nils-herrmann/clean_scirepeval huggingface dataset:
A framework analysis of the open source software development paradigm

Open Source Software (OSS) has become the subject of much commercial interest of late. Certainly, OSS seems to hold much promise in addressing the core issues of the software crisis, namely that of software taking too long to develop, exceeding its budget, and not working very well. Indeed, there have been several examples of significant OSS success stories—the Linux operating system, the Apache web server, the BIND domain name resolution utility, to name but a few. However, little by way of rigorous academic research on OSS has been conducted to date. In this study, a framework was derived from two previous frameworks which have been very influential in the IS field, namely that of Zachman’s IS architecture (ISA) and Checkland’s CATWOE framework from Soft Systems Methodology (SSM). The resulting framework is used to analyze the OSS approach in detail. The potential future of OSS research is also discussed.
Risk-aware multi-armed bandit problem with application to portfolio selection

Sequential portfolio selection has attracted increasing interest in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision-making under uncertainty, namely the exploration versus exploitation dilemma, and therefore provides a natural connection to portfolio selection. In this paper, we incorporate risk awareness into the classic multi-armed bandit setting and introduce an algorithm to construct portfolio. Through filtering assets based on the topological structure of the financial market and combining the optimal multi-armed bandit policy with the minimization of a coherent risk measure, we achieve a balance between risk and return.
Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank's VIP Customer Churn as the Example

Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise. According to the bank's customer churn data which is large scale and imbalance, this paper presented a support vector machine model to predict customer churn. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for a commercial bank's VIP customers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for bank's customer churn prediction.
NIMS-PL: A Cable-Driven Robot With Self-Calibration Capabilities

We present the Networked InfoMechanical System for Planar Translation, which is a novel two-degree-of-freedom (2-DOF) cable-driven robot with self-calibration and online drift-correction capabilities. This system is intended for actuated sensing applications in aquatic environments. The actuation redundancy resulting from in-plane translation driven by four cables results in an infinite set of tension distributions, thus requiring real-time computation of optimal tension distributions. To this end, we have implemented a highly efficient, iterative linear programming solver, which requires a very small number of iterations to converge to the optimal value. In addition, two novel self-calibration methods have been developed that leverage the robot's actuation redundancy. The first uses an incremental displacement, or jitter method, whereas the second uses variations in cable tensions to determine end-effector location. We also propose a novel least-squares drift-detection algorithm, which enables the robot to detect long-term drift. Combined with self-calibration capabilities, this drift-monitoring algorithm enables long-term autonomous operation. To verify the performance of our algorithms, we have performed extensive experiments in simulation and on a real system.
Datasets for Aspect-Based Sentiment Analysis in French

Aspect Based Sentiment Analysis (ABSA) is the task of mining and summarizing opinions from text about specific entities and their aspects. This article describes two datasets for the development and testing of ABSA systems for French which comprise user reviews annotated with relevant entities, aspects and polarity values. The first dataset contains 457 restaurant reviews (2365 sentences) for training and testing ABSA systems, while the second contains 162 museum reviews (655 sentences) dedicated to out-of-domain evaluation. Both datasets were built as part of SemEval-2016 Task 5 “Aspect-Based Sentiment Analysis” where seven different languages were represented, and are publicly available for research purposes. This article provides examples and statistics by annotation type, summarizes the annotation guidelines and discusses their cross-lingual applicability. It also explains how the data was used for evaluation in the SemEval ABSA task and briefly presents the results obtained for French.
An empirical study on predicting user acceptance of e-shopping on the Web

This study develops an extended model to predict consumer acceptance of electronic-shopping (e-shopping) based on the theory of reasoned action (TRA) and the technology acceptance model (TAM). Our model was tested using data collected from 212 questionnaires and analyzed using multiple regression. The results show that individual attitudes toward e-shopping are strongly and positively correlated with user acceptance. The empirical results confirmed that perceived ease of use of trading online (PEOUT) and perceived usefulness (PU) significantly determine individual attitudes toward e-shopping, as well as confirming the significant effect of perceived ease of use of the Web on PEOUT, which in turn affects PU. However, PU was not found to affect user acceptance significantly. Additionally, user satisfaction with the Internet/WWW and perceptions of information, system and service were shown to affect user acceptance significantly. Overall, the proposed model could be used to predict consumer willingness to shop on the Web.
Digital Forensic Analysis of SIM Cards

Smart cards are fundamental technology in modern life. It is embedded in numerous devices such as GPS devices, ATM cards, Mobile SIM cards and many others. Mobile devices became the evolution of technology. It becomes smaller, faster and supports large storage capabilities. Digital forensics of mobile devices that maybe found in crime scene is becoming inevitable. The purpose of this research is to address the SIM cards digital forensics analysis. It presents sound forensic methodology and process of SIM cards forensic examination. In particular, the main aim of the research is to answer the following research questions: (1) what forensic evidence could be extracted from a SIM card and (2) what are limitations that may hinder a forensic
Three learning phases for radial-basis-function networks

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.
Real-Time Top-View People Counting Based on a Kinect and NVIDIA Jetson TK1 Integrated Platform

In this paper, we describe how to establish an embedded framework for real-time top-view people counting. The development of our system consists of two parts, i.e. establishing an embedded signal processing platform and designing a people counting algorithm for the embedded system. For the hardware platform construction, we use Kinect as the camera and exploit NVIDIA Jetson TK1 board as the embedded processing platform. We describe how to build a channel to make Kinect for windows version 2.0 communicate with Jetson TK1. Based on the embedded system, we adapt a water filling based scheme for top-view people counting, which integrates head detection based on water drop, people tracking and counting. Gaussian Mixture Model is used to construct and update the background model. The moving people in each video frame are extracted using background subtraction method. Additionally, the water filling algorithm is used to segment head area as Region Of Interest(ROI). Tracking and counting people are performed by calculating the distance of ROI center point before and after the frame. The whole framework is flexible and practical for real-time application.
Degradation of mechanical properties of class-H winding insulation

Thermal ageing is one of the key factors that result in insulation degradation leading to lower dielectric strength and thereafter occurrence of partial discharge in high voltage machines. However, in low voltage high current machines, the occurrence of partial discharge is minimal and degradation of dielectric strength alone will not cause the breakdown of the insulation. This paper hypothesizes that the breakdown of insulation in low voltage/high current machines occur as a result of insulation cracking among other factors. In this paper, it is shown that thermal degradation leads to degradation of mechanical properties of Polyamide-imide insulation, particularly reduction of the ultimate tensile strength and leads to enhanced brittleness. It follows that once a crack in the insulation is formed, the breakdown strength at the point of cracking will be lower. Multiple such cracks may lead to the formation of a breakdown path thereafter leading to aggravated heating and further accelerated deterioration of the insulation. In this paper, the methodology for testing the mechanical properties of insulation material is presented. A model to account for the variation of such properties due to thermal degradation is developed.
Towards Modeling and Reasoning Support for Early-Phase Requirements Engineering

Requirements are usually understood as stating what a system is supposed to do, as opposed to how it should do it. Howevel; understanding the organizational context and rationales (the “Whys”) that lead up to systems requirements can be just as important for the ongoing success of the system. Requirements modelling techniques can be used to help deal with the knowledge and reasoning needed in this earlier phase of requirements engineering. Howevel; most existing requirements techniques are intended more for the later phase of requirements engineering, which focuses on completeness, consistency, and automated veriJcation of requirements. In contrast, the early phase aims to model and analyze stakeholder interests and how they might be addressed, or compromised, by various system-and-environment alternatives. This paper argues, therefore, that a direrent kind of modelling and reasoning support is needed for the early phase. An outline of the i* framework is given as an example of a step in this direction. Meeting scheduling is used as a domain example.
Semantic Representation

In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief “opinionated survey” of broadcoverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR–some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on imple-
Active Release of Microobjects Using a MEMS Microgripper to Overcome Adhesion Forces

Due to force scaling laws, large adhesion forces at the microscale make rapid accurate release of microobjects a long-standing challenge in pick-place micromanipulation. This paper presents a new microelectromechanical systems (MEMS) microgripper integrated with a plunging mechanism to impact the microobject for it to gain sufficient momentum to overcome adhesion forces. The performance was experimentally quantified through the manipulation of 7.5-10.9-mum borosilicate glass spheres in an ambient environment under an optical microscope. Experimental results demonstrate that this microgripper, for the first time, achieves a 100% successful release rate (based on 200 trials) and a release accuracy of 0.70 plusmn0.46 mum. Experiments with conductive and nonconductive substrates also confirmed that the release process is not substrate dependent. Theoretical analyses were conducted to understand the release principle. Based on this paper, further scaling down the end structure of this microgripper will possibly provide an effective solution to the manipulation of submicrometer-sized objects.
Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away.

In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible.
An empirical study of the correlation between code coverage and reliability estimation

Existing time-domain models for software reliability often result in an overestimation of such reliability because they do not take the nature of testing techniques into account. Since every testing technique has a limit to its ability to reveal faults in a given system , as a technique approaches its saturation region fewer faults are discovered and reliability growth phenomena are predicted from the models. When the software is turned over to eld operation, signiicant overestimates of reliability are observed. In this paper, we present a technique to solve this problem by addressing both time and coverage measures for the prediction of software failures. Our technique uses coverage information collected during testing to extract only eeective data from a given operational proole. Execution time between test cases which neither increase coverage nor cause a failure is reduced by a parameterized factor. Experiments using this technique were conducted on a program created in a simulation environment with simulated faults and on an industrial automatic ight control project which contained several natural faults. Results from both experiments indicate that overesti-mation of reliability is reduced signiicantly using our technique. This new approach not only helps reliability growth models make more accurate predictions, but also reveals the eeciency of a testing proole so that more eeective testing techniques can be conducted.
Emotion recognition in the wild from videos using images

This paper presents the implementation details of the proposed solution to the Emotion Recognition in the Wild 2016 Challenge, in the category of video-based emotion recognition. The proposed approach takes the video stream from the audio-video trimmed clips provided by the challenge as input and produces the emotion label corresponding to this video sequence. This output is encoded as one out of seven classes: the six basic emotions (Anger, Disgust, Fear, Happiness, Sad, Surprise) and Neutral. Overall, the system consists of several pipelined modules: face detection, image pre-processing, deep feature extraction, feature encoding and, finally, an SVM classification. 
 This system achieves 59.42% validation accuracy, surpassing the competition baseline of 38.81%. With regard to test data, our system achieves 56.66% recognition rate, also improving the competition baseline of 40.47%.
Efficient SAT Approach to Multi-Agent Path Finding Under the Sum of Costs Objective

In the multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents. In this paper we present the first SAT-solver for the sum-of-costs variant of MAPF which was previously only solved by search-based methods. Using both a lower bound on the sum-of-costs and an upper bound on the makespan, we are able to have a reasonable number of variables in our SAT encoding. We then further improve the encoding by borrowing ideas from ICTS, a search-based solver. Experimental evaluation on several domains showed that there are many scenarios where the new SAT-based method outperforms the best variants of previous sumof-costs search solvers the ICTS and ICBS algorithms.
Adding the Next Nine: An Investigation of Mobile Broadband Networks Availability

The near ubiquitous availability and success of mobile broadband networks has motivated verticals that range from public safety communication to intelligent transportation systems and beyond to consider choosing them as the communication mean of choice. Several of these verticals, however, expect high availability of multiple nines. This paper leverages end-to-end measurements to investigate the potential of current mobile broadband networks to support these expectations. We conduct a large-scale measurement study of network availability in four networks in Norway. This study is based on three years of measurements from hundreds of stationary measurement nodes and several months of measurements from four mobile nodes. We find that the mobile network centralized architecture and infrastructure sharing between operators are responsible for a non-trivial fraction of network failures. Most episodes of degraded availability, however, are uncorrelated. We also find that using two networks simultaneously can result in more than five nines of availability for stationary nodes and three nines of availability for mobile nodes. Our findings point to potential avenues for enhancing the availability of future mobile networks.
An iterative maximum-likelihood polychromatic algorithm for CT

A new iterative maximum-likelihood reconstruction algorithm for X-ray computed tomography is presented. The algorithm prevents beam hardening artifacts by incorporating a polychromatic acquisition model. The continuous spectrum of the X-ray tube is modeled as a number of discrete energies. The energy dependence of the attenuation is taken into account by decomposing the linear attenuation coefficient into a photoelectric component and a Compton scatter component. The relative weight of these components is constrained based on prior material assumptions. Excellent results are obtained for simulations and for phantom measurements. Beam-hardening artifacts are effectively eliminated. The relation with existing algorithms is discussed. The results confirm that improving the acquisition model assumed by the reconstruction algorithm results in reduced artifacts. Preliminary results indicate that metal artifact reduction is a very promising application for this new algorithm.
Adversarial Localization Network

We propose the Adversarial Localization Network, a novel weakly supervised approach to generate object masks in an image. We train a corruption net to mask out regions of the image to reduce prediction confidence of the classifier. To avoid generating adversarial artifacts due to vulnerability of deep networks to adversarial perturbation, we also co-train the classifier to correctly predict the corrupted images. Our approach could efficiently produce reasonable object masks with a simple architecture and few hyper-parameters. We achieve competitive results on the ILSVRC2012 dataset for object localization with only image level labels and no bounding boxes for training.
Morpho-syntactically Annotated Amharic Treebank

In this paper, we describe an ongoing project of developing a treebank for Amharic. The main objective of developing the treebank is to use it as an input for the development of a parser. Morphologically-rich Languages like Arabic, Amharic and other Semitic languages present challenges to the state-of-art in parsing. In such language morphemes play important functions in both morphology and syntax. In addition to the existence of high lexical variations due the morphology, Amharic has a number of clitics which are not indicated with any special marker in the orthography. Considering the status of Amharic resources and challenges to the existing approach to parsing, we suggest to develop a treebank where clitics are separated manually from content words and annotated semi-automatically for part-of-speech, morphological features and syntactic relations.