nils-herrmann/clean_scirepeval huggingface dataset:
Colorgorical: Creating discriminable and preferable color palettes for information visualization

We present an evaluation of Colorgorical, a web-based tool for creating discriminable and aesthetically preferable categorical color palettes. Colorgorical uses iterative semi-random sampling to pick colors from CIELAB space based on user-defined discriminability and preference importances. Colors are selected by assigning each a weighted sum score that applies the user-defined importances to Perceptual Distance, Name Difference, Name Uniqueness, and Pair Preference scoring functions, which compare a potential sample to already-picked palette colors. After, a color is added to the palette by randomly sampling from the highest scoring palettes. Users can also specify hue ranges or build off their own starting palettes. This procedure differs from previous approaches that do not allow customization (e.g., pre-made ColorBrewer palettes) or do not consider visualization design constraints (e.g., Adobe Color and ACE). In a Palette Score Evaluation, we verified that each scoring function measured different color information. Experiment 1 demonstrated that slider manipulation generates palettes that are consistent with the expected balance of discriminability and aesthetic preference for 3-, 5-, and 8-color palettes, and also shows that the number of colors may change the effectiveness of pair-based discriminability and preference scores. For instance, if the Pair Preference slider were upweighted, users would judge the palettes as more preferable on average. Experiment 2 compared Colorgorical palettes to benchmark palettes (ColorBrewer, Microsoft, Tableau, Random). Colorgorical palettes are as discriminable and are at least as preferable or more preferable than the alternative palette sets. In sum, Colorgorical allows users to make customized color palettes that are, on average, as effective as current industry standards by balancing the importance of discriminability and aesthetic preference.
Analytical model for effects of twisting on litz-wire losses

Litz wire uses complex twisting to balance currents between strands. Most models assume that the twisting works perfectly to accomplish this balancing, and thus are not helpful in choosing the details of the twisting configuration. A complete model that explicitly models the effect of twisting on loss is introduced. Skin effect and proximity effect are each modeled at the level of the individual strands and at each level of the twisting construction. Comparisons with numerical simulations are used to verify the model. The results are useful for making design choices for the twisting configuration and the pitches of each twisting step. Windings with small numbers of turns are the most likely to have significant bundle-level effects that are not captured by conventional models, and are the most important to model and optimize with this approach.
Speaking swiss: languages and venues in foursquare

Due to increasing globalization, urban societies are becoming more multicultural. The availability of large-scale digital mobility traces e.g. from tweets or checkins provides an opportunity to explore multiculturalism that until recently could only be addressed using survey-based methods. In this paper we examine a basic facet of multiculturalism through the lens of language use across multiple cities in Switzerland. Using data obtained from Foursquare over 330 days, we present a descriptive analysis of linguistic differences and similarities across five urban agglomerations in a multicultural, western European country.
3D modelling and simulation of a crawler robot in ROS/Gazebo

Modelling and animation of crawler UGV's caterpillars is a complicated task, which has not been completely resolved in ROS/Gazebo simulators. In this paper, we proposed an approximation of track-terrain interaction of a crawler UGV, perform modelling and simulation of Russian crawler robot "Engineer" within ROS/Gazebo and visualize its motion in ROS/RViz software. Finally, we test the proposed model in heterogeneous robot group navigation scenario within uncertain Gazebo environment.
Voronoi++: A Dynamic Page Segmentation Approach Based on Voronoi and Docstrum Features

This paper presents a dynamic approach to document page segmentation. Current page segmentation algorithms lack the ability to dynamically adapt local variations in the size, orientation and distance of components within a page. Our approach builds upon one of the best algorithms, Kise et. al. work based on Area Voronoi Diagrams, which adapts globally to page content to determine algorithm parameters. In our approach, local thresholds are determined dynamically based on parabolic relations between components, and Docstrum based angular and neighborhood features are integrated to improve accuracy. Zone-based evaluation was performed on four sets of printed and handwritten documents in English and Arabic scripts and an increase of 33% in accuracy is reported.
Semantics-preserving hashing for cross-view retrieval

With benefits of low storage costs and high query speeds, hashing methods are widely researched for efficiently retrieving large-scale data, which commonly contains multiple views, e.g. a news report with images, videos and texts. In this paper, we study the problem of cross-view retrieval and propose an effective Semantics-Preserving Hashing method, termed SePH. Given semantic affinities of training data as supervised information, SePH transforms them into a probability distribution and approximates it with to-be-learnt hash codes in Hamming space via minimizing the Kullback-Leibler divergence. Then kernel logistic regression with a sampling strategy is utilized to learn the nonlinear projections from features in each view to the learnt hash codes. And for any unseen instance, predicted hash codes and their corresponding output probabilities from observed views are utilized to determine its unified hash code, using a novel probabilistic approach. Extensive experiments conducted on three benchmark datasets well demonstrate the effectiveness and reasonableness of SePH.
Guidelines , Criteria , and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology

In the context of the development of prototypic assessment instruments in the areas of cognition, personality, and adaptive functioning, the issues of standardization, norming procedures, and the important psychometrics of test reliability and validity are evaluated critically. Criteria, guidelines, and simple rules of thumb are provided to assist the clinician faced with the challenge of choosing an appropriate test instrument for a given psychological assessment.
Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain

Visualizing entire neuronal networks for analysis in the intact brain has been impossible up to now. Techniques like computer tomography or magnetic resonance imaging (MRI) do not yield cellular resolution, and mechanical slicing procedures are insufficient to achieve high-resolution reconstructions in three dimensions. Here we present an approach that allows imaging of whole fixed mouse brains. We modified 'ultramicroscopy' by combining it with a special procedure to clear tissue. We show that this new technique allows optical sectioning of fixed mouse brains with cellular resolution and can be used to detect single GFP-labeled neurons in excised mouse hippocampi. We obtained three-dimensional (3D) images of dendritic trees and spines of populations of CA1 neurons in isolated hippocampi. Also in fruit flies and in mouse embryos, we were able to visualize details of the anatomy by imaging autofluorescence. Our method is ideally suited for high-throughput phenotype screening of transgenic mice and thus will benefit the investigation of disease models.
Intrusion Detection: A Survey

The rapid proliferation of computer networks has changed the prospect of network security. An easy accessibility condition cause computer networkpsilas vulnerable against several threats from hackers. Threats to networks are numerous and potentially devastating. Up to the moment, researchers have developed Intrusion Detection Systems (IDS) capable of detecting attacks in several available environments. A boundlessness of methods for misuse detection as well as anomaly detection has been applied. Many of the technologies proposed are complementary to each other, since for different kind of environments some approaches perform better than others. This paper presents a taxonomy of intrusion detection systems that is then used to survey and classify them. The taxonomy consists of the detection principle, and second of certain operational aspects of the intrusion detection system.
STransE: a novel embedding model of entities and relationships in knowledge bases

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a lowdimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.
Black-Box Variational Inference for Stochastic Differential Equations

Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process. Working with an EulerMaruyama discretisation for the diffusion, we use variational inference to jointly learn the parameters and the diffusion paths. We use a standard mean-field variational approximation of the parameter posterior, and introduce a recurrent neural network to approximate the posterior for the diffusion paths conditional on the parameters. This neural network learns how to provide Gaussian state transitions which bridge between observations as the conditioned diffusion process does. The resulting black-box inference method can be applied to any SDE system with light tuning requirements. We illustrate the method on a LotkaVolterra system and an epidemic model, producing accurate parameter estimates in a few hours.
A web enabled secured system for attendance monitoring and real time location tracking using Biometric and Radio Frequency Identification (RFID) technology

Automated Information Systems has played a major role in the growth, advancement, and modernization of our daily work processes. The main purpose of this paper is to develop a safe and secure web based attendance monitoring system using Biometrics and Radio Frequency Identification (RFID) Technology based on multi-tier architecture, for both computers and smartphones. The system can maintain the attendance records of both students and teachers/staff members of an institution. The system can also detect the current location of the students, faculties, and other staff members anywhere within the domain of institution campus. With the help of android application one can receive live feeds of various campus activities, keep updated with the current topics in his/her enrolled courses as well as track his/her friends on a real time basis. An automated SMS service is facilitated in the system, which sends an SMS automatically to the parents in order to notify that their ward has successfully reached the college. Parents as well as student will be notified via e-mail, if the student is lagging behind in attendance. There is a functionality of automatic attendance performance graph in the system, which gives an idea of the student's consistency in attendance throughout the semester.
SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor

This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead. The task is based on a new dataset of funny tweets posted in response to shared hashtags, collected from the ‘Hashtag Wars’ segment of the TV show @midnight. The results are evaluated in two subtasks that require the participants to generate either the correct pairwise comparisons of tweets (subtask A), or the correct ranking of the tweets (subtask B) in terms of how funny they are. 7 teams participated in subtask A, and 5 teams participated in subtask B. The best accuracy in subtask A was 0.675. The best (lowest) rank edit distance for subtask B was 0.872.
A new isolated SEPIC converter with coupled inductors for photovoltaic applications

In this paper a new isolated SEPIC converter which is a proper choice for PV applications, is introduced and analyzed. The proposed converter has the advantage of high voltage gain while the switch voltage stress is same as a regular SEPIC converter. The converter operating modes are discussed and design considerations are presented. Also simulation results are illustrated which justifies the theoretical analysis. Finally the proposed converter is improved using active clamp technique.
The Jpred 3 secondary structure prediction server

Jpred (http://www.compbio.dundee.ac.uk/jpred) is a secondary structure prediction server powered by the Jnet algorithm. Jpred performs over 1000 predictions per week for users in more than 50 countries. The recently updated Jnet algorithm provides a three-state (alpha-helix, beta-strand and coil) prediction of secondary structure at an accuracy of 81.5%. Given either a single protein sequence or a multiple sequence alignment, Jpred derives alignment profiles from which predictions of secondary structure and solvent accessibility are made. The predictions are presented as coloured HTML, plain text, PostScript, PDF and via the Jalview alignment editor to allow flexibility in viewing and applying the data. The new Jpred 3 server includes significant usability improvements that include clearer feedback of the progress or failure of submitted requests. Functional improvements include batch submission of sequences, summary results via email and updates to the search databases. A new software pipeline will enable Jnet/Jpred to continue to be updated in sync with major updates to SCOP and UniProt and so ensures that Jpred 3 will maintain high-accuracy predictions.
On discovery of gathering patterns from trajectories

The increasing pervasiveness of location-acquisition technologies has enabled collection of huge amount of trajectories for almost any kind of moving objects. Discovering useful patterns from their movement behaviours can convey valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called gathering, which is a trajectory pattern modelling various group incidents such as celebrations, parades, protests, traffic jams and so on. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. Since the process of discovering gathering patterns over large-scale trajectory databases can be quite lengthy, we further develop a set of well thought out techniques to improve the performance. These techniques, including effective indexing structures, fast pattern detection algorithms implemented with bit vectors, and incremental algorithms for handling new trajectory arrivals, collectively constitute an efficient solution for this challenging task. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on a real taxicab trajectory dataset.
Video Anomaly Detection With Compact Feature Sets for Online Performance

Over the past decade, video anomaly detection has been explored with remarkable results. However, research on methodologies suitable for online performance is still very limited. In this paper, we present an online framework for video anomaly detection. The key aspect of our framework is a compact set of highly descriptive features, which is extracted from a novel cell structure that helps to define support regions in a coarse-to-fine fashion. Based on the scene’s activity, only a limited number of support regions are processed, thus limiting the size of the feature set. Specifically, we use foreground occupancy and optical flow features. The framework uses an inference mechanism that evaluates the compact feature set via Gaussian Mixture Models, Markov Chains, and Bag-of-Words in order to detect abnormal events. Our framework also considers the joint response of the models in the local spatio-temporal neighborhood to increase detection accuracy. We test our framework on popular existing data sets and on a new data set comprising a wide variety of realistic videos captured by surveillance cameras. This particular data set includes surveillance videos depicting criminal activities, car accidents, and other dangerous situations. Evaluation results show that our framework outperforms other online methods and attains a very competitive detection performance compared with state-of-the-art non-online methods.
The Broadcast Storm Problem in a Mobile ad hoc Network

Broadcasting is a common operation in a network to resolve many issues. In a mobile ad hoc network (MANET) in particular, due to host mobility, such operations are expected to be executed more frequently (such as finding a route to a particular host, paging a particular host, and sending an alarm signal). Because radio signals are likely to overlap with others in a geographical area, a straightforward broadcasting by flooding is usually very costly and will result in serious redundancy, contention, and collision, to which we call the broadcast storm problem. In this paper, we identify this problem by showing how serious it is through analyses and simulations. We propose several schemes to reduce redundant rebroadcasts and differentiate timing of rebroadcasts to alleviate this problem. Simulation results are presented, which show different levels of improvement over the basic flooding approach.
Binary Histogram in Image Classification for Retrieval Purposes

Image retrieval can be considered as a classification problem. Classification is usually based on some image features. In the feature extraction image segmentation is commonly used. In this paper we introduce a new feature for image classification for retrieval purposes. This feature is based on the gray level histogram of the image. The feature is called binary histogram and it can be used for image classification without segmentation. Binary histogram can be used for image retrieval as such by using similarity calculation. Another approach is to extract some features from it. In both cases indexing and retrieval do not require much computational time. We test the similarity measurement and the feature-based retrieval by making classification experiments. The proposed features are tested using a set of paper defect images, which are acquired from an industrial imaging application.
XFS: The Big Storage File System for Linux

hch@lst.de X F s I s a F I l e s y s t e m t h at w a s d e signed from day one for computer systems with large numbers of CPUs and large disk arrays. It focuses on supporting large files and good streaming I/O performance. It also has some interesting administrative features not supported by other Linux file systems. This article gives some background information on why XFS was created and how it differs from the familiar Linux file systems. You may discover that XFS is just what your project needs instead of making do with the default Linux file system.
Symmetry-breaking Convergence Analysis of Certain Two-layered Neural Networks with Relu Nonlinearity

In this paper, we use dynamical system to analyze the nonlinear weight dynamics of two-layered bias-free networks in the form of g(x;w) = ∑K j=1 σ(w T j x), where σ(·) is ReLU nonlinearity. We assume that the input x follow Gaussian distribution. The network is trained using gradient descent to mimic the output of a teacher network of the same size with fixed parameters w∗ using l2 loss. We first show that when K = 1, the nonlinear dynamics can be written in close form, and converges to w∗ with at least (1 − )/2 probability, if random weight initializations of proper standard derivation (∼ 1/ √ d) is used, verifying empirical practice [Glorot & Bengio (2010); He et al. (2015); LeCun et al. (2012)]. For networks with many ReLU nodes (K ≥ 2), we apply our close form dynamics and prove that when the teacher parameters {w∗ j}j=1 forms orthonormal bases, (1) a symmetric weight initialization yields a convergence to a saddle point and (2) a certain symmetry-breaking weight initialization yields global convergence to w∗ without local minima. To our knowledge, this is the first proof that shows global convergence in nonlinear neural network without unrealistic assumptions on the independence of ReLU activations. In addition, we also give a concise gradient update formulation for a multilayer ReLU network when it follows a teacher of the same size with l2 loss. Simulations verify our theoretical analysis.
A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.
Big data: A review

Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. These useful informations for companies or organizations with the help of gaining richer and deeper insights and getting an advantage over the competition. For this reason, big data implementations need to be analyzed and executed as accurately as possible. This paper presents an overview of big data's content, scope, samples, methods, advantages and challenges and discusses privacy concern on it.