nils-herrmann/clean_scirepeval huggingface dataset:
DESIGN AND IMPLEMENTATION OF A VIRTUAL CLASSROOM SYSTEM

In the last few decades, education has witnessed some advances in technologies involving computeraided learning that promises to drastically change the methods of teaching and learning. The World Wide Web has played a major role in information storage and dissemination in the educational community. Conventional classroom based teaching involves the delivery of course materials by the lecturer in a particular place at a defined time. Hence it imposes a constraint of time and place on both the instructor and the student. Due to human factor arising from the traditional classroom method, the lecturer may not always be able to put in optimum effort towards preparing and delivering course materials. There may also be inconsistencies in the pedagogy and learning style due to repetitive nature of teaching/learning. The objective of this paper is to develop a virtual classroom system to enhance learning on campus. The system was developed using PHP and MySQL as server side programming and database respectively. The web-based virtual classroom provides a web enabled interactive model for e-learning in which the course material is presented using multimedia and hypermedia.
Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions..



The aim is to summarize and review published studies describing computer-based approaches for predicting patients' future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research.



The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results.



After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods.



Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes.
Maximizing product adoption in social networks

One of the key objectives of viral marketing is to identify a small set of users in a social network, who when convinced to adopt a product will influence others in the network leading to a large number of adoptions in an expected sense. The seminal work of Kempe et al.  approaches this as the problem of influence maximization. This and other previous papers tacitly assume that a user who is influenced (or, informed) about a product necessarily adopts the product and encourages her friends to adopt it. However, an influenced user may not adopt the product herself, and yet form an opinion based on the experiences of her friends, and share this opinion with others. Furthermore, a user who adopts the product may not like the product and hence not encourage her friends to adopt it to the same extent as another user who adopted and liked the product. This is independent of the extent to which those friends are influenced by her. Previous works do not account for these phenomena.
 We argue that it is important to distinguish product adoption from influence. We propose a model that factors in a user's experience (or projected experience) with a product. We adapt the classical Linear Threshold (LT) propagation model by defining an objective function that explicitly captures product adoption, as opposed to influence. We show that under our model, adoption maximization is NP-hard and the objective function is monotone and submodular, thus admitting an approximation algorithm. We perform experiments on three real popular social networks and show that our model is able to distinguish between influence and adoption, and predict product adoption much more accurately than approaches based on the classical LT model.
IoT based urban climate monitoring using Raspberry Pi

Internet of Things is the web of physical objects that contain the embedded technology which is helping to develop man to machine or machine to machine communication. This paper mainly propounds a stand-alone system which is providing a dynamic datasheet about the parameters of the city environment. The system is using low cost low power ARM based minicomputer that is Raspberry Pi. It can communicate through Local Area Network (LAN) or the external Wi-Fi module. Commands from user are processed at the Raspberry Pi using Python language. The data can be monitored with other terminal devices like Laptop, Smart Phone and Tablet which is endowed with the internet facility. This framework is giving access to real-time information about an urban environment which includes the parameters: temperature, humidity, pressure, CO and harmful air pollutants.
Expressing emotion through posture and gesture Introduction

Emotion and its physical expression are an integral part of social interaction, informing others about how we are feeling and affecting social outcomes (Vosk, Forehand, and Figueroa 1983). Studies on the physical expression of emotion can be traced back to the 19th century with Darwin’s seminal book “The Expression of the Emotions in Man and Animals” that reveals the key role of facial expressions and body movement in communicating status and emotion (Darwin 1872).
Privacy-Preserving Collaborative Filtering

Collaborative filtering (CF) techniques are becoming very popular on the Internet and are widely used in several domains to cope with information overload. E-commerce sites use filtering systems to recommend products to customers based on the preferences of like-minded customers, but their systems do not protect user privacy. Because users concerned about privacy may give false information, it is not easy to collect high-quality user data for collaborative filtering, and recommendation systems using poor data produce inaccurate recommendations. This means that privacy measures are key to the success of collecting high-quality data and providing accurate recommendations. This article discusses collaborative filtering with privacy based on both correlation and singular-value decomposition (SVD) and proposes the use of randomized perturbation techniques to protect user privacy while producing reasonably accurate recommendations. Such techniques add randomness to the original data, preventing the data collector (the server) from learning private user data, but this scheme can still provide accurate recommendations. Experiments were conducted with real datasets to evaluate the overall performance of the proposed scheme. The results were used for analysis of how different parameters affect accuracy. Collaborative filtering systems using randomized perturbation techniques were found to provide accurate recommendations while preserving user privacy.
A Chinese Handwriting Education System with Automatic Error Detection

We build a distance education application of a Chinese handwriting education system that allows students to do practice at anytime and anywhere. As an intelligent tutor, the system can automatically check the handwriting errors, such as the stroke production errors, stroke sequence error and stroke relationship error. Then our system should provide useful feedback to the student. In this paper, attributed relational graph matching is used to locate the handwriting errors. The pruning strategy is applied to reduce the computational time. The experiment results show that our proposal can handle more handwriting error cases than existing methods with a higher accuracy.
Rear end planar slot antenna of a substrate integrated waveguide cavity fed by grounded coplanar waveguide

A design of simple profile planar slot antenna which is placed at the rear end of the substrate integrated waveguide is been studied in this paper. The substrate Integrated Waveguide (SIW) adopts the mm wave technology and it facilitates the feeding elemen and the antennato be built over the same substrate. The return loss is obtained as −30dB at 10GHz. Also the performance is analysed by varying the height of the substrate, the length and width of the slot. The simulated antenna has good performance when compared to conventional slot antenna along with miniaturisation by the use of SIW technology. The merits of the antenna include simple profile, low weight, easy fabrication and simple integration with planar circuits.
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems

Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various imaging problems. However, it is still unclear why these deep learning architectures work for specific inverse problems. Moreover, in contrast to the usual evolution of signal processing theory around the classical theories, the link between deep learning and the classical signal processing approaches such as wavelets, non-local processing, compressed sensing, etc, are not yet well understood. To address these issues, here we show that the long-searched-for missing link is the convolutional framelets for representing a signal by convolving local and non-local bases. The convolutional framelets was originally developed to generalize the theory of low-rank Hankel matrix approaches for inverse problems, and this paper further extends the idea so that we can obtain a deep neural network using multilayer convolutional framelets with perfect reconstruction (PR) under rectilinear linear unit nonlinearity (ReLU). Our analysis also shows that the popular deep network components such as residual block, redundant filter channels, and concatenated ReLU (CReLU) do indeed help to achieve the PR, while the pooling and unpooling layers should be augmented with high-pass branches to meet the PR condition. Moreover, by changing the number of filter channels and bias, we can control the shrinkage behaviors of the neural network. This discovery reveals the limitations of many existing deep learning architectures for inverse problems, and leads us to propose a novel theory for deep convolutional framelets neural network. Using numerical experiments with various inverse problems, we demonstrated that our deep convolution framelets network shows consistent improvement over existing deep architectures. This discovery suggests that the success of deep learning is not from a magical power of a black-box, but rather comes from the power of a novel signal representation using non-local basis combined with data-driven local basis, which is indeed a natural extension of classical signal processing theory.
Using ontologies with UML class-based modeling: The TwoUse approach

UML class-based models and OWL ontologies constitute modeling approaches with different strength and weaknesses that make them appropriate for specifying different aspects of software systems. We propose an integrated use of both modeling approaches in a coherent framework – TwoUse. We present a framework involving different concrete syntaxes for developing integrated models and use an OCL-like approach for writing query operations. We illustrate TwoUse’s applicability with a case study and conclude that TwoUse achieves enhancements of nonfunctional software requirements like maintainability, reusability and extensibility.
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-Resolution

While implicit generative models such as GANs have shown impressive results in high quality image reconstruction and manipulation using a combination of various losses, we consider a simpler approach leading to surprisingly strong results. We show that texture loss[1] alone allows the generation of perceptually high quality images. We provide a better understanding of texture constraining mechanism and develop a novel semantically guided texture constraining method for further improvement. Using a recently developed perceptual metric employing “deep features” and termed LPIPS , the method obtains state-of-the-art results. Moreover, we show that a texture representation of those deep features better capture the perceptual quality of an image than the original deep features. Using texture information, off-the-shelf deep classification networks (without training) perform as well as the best performing (tuned and calibrated) LPIPS metrics. The code is publicly available.
Influence analysis of Github repositories

With the support of cloud computing techniques, social coding platforms have changed the style of software development. Github is now the most popular social coding platform and project hosting service. Software developers of various levels keep entering Github, and use Github to save their public and private software projects. The large amounts of software developers and software repositories on Github are posing new challenges to the world of software engineering. This paper tries to tackle one of the important problems: analyzing the importance and influence of Github repositories. We proposed a HITS based influence analysis on graphs that represent the star relationship between Github users and repositories. A weighted version of HITS is applied to the overall star graph, and generates a different set of top influential repositories other than the results from standard version of HITS algorithm. We also conduct the influential analysis on per-month star graph, and study the monthly influence ranking of top repositories.
An Empirical Evaluation of True Online TD({\lambda})

The true online TD(λ) algorithm has recently been proposed (van Seijen and Sutton, 2014) as a universal replacement for the popular TD(λ) algorithm, in temporal-difference learning and reinforcement learning. True online TD(λ) has better theoretical properties than conventional TD(λ), and the expectation is that it also results in faster learning. In this paper, we put this hypothesis to the test. Specifically, we compare the performance of true online TD(λ) with that of TD(λ) on challenging examples, random Markov reward processes, and a real-world myoelectric prosthetic arm. We use linear function approximation with tabular, binary, and non-binary features. We assess the algorithms along three dimensions: computational cost, learning speed, and ease of use. Our results confirm the strength of true online TD(λ): 1) for sparse feature vectors, the computational overhead with respect to TD(λ) is minimal; for non-sparse features the computation time is at most twice that of TD(λ), 2) across all domains/representations the learning speed of true online TD(λ) is often better, but never worse than that of TD(λ), and 3) true online TD(λ) is easier to use, because it does not require choosing between trace types, and it is generally more stable with respect to the step-size. Overall, our results suggest that true online TD(λ) should be the first choice when looking for an efficient, general-purpose TD method.
Information Theoretic Model Validation for Spectral Clustering

Model validation constitutes a fundamental step in data clustering. The central question is: Which cluster model and how many clusters are most appropriate for a certain application? In this study, we introduce a method for the validation of spectral clustering based upon approximation set coding. In particular, we compare correlation and pairwise clustering to analyze the correlations of temporal gene expression profiles. To evaluate and select clustering models, we calculate their reliable informativeness. Experimental results in the context of gene expression analysis show that pairwise clustering yields superior amounts of reliable information. The analysis results are consistent with the Bayesian Information Criterion (BIC), and exhibit higher generality than BIC.
Morpho-syntactic Lexical Generalization for CCG Semantic Parsing

In this paper, we demonstrate that significant performance gains can be achieved in CCG semantic parsing by introducing a linguistically motivated grammar induction scheme. We present a new morpho-syntactic factored lexicon that models systematic variations in morphology, syntax, and semantics across word classes. The grammar uses domain-independent facts about the English language to restrict the number of incorrect parses that must be considered, thereby enabling effective learning from less data. Experiments in benchmark domains match previous models with one quarter of the data and provide new state-of-the-art results with all available data, including up to 45% relative test-error reduction.
Integrated virtual commissioning an essential activity in the automation engineering process: From virtual commissioning to simulation supported engineering

Production plants within manufacturing and process industries are becoming more and more complex systems. For a safe and demand sufficient production the automation system of a production plant is mission critical. Therefore the correct functioning and engineering of the automation system is essential. Nowadays the use of a virtual commissioning step before deploying the automation software on the real production plant is used more often. Within the virtual commissioning the automation software is tested against a virtual plant model, sufficient to check out the correct functioning of the automation system. Usually virtual commissioning is used as a step at the end of the automation engineering, as proposed by VDI 4499. Within this paper an integrated virtual commissioning is proposed, where the automation software is continuously tested against a virtual plant model during the overall engineering phase. The virtual plant is growing together with the automation software and thus enables simulation supported automation engineering. Benefits of the proposed workflow are that errors can be detected immediately after the implementation and the use of the virtual plant model could be extended to various stages of the engineering workflow.
Automotive Radar – Status and Trends

The paper gives a brief overview of automotive radar. The status of the frequency regulation for short and long range radar is summarized because of its importance for car manufacturers and their sensor suppliers. Front end concepts and antenna techniques of 24 GHz and 77 GHz sensors are briefly described. Their impact on the sensor’s field of view and on the angular measurement capability is discussed. Esp. digital beamforming concepts are considered and promising results are presented.
Does the use of social networking sites increase children's risk of harm?

Although research findings have been equivocal as to whether the use of social networking sites (SNS) increases experiences of online risk among children, the affordances of SNS lend support to this possibility, attracting much policy and public concern. The present article examines whether the use of such services increases the risks that children and young people encounter by analyzing data from a random stratified sample of approximately 1000 internetusing children aged 9-16 years in each of 25 European countries. Four hypotheses were formulated and tested. The first hypothesis, namely that children who use social networking sites will encounter more risks online than those who do not, is supported by the data. The second hypothesis stated that SNS users with more digital competence will encounter more online risk than those with less competence; this was also supported, despite being counter to common assumptions. Thirdly, we hypothesized that SNS users with more risky SNS practices (e.g. a public profile, displaying identifying information, with a very large number of contacts) will encounter more online risk than those with fewer risky practices: this too was supported by the data; thus what matters for risk is how SNS are used, a useful point for awareness-raising initiatives. The fourth hypothesis stated that SNS users with more digital competence in using the internet will experience less harm associated with online risk. The data did not support this hypothesis, since digital competence did not reduce the probability of children saying that they have been bothered or upset by something on the internet. Finally, the study found that, although this had not been predicted, whether or not risks are experienced as harmful depends on the specific relation between risks and platforms (website, instant messaging, gaming or social networking). We call on future research to explore how particular affordances sustain particular communicative conditions and, in turn, are responded to differently by children. The research and policy implications of the findings are discussed.
Thorough static analysis of device drivers

Bugs in kernel-level device drivers cause 85% of the system crashes in the Windows XP operating system [44]. One of the sources of these errors is the complexity of the Windows driver API itself: programmers must master a complex set of rules about how to use the driver API in order to create drivers that are good clients of the kernel. We have built a static analysis engine that finds API usage errors in C programs. The Static Driver Verifier tool (SDV) uses this engine to find kernel API usage errors in a driver. SDV includes models of the OS and the environment of the device driver, and over sixty API usage rules. SDV is intended to be used by driver developers "out of the box." Thus, it has stringent requirements: (1) complete automation with no input from the user; (2) a low rate of false errors. We discuss the techniques used in SDV to meet these requirements, and empirical results from running SDV on over one hundred Windows device drivers.
A robust-coded pattern projection for dynamic 3D scene measurement

This paper presents a new coded structured light pattern which permits to solve the correspondence problem by a single shot and without using geometrical constraints. The pattern is composed by the projection of a grid made by coloured slits in such a way that each slit with its two neighbours appears only once in the pattern. The technique proposed permits a rapid and robust 3D scene measurement, even with moving objects. Ó 1998 Elsevier