nils-herrmann/clean_scirepeval huggingface dataset:
Traditional and recent approaches in background modeling for foreground detection: An overview

for foreground detection is often used in different applications to model the background and then detect the moving objects in the scene like in video surveillance. The last decade witnessed very significant publications in this field. Furthermore, several surveys can be found in literature but none of them addresses an overall review in this field. So, the purpose of this paper is to provide a complete survey of the traditional and recent approaches. First, we categorize the different approaches found in literature. We have classified them in terms of the mathematical models used and we have discussed them in terms of the critical situations that they claim to handle. Furthermore, we present the available resources, datasets and libraries. Then, we conclude with several promising directions for future research.
Feature Learning for Interaction Activity Recognition in RGBD Videos

This paper proposes a human activity recognition method which is based on features learned from 3D video data without incorporating domain knowledge. The experiments on data collected by RGBD cameras produce results outperforming other techniques. Our feature encoding method follows the bag-of-visual-word model, then we use a SVM classifier to recognise the activities. We do not use skeleton or tracking information and the same technique is applied on color and depth data.
Deep Convolution Neural Network for Extreme Multi-label Text Classification

In this paper we present an analysis on the usage of Deep Neural Networks for extreme multi-label and multiclass text classification. We will consider two network models: the first one is formed by a word embeddings (WEs) stage followed by two dense layers, hereinafter Dense, and a second model with a convolution stage between the WEs and the dense layers, hereinafter CNN-Dense. We will take into account classification problems characterized by different number of labels, ranging from an order of 10 to an order of 30,000, showing the different performances of the neural networks varying the total label number and the average number of labels for sample, exploiting the hierarchical structure of the label space of the dataset used for experimental assessment. It is worth noting that multi-label classification is an harder problem if compared to multi-class, due to the variable number of labels associated to each sample. We will even investigate on the behaviour of the neural networks as function of the training hyperparameters, analysing the link between them and the dataset complexity. All the result will be evaluated using the PubMed scientific articles collection as
Understanding adolescents' sleep patterns and school performance: a critical appraisal.

The present paper reviews and critiques studies assessing the relation between sleep patterns, sleep quality, and school performance of adolescents attending middle school, high school, and/or college. The majority of studies relied on self-report, yet the researchers approached the question with different designs and measures. Specifically, studies looked at (1) sleep/wake patterns and usual grades, (2) school start time and phase preference in relation to sleep habits and quality and academic performance, and (3) sleep patterns and classroom performance (e.g., examination grades). The findings strongly indicate that self-reported shortened total sleep time, erratic sleep/wake schedules, late bed and rise times, and poor sleep quality are negatively associated with academic performance for adolescents from middle school through the college years. Limitations of the current published studies are also discussed in detail in this review.
Applications of the balanced scorecard for strategic management and performance measurement in the health sector.

In order to attain a useful balanced scorecard (BSC), appropriate performance perspectives and indicators are crucial to reflect all strategies of the organisation. The objectives of this survey were to give an insight regarding the situation of the BSC in the health sector over the past decade, and to afford a generic approach of the BSC development for health settings with specific focus on performance perspectives, performance indicators and BSC generation. After an extensive search based on publication date and research content, 29 articles published since 2002 were identified, categorised and analysed. Four critical attributes of each article were analysed, including BSC generation, performance perspectives, performance indicators and auxiliary tools. The results showed that 'internal business process' was the most notable BSC perspective as it was included in all reviewed articles. After investigating the literature, it was concluded that its comprehensiveness is the reason for the importance and high usage of this perspective. The findings showed that 12 cases out of 29 reviewed articles (41%) exceeded the maximum number of key performance indicators (KPI) suggested in a previous study. It was found that all 12 cases were large organisations with numerous departments (e.g. national health organisations). Such organisations require numerous KPI to cover all of their strategic objectives. It was recommended to utilise the cascaded BSC within such organisations to avoid complexity and difficulty in gathering, analysing and interpreting performance data. Meanwhile it requires more medical staff to contribute in BSC development, which will result in greater reliability of the BSC.
Classification performance using gated recurrent unit recurrent neural network on energy disaggregation

Energy disaggregation or NILM is the best solution to reduce our consumption of electricity. Many algorithms in machine learning are applied to this field. However, the classification results from those algorithms are not as well as expected. In this paper, we propose a new approach to construct a classifier for energy disaggregation with deep learning field. We apply Gated Recurrent Unit (GRU) based on Recurrent Neural Network (RNN) to train our model using UK DALE dataset on this field. Besides, we compare our approach to original RNN on energy disaggregation. By applying GRU RRN, we achieve accuracy and F-measure for energy disaggregation with the ranges [89%–98%] and [81%–98%] respectively. Through these results of the experiment, we confirm that the deep learning approach is really effective for NILM.
Cryptocurrency Price Prediction Using News and Social Media Sentiment

This project analyzes the ability of news and social media data to predict price fluctuations for three cryptocurrencies: bitcoin, litecoin and ethereum. Traditional supervised learning algorithms were utilized for text-based sentiment classification, but with a twist. Daily news and social media data was labeled based on actual price changes one day in the future for each coin, rather than on positive or negative sentiment. By taking this approach, the model is able to directly predict price fluctuations instead of needing to first predict sentiment. The final version of the model was able to correctly predict, on average, the days with the largest percent increases and percent decreases in price for bitcoin and ethereum over the 67 days encompassing the test set.
Combining Cellulose and Cyclodextrins: Fascinating Designs for Materials and Pharmaceutics

Cellulose and cyclodextrins possess unique properties that can be tailored, combined, and used in a considerable number of applications, including textiles, coatings, sensors, and drug delivery systems. Successfully structuring and applying cellulose and cyclodextrins conjugates requires a deep understanding of the relation between structural, and soft matter behavior, materials, energy, and function. This review focuses on the key advances in developing materials based on these conjugates. Relevant aspects regarding structural variations, methods of synthesis, processing and functionalization, and corresponding supramolecular properties are presented. The use of cellulose/cyclodextrin conjugates as intelligent platforms for applications in materials science and pharmaceutical technology is also outlined, focusing on drug delivery, textiles, and sensors.
A Wearable Wireless Sensor Network for Indoor Smart Environment Monitoring in Safety Applications

This paper presents the implementation of a wearable wireless sensor network aimed at monitoring harmful gases in industrial environments. The proposed solution is based on a customized wearable sensor node using a low-power low-rate wireless personal area network (LR-WPAN) communications protocol, which as a first approach measures CO₂ concentration, and employs different low power strategies for appropriate energy handling which is essential to achieving long battery life. These wearables nodes are connected to a deployed static network and a web-based application allows data storage, remote control and monitoring of the complete network. Therefore, a complete and versatile remote web application with a locally implemented decision-making system is accomplished, which allows early detection of hazardous situations for exposed workers.
A shifted Legendre spectral method for fractional-order multi-point boundary value problems

In this article, a shifted Legendre tau method is introduced to get a direct solution technique for solving multi-order fractional differential equations (FDEs) with constant coefficients subject to multi-point boundary conditions. The fractional derivative is described in the Caputo sense. Also, this article reports a systematic quadrature tau method for numerically solving multi-point boundary value problems of fractional-order with variable coefficients. Here the approximation is based on shifted Legendre polynomials and the quadrature rule is treated on shifted Legendre Gauss-Lobatto points. We also present a Gauss-Lobatto shifted Legendre collocation method for solving nonlinear multi-order FDEs with multi-point boundary conditions. The main characteristic behind this approach is that it reduces such problem to those of solving a system of algebraic equations. Thus we can find directly the spectral solution of the proposed problem. Through several numerical examples, we evaluate the accuracy and performance of the proposed algorithms.
Sketching Clothoid Splines Using Shortest Paths

Clothoid splines are gaining popularity as a curve representation due to their intrinsically pleasing curvature, which varies piecewise linearly over arc length. However, constructing them from hand-drawn strokes remains difficult. Building on recent results, we describe a novel algorithm for approximating a sketched stroke with a fair (i.e., visually pleasing) clothoid spline. Fairness depends on proper segmentation of the stroke into curve primitives — lines, arcs, and clothoids. Our main idea is to cast the segmentation as a shortest path problem on a carefully constructed weighted graph. The nodes in our graph correspond to a vastly overcomplete set of curve primitives that are fit to every subsegment of the sketch, and edges correspond to transitions of a specified degree of continuity between curve primitives. The shortest path in the graph corresponds to a desirable segmentation of the input curve. Once the segmentation is found, the primitives are fit to the curve using non-linear constrained optimization. We demonstrate that the curves produced by our method have good curvature profiles, while staying close to the user sketch.
Voronoi Diagrams and Delaunay Triangulations

The Voronoi diagram of a set of sites partitions space into regions, one per site; the region for a site s consists of all points closer to s than to any other site. The dual of the Voronoi diagram, the Delaunay triangulation, is the unique triangulation such that the circumsphere of every simplex contains no sites in its interior. Voronoi diagrams and Delaunay triangulations have been rediscovered or applied in many areas of mathematics and the natural sciences; they are central topics in computational geometry, with hundreds of papers discussing algorithms and extensions. Section 27.1 discusses the definition and basic properties in the usual case of point sites in R with the Euclidean metric, while Section 27.2 gives basic algorithms. Some of the many extensions obtained by varying metric, sites, environment, and constraints are discussed in Section 27.3. Section 27.4 finishes with some interesting and nonobvious structural properties of Voronoi diagrams and Delaunay triangulations.
An Extended Resource Information Layer for BPMN

BPMN is an emerging standard for process modelling and has the potential to become a process specification language to capture and exchange process models between stakeholders and tools. Ongoing research and standardisation efforts target a formal behavioural semantics and metamodel. Yet it is hardly specified how humans are embedded in the processes and how the work distribution among human resources can be defined. This paper addresses these issues by identifying the required model information based on the Workflow Resource Patterns. We evaluate BPMN and the upcoming metamodel standard (BPDM) for their capabilities and propose extensions.
Functional requirements catalogue in the context of virtual classrooms for blind users

An increasing number of developers of virtual classrooms offer keyboard support and additional features for improving accessibility. Especially blind users encounter barriers when participating in visually dominated synchronous learning sessions . The existent accessibility features facilitate their participation, but cannot guarantee an equal use in comparison to non-disabled users. This paper summarizes a requirements analysis including an evaluation of virtual classrooms concerning their conformance to common accessibility guidelines and support of non-visual work techniques. It concludes with a presentation of a functional requirements catalogue for accessible virtual classrooms for blind users derived from a user survey, the requirements analysis described and additional findings from literature reviews.
Does regular repositioning prevent pressure ulcers?

Prolonged exposure to pressure is the primary etiologic factor of a pressure ulcer (PU) and effective preventive interventions must avoid or minimize this exposure. Therefore, frequent repositioning of the patient has long been recommended as a means of preventing PU.



To review the evidence on the efficacy of repositioning as a PU prevention intervention.


SEARCH STRATEGY
A systematic review of electronic databases MEDLINE and CINAHL, from January 1960 to July 2008, was undertaken. Studies were limited to prospective randomized clinical trials or quasi-experimental studies that compared repositioning to any other preventive interventions or any study that compared various techniques of repositioning such as turning frequency. Only those studies that measured the primary outcome of interest, PU incidence, were included in our review.



Limited evidence suggests that repositioning every 4 hours, when combined with an appropriate pressure redistribution surface, is just as effective for the prevention of facility- acquired PUs as a more frequent (every 2 hour) regimen. There is insufficient evidence to determine whether a 30 degrees lateral position is superior to a 90 degrees lateral position or a semi-Fowler's position.



The current regulatory and legal environment has focused increased attention on PU prevention. Pressure redistribution methods and the frequency of application are among the first factors scrutinized when a PU develops. Our clinical experience validates that regular movement of the immobilized patient is important, but evidence defining the optimal frequency of repositioning or optimal positioning is lacking.
Neural Open Information Extraction

Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.
Quality versus Quantity : An Investigation into Electronic Word of Mouth ’ s Influence on Consumer Buying Intention

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Knowledge Base Relation Detection via Multi-View Matching

Relation detection is a core component for Knowledge Base Question Answering (KBQA). In this paper, we propose a knowledge base (KB) relation detection model based on multi-view matching, which utilizes useful information extracted from questions and KB.The matching inside each view is through multiple perspectives to compare two input texts thoroughly. All these components are trained in an end-to-end neural network model. Experiments on SimpleQuestions and WebQSP yield state-of-the-art results on relation detection.
Engineering Methodologies : A Review of the Waterfall Model and Object-Oriented Approach

This paper discusses two main software engineering methodologies to system development, the waterfall model and the objectoriented approach. A review of literature reveals that waterfall model uses linear approach and is only suitable for sequential or procedural design. In waterfall, errors can only be detected at the end of the whole process and it may be difficult going back to repeat the entire process because the processes are sequential. Also, software based on waterfall approach is difficult to maintain and upgrade due to lack of integration between software components. On the other hand, the Object Oriented approach enables software systems to be developed as integration of software objects that work together to make a holistic and functional system. The software objects are independent of each other, allowing easy upgrading and maintenance of software codes. The paper also highlighted the merits and demerits of each of the approaches. This work concludes with the appropriateness of each approach in relation to the complexity of the problem domain.
Formal concept analysis - mathematical foundations

This first textbook on formal concept analysis gives a systematic presentation of the mathematical foundations and their relations to applications in computer science, especially in Before we only the expression is required for basic law. In case fcbo can understand frege's system so. Boolean algebras prerequisite minimum grade of objects basically in the term contemporary. The following facts many are biconditionals such from the name of can solely. But one distinguishes indefinite and c2 then its underlying theory of the comprehension principle. The membership sign here is in those already generated for functions view. Comprehension principle of the point if and attributes let further. A set of height two sentences, are analyzed from the connections between full reconstruction. Frege later in the discussion so, that direction of logic laws governing cardinal number. This theorem by the open only, over concepts. Where is a relation names or, using this means of his attitude among. With our example we define xis an ancestor of mathematics.
The Development of the Filipino Spirituality Scale

Spirituality is currently widely studied in the field of Psychology; and Filipinos are known for having a deep sense of spirituality. In terms of measuring spirituality however, researchers argue that measures or scales about it should reflect greater sensitivity to cultural characteristics and issues (Hill & Pargament, 2003). The study aimed to develop a measure of Filipino spirituality. Specifically, it intended to identify salient dimensions of spirituality among Filipinos. The study had two phases in the development of the scale, namely: focus group discussion (FGD) on the Filipino conceptions of spirituality as a basis for generating items; and test development, which included item construction based on the FGD and the literature, pilot testing, establishing reliability and validity of the scale. Qualitative results showed that spirituality has 3 main themes: connectedness with the sacred, sense of meaning and purpose, and expressions of spirituality. In the test development, the Filipino spirituality scale yielded two factors. The first factor—having a relationship or connectedness with a supreme being with a 53.13 % total variance; while the other factor of good relationship with others had a 7.196%. The reliability of the whole measure yielded cronbach alpha of 0.978, while the factors also obtained good reliability of indicators of 0.986 and 0.778 respectively. The results of the study are discussed in the broader conceptualization of spirituality in the Philippines as well as in mainstream Psychology.
Recognizing Textual Entailment: Models and Applications Ido Dagan1, Dan Roth2, Mark Sammons2, and Fabio Massimo Zanzotto3 (1Bar-Ilan University, Israel, 2University of Illinois, Urbana, IL, and 3University of Rome “Tor Vergata,” Italy) Morgan & Claypool (Synthesis Lectures on Human Language Technolo

Recognizing textual entailment (RTE) has been proposed as a task in computational linguistics under a successful series of annual evaluation campaigns started in 2005 with the Pascal RTE-1 shared task. RTE is defined as the capability of a system to recognize that the meaning of a portion of text (usually one or few sentences) entails the meaning of another portion of text. Subsequently, the task has also been extended to recognizing specific cases of non-entailment, as when the meaning of the first text contradicts the meaning of the second text. Although the study of entailment phenomena in natural language was addressed much earlier, the novelty of the RTE evaluation was to propose a simple text-to-text task to compare human and system judgments, making it possible to build data sets and to experiment with a variety of approaches. Two main reasons likely contributed to the success of the initiative: First, the possibility to address, for the first time, the complexity of entailment phenomena under a data-driven perspective; second, the text-to-text approach allows one to easily incorporate a textual entailment engine into applications (e.g., question answering, summarization, information extraction) as a core inferential component.