nils-herrmann/clean_scirepeval huggingface dataset:
Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia

In this article, we present a novel approach for computing semantic relatedness and conduct a large-scale study of it on Wikipedia. Unlike existing semantic analysis methods that utilize Wikipedia’s content or link structure, we propose to use human navigational paths on Wikipedia for this task. We obtain 1.8 million human navigational paths from a semi-controlled navigation experiment – a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. Our results are intriguing: They suggest that (i) semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia’s plain link structure alone and (ii) that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. Our work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.
Comparative Study on Generative Adversarial Networks

In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative models in the field of computer vision and image classification. These generative models have been popularized through a new framework called Generative Adversarial Networks. Moreover, many modified versions of this framework have been proposed in the last two years. We study the original model proposed by Goodfellow et al. (2014) as well as modifications over the original model and provide a comparative analysis of these models.
Handwriting difficulties in primary school children: a search for underlying mechanisms.

This study investigated the contribution of perceptual-motor dysfunction and cognitive planning problems to the quality or speed of handwriting in children with handwriting problems (HWP).



Twenty-nine children with HWP and 20 classroom peers attending regular schools (grade 2 and grade 3) were tested with regard to visual perception, visual-motor integration, fine motor coordination, and cognitive planning abilities.



The HWP group scored significantly lower on visual perception, visual-motor integration, fine motor coordination, and cognitive planning in comparison with classroom controls. Regression analyses showed that visual-motor integration was the only significant predictor for quality of handwriting in the HWP group, whereas fine motor coordination (i.e., unimanual dexterity) was the only significant predictor of quality of handwriting in the control group.



Results suggest that two different mechanisms underlie the quality of handwriting in children with and without handwriting problems. Poor quality of handwriting of children with HWP seems particularly related to a deficiency in visual-motor integration.
Subspace Clustering with Priors via Sparse Quadratically Constrained Quadratic Programming

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem can be formulated as a convex semi-definite optimization problem subject to an additional rank constrain that involves only a very small number of variables. This is established by first reducing the problem to a quadratically constrained quadratic problem and then using its special structure to find conditions guaranteeing that a suitably built convex relaxation is indeed exact. When combined with the standard nuclear norm relaxation for rank, the results above lead to computationally efficient algorithms with optimality guarantees. A salient feature of the proposed approach is its ability to incorporate existing a-priori information about the noise, co-ocurrences, and percentage of outliers. These results are illustrated with several examples.
An approach for offensive text detection and prevention in Social Networks

Social Network has become a place where people from every corner of the world has established a virtual civilization. In this virtual community, people used to share their views, express their feelings, photos, videos, blogs, etc. Social Networking Sites like Facebook, Twitters, etc. has given a platform to share innumerable contents with just a click of a button. However, there is no restriction applied by them for the uploaded content. These uploaded content may contains abusive words, explicit images which may be unsuitable for social platforms. As such there is no defined mechanism for restricting offensive contents from publishing on social sites. To solve this problem we have used our proposed approach. In our approach we are developing a social network prototype for implementing our approach for automatic filtering of offensive content in social network. Many popular social networking sites today don't have proper mechanism for restricting offensive contents. They use reporting methods in which user report if the content is abuse. This requires substantial human efforts and time. In this paper, we applied pattern matching algorithm for offensive keyword detection from social networking comments and prevent it from publishing on social platform. Apart from conventional method of reporting abusive contents by users our approach does not requires any human intervention and thus restrict offensive words by detecting and preventing it automatically.
Distinguishing Opinion from News

Newspapers have separate sections for opinion articles and news articles. The goal of this project is to classify articles as opinion versus news and also to do analysis of the results to figure out the factors that distinguish the two. Preliminary results show that classification is possible with unigram features in an SVM with F1 of .94.  This project focuses on subjectivity classification for news articles. Much prior work on subjectivity has focused on distinguishing positive and negative sentiment (for instance, in product reviews) or classifying phrases or clauses as subjective (Liu, 2008). Here we attempt to distinguish entire articles as reporting news or expressing opinion. The task is related but has some key differences. For instance, review-type sentiment analysis often relies on pre-made lexicons or focuses on classifying words as positive or negative (Toprak and Gurevych, 2009; Turney and Littman, 2010; Potts). Words associated with positivity and negativity are not necessarily those associated with editorials and opinion pieces in which authors pose sophisticated arguments about current events, policies, etc. One goal of the project was to gain a lexical understanding of words that can distinguish the two categories, and thus be able to generate a lexicon similar those already existing for sentiment analysis of reviews that would work for articles. Prior Work There has been thorough research into document classification."Machine Learning in Automated Text Categorization" (Sebastiani, 2003) provides an overview of work up to 2002. Within the area of subjectivity/sentiment analysis there is also a wide variety of work. Pang and Lee give an overview of the field of subjectivity (Pang and Lee, 2008). Liu defines many different problems within the field including sentiment and subjectivity classification: (1) classifying an opinionated document as expressing a positive or negative opinion, and (2) classifying a sentence or a clause of the sentence as subjective or objective (Liu, 2010) Liu also gives an overview of the field thus far from a teaching perspective. Turney and Littman provide a method for sentiment for particular words based on their context (Turney and Littman, 2003). Yu and Hatzivassiloglou specifically address distinguishing opinion from news using a Naive Bayes classifier and are able to achieve very high results (Yu and Hatzivassiloglou, 2003). However, their method did not generalize to my dataset. Data I use two datasets, both consisting of articles from the New York Times. The primary dataset consists of 140 articles over the course of the 7 years up to and including 2012. For comparison, I also test on a dataset of articles from October and November 2012 in which news events are covered repeatedly. The data was collected by scraping the New York Times website. The first
A Kalman Filter Based Tracking Scheme with Input Estimation

Beginning with the derivation of a least squares estimator that yields an estimate of the acceleration input vector, this paper first develops a detector for sensing target maneuvers and then develops the combination of the estimator, detector, and a "simple" Kalman filter to form a tracker for maneuvering targets. Finally, some simulation results are presented. A relationship between the actual residuals, assuming target maneuvers, and the theoretical residuals of the "simple" Kalman filter that assumes no maneuvers, is first formulated. The estimator then computes a constant acceleration input vector that best fits that relationship. The result is a least squares estimator of the input vector which can be used to update the "simple" Kalman filter. Since typical targets spend considerable periods of time in the constant course and speed mode, a detector is used to guard against automatic updating of the "simple" Kalman filter. A maneuver is declared, and updating performed, only if the norm of the estimated input vector exceeds a threshold. The tracking sclheme is easy to implement and its capability is illustrated in three tracking examples.
Exact Inference Techniques for the Dynamic Analysis of Bayesian Attack Graphs

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise valuable network resources. The uncertainty about the attacker’s behaviour and capabilities make Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of traditional attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our proposed approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.
Model-Checking Markov Chains in the Presence of Uncertainties

We investigate the problem of model checking Interval-valued Discrete-time Markov Chains (IDTMC). IDTMCs are discrete-time finite Markov Chains for which the exact transition probabilities are not known. Instead in IDTMCs, each transition is associated with an interval in which the actual transition probability must lie. We consider two semantic interpretations fo r the uncertainty in the transition probabilities of an IDTMC. In the first interpre tation, we think of an IDTMC as representing a (possibly uncountable) fam ily of (classical) discrete-time Markov Chains, where each member of the fam ily is a Markov Chain whose transition probabilities lie within the interval range given in the IDTMC. This semantic interpretation we call Uncertain Markov Chains (UMC). In the second semantics for an IDTMC, which we call Interval M rkov Decision Process (IMDP), we view the uncertainty as being resolved thro ug non-determinism. In other words, each time a state is visited, we adversar ially pick a transition distribution that respects the interval constraints, and take a probabilistic step according to the chosen distribution. We show that the PCTL model checking problem for both Uncertain Markov Chain semantics and Interval Markov Decision Process semantics is decidable in PSPACE. We also p rove lower bounds for these model checking problems.
Perceptually based brush strokes for nonphotorealistic visualization

An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the low-level human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.
Sharing Visual Features for Multiclass and Multiview Object Detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity and the (training-time) sample complexity scale linearly with the number of classes to be detected. We present a multitask learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required and, therefore, the runtime cost of the classifier, is observed to scale approximately logarithmically with the number of classes. The features selected by joint training are generic edge-like features, whereas the features chosen by training each class separately tend to be more object-specific. The generic features generalize better and considerably reduce the computational cost of multiclass object detection
Screening of Wrightia tinctoria leaves for Anti psoriatic activity

The hydro alcoholic extract of Wrightia tinctoria leaves was evaluated for antipsoriatic activity by mouse tail test.  Antipsoriatic activity was performed at a dose 200 mg/kg body weight in mice (25-30 g). Isoretinoic acid (0.5 mg/kg) was used as the standard. Degree of orthokeratosis, drug activity and the relative epidermal thicknesses were calculated and statistically analyzed. The extract was also evaluated for its antioxidant potential by DPPH, nitric oxide and hydrogen peroxide radical scavenging assays.  The extract produced significant (p<0.01) degree of orthokeratosis compared to control and the drug activity was found to be 70.18%, which is more potent than the standard (57.43%).. The extract showed prominent antioxidant activity in all the assays. The present study concludes that the selected plant has antipsoriatic activity and can be used for psoriasis treatment.
Tweeting celebrity suicides: Users' reaction to prominent suicide deaths on Twitter and subsequent increases in actual suicides.

A substantial amount of evidence indicates that news coverage of suicide deaths by celebrities is followed by an increase in suicide rates, suggesting a copycat behavior. However, the underlying process by which celebrity status and media coverage leads to increases in subsequent suicides is still unclear. This study collected over 1 million individual messages ("tweets") posted on Twitter that were related to 26 prominent figures in Japan who died by suicide between 2010 and 2014 and investigated whether media reports on suicide deaths that generated a greater level of reactions by the public are likely to be followed by a larger increase in actual suicides. We also compared the number of Twitter posts and the number of media reports in newspaper and on television to understand whether the number of messages on Twitter in response to the deaths corresponds to the amount of coverage in the traditional media. Using daily data from Japan's national death registry between 2010 and 2014, our analysis found an increase in actual suicides only when suicide deaths generated a large reaction from Twitter users. In contrast, no discernible increase in suicide counts was observed when the analysis included suicide deaths to which Twitter users did not show much interest, even when these deaths were covered considerably by the traditional media. This study also found suicides by relatively young entertainers generated a large number of posts on Twitter. This sharply contrasts with the relatively smaller volume of reaction to them generated by traditional forms of media, which focuses more on the deaths of non-entertainers. The results of this study strongly suggest that it is not sufficient to examine only traditional news media when investigating the impact of media reports on actual suicides.
Multi-criteria supplier segmentation using a fuzzy preference relations based AHP

0377-2217/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.ejor.2012.09.037 ⇑ Corresponding author. ; fax E-mail addresses: j.rezaei@tudelft.nl (J. Rezaei), j.r. 1 Tel.: +31 15 27 84815; fax: +31 15 27 83177. One of the strategic activities of a firm is supplier segmentation, whereby a firm creates groups of suppliers to handle them differently. Existing literature provides several typologies of suppliers, each of which uses different dimensions/variables. In this paper, different typologies are combined by distinguishing two overarching dimensions, the capabilities and the willingness of suppliers to cooperate with a particular firm. These dimensions cover almost all the existing supplier segmentation criteria mentioned in existing literature. For each particular situation, these dimensions can be specified using a multi-criteria decision-making method. A methodology is proposed that includes a fuzzy Analytic Hierarchy Process (AHP) which uses fuzzy preference relations to incorporate the ambiguities and uncertainties that usually exist in human judgment. The proposed methodology is used to segment the suppliers of a broiler company. The result is a segmentation of suppliers based on two aggregated dimensions. Finally some strategies to handle different segments are discussed and concluding remarks and suggestions for future research are provided.
Representation learning with complete semantic description of knowledge graphs

Learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low dimensional space. There exits two kinds of representation methods for entities in Knowledge Graphs (KGs), including structure-based representation and description-based representation. Most methods represent entities with fact triples of KGs through translating embedding models, which can't integrate the rich information in entities descriptions with triple structure information. In this paper, we propose a novel RL method named as Representation Learning with Complete semantic Description of Knowledge Graphs (RLCD), which can exploit all semantic information of entity descriptions and fact triples of KGs, to enrich the semantic representations of KGs. More specifically, we explore Doc2Vec encoder model to encode all semantic information of entity descriptions without losing the relevance in the context of entities descriptions, and further learn knowledge representations from triples with entity descriptions. The experiment results show that RLCD gets better performance that state-of-the-art method DKRL, in terms of mean rank value and HITS. Moreover, RLCD is much faster than DKRL.
TCS-TRA-1260 TCS Technical Report ZDD-Based Computation of the Number of Paths in a Graph by

Counting the number of paths in a graph, for example the number of nonintersecting (or self-avoiding) rook paths joining opposite corners of an n × n grid, is not an easy problem since no mathematical formula is found and the number becomes too large to enumerate one by one except for small graphs. We have implemented software based on the ZDD technique in Knuth's " The Art of Computer Programming " carefully so as to achieve better memory efficiency, and have succeeded in computing the exact numbers for some graphs that were not known until now.
3 Materials and Methods 3 . 1 Species and sequence selection

A number of phylogenetic studies have been performed on the family Sciuridae. This work seeks to validate and extend existing phylogenetic studies of Sciuridae with an analysis of the divergence times of a selected set of species. A total of five genes; 2 mitochrontrial ribosomal RNA genes (12S, 16S), a mitochrontrial protein coding gene (cytb) and two nuclear genes (IRBP, RAG1) were studied across 65 representative species. Phylogenetic inference using a variety of methods validated prior molecular studies. Analysis of species divergence times using representative fossil data was performed to better define the patterns of speciation.
Flexible Developable Surfaces

We introduce a discrete paradigm for developable surface modeling. Unlike previous attempts at interactive developable surface modeling, our system is able to enforce exact developability at every step, ensuring that users do not inadvertently suggest configurations that leave the manifold of admissible folds of a flat two-dimensional sheet. With methods for navigation of this highly nonlinear constraint space in place, we show how to formulate a discrete mean curvature bending energy measuring how far a given discrete developable surface is from being flat. This energy enables relaxation of user-generated configurations and suggests a straightforward subdivision scheme that produces admissible smoothed versions of bent regions of our discrete developable surfaces.
Parameterized Shape Grammar for Generating n-fold Islamic Geometric Motifs

The complex formation of Islamic geometric Patterns (IGP) is one of the distinctive features in Islamic art and architecture. Many have attempted to reproduce these patterns in digital form, using various pattern generation techniques. Shape grammars are an effective pattern generation method, providing good aesthetic results. In this paper we present a novel approach in generating 3D IGP using an extended shape grammar: Parametrized Shape Grammar (PSG). PSG allows a user to generate both original and novel forms of Islamic geometric motifs (a repeated unit of a pattern). PSG is generalized to generate n-fold Islamic geometric motifs in a 3D environment, practically implemented as a 3D modeling tool within Autodesk Maya. The parametrization within each grammar rule is the key to generating numerous original and novel Islamic geometric motifs.