nils-herrmann/clean_scirepeval huggingface dataset:
Metrics for Requirements Engineering and Automated Requirements Tools

Software requirements are the foundations from which quality is measured. Measurement enables to improve the software process; assist in planning, tracking and controlling the software project and assess the quality of the software thus produced. Quality issues such as accuracy, security and performance are often crucial to the success of a software system. Quality should be maintained from starting phase of software development. Requirements management, play an important role in maintaining quality of software. A project can deliver the right solution on time and within budget with proper requirements management. Software quality can be maintained by checking quality attributes in requirements document. Requirements metrics such as volatility, traceability, size and completeness are used to measure requirements engineering phase of software development lifecycle. Manual measurement is expensive, time consuming and prone to error therefore automated tools should be used. Automated requirements tools are helpful in measuring requirements metrics. The aim of this paper is to study, analyze requirements metrics and automated requirements tools, which will help in choosing right metrics to measure software development based on the evaluation of Automated Requirements Tools
Sketch-based modeling with few strokes

We present a novel sketch-based system for the interactive modeling of a variety of free-form 3D objects using just a few strokes. Our technique is inspired by the traditional illustration strategy for depicting 3D forms where the basic geometric forms of the subjects are identified, sketched and progressively refined using few key strokes. We introduce two parametric surfaces, rotational and cross sectional blending, that are inspired by this illustration technique. We also describe orthogonal deformation and cross sectional oversketching as editing tools to complement our modeling techniques. Examples with models ranging from cartoon style to botanical illustration demonstrate the capabilities of our system.
Soil macronutrient sensing for precision agriculture.

Accurate measurements of soil macronutrients (i.e., nitrogen, phosphorus, and potassium) are needed for efficient agricultural production, including site-specific crop management (SSCM), where fertilizer nutrient application rates are adjusted spatially based on local requirements. Rapid, non-destructive quantification of soil properties, including nutrient levels, has been possible with optical diffuse reflectance sensing. Another approach, electrochemical sensing based on ion-selective electrodes or ion-selective field effect transistors, has been recognized as useful in real-time analysis because of its simplicity, portability, rapid response, and ability to directly measure the analyte with a wide range of sensitivity. Current sensor developments and related technologies that are applicable to the measurement of soil macronutrients for SSCM are comprehensively reviewed. Examples of optical and electrochemical sensors applied in soil analyses are given, while advantages and obstacles to their adoption are discussed. It is proposed that on-the-go vehicle-based sensing systems have potential for efficiently and rapidly characterizing variability of soil macronutrients within a field.
A Boost Converter With Capacitor Multiplier and Coupled Inductor for AC Module Applications

The grid-connected AC module is an alternative solution in photovoltaic (PV) generation systems. It combines a PV panel and a micro-inverter connected to grid. The use of a high step-up converter is essential for the grid-connected micro-inverter because the input voltage is about 15 V to 40 V for a single PV panel. The proposed converter employs a Zeta converter and a coupled inductor, without the extreme duty ratios and high turns ratios generally needed for the coupled inductor to achieve high step-up voltage conversion; the leakage-inductor energy of the coupled inductor is efficiently recycled to the load. These features improve the energy-conversion efficiency. The operating principles and steady-state analyses of continuous and boundary conduction modes, as well as the voltage and current stresses of the active components, are discussed in detail. A 25 V input voltage, 200 V output voltage, and 250 W output power prototype circuit of the proposed converter is implemented to verify the feasibility; the maximum efficiency is up to 97.3%, and full-load efficiency is 94.8%.
Moving object detection and tracking by using annealed background subtraction method in videos: Performance optimization

In computer vision, moving object detection and tracking methods are the most important preliminary steps for higher-level video analysis applications. In this frame, background subtraction (BS) method is a well-known method in video processing and it is based on frame differencing. The basic idea is to subtract the current frame from a background image and to classify each pixel either as foreground or background by comparing the difference with a threshold. Therefore, the moving object is detected and tracked by using frame differencing and by learning an updated background model. In addition, simulated annealing (SA) is an optimization technique for soft computing in the artificial intelligence area. The p-median problem is a basic model of discrete location theory of operational research (OR) area. It is a NP-hard combinatorial optimization problem. The main aim in the p-median problem is to find p number facility locations, minimize the total weighted distance between demand points (nodes) and the closest facilities to demand points. The SA method is used to solve the p-median problem as a probabilistic metaheuristic. In this paper, an SA-based hybrid method called entropy-based SA (EbSA) is developed for performance optimization of BS, which is used to detect and track object(s) in videos. The SA modification to the BS method (SA–BS) is proposed in this study to determine the optimal threshold for the foreground-background (i.e., bi-level) segmentation and to learn background model for object detection. At these segmentation and learning stages, all of the optimization problems considered in this study are taken as p-median problems. Performances of SA–BS and regular BS methods are measured using four videoclips. Therefore, these results are evaluated quantitatively as the overall results of the given method. The obtained performance results and statistical analysis (i.e., Wilcoxon median test) show that our proposed method is more preferable than regular BS method. Meanwhile, the contribution of this
Optical music recognition: state-of-the-art and open issues

For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores is required. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones.
Relation extraction and scoring in DeepQA

scoring in DeepQA C. Wang A. Kalyanpur J. Fan B. K. Boguraev D. C. Gondek Detecting semantic relations in text is an active problem area in natural-language processing and information retrieval. For question answering, there are many advantages of detecting relations in the question text because it allows background relational knowledge to be used to generate potential answers or find additional evidence to score supporting passages. This paper presents two approaches to broad-domain relation extraction and scoring in the DeepQA question-answering framework, i.e., one based on manual pattern specification and the other relying on statistical methods for pattern elicitation, which uses a novel transfer learning technique, i.e., relation topics. These two approaches are complementary; the rule-based approach is more precise and is used by several DeepQA components, but it requires manual effort, which allows for coverage on only a small targeted set of relations (approximately 30). Statistical approaches, on the other hand, automatically learn how to extract semantic relations from the training data and can be applied to detect a large amount of relations (approximately 7,000). Although the precision of the statistical relation detectors is not as high as that of the rule-based approach, their overall impact on the system through passage scoring is statistically significant because of their broad coverage of knowledge.
Setting Goals and Choosing Metrics for Recommender System Evaluations

Recommender systems have become an important personalization technique on the web and are widely used especially in e-commerce applications. However, operators of web shops and other platforms are challenged by the large variety of available algorithms and the multitude of their possible parameterizations. Since the quality of the recommendations that are given can have a significant business impact, the selection of a recommender system should be made based on well-founded evaluation data. The literature on recommender system evaluation offers a large variety of evaluation metrics but provides little guidance on how to choose among them. This paper focuses on the often neglected aspect of clearly defining the goal of an evaluation and how this goal relates to the selection of an appropriate metric. We discuss several well-known accuracy metrics and analyze how these reflect different evaluation goals. Furthermore we present some less well-known metrics as well as a variation of the area under the curve measure that are particularly suitable for the evaluation of recommender systems in e-commerce applications.
Joint Dependency Parsing and Multiword Expression Tokenization

Complex conjunctions and determiners are often considered as pretokenized units in parsing. This is not always realistic, since they can be ambiguous. We propose a model for joint dependency parsing and multiword expressions identification, in which complex function words are represented as individual tokens linked with morphological dependencies. Our graphbased parser includes standard secondorder features and verbal subcategorization features derived from a syntactic lexicon.We train it on a modified version of the French Treebank enriched with morphological dependencies. It recognizes 81.79% of ADV+que conjunctions with 91.57% precision, and 82.74% of de+DET determiners with 86.70% precision.
Assessment of Customer Satisfaction in a Five Star Hotel-A Case Study

Tourism industry has become one of the most profitable industries in the world. Customer satisfaction has been identified as a key performance indicator in hotel industry. This study focused on customer satisfaction of a five star hotel in Kandy district. Servqual model was employed in the assessment of customer satisfaction of the hotel. The overall objective of this study was to examine the level of customer satisfaction and major factors contributing to customer satisfaction in a five star hotel. The data were collected using a questionnaire containing 49 questions based on 22 variables of the five dimensions of Tangibility, Reliability, Responsiveness, Assurance and Empathy. Sixty residential customers of the hotel were randomly selected. Focus group discussions and a perception survey among hotel staff were also conducted to enrich the findings. Data were analyzed using descriptive statistics, MINITAB Version 14 with Two Sample Ttest. Majority of the customers expressed their satisfaction with the overall service they received from the hotel, especially regarding Tangibility, Responsiveness and Assurance. Findings revealed that the hotel had not fulfilled the customers’ satisfaction with regard to Reliability and Empathy. It was note-worthy that a minority of customers felt overall dissatisfied with the service of the hotel. Customers seemed to have perceived the same service differently. Customers’ expectations had been influenced by their knowledge about general standards of hotel practices.
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose

This paper addresses the challenge of 3D human pose estimation from a single color image. Despite the general success of the end-to-end learning paradigm, top performing approaches employ a two-step solution consisting of a Convolutional Network (ConvNet) for 2D joint localization and a subsequent optimization step to recover 3D pose. In this paper, we identify the representation of 3D pose as a critical issue with current ConvNet approaches and make two important contributions towards validating the value of end-to-end learning for this task. First, we propose a fine discretization of the 3D space around the subject and train a ConvNet to predict per voxel likelihoods for each joint. This creates a natural representation for 3D pose and greatly improves performance over the direct regression of joint coordinates. Second, to further improve upon initial estimates, we employ a coarse-to-fine prediction scheme. This step addresses the large dimensionality increase and enables iterative refinement and repeated processing of the image features. The proposed approach outperforms all state-of-the-art methods on standard benchmarks achieving a relative error reduction greater than 30% on average. Additionally, we investigate using our volumetric representation in a related architecture which is suboptimal compared to our end-to-end approach, but is of practical interest, since it enables training when no image with corresponding 3D groundtruth is available, and allows us to present compelling results for in-the-wild images.
Splatting indirect illumination

In this paper we present a novel method for plausible real-time rendering of indirect illumination effects for diffuse and non-diffuse surfaces. The scene geometry causing indirect illumination is captured by an extended shadow map, as proposed in previous work, and secondary light sources are distributed on directly lit surfaces. One novelty is the rendering of these secondary lights' contribution by splatting in a deferred shading process, which decouples rendering time from scene complexity. An importance sampling strategy, implemented entirely on the GPU, allows efficient selection of secondary light sources. Adapting the light's splat shape to surface glossiness also allows efficient rendering of caustics. Unlike previous approaches the approximated indirect lighting does barely exhibit coarse artifacts - even under unfavorable viewing and lighting conditions. We describe an implementation on contemporary graphics hardware, show a comparison to previous approaches, and present adaptation to and results in game-typical applications.
On the relation between SPARQL1.1 and Answer Set Programming

In the context of the emerging Semantic Web and the quest for a common logical framework underpinning its architecture, the relation of rule-based languages such as Answer Set Programming (ASP) and ontology languages such as OWL has attracted a lot of attention in the literature over the past years. With its roots in Deductive Databases and Datalog though, ASP shares much more commonality with another Semantic Web standard, namely the query language SPARQL. In this paper, we take the recent approval of the SPARQL1.1 standard by the World Wide Web consortium (W3C) as an opportunity to introduce this standard to the Logic Programming community by providing a translation of SPARQL1.1 into ASP. In this translation, we explain and highlight peculiarities of the new W3C standard. Along the way, we survey existing literature on foundations of SPARQL and SPARQL1.1, and also combinations of SPARQL with ontology and rules languages. Thereby, apart from providing means to implement and support SPARQL natively within Logic Programming engines and particularly ASP engines, we hope to pave the way for further research on a common logical framework for Semantic Web languages, including query languages, from an ASP point of view. 1Vienna University of Economics and Business (WU Wien), Welthandelsplatz 1, 1020 Vienna, Austria E-mail: axel.polleres@wu.ac.at 2Institute for Information Systems 184/2, Technische Universität Wien, Favoritenstrasse 9-11, 1040 Vienna, Austria. E-mail: wallner@dbai.tuwien.ac.at   On the relation between SPARQL1.1 and Answer Set Programming. Journal of Applied Non-Classical Logics (JANCL), 23(1-2):159-212, 2013. Special issue on Equilibrium Logic and Answer Set Programming.
A pipeline for structured light bathymetric mapping

This paper details a methodology for using structured light laser imaging to create high resolution bathymetric maps of the sea floor. The system includes a pair of stereo cameras and an inclined 532nm sheet laser mounted to a remotely operated vehicle (ROV). While a structured light system generally requires a single camera, a stereo vision set up is used here for in-situ calibration of the laser system geometry by triangulating points on the laser line. This allows for quick calibration at the survey site and does not require precise jigs or a controlled environment. A batch procedure to extract the laser line from the images to sub-pixel accuracy is also presented. The method is robust to variations in image quality and moderate amounts of water column turbidity. The final maps are constructed using a reformulation of a previous bathymetric Simultaneous Localization and Mapping (SLAM) algorithm called incremental Smoothing and Mapping (iSAM). The iSAM framework is adapted from previous applications to perform sub-mapping, where segments of previously visited terrain are registered to create relative pose constraints. The resulting maps can be gridded at one centimeter and have significantly higher sample density than similar surveys using high frequency multibeam sonar or stereo vision. Results are presented for sample surveys at a submerged archaeological site and sea floor rock outcrop.
An RNN-based prosodic information synthesizer for Mandarin text-to-speech

A new RNN-based prosodic information synthesizer for Mandarin Chinese text-to-speech (TTS) is proposed in this paper. Its four-layer recurrent neural network (RNN) generates prosodic information such as syllable pitch contours, syllable energy levels, syllable initial and final durations, as well as intersyllable pause durations. The input layer and first hidden layer operate with a word-synchronized clock to represent currentword phonologic states within the prosodic structure of text to be synthesized. The second hidden layer and output layer operate on a syllable-synchronized clock and use outputs from the preceding layers, along with additional syllable-level inputs fed directly to the second hidden layer, to generate desired prosodic parameters. The RNN was trained on a large set of actual utterances accompanied by associated texts, and can automatically learn many human-prosody phonologic rules, including the wellknown Sandhi Tone 3 F0-change rule. Experimental results show that all synthesized prosodic parameter sequences matched quite well with their original counterparts, and a pitch-synchronousoverlap-add-based (PSOLA-based) Mandarin TTS system was also used for testing of our approach. While subjective tests are difficult to perform and remain to be done in the future, we have carried out informal listening tests by a significant number of native Chinese speakers and the results confirmed that all synthesized speech sounded quite natural.
I would DiYSE for it!: a manifesto for do-it-yourself internet-of-things creation

This paper presents a manifesto directed at developers and designers of internet-of-things creation platforms. Currently, most existing creation platforms are tailored to specific types of end-users, mostly people with a substantial background in or affinity with technology. The thirteen items presented in the manifesto however, resulted from several user studies including non-technical users, and highlight aspects that should be taken into account in order to open up internet-of-things creation to a wider audience. To reach out and involve more people in internet-of-things creation, a relation is made to the social phenomenon of do-it-yourself, which provides valuable insights into how society can be encouraged to get involved in creation activities. Most importantly, the manifesto aims at providing a framework for do-it-yourself systems enabling non-technical users to create internet-of-things applications.
Into the Mind of the Seller: Using Neurophysiological Tools to Understand Sales Techniques

Neurophysiological recording techniques are helping provide marketers and salespeople with an increased understanding of their targeted customers. Such tools are also providing information systems researchers more insight to their end-users. These techniques may also be used introspectively to help researchers learn more about their own techniques. Here we look to help salespeople have an increased understanding of their selling methods by looking through their eyes instead of through the eyes of the customer. A preliminary study is presented using electroencephalography of three sales experts while watching the first moments of a video of a sales pitch to understand mental processing during the approach phase. Follow on work is described and considerations for interpreting data in light of individual differences.
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

In this work, we investigate the value of uncertainty modelling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.