nils-herrmann/clean_scirepeval huggingface dataset:
The reciprocal links between school engagement, youth problem behaviors, and school dropout during adolescence.

Drawing on the self-system model, this study conceptualized school engagement as a multidimensional construct, including behavioral, emotional, and cognitive engagement, and examined whether changes in the three types of school engagement related to changes in problem behaviors from 7th through 11th grades (approximately ages 12-17). In addition, a transactional model of reciprocal relations between school engagement and problem behaviors was tested to predict school dropout. Data were collected on 1,272 youth from an ethnically and economically diverse county (58% African American, 36% European American; 51% females). Results indicated that adolescents who had declines in behavioral and emotional engagement with school tended to have increased delinquency and substance use over time. There were bidirectional associations between behavioral and emotional engagement in school and youth problem behaviors over time. Finally, lower behavioral and emotional engagement and greater problem behaviors predicted greater likelihood of dropping out of school.
What one intelligence test measures: a theoretical account of the processing in the Raven Progressive Matrices Test.

The cognitive processes in a widely used, nonverbal test of analytic intelligence, the Raven Progressive Matrices Test (Raven, 1962), are analyzed in terms of which processes distinguish between higher scoring and lower scoring subjects and which processes are common to all subjects and all items on the test. The analysis is based on detailed performance characteristics, such as verbal protocols, eye-fixation patterns, and errors. The theory is expressed as a pair of computer simulation models that perform like the median or best college students in the sample. The processing characteristic common to all subjects is an incremental, reiterative strategy for encoding and inducing the regularities in each problem. The processes that distinguish among individuals are primarily the ability to induce abstract relations and the ability to dynamically manage a large set of problem-solving goals in working memory.
Deep learning for computational biology

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.
Credit card fraud and detection techniques : a review

Fraud is one of the major ethical issues in the credit card industry. The main aims are, firstly, to identify the different types of credit card fraud, and, secondly, to review alternative techniques that have been used in fraud detection. The sub-aim is to present, compare and analyze recently published findings in credit card fraud detection. This article defines common terms in credit card fraud and highlights key statistics and figures in this field. Depending on the type of fraud faced by banks or credit card companies, various measures can be adopted and implemented. The proposals made in this paper are likely to have beneficial attributes in terms of cost savings and time efficiency. The significance of the application of the techniques reviewed here is in the minimization of credit card fraud. Yet there are still ethical issues when genuine credit card customers are misclassified as fraudulent.
Automata Guided Hierarchical Reinforcement Learning for Zero-shot Skill Composition

Skills learned through (deep) reinforcement learning often generalizes poorly across domains and re-training is necessary when presented with a new task. We present a framework that combines techniques in formal methods with reinforcement learning (RL). The methods we provide allows for convenient specification of tasks with logical expressions, learns hierarchical policies (meta-controller and low-level controllers) with well-defined intrinsic rewards, and construct new skills from existing ones with little to no additional exploration. We evaluate the proposed methods in a simple grid world simulation as well as a more complicated kitchen environment in AI2Thor (Kolve et al. .
Efficient Quadrature for NURBS-based Isogeometric Analysis

We initiate the study of efficient quadrature rules for NURBS -based isogeometric analysis. A rule of thumb emerges, the “half-point rule”, indicat ng that optimal rules involve a number of points roughly equal to half the number of degreesof-freedom, or equivalently half the number of basis functions of the space under conside rat on. The half-point rule is independent of the polynomial order of the basis. Efficien t rules require taking into account the precise smoothness of basis functions across elem ent boundaries. Several rules of practical interest are obtained, and a numerical procedu re for determining efficient rules is presented. We compare the cost of quadrature for typical situations ari ing in structural mechanics and fluid dynamics. The new rules represent improvements ove r those used previously in isogeometric analysis.
Geospatial Object Detection in Remote Sensing Imagery Based on Multiscale Single-Shot Detector with Activated Semantics

Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a heated and challenging problem in the field of automatic image interpretation. Despite convolutional neural networks (CNNs) having facilitated the development in this domain, the computation efficiency under real-time application and the accurate positioning on relatively small objects in HSR images are two noticeable obstacles which have largely restricted the performance of detection methods. To tackle the above issues, we first introduce semantic segmentation-aware CNN features to activate the detection feature maps from the lowest level layer. In conjunction with this segmentation branch, another module which consists of several global activation blocks is proposed to enrich the semantic information of feature maps from higher level layers. Then, these two parts are integrated and deployed into the original single shot detection framework. Finally, we use the modified multi-scale feature maps with enriched semantics and multi-task training strategy to achieve end-to-end detection with high efficiency. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset have demonstrated the superiority of the presented method.
A Combination Method for Improving Text Summarization

As the volume of online and electronic information increasingly has grown, quickly and accurately access to these important resources is a big challenge. Text analytics can help by transposing words and sentences in unstructured data into high-quality information. Text summarization is one of the applications of text mining, has been of interest to researchers. In addition to text summarization, using optimization algorithms can be influenced results. In this paper, has been presented a hybrid approach for English multi-document summarization. As name suggest, a text summarization system produces summary of original documents. Combination of text mining and optimization algorithms is main ways this research, to improve results and reduce redundancy in summary sentences and simultaneously summary sentences have the most relevant. Similarity measures are cosine and overlap. Using multi-objective particle swarm optimization algorithm improved results. The experimental results of the method on two data sets DUC2005 and DUC2007 show improvements in the three assessment criteria associated. Result of summarization about 3 percent in ROUGE-1, in ROUGE-2 at 2 percent and the benchmark ROUGE-SU for approximately 1.5% compared with the previous methods
Techniques of Data Mining In Healthcare: A Review

Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.
Software architecture: foundations, theory, and practice

Software architecture has become a centerpiece subject for software engineers, both researchers and practitioners alike. At the heart of every software system is its software architecture, i.e., "the set of principal design decisions about the system". Architecture permeates all major facets of a software system, for principal design decisions may potentially be made at any time during a system's lifetime, and potentially by any stakeholder. Such decisions encompass structural concerns, such as the system's high-level building blocks---components, connectors, and configurations; the system's deployment; the system's non-functional properties; and the system's evolution patterns, including runtime adaptation. Software architectures found particularly useful for families of systems---product lines---are often codified into architectural patterns, architectural styles, and reusable, parameterized reference architectures. This tutorial affords the participant an extensive treatment of the field of software architecture, its foundation, principles, and elements, including those mentioned above. Additionally, the tutorial introduces the participants to the state-of-the-art as well as the state-of-the-practice in software architecture, and looks at emerging and likely future trends in this field. The discussion is illustrated with numerous real-world examples. One example given prominent treatment is the architecture of the World Wide Web and its underlying architectural style, REpresentational State Transfer (REST).
The Evolution of Current Research Impact Metrics: From Bibliometrics to Altmetrics?

The prestige of publication has been based on traditional citation metrics, most commonly journal impact factor. However, the Internet has radically changed the speed, flow, and sharing of medical information. Furthermore, the explosion of social media, along with development of popular professional and scientific websites and blogs, has led to the need for alternative metrics, known as altmetrics, to quantify the wider impact of research. We explore the evolution of current research impact metrics and examine the evolving role of altmetrics in measuring the wider impact of research. We suggest that altmetrics used in research evaluation should be part of an informed peer-review process such as traditional metrics. Moreover, results based on altmetrics must not lead to direct decision making about research, but instead, should be used to assist experts in making decisions. Finally, traditional and alternative metrics should complement, not replace, each other in the peer-review process.
Domain Randomization and Generative Models for Robotic Grasping

Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often trained on as few as hundreds or thousands of unique object instances, and as a result generalization can be a challenge. In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis. We generate millions of unique, unrealistic procedurally generated objects, and train a deep neural network to perform grasp planning on these objects. Since the distribution of successful grasps for a given object can be highly multimodal, we propose an autoregressive grasp planning model that maps sensor inputs of a scene to a probability distribution over possible grasps. This model allows us to sample grasps efficiently at test time (or avoid sampling entirely). We evaluate our model architecture and data generation pipeline in simulation and the real world. We find we can achieve a >90% success rate on previously unseen realistic objects at test time in simulation despite having only been trained on random objects. We also demonstrate an 80% success rate on real-world grasp attempts despite having only been trained on random simulated objects.
Eight Questions about Corruption

S ome years ago I interviewed the chief executive officer of a successful Thai manufacturing firm as part of a pilot survey project. While trying to figure out a good way to quantify the firm’s experience with government regulations and corruption in the foreign trade sector, the CEO exclaimed: “I hope to be reborn as a custom official.” When a well-paid CEO wishes for a job with low official pay in the government sector, corruption is almost surely a problem! The most devastating forms of corruption include the diversion and outright theft of funds for public programs and the damage caused by firms and individuals that pay bribes to avoid health and safety regulations intended to benefit the public. Examples abound. A conservative estimate is that the former President of Zaire, Mobutu Sese Seko, looted the treasury of some $5 billion—an amount equal to the country’s entire external debt at the time he was ousted in 1997. The funds allegedly embezzled by the former presidents of Indonesia and Philippines, Mohamed Suharto and Ferdinand Marcos, are estimated to be two and seven times higher (Transparency International, 2004). In the Goldenberg scam in Kenya in the early 1990s, the Goldenberg firm received as much as $1 billion from the government as part of an export compensation scheme for fictitious exports of commodities of which Kenya either produced little (gold) or nothing at all (diamonds) (“Public Inquiry into Kenya Gold Scam,” 2003). An internal IMF report found that nearly $1 billion of oil revenues, or $77 per capita, vanished from Angolan state coffers in 2001 alone (Pearce, 2002). This amount was about three times the value of the humanitarian aid received by Angola in 2001—in a country where three-quarters of the population survives on less than $1 a day and where one
A Map for Big Data Research in Digital Humanities

How can dense representations be created out of large amount of data (Tufte 2001)? How can users navigate within abstract representations? How can multi-scale navigation be realized? How can users use data visualization to detect new patterns? Linguistic How can large quantities of text be visualized and sorted (Rockwell et al. 2010)? How can users navigate within different text layers? How can distant and close reading be combined? Frontiers in Digital Humanities | www.frontiersin.org May 2015 | Volume 2 | Article 1 5 Kaplan Big data digital humanities
The Impact of a Corporate Culture of Sustainability on Corporate Behavior and Performance

We investigate the effect of a corporate culture of sustainability on multiple facets of corporate behavior and performance outcomes. Using a matched sample of 180 companies, we find that corporations that voluntarily adopted environmental and social policies many years ago termed as High Sustainability companies exhibit fundamentally different characteristics from a matched sample of firms that adopted almost none of these policies termed as Low Sustainability companies. In particular, we find that the boards of directors of these companies are more likely to be responsible for sustainability and top executive incentives are more likely to be a function of sustainability metrics. Moreover, they are more likely to have organized procedures for stakeholder engagement, to be more long-term oriented, and to exhibit more measurement and disclosure of nonfinancial information. Finally, we provide evidence that High Sustainability companies significantly outperform their counterparts over the long-term, both in terms of stock market and accounting performance. The outperformance is stronger in sectors where the customers are individual consumers instead of companies, companies compete on the basis of brands and reputations, and products significantly depend upon extracting large amounts of natural resources. Robert G. Eccles is a Professor of Management Practice at Harvard Business School. Ioannis Ioannou is an Assistant Professor of Strategic and International Management at London Business School. George Serafeim is an Assistant Professor of Business Administration at Harvard Business School, contact email: gserafeim@hbs.edu. Robert Eccles and George Serafeim gratefully acknowledge financial support from the Division of Faculty Research and Development of the Harvard Business School. We would like to thank Christopher Greenwald for supplying us with the ASSET4 data. Moreover, we would like to thank Cecile Churet and Iordanis Chatziprodromou from Sustainable Asset Management for giving us access to their proprietary data. We are grateful to Chris Allen, Jeff Cronin, Christine Rivera, and James Zeitler for research assistance. We thank Ben Esty, Joshua Margolis, Costas Markides, Catherine Thomas and seminar participants at Boston College for helpful comments. We are solely responsible for any errors in this manuscript.
Chinese Segmentation with a Word-Based Perceptron Algorithm

Standard approaches to Chinese word segmentation treat the problem as a tagging task, assigning labels to the characters in the sequence indicating whether the character marks a word boundary. Discriminatively trained models based on local character features are used to make the tagging decisions, with Viterbi decoding finding the highest scoring segmentation. In this paper we propose an alternative, word-based segmentor, which uses features based on complete words and word sequences. The generalized perceptron algorithm is used for discriminative training, and we use a beamsearch decoder. Closed tests on the first and secondSIGHAN bakeoffs show that our system is competitive with the best in the literature, achieving the highest reported F-scores for a number of corpora.
Design of a highly biomimetic anthropomorphic robotic hand towards artificial limb regeneration

A wide range of research areas, from telemanipulation in robotics to limb regeneration in tissue engineering, could benefit from an anthropomorphic robotic hand that mimics the salient features of the human hand. The challenges of designing such a robotic hand are mainly resulted from our limited understanding of the human hand from engineering point of view and our ability to replicate the important biomechanical features with conventional mechanical design. We believe that the biomechanics of human hand is an essential component of the hand dexterity and can be replicated with highly biomimetic design. To this end, we reinterpret the important biomechanical advantages of the human hand from roboticist's perspective and design a biomimetic robotic hand that closely mimics its human counterpart with artificial joint capsules, crocheted ligaments and tendons, laser-cut extensor hood, and elastic pulley mechanisms. We experimentally identify the workspaces of the fingertips and successfully demonstrate that our proofof- concept design can be teleoperated to grasp and manipulate daily objects with a variety of natural hand postures based on hand taxonomy.
Mixture of PLDA Models in i-vector Space for Gender-Independent Speaker Recognition

The Speaker Recognition community that participates in NIST evaluations has concentrated on designing genderand channel-conditioned systems. In the real word, this conditioning is not feasible. Our main purpose in this work is to propose a mixture of Probabilistic Linear Discriminant Analysis models (PLDA) as a solution for making systems independent of speaker gender. In order to show the effectiveness of the mixture model, we first experiment on 2010 NIST telephone speech (det5), where we prove that there is no loss of accuracy compared with a baseline gender-dependent model. We also test with success the mixture model on a more realistic situation where there are cross-gender trials. Furthermore, we report results on microphone speech for the det1, det2, det3 and det4 tasks to confirm the effectiveness of the mixture model.
The extended mind

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Survey of Recent Advances in Visual Question Answering

Visual Question Answering (VQA) presents a unique challenge as it requires the ability to understand and encode the multi-modal inputs in terms of image processing and natural language processing. The algorithm further needs to learn how to perform reasoning over this multi-modal representation so it can answer the questions correctly. This paper presents a survey of different approaches proposed to solve the problem of Visual Question Answering. We also describe the current state of the art model in later part of paper. In particular, the paper describes the approaches taken by various algorithms to extract image features, text features and the way these are employed to predict answers. We also briefly discuss the experiments performed to evaluate the VQA models and report their performances on diverse datasets including newly released VQA2.0[8].
Bottom-up Game Studies

In this paper, we endorse and advance an emerging bottomup approach to game studies that utilizes techniques from natural language processing. Our contribution is threefold: we present the first complete review of the growing body of work through which this methodology has been innovated; we present a latent semantic analysis model that constitutes the first application of this fundamental bottom-up technique to the domain of digital games; and finally, unlike earlier projects that have only written about their models, we introduce and evaluate a tool that serves as an interface to ours. This tool is GameNet, in which nearly 12,000 games are linked to the games to which they are most related. From an expert evaluation, we demonstrate that, beyond being an interface to our model, GameNet may be used more generally as a research tool for game scholars. Specifically, we find that it is especially useful for the scholar who wishes to explore a relatively unfamiliar area of games, but that it may also be used to discover unforeseen cases related to topics that have already been thoroughly researched.