nils-herrmann/clean_scirepeval huggingface dataset:
mclust Version 4 for R : Normal Mixture Modeling for Model-Based Clustering , Classification , and Density Estimation

mclust is a contributed R package for model-based clustering, classification, and density estimation based on finite normal mixture modeling. It provides functions for parameter estimation via the EM algorithm for normal mixture models with a variety of covariance structures, and functions for simulation from these models. Also included are functions that combine model-based hierarchical clustering, EM for mixture estimation and the Bayesian Information Criterion (BIC) in comprehensive strategies for clustering, density estimation and discriminant analysis. There is additional functionality for displaying and visualizing the models along with clustering, classification, and density estimation results. Several features of the software have been changed in this version, in particular the functionality for discriminant analysis and density estimation has been largely expanded. mclust is licensed under the GPL and distributed through CRAN; see http://cran.r-project.org/web/packages/mclust/index.html.
Statistical techniques for online personalized advertising: a survey

Online advertising is the major source of revenue for most web service providers. Displaying advertisements that match user interests will not only lead to user satisfaction, but it will also maximize the revenues of both advertisers and web publishers. Online Advertisement systems use web mining and machine learning techniques to personalize advertisement selection to a particular user based on certain features such as his browsing behavior or demographic data. This paper presents an overview of online advertisement selection and summarizes the main technical challenges and open issues in this field. The paper investigates most of the relevant existing approaches carried out towards this perspective and provides a comparison and classification of these approaches.
Bulk Current Injection in Twisted Wire Pairs With Not Perfectly Balanced Terminations

In this paper, common mode (CM) and differential mode (DM) voltages induced by bulk current injection at the terminations of a differential interconnection are predicted and correlated to the degree of unbalance of the terminal units. This is done by extending a previously derived lumped-parameter representation of the injection probe and by combining modal analysis with a multiconductor transmission line theory. Under nonrestrictive assumptions on termination unbalance, it is shown that CM and DM voltages can be readily predicted by two equivalent circuits. The circuit for CM prediction is independent on termination unbalance and directly excited by the injection probe. The circuit for DM prediction does not involve any of the probe model parameters, and is driven by two voltage sources related to the induced CM voltages through the common-mode rejection ratio of each termination. Model accuracy was proven by measurements carried out on a test bench setup according to the BCI Standards.
Calibrating multiple cameras with non-overlapping views using coded checkerboard targets

This paper presents an approach for combined intrinsic and extrinsic calibration of multi-camera rigs using coded targets. It is suited for conventional mono or stereo camera setups as well as for arrangements of multiple cameras with non-overlapping fields of view. We use a static scene consisting of multiple checkerboard targets and a sequence of images. This gives us a large amount of different observations and leads to precise calibration results. To solve the association problem of checkerboard corners over time and between different cameras, we use binary patterns that surround each checkerboard. A sparse nonlinear least squares solver is finally used to estimate the optimal parameter set. The parameter set contains intrinsic and extrinsic parameters, time series of the multi-camera rig pose, board poses and a description of board deformation.
Teachers’ Performance Evaluation in Higher Educational Institution using Data Mining Technique

Educational Data Mining (EDM) is an evolving field exploring pedagogical data by applying different machine learning techniques/tools. It can be considered as interdisciplinary research field which provides intrinsic knowledge of teaching and learning process for effective education. The main objective of any educational institution is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge that predicts teachers’ performance. This study presents an efficient system model for evaluation and prediction of teachers’ performance in higher institutions of learning using data mining technologies. To achieve the objectives of this work, a two-layered classifier system was designed; it consists of an Artificial Neural Network (ANN) and Decision Tree. The classifier system was tested successfully using case study data from a Nigerian University in the South West of Nigeria. The data consists of academic qualifications for teachers as well as their experiences and grades of students in courses they taught among others. The attribute selected were evaluated using two feature selection methods in order to get a subset of the attributes that would make for a compact and accurate predictive model. The WEKA machine learning tool was used for the mining. The results show that, among the six attributes used, Working Experience, and Rank are rated the best two attributes that contributed mostly to the performance of teachers in this study. Also, considering the time taken to build the models and performance accuracy level, C4.5 decision tree outperformed the other two algorithms (ID3 and MLP) with good performance of 83.5% accuracy level and acceptable kappa statistics of 0.743. It does mean that C4.5 decision tree is best algorithm suitable for predicting teachers’ performance in relation to the other two algorithms in this work. General Terms Data Mining
An Inference Model for Semantic Entailment in Natural Language

Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. We present a principled approach to this problem that builds on inducing rerepresentations of text snippets into a hierarchical knowledge representation along with a sound inferential mechanism that makes use of it to prove semantic entailment.
Hockey Action Recognition via Integrated Stacked Hourglass Network

A convolutional neural network (CNN) has been designed to interpret player actions in ice hockey video. The hourglass network is employed as the base to generate player pose estimation and layers are added to this network to produce action recognition. As such, the unified architecture is referred to as action recognition hourglass network, or ARHN. ARHN has three components. The first component is the latent pose estimator, the second transforms latent features to a common frame of reference, and the third performs action recognition. Since no benchmark dataset for pose estimation or action recognition is available for hockey players, we generate such an annotated dataset. Experimental results show action recognition accuracy of 65% for four types of actions in hockey. When similar poses are merged to three and two classes, the accuracy rate increases to 71% and 78%, proving the efficacy of the methodology for automated action recognition in hockey.
Optimal crawling strategies for web search engines

Web Search Engines employ multiple so-called crawlers to maintain local copies of web pages. But these web pages are frequently updated by their owners, and therefore the crawlers must regularly revisit the web pages to maintain the freshness of their local copies. In this paper, we propose a two-part scheme to optimize this crawling process. One goal might be the minimization of the average level of staleness over all web pages, and the scheme we propose can solve this problem. Alternatively, the same basic scheme could be used to minimize a possibly more important search engine embarrassment level metric: The frequency with which a client makes a search engine query and then clicks on a returned url only to find that the result is incorrect. The first part our scheme determines the (nearly) optimal crawling frequencies, as well as the theoretically optimal times to crawl each web page. It does so within an extremely general stochastic framework, one which supports a wide range of complex update patterns found in practice. It uses techniques from probability theory and the theory of resource allocation problems which are highly computationally efficient -- crucial for practicality because the size of the problem in the web environment is immense. The second part employs these crawling frequencies and ideal crawl times as input, and creates an optimal achievable schedule for the crawlers. Our solution, based on network flow theory, is exact as well as highly efficient. An analysis of the update patterns from a highly accessed and highly dynamic web site is used to gain some insights into the properties of page updates in practice. Then, based on this analysis, we perform a set of detailed simulation experiments to demonstrate the quality and speed of our approach.
Forward and inverse kinematics model for robotic welding process using KR-16KS KUKA robot

This paper aims to model the forward and inverse kinematics of a KUKA KR-16KS robotic arm in the application of a simple welding process. A simple welding task to weld a block onto a metal sheet is carried out in order to investigate the forward and inverse kinematics models of KR-16KS. A movement flow planning is designed and further developed into the KR-16KS programming. Eleven points of movement are studied for the forward kinematic modeling. A summary of calculation is obtained. A general D-H representation of forward and inverse matrix is obtained. This can be used in each of the welding operation movement based on KUKA KR-16KS robotic arm. A forward kinematic and an inverse kinematic aspect of KUKA KR-16KS is successfully modeled based on a simple welding task.
Scaling HDFS to More Than 1 Million Operations Per Second with HopsFS

that replaces the main scalability bottleneck in HDFS, single node in-memory metadata service, with a no-sharedstate distributed system built on a NewSQL database. By removing the metadata bottleneck in Apache HDFS, HopsFS enables significantly larger cluster sizes, more than an order of magnitude higher throughput, and significantly lower clientlatencies for large clusters. In this paper, we detail the techniques and optimizations that enable HopsFS to surpass 1 million file system operations per second - at least 16 times higher throughput than HDFS. In particular, we discuss how we exploit recent high performance features from NewSQL databases, such as application defined partitioning, partition-pruned index scans, and distribution aware transactions. Together with more traditional techniques, such as batching and write-ahead caches, we show how many incremental optimizations have enabled a revolution in distributed hierarchical file system performance.
AMPK regulates energy expenditure by modulating NAD+ metabolism and SIRT1 activity

AMP-activated protein kinase (AMPK) is a metabolic fuel gauge conserved along the evolutionary scale in eukaryotes that senses changes in the intracellular AMP/ATP ratio. Recent evidence indicated an important role for AMPK in the therapeutic benefits of metformin, thiazolidinediones and exercise, which form the cornerstones of the clinical management of type 2 diabetes and associated metabolic disorders. In general, activation of AMPK acts to maintain cellular energy stores, switching on catabolic pathways that produce ATP, mostly by enhancing oxidative metabolism and mitochondrial biogenesis, while switching off anabolic pathways that consume ATP. This regulation can take place acutely, through the regulation of fast post-translational events, but also by transcriptionally reprogramming the cell to meet energetic needs. Here we demonstrate that AMPK controls the expression of genes involved in energy metabolism in mouse skeletal muscle by acting in coordination with another metabolic sensor, the NAD+-dependent type III deacetylase SIRT1. AMPK enhances SIRT1 activity by increasing cellular NAD+ levels, resulting in the deacetylation and modulation of the activity of downstream SIRT1 targets that include the peroxisome proliferator-activated receptor-γ coactivator 1α and the forkhead box O1 (FOXO1) and O3 (FOXO3a) transcription factors. The AMPK-induced SIRT1-mediated deacetylation of these targets explains many of the convergent biological effects of AMPK and SIRT1 on energy metabolism.
Evaluation of Different Control Strategies for Teams of Unmanned Marine Vehicles ( UMVs ) Comparing the Requirements of Underwater Communication and Navigation in MATLAB ® Simulations

Thomas Glotzbach, TU Ilmenau; Fraunhofer Center for Applied Systems Technology, Ilmenau/Germany, thomas.glotzbach@tu-ilmenau.de Andrea Picini, INNOVA S.p.A., Rome/Italy, a.picini@innova-eu.net Antonio Zangrilli, INNOVA S.p.A., Rome/Italy, a.zangrilli@innova-eu.net Mike Eichhorn, TU Ilmenau/Germany, mike.eichhorn@tu-ilmenau.de Peter Otto, TU Ilmenau/Germany, peter.otto@tu-ilmenau.de Matthias Schneider, TU Ilmenau/Germany, schneider.matthias@tu-ilmenau.de
Stepfather Involvement and Stepfather-Child Relationship Quality: Race and Parental Marital Status as Moderators.

Stepparent-child relationship quality is linked to stepfamily stability and children's well-being. Yet, the literature offers an incomplete understanding of factors that promote high-quality stepparent-child relationships, especially among socio-demographically diverse stepfamilies. In this study, we explore the association between stepfather involvement and stepfather-child relationship quality among a racially diverse and predominately low-income sample of stepfamilies with preadolescent children. Using a subsample of 467 mother-stepfather families from year 9 of the Fragile Families and Child Wellbeing Study, results indicate that stepfather involvement is positively associated with stepfather-child relationship quality. This association is statistically indistinguishable across racial groups, although the association is stronger among children in cohabiting stepfamilies compared to children in married stepfamilies.
Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor

Can we tell a good professor from their students’ comments? And are there differences between what is considered to be a good professor by different student groups? We use a large corpus of student evaluations collected from the RateMyProfessors website, covering different institutions, disciplines, and cultures, and perform several comparative experiments and analyses aimed to answer these two questions. Our results indicate that (1) we can reliably classify good professors from poor professors with an accuracy of over 90%, and (2) we can separate the evaluations made for good professors by different groups with accuracies in the range of 71-89%. Furthermore, a qualitative analysis performed using topic modeling highlights the aspects of interest for different student groups.
A Chinese Text Corrector Based on Seq2Seq Model

In this paper, we build a Chinese text corrector which can correct spelling mistakes precisely in Chinese texts. Our motivation is inspired by the recently proposed seq2seq model which consider the text corrector as a sequence learning problem. To begin with, we propose a biased-decoding method to improve the bilingual evaluation understudy (BLEU) score of our model. Secondly, we adopt a more reasonable OOV token scheme, which enhances the robustness of our correction mechanism. Moreover, to test the performance of our proposed model thoroughly, we establish a corpus which includes 600,000 sentences from news data of Sogou Labs. Experiments show that our corrector model can achieve better corrector results based on the corpus.
Provenance in neuroimaging

Provenance, the description of the history of a set of data, has grown more important with the proliferation of research consortia-related efforts in neuroimaging. Knowledge about the origin and history of an image is crucial for establishing data and results quality; detailed information about how it was processed, including the specific software routines and operating systems that were used, is necessary for proper interpretation, high fidelity replication and re-use. We have drafted a mechanism for describing provenance in a simple and easy to use environment, alleviating the burden of documentation from the user while still providing a rich description of an image's provenance. This combination of ease of use and highly descriptive metadata should greatly facilitate the collection of provenance and subsequent sharing of data.
Evaluation of salient point methods

Processing visual content in images and videos is a challenging task associated with the development of modern computer vision. Because salient point approaches can represent distinctive and affine invariant points in images, many approaches have been proposed over the past decade. Each method has particular advantages and limitations and may be appropriate in different contexts. In this paper we evaluate the performance of a wide set of salient point detectors and descriptors. We begin by comparing diverse salient point algorithms (SIFT, SURF, BRIEF, ORB, FREAK, BRISK, STAR, GFTT and FAST) with regard to repeatability, recall and precision and then move to accuracy and stability in real-time video tracking.
PCN: Point Completion Network

Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.
Architectural optimizations for high performance and energy efficient Smith-Waterman implementation on FPGAs using OpenCL

Smith-Waterman is a dynamic programming algorithm that plays a key role in the modern genomics pipeline as it is guaranteed to find the optimal local alignment between two strings of data. The state of the art presents many hardware acceleration solutions that have been implemented in order to exploit the high degree of parallelism available in this algorithm. The majority of these implementations use heuristics to increase the performance of the system at the expense of the accuracy of the result. In this work, we present an implementation of the pure version of the algorithm. We include the key architectural optimizations to achieve highest possible performance for a given platform and leverage the Berkeley roofline model to track the performance and guide the optimizations. To achieve scalability, our custom design comprises of systolic arrays, data compression features and shift registers, while a custom port mapping strategy aims to maximize performance. Our designs are built leveraging an OpenCL-based design entry, namely Xilinx SDAccel, in conjunction with a Xilinx Virtex 7 and Kintex Ultrascale platform. Our final design achieves a performance of 42.47 GCUPS (giga cell updates per second) with an energy efficiency of 1.6988 GCUPS/W. This represents an improvement of 1.72x in performance and energy efficiency over previously published FPGA implementations and 8.49x better in energy efficiency over comparable GPU implementations.
Through a Glass Darkly: Information Technology Design, Identity Verification, and Knowledge Contribution in Online Communities

A of information technology (IT) artifacts, such as those supporting reputation management and digital archives of past interactions, are commonly deployed to support online communities. Despite their ubiquity, theoretical and empirical research investigating the impact of such IT-based features on online community communication and interaction is limited. Drawing on the social psychology literature, we describe an identitybased view to understand how the use of IT-based features in online communities is associated with online knowledge contribution. Specifically, the use of four categories of IT artifacts—those supporting virtual copresence, persistent labeling, self-presentation, and deep profiling—is proposed to enhance perceived identity verification, which thereafter promotes satisfaction and knowledge contribution. To test the theoretical model, we surveyed more than 650 members of two online communities. In addition to the positive effects of community IT artifacts on perceived identity verification, we also find that perceived identity verification is strongly linked to member satisfaction and knowledge contribution. This paper offers a new perspective on the mechanisms through which IT features facilitate computer-mediated knowledge sharing, and it yields important implications for the design of the supporting IT infrastructure.
Inferring templates from spreadsheets

We present a study investigating the performance of a system for automatically inferring spreadsheet templates. These templates allow users to safely edit spreadsheets, that is, certain kinds of errors such as range, reference, and type errors can be provably prevented. Since the inference of templates is inherently ambiguous, such a study is required to demonstrate the effectiveness of any such automatic system. The study results show that the system considered performs significantly better than subjects with intermediate to expert level programming expertise. These results are important because the translation of the huge body of existing spreadsheets into a system based on safety-guaranteeing templates cannot be performed without automatic support. We also carried out post-hoc analyses of the video recordings of the subjects' interactions with the spreadsheets and found that although expert-level subjects needed less time and developed more accurate templates than less experienced subjects, they did not inspect fewer cells in the spreadsheet. %and found that expert-level subjects spend less time and inspect fewer cells in the spreadsheet and develop more accurate templates than subjects with less experience.
Cnvlutin2: Ineffectual-Activation-and-Weight-Free Deep Neural Network Computing

We discuss several modifications and extensions over the previous proposed Cnvlutin (CNV) accelerator for convolutional and fully-connected layers of Deep Learning Network. We first describe different encodings of the activations that are deemed ineffectual. The encodings have different memory overhead and energy characteristics. We propose using a level of indirection when accessing activations from memory to reduce their memory footprint by storing only the effectual activations. We also present a modified organization that detects the activations that are deemed as ineffectual while fetching them from memory. This is different than the original design that instead detected them at the output of the preceding layer. Finally, we present an extended CNV that can also skip ineffectual weights.