nils-herrmann/clean_scirepeval huggingface dataset:
Using Cohesion and Coupling for Software Remodularization: Is It Enough?

Refactoring and, in particular, remodularization operations can be performed to repair the design of a software system and remove the erosion caused by software evolution. Various approaches have been proposed to support developers during the remodularization of a software system. Most of these approaches are based on the underlying assumption that developers pursue an optimal balance between cohesion and coupling when modularizing the classes of their systems. Thus, a remodularization recommender proposes a solution that implicitly provides a (near) optimal balance between such quality attributes. However, there is still no empirical evidence that such a balance is the desideratum by developers. This article aims at analyzing both objectively and subjectively the aforementioned phenomenon. Specifically, we present the results of (1) a large study analyzing the modularization quality, in terms of package cohesion and coupling, of 100 open-source systems, and (2) a survey conducted with 29 developers aimed at understanding the driving factors they consider when performing modularization tasks. The results achieved have been used to distill a set of lessons learned that might be considered to design more effective remodularization recommenders.
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics

Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. The problem occurs, in the binary case, when the number of instances in one class significantly outnumbers the number of instances in the other class. This situation is a handicap when trying to identify the minority class, as the learning algorithms are not usually adapted to such characteristics. The approaches to deal with the problem of imbalanced datasets fall into two major categories: data sampling and algorithmic modification. Cost-sensitive learning solutions incorporating both the data and algorithm level approaches assume higher misclassification costs with samples in the minority class and seek to minimize high cost errors. Nevertheless, there is not a full exhaustive comparison between those models which can help us to determine the most appropriate one under different scenarios. The main objective of this work is to analyze the performance of data level proposals against algorithm level proposals focusing in cost-sensitive models and versus a hybrid procedure that combines those two approaches. We will show, by means of a statistical comparative analysis, that we cannot highlight an unique approach among the rest. This will lead to a discussion about the data intrinsic characteristics of the imbalanced classification problem which will help to follow new paths that can lead to the improvement of current models mainly focusing on class overlap and dataset shift in imbalanced classification.
Tunneling Neural Perception and Logic Reasoning through Abductive Learning

Perception and reasoning are basic human abilities that are seamlessly connected as part of human intelligence. However, in current machine learning systems, the perception and reasoning modules are incompatible. Tasks requiring joint perception and reasoning ability are difficult to accomplish autonomously and still demand human intervention. Inspired by the way language experts decoded Mayan scripts by joining two abilities in an abductive manner, this paper proposes the abductive learning framework. The framework learns perception and reasoning simultaneously with the help of a trial-and-error abductive process. We present the Neural-Logical Machine as an implementation of this novel learning framework. We demonstrate thatusing human-like abductive learningthe machine learns from a small set of simple hand-written equations and then generalizes well to complex equations, a feat that is beyond the capability of state-of-the-art neural network models. The abductive learning framework explores a new direction for approaching human-level learning ability.
DiSCO: Distributed Optimization for Self-Concordant Empirical Loss

We propose a new distributed algorithm for empirical risk minimization in machine learning. The algorithm is based on an inexact damped Newton method, where the inexact Newton steps are computed by a distributed preconditioned conjugate gradient method. We analyze its iteration complexity and communication efficiency for minimizing self-concordant empirical loss functions, and discuss the results for distributed ridge regression, logistic regression and binary classification with a smoothed hinge loss. In a standard setting for supervised learning, where the n data points are i.i.d. sampled and when the regularization parameter scales as 1/ √ n, we show that the proposed algorithm is communication efficient: the required round of communication does not increase with the sample size n, and only grows slowly with the number of machines.
Employee Turnover in banking sector : Empirical evidence 1

“Employee turnover” as a term is widely used in business circles. Although several studies have been conducted on this topic, most of the researchers focus on the causes of employee turnover. This research looked at extent of influence of various factors on employee turnover in urban and semi urban banks. The research was aimed at achieving the following objectives: identify the key factors of employee turnover; determine the extent to which the identified factors are influencing employees’ turnover. The study is based on the responses of the employees of leading banks. A self-developed questionnaire, measured on a Likert Scale was used to collect data from respondents. Quantitative research design was used and this design was chosen because its findings are generaliseable and data objective. The reliability of the data collected is done by split half method.. The collected data were being analyzed using a program called Statistical Package for Social Science (SPSS ver.16.0 For Windows). The data analysis is carried out by calculating mean, standard deviation and linear correlation. The difference between means of variable was estimated by using t-test. The following factors have significantly influenced employee turnover in banking sector: Work Environment, Job Stress, Compensation (Salary), Employee relationship with management, Career Growth.
A music notation construction engine for optical music recognition

Optical music recognition (OMR) systems are used to convert music scanned from paper into a format suitable for playing or editing on a computer. These systems generally have two phases: recognizing the graphical symbols (such as note-heads and lines) and determining the musical meaning and relationships of the symbols (such as the pitch and rhythm of the notes). In this paper we explore the second phase and give a two-step approach that admits an economical representation of the parsing rules for the system. The approach is flexible and allows the system to be extended to new notations with little effort—the current system can parse common music notation, Sacred Harp notation and plainsong. It is based on a string grammar and a customizable graph that specifies relationships between musical objects. We observe that this graph can be related to printing as well as recognizing music notation, bringing the opportunity for cross-fertilization between the two areas of research.
Gamification of ERP Systems – Exploring Gamification Effects on User Acceptance Constructs

The adoption of game mechanics into serious contexts such as business applications (gamification) is a promising trend to improve the user’s participation and engagement with the software in question and on the job. However, this topic is mainly driven by practitioners. A theoretical model for gamification with appropriate empirical validation is missing. In this paper, we introduce a prototype for gamification using SAP ERP as example. Moreover, we have evaluated the concept within a comprehensive user study with 112 participants based on the technology acceptance model (TAM) using partial least squares (PLS) for analysis. Finally, we show that this gamification approach yields significant improvements in latent variables such as enjoyment, flow or perceived ease of use. Moreover, we outline further research requirements in the domain of gamification.
Dynamic control of autonomous quadrotor flight in an estimated wind field

We present a nonlinear, dynamic controller for a 6DOF quadrotor operating in an estimated, spatially varying, turbulent wind field. The quadrotor dynamics include the aerodynamic effects of drag, rotor blade flapping, and induced thrust due to translational velocity and external wind fields. To control the quadrotor we use a dynamic input/output feedback linearization controller that estimates a parametric model of the wind field using a recursive Bayesian filter. Each rotor experiences a possibly different wind field, which introduces moments that are accounted for in the controller and allows flight in wind fields that vary over the length of the vehicle. We add noise to the wind field in the form of Dryden turbulence to simulate the algorithm in two applications: autonomous ship landing and quadrotor proximity flight.
Stepping out of the box: towards analytics outside the learning management system

Most of the current learning analytic techniques have as starting point the data recorded by Learning Management Systems (LMS) about the interactions of the students with the platform and among themselves. But there is a tendency on students to rely less on the functionality offered by the LMS and use more applications that are freely available on the net. This situation is magnified in studies in which students need to interact with a set of tools that are easily installed on their personal computers. This paper shows an approach using Virtual Machines by which a set of events occurring outside of the LMS are recorded and sent to a central server in a scalable and unobtrusive manner.
Refactoring - improving coupling and cohesion of existing code

Refactorings are widely recognised as ways to improve the internal structure of object-oriented software while maintaining its external behaviour. Unfortunately, refactorings concentrate on the treatment of symptoms (the so called code-smells), thus improvements depend a lot on the skills of the maintained coupling and cohesion on the other hand are quality attributes which are generally recognized as being among the most likely quantifiable indicators for software maintainability. Therefore, this paper analyzes how refactorings manipulate coupling/cohesion characteristics, and how to identify refactoring opportunities that improve these characteristics. As such we provide practical guidelines for the optimal usage of refactoring in a software maintenance process.
On the Foundations of Statistical Inference Author ( s ) :

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Multimode Digital Controller for Synchronous Buck Converters Operating Over Wide Ranges of Input Voltages and Load Currents

This paper describes a digital pulsewidth modulation/pulse-frequency modulation (PWM/PFM) controller with input-voltage feedforward (IVFF) for synchronous buck dc-dc converters. The controller includes automatic PWM/PFM mode switching and effective synchronous operation with a minimum number of active components and without the need for current sensing in PFM mode of operation. IVFF improves efficiency and dynamic performance over a wide range of input voltages. Controller parameters, including the PWM switching frequency, the PFM pulse period, and the mode transition point are programmable, which enables efficiency optimization. Experimental results are shown for a synchronous buck converter with 5-12 V input voltage, and 1.3 V, 0-10 A output.
A Novel Slotless Halbach-Array Permanent-Magnet Brushless DC Motor for Spacecraft Applications

This paper presents the design and analysis of a novel machine family-the enclosed-rotor Halbach-array permanent-magnet brushless dc motors for spacecraft applications. The initial design, selection of major parameters, and air-gap magnetic flux density are estimated using the analytical model of the machine. The proportion of the Halbach array in the machine is optimized using finite element analysis to obtain a near-trapezoidal flux pattern. The machine is found to provide uniform air-gap flux density along the radius, thus avoiding circulating currents in stator conductors and thereby reducing torque ripple. Furthermore, the design is validated with experimental results on a fabricated machine and is found to suit the design requirements of critical spacecraft applications.
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement

We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is generally applicable to any sequence generation task. We extensively evaluate the proposed model on machine translation (En↔De and En↔Ro) and image caption generation, and observe that it significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart.
An improved Canny edge detection algorithm

The traditional Canny edge detection algorithm is sensitive to noise, therefore, it's easy to lose weak edge information when filtering out the noise, and its fixed parameters show poor adaptability. In response to these problems, this paper proposed an improved algorithm based on Canny algorithm. This algorithm introduced the concept of gravitational field intensity to replace image gradient, and obtained the gravitational field intensity operator. Two adaptive threshold selection methods based on the mean of image gradient magnitude and standard deviation were put forward for two kinds of typical images (one has less edge information, and the other has rich edge information) respectively. The improved Canny algorithm is simple and easy to realize. Experimental results show that the algorithm can preserve more useful edge information and more robust to noise.
Localized Supervised Metric Learning on Temporal Physiological Data

Effective patient similarity assessment is important for clinical decision support. It enables the capture of past experience as manifested in the collective longitudinal medical records of patients to help clinicians assess the likely outcomes resulting from their decisions and actions. However, it is challenging to devise a patient similarity metric that is clinically relevant and semantically sound. Patient similarity is highly context sensitive: it depends on factors such as the disease, the particular stage of the disease, and co-morbidities. One way to discern the semantics in a particular context is to take advantage of physicians’ expert knowledge as reflected in labels assigned to some patients. In this paper we present a method that leverages localized supervised metric learning to effectively incorporate such expert knowledge to arrive at semantically sound patient similarity measures. Experiments using data obtained from the MIMIC II database demonstrate the effectiveness of this approach.
Asymmetric Discrete Graph Hashing

Recently, many graph based hashing methods have been emerged to tackle large-scale problems. However, there exists two major bottlenecks: (1) directly learning discrete hashing codes is an NP-hard optimization problem; (2) the complexity of both storage and computational time to build a graph with n data points is O(n2). To address these two problems, in this paper, we propose a novel yet simple supervised graph based hashing method, asymmetric discrete graph hashing, by preserving the asymmetric discrete constraint and building an asymmetric affinity matrix to learn compact binary codes. Specifically, we utilize two different instead of identical discrete matrices to better preserve the similarity of the graph with short binary codes.We generate the asymmetric affinity matrix using m (m << n) selected anchors to approximate the similarity among all training data so that computational time and storage requirement can be significantly improved. In addition, the proposed method jointly learns discrete binary codes and a low-dimensional projection matrix to further improve the retrieval accuracy. Extensive experiments on three benchmark large-scale databases demonstrate its superior performance over the recent state of the arts with lower training time costs.
Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids

Alternative vehicles based on internal combustion engines (ICE), such as the hybrid electric vehicle (HEV), the plug-in hybrid electric vehicle (PHEV) and the fuel-cell electric vehicle (FCEV), are becoming increasingly popular. HEVs are currently commercially available and PHEVs will be the next phase in the evolution of hybrid and electric vehicles. The batteries of the PHEVs are designed to be charged at home, from a standard outlet in the garage, or on a corporate car park. The electrical consumption for charging PHEVs may take up to 5% of the total electrical consumption in Belgium by 2030. These extra electrical loads have an impact on the distribution grid which is analyzed in terms of power losses and voltage deviations. Firstly, the uncoordinated charging is described where the vehicles are charged immediately when they are plugged in or after a fixed start delay. This uncoordinated power consumption on a local scale can lead to grid problems. Therefore coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor. The optimal charge profile of the PHEVs is computed by minimizing the power losses. The exact forecasting of household loads is not possible, so stochastic programming is introduced.
Fidelity vs. simplicity: a global approach to line drawing vectorization

Vector drawing is a popular representation in graphic design because of the precision, compactness and editability offered by parametric curves. However, prior work on line drawing vectorization focused solely on faithfully capturing input bitmaps, and largely overlooked the problem of producing a compact and editable curve network. As a result, existing algorithms tend to produce overly-complex drawings composed of many short curves and control points, especially in the presence of thick or sketchy lines that yield spurious curves at junctions. We propose the first vectorization algorithm that explicitly balances fidelity to the input bitmap with simplicity of the output, as measured by the number of curves and their degree. By casting this trade-off as a global optimization, our algorithm generates few yet accurate curves, and also disambiguates curve topology at junctions by favoring the simplest interpretations overall. We demonstrate the robustness of our algorithm on a variety of drawings, sketchy cartoons and rough design sketches.
What is coordination theory

The primary purpose of this paper is to stimulate discussion about a research agenda for a new interdisciplinary field. This field-the study of coordination-draws upon a variety of different disciplines including computer science, organization theory, management science, economics, and psychology. Work in this new area will include developing a body of scientific theory, which we will call "coordination theory," about how the activities of separate actors can be coordinated. One important use for coordination theory will be in developing and using computer and communication systems to help people coordinate their activities in new ways. We will call these systems "coordination technology."  There are four reasons why work in this area is timely: (1) In recent years, large numbers of people have acquired direct access to computers. These computers are now beginning to be connected to each other. Therefore, we now have, for the first time, an opportunity for vastly larger numbers of people to use computing and communications capabilities to help coordinate their work. For example, specialized new software has been developed to (a) support multiple authors working together on the same document, (b) help people display and manipulate information more effectively in face-to-face meetings, and (c) help people intelligently route and process electronic messages. It already appears likely that there will be commercially successful products of this new type (often called "computer supported cooperative work" or "groupware"), and to some observers these applications herald a paradigm shift in computer usage as significant as the earlier shifts to time-sharing and personal computing. It is less clear whether the continuing development of new computer applications in this area will depend solely on the intuitions of successful designers or whether it will also be guided by a coherent underlying theory of how people coordinate their activities now and how they might do so differently with computer support. (2) In the long run, the dramatic improvements in the costs and capabilities of information technologies are changing-by orders of magnitude-the constraints on how certain kinds of communication and coordination can occur. At the same time, there is a pervasive feeling in American business that the pace of change is accelerating and that we need to create more flexible and adaptive organizations. Together, these changes may soon lead us across a threshhold where entirely new ways of organizing human activities become desirable. For 2 example, new capabilities for communicating information faster, less expensively, and …
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision

Better shape priors improve the mask accuracy and reduce false removal. Moving down the table from the no prior case to the box priors and then to the class specific shape priors from the Pascal dataset masks the masks smaller, improves the mIoU and also reduces the false removal rate. Input image Global loss Local loss Qualitative comparison of global vs local loss. Local real-fake loss improves the in-painting results producing sharper, texture-rich images, compared to smooth blurry results obtained by the global loss.
Improving Distributed Representation of Word Sense via WordNet Gloss Composition and Context Clustering

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentencelevel embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.