradition of billing framework. General Terms Central office
Partially Observable Markov Decision Processes for Spoken Dialogue Management

Partially Observable Markov Decision Processes for Spoken Dialogue Management Jason D. Williams The design of robust spoken dialog systems is a significant research challenge. Speech recognition errors are common and hence the state of the conversation can never be known with certainty, and users can react in a variety of ways making deterministic forward planning impossible. This thesis argues that a partially observable Markov decision process (POMDP) provides a principled formalism for modelling human-machine conversation. Further, this thesis introduces the SDS-POMDP framework which enables statistical models of users’ behavior and the speech recognition process to be combined with handcrafted heuristics into a single framework that supports global optimization. A combination of theoretical and empirical studies confirm that the SDS-POMDP framework unifies and extends existing techniques, such as local use of confidence score, maintaining parallel dialog hypotheses, and automated planning. Despite its potential, the SDS-POMDP model faces important scalability challenges, and this thesis next presents two methods for scaling up the SDS-POMDP model to realistically sized spoken dialog systems. First, summary point-based value iteration (SPBVI) enables a single slot (a dialog variable such as a date, time, or location) to take on an arbitrary number of values by restricting the planner to consider only the likelihood of the best hypothesis. Second, composite SPBVI (CSPBVI) enables dialog managers consisting of many slots to be created by planning locally within each slot, and combining these local plans into a global plan using a simple heuristic. Results from dialog simulation show that these techniques enable the SDS-POMDP model to handle real-world dialog problems while continuing to out-perform established techniques and hand-crafted dialog managers. Finally, application to a real spoken dialog system is demonstrated.
Hemodynamic shear stress and its role in atherosclerosis.

Atherosclerosis, the leading cause of death in the developed world and nearly the leading cause in the developing world, is associated with systemic risk factors including hypertension, smoking, hyperlipidemia, and diabetes mellitus, among others. Nonetheless, atherosclerosis remains a geometrically focal disease, preferentially affecting the outer edges of vessel bifurcations. In these predisposed areas, hemodynamic shear stress, the frictional force acting on the endothelial cell surface as a result of blood flow, is weaker than in protected regions. Studies have identified hemodynamic shear stress as an important determinant of endothelial function and phenotype. Arterial-level shear stress (>15 dyne/cm2) induces endothelial quiescence and an atheroprotective gene expression profile, while low shear stress (<4 dyne/cm2), which is prevalent at atherosclerosis-prone sites, stimulates an atherogenic phenotype. The functional regulation of the endothelium by local hemodynamic shear stress provides a model for understanding the focal propensity of atherosclerosis in the setting of systemic factors and may help guide future therapeutic strategies.