nils-herrmann/clean_scirepeval huggingface dataset:
Detailed Human Shape and Pose from Images

Much of the research on video-based human motion capture assumes the body shape is known a priori and is represented coarsely (e.g. using cylinders or superquadrics to model limbs). These body models stand in sharp contrast to the richly detailed 3D body models used by the graphics community. Here we propose a method for recovering such models directly from images. Specifically, we represent the body using a recently proposed triangulated mesh model called SCAPE which employs a low-dimensional, but detailed, parametric model of shape and pose-dependent deformations that is learned from a database of range scans of human bodies. Previous work showed that the parameters of the SCAPE model could be estimated from marker-based motion capture data. Here we go further to estimate the parameters directly from image data. We define a cost function between image observations and a hypothesized mesh and formulate the problem as optimization over the body shape and pose parameters using stochastic search. Our results show that such rich generative models enable the automatic recovery of detailed human shape and pose from images.
A structured experiment of test-driven development

Test Driven Development (TDD) is a software development practice in which unit test cases are incrementally written prior to code implementation. We ran a set of structured experiments with 24 professional pair programmers. One group developed a small Java program using TDD while the other (control group), used a waterfall-like approach. Experimental results, subject to external validity concerns, tend to indicate that TDD programmers produce higher quality code because they passed 18% more functional black-box test cases. However, the TDD programmers took 16% more time. Statistical analysis of the results showed that a moderate statistical correlation existed between time spent and the resulting quality. Lastly, the programmers in the control group often did not write the required automated test cases after completing their code. Hence it could be perceived that waterfall-like approaches do not encourage adequate testing. This intuitive observation supports the perception that TDD has the potential for increasing the level of unit testing in the software industry. q 2003 Elsevier B.V. All rights reserved.
LIDE: Language Identification from Text Documents

The increase in the use of microblogging came along with the rapid growth on short linguistic data. On the other hand deep learning is considered to be the new frontier to extract meaningful information out of large amount of raw data in an automated manner. In this study, we engaged these two emerging fields to come up with a robust language identifier on demand, namely Language Identification Engine (LIDE). As a result, we achieved 95.12% accuracy in Discriminating between Similar Languages (DSL) Shared Task 2015 dataset, which is comparable to the maximum reported accuracy of 95.54% achieved so far.
Analyzing and evaluating query reformulation strategies in web search logs

Users frequently modify a previous search query in hope of retrieving better results. These modifications are called query reformulations or query refinements. Existing research has studied how web search engines can propose reformulations, but has given less attention to how people perform query reformulations. In this paper, we aim to better understand how web searchers refine queries and form a theoretical foundation for query reformulation. We study users' reformulation strategies in the context of the AOL query logs. We create a taxonomy of query refinement strategies and build a high precision rule-based classifier to detect each type of reformulation. Effectiveness of reformulations is measured using user click behavior. Most reformulation strategies result in some benefit to the user. Certain strategies like add/remove words, word substitution, acronym expansion, and spelling correction are more likely to cause clicks, especially on higher ranked results. In contrast, users often click the same result as their previous query or select no results when forming acronyms and reordering words. Perhaps the most surprising finding is that some reformulations are better suited to helping users when the current results are already fruitful, while other reformulations are more effective when the results are lacking. Our findings inform the design of applications that can assist searchers; examples are described in this paper.
EOS: A multilingual text archive of international newspaper & blog articles

The Expandable Open Source (EOS) database maintains an archive of Internet accessible newspaper and blog articles from across the world. Today, the archive contains over 700 million articles and is growing by approximately 100,000 articles each day. In this work, we describe the components of EOS, our community of data users and data providers, some real world use cases, challenges associated with maintaining this archive, and a vision for its future as a text analytic portal.
Insulin sensitiser agents alone and in co-treatment with r-FSH for ovulation induction in PCOS women.

The aim of this study was to compare the effectiveness of myo-inositol (MYO) and metformin, in monotherapy or in association with recombinant follicle stimulating hormone (r-FSH), in the treatment of menstrual irregularities, chronic anovulation, and female infertility in patients with polycystic ovary syndrome (PCOS).



One hundred twenty patients were randomly treated with metformin 1500 mg/day orally (n = 60), or 4 g MYO plus 400 microg folic acid daily (n = 60), continuously. If no pregnancy occurred, r-FSH (37.5 units/day) was added to the treatment for a maximum of three attempts.



Fifty percent of the patients who assumed metformin restored spontaneous ovulation, 18.3% of these obtained pregnancy. The remaining 42 patients were treated with metformin plus r-FSH. Pregnancy occurred in a total of 11 women (26.1%). The total pregnancy rate was 36.6%. Sixty-five percent of the patients treated with MYO plus folic acid restored spontaneous ovulation activity, 30% of these obtained pregnancy. The remaining 38 patients were treated with MYO, folic acid plus r-FSH. Pregnancy occurred in a total of 11 women (28.9%). The total pregnancy rate was 48.4%.



Both metformin and MYO, can be considered as first line treatment for restoring normal menstrual cycles in most patients with PCOS, even if MYO treatment seems to be more effective than metformin.
ISO 9241-11 Revised: What Have We Learnt About Usability Since 1998?

A revision is currently being undertaken of ISO 9241-11, published in 1998 to provide guidance on usability. ISO-9241-11 defines usability in terms of effectiveness, efficiency and satisfaction in a particular context of use. The intention was to emphasise that usability is an outcome of interaction rather than a property of a product. This is now widely accepted. However, the standard also places emphasis on usability measurement and it is now appreciated that there is more to usability evaluation than measurement. Other developments include an increasing awareness of the importance of the individual user's emotional experience as discretionary usage of complex consumer products and use of the World Wide Web have became more widespread. From an organisational perspective, it is now appreciated that usability plays an important role in managing the potentials risks that can arise from inappropriate outcomes of interaction. The revision of ISO 9241-11 takes account of these issues and other feedback.
Pose optimization in edge distance field for textureless 3D object tracking

This paper presents a monocular model-based 3D tracking approach for textureless objects. Instead of explicitly searching for 3D-2D correspondences as previous methods, which unavoidably generates individual outlier matches, we aim to minimize the holistic distance between the predicted object contour and the query image edges. We propose a method that can directly solve 3D pose parameters in unsegmented edge distance field. We derive the differentials of edge matching distance with respect to the pose parameters, and search the optimal 3D pose parameters using standard gradient-based non-linear optimization techniques. To avoid being trapped in local minima and to deal with potential large inter-frame motions, a particle filtering process with a first order autoregressive state dynamics is exploited. Occlusions are handled by a robust estimator. The effectiveness of our approach is demonstrated using comparative experiments on real image sequences with occlusions, large motions and cluttered backgrounds.
A Multitask Objective to Inject Lexical Contrast into Distributional Semantics

Distributional semantic models have trouble distinguishing strongly contrasting words (such as antonyms) from highly compatible ones (such as synonyms), because both kinds tend to occur in similar contexts in corpora. We introduce the multitask Lexical Contrast Model (mLCM), an extension of the effective Skip-gram method that optimizes semantic vectors on the joint tasks of predicting corpus contexts and making the representations of WordNet synonyms closer than that of matching WordNet antonyms. mLCM outperforms Skip-gram both on general semantic tasks and on synonym/antonym discrimination, even when no direct lexical contrast information about the test words is provided during training. mLCM also shows promising results on the task of learning a compositional negation operator mapping adjectives to their antonyms.
Semi-Supervised Learning on Graphs Based on Local Label Distributions

In this work, we propose a novel approach for the semi-supervised node classification. Precisely, we propose a method which takes labels in the local neighborhood of different locality levels into consideration. Most previous approaches that tackle the problem of node classification consider nodes to be similar, if they have shared neighbors or are close to each other in the graph. Recent methods for attributed graphs additionally take attributes of the neighboring nodes into account. We argue that the labels of the neighbors bear important information and considering them helps to improve classification quality. Two nodes which are similar based on labels in their neighborhood do not need to lie close-by in the graph and may even belong to different connected components. Considering labels can improve node classification for graphs with and without node attributes. However, as we will show, existing methods cannot be adapted to consider the labels of neighboring nodes in a straightforward fashion. Therefore, we propose a new method to learn label-based node embeddings which can mirror a variety of relations between the class labels of neighboring nodes. Furthermore, we propose several network architectures which combine multiple representations of the label distribution in the neighborhood with different localities. Our experimental evaluation demonstrates that our new methods can significantly improve the prediction quality on real world data sets.
Predicting software change-proneness with code smells and class imbalance learning

The objective of this paper is to study the relationship between different types of object-oriented software metrics, code smells and actual changes in software code that occur during maintenance period. It is hypothesized that code smells are indicators of maintenance problems. To understand the relationship between code smells and maintenance problems, we extract code smells in a Java based mobile application called MOBAC. Four versions of MOBAC are studied. Machine learning techniques are applied to predict software change-proneness with code smells as predictor variables. The results of this paper indicate that codes smells are more accurate predictors of change-proneness than static code metrics for all machine learning methods. However, class imbalance techniques did not outperform class balance machine learning techniques in change-proneness prediction. The results of this paper are based on accuracy measures such as F-measure and area under ROC curve.
Fine-Grained Sentiment Analysis on Financial Microblogs and News Headlines

Sentiment analysis in the financial domain is quickly becoming a prominent research topic as it provides a powerful method to predict market dynamics. In this work, we leverage advances in Semantic Web area to develop a fine-grained approach to predict real-valued sentiment scores. We compare several classifiers trained on two different datasets. The first dataset consists of microblog messages focusing on stock market events, while the second one consists of financially relevant news headlines crawled from different sources on the Internet. We test our approach using several feature sets including lexical features, semantic features and a combination of lexical and semantic features. Experimental results show that the proposed approach allows achieving an accuracy level of more than 72%.
FEATURE SELECTION METHODS AND ALGORITHMS

Feature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of a learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy; we discard the remaining, unimportant dimensions. This is an important stage of preprocessing and is one of two ways of avoiding the curse of dimensionality (the other is feature extraction). There are two approaches in Feature selection known as Forward selection and backward selection. Feature selection has been an active research area in pattern recognition, statistics, and data mining communities. The main idea of feature selection is to choose a subset of input variables by eliminating features with little or no predictive information. Feature selection methods can be decomposed into three broad classes. One is Filter methods and another one is Wrapper method and the third one is Embedded method. This paper presents an empirical comparison of feature selection methods and its algorithms. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situations. This work reviews several fundamental algorithms found in the literature and assesses their performance in a controlled scenario.  Selection, Feature Selection Methods, Feature Selection Algorithms.
Analyzing Uber's Ride-sharing Economy

Uber is a popular ride-sharing application that matches people who need a ride (or riders) with drivers who are willing to provide it using their personal vehicles. Despite its growing popularity, there exist few studies that examine large-scale Uber data, or in general the factors affecting user participation in the sharing economy. We address this gap through a study of the Uber market that analyzes large-scale data covering 59 million rides which spans a period of 7 months. The data were extracted from email receipts sent by Uber collected on Yahoo servers, allowing us to examine the role of demographics (e.g., age and gender) on participation in the ride-sharing economy. In addition, we evaluate the impact of dynamic pricing (i.e., surge pricing) and income on both rider and driver behavior. We find that the surge pricing does not bias Uber use towards higher income riders. Moreover, we show that more homophilous matches (e.g., riders to drivers of a similar age) can result in higher driver ratings. Finally, we focus on factors that affect retention and use information from earlier rides to accurately predict which riders or drivers will become active Uber users.
A practical path loss model for indoor WiFi positioning enhancement

Positioning within a local area refers to technology whereby each node is self-aware of its position. Based on empirical study, this paper proposes an enhancement to the path loss model in the indoor environment for improved accuracy in the relationship between distance and received signal strength. We further demonstrate the potential of our model for the WiFi positioning system, where the mean errors in the distance estimation are 2.3 m and 2.9 m for line of sight and non line of sight environments, respectively.
Enhancing Test Compression With Dependency Analysis for Multiple Expansion Ratios

Scan test data compression is widely used in industry to reduce test data volume (TDV) and test application time (TAT). This paper shows how multiple scan chain expansion ratios can help to obtain high test data compression in system-on-chips. Scan chains are partitioned with a higher expansion ratio than normal in scan compression mode and then are gradually concatenated based on a cost function to detect any faults that could not be detected at the higher expansion ratios. It improves the overall test compression ratio since it potentially allows faults to be detected at the highest expansion ratio. This paper introduces a new cost function to choose scan chain concatenation candidates for concatenation for multiple expansion ratios. To avoid TDV and TAT increase by scan concatenation, the proposed method takes a logic structure and scan chain length into consideration. Experiment results show the proposed method reduces TAT and TDV by 53%–64% compared with a traditional scan compression method.
Analysis of jitter due to power-supply noise in phase-locked loops

Phase-locked loops (PLL) in RF and mixed signal VLSI circuits experience supply noise which translates to a timing jitter. In this paper an analysis of the timing jitter due to the noise on the power supply rails is presented. Stochastic models of the power supply noise in VLSI circuits for different values of on-chip decoupling capacitances are presented first. This is followed by calculation of the phase noise of the voltage-controlled oscillator (VCO) in terms of the statistical properties of supply noise. Finally the timing jitter of PLL is predicted in response to the VCO phase noise. A PLL circuit has been designed in 0.35μ CMOS process, and our mathematical model was applied to determine the timing jitter. Experimental results prove the accuracy of the predicted model.
Supervised Hashing with Deep Neural Networks

In this paper, we propose training very deep neural networks (DNNs) for supervised learning of hash codes. Existing methods in this context train relatively “shallow” networks limited by the issues arising in back propagation (e.g. vanishing gradients) as well as computational efficiency. We propose a novel and efficient training algorithm inspired by alternating direction method of multipliers (ADMM) that overcomes some of these limitations. Our method decomposes the training process into independent layer-wise local updates through auxiliary variables. Empirically we observe that our training algorithm always converges and its computational complexity is linearly proportional to the number of edges in the networks. Empirically we manage to train DNNs with 64 hidden layers and 1024 nodes per layer for supervised hashing in about 3 hours using a single GPU. Our proposed very deep supervised hashing (VDSH) method significantly outperforms the state-of-theart on several benchmark datasets.
A bidirectional soft pneumatic fabric-based actuator for grasping applications

THIS paper presents the development of a bidirectional fabric-based soft pneumatic actuator requiring low fluid pressurization for actuation, which is incorporated into a soft robotic gripper to demonstrate its utility. The bidirectional soft fabric-based actuator is able to provide both flexion and extension. Fabrication of the fabric actuators is simple as compared to the steps involved in traditional silicone-based approach. In addition, the fabric actuators are able to generate comparably larger vertical grip resistive force at lower operating pressure than elastomeric actuators and 3D-printed actuators, being able to generate resistive grip force up to 20N at 120 kPa. Five of the bidirectional soft fabric-based actuators are deployed within a five-fingered soft robotic gripper, complete with five casings and a base. It is capable of grasping a variety of objects with maximum width or diameter closer to its bending curvature. A cutting task involved bimanual manipulation was demonstrated successfully with the gripper. To incorporate intelligent control for such a task, a soft force made completely of compliant material was attached to the gripper, which allows determination of whether the cutting task is completed. To the authors' knowledge, this work is the first study which incorporates two soft robotic grippers for bimanual manipulation with one of the grippers sensorized to provide closed loop control.
Digital Infrastructures : The Missing IS Research Agenda

S the inauguration of information systems research (ISR) two decades ago, the information systems (IS) field’s attention has moved beyond administrative systems and individual tools. Millions of users log onto Facebook, download iPhone applications, and use mobile services to create decentralized work organizations. Understanding these new dynamics will necessitate the field paying attention to digital infrastructures as a category of IT artifacts. A state-of-the-art review of the literature reveals a growing interest in digital infrastructures but also confirms that the field has yet to put infrastructure at the centre of its research endeavor. To assist this shift we propose three new directions for IS research: (1) theories of the nature of digital infrastructure as a separate type of IT artifact, sui generis; (2) digital infrastructures as relational constructs shaping all traditional IS research areas; (3) paradoxes of change and control as salient IS phenomena. We conclude with suggestions for how to study longitudinal, large-scale sociotechnical phenomena while striving to remain attentive to the limitations of the traditional categories that have guided IS research.
Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity

Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.