nils-herrmann/clean_scirepeval huggingface dataset:
Scattered data interpolation and approximation for computer graphics

The goal of scattered data interpolation techniques is to construct a (typically smooth) function from a set of unorganized samples. These techniques have a wide range of applications in computer graphics. For instance they can be used to model a surface from a set of sparse samples, to reconstruct a BRDF from a set of measurements, to interpolate motion capture data, or to compute the physical properties of a fluid. This course will survey and compare scattered interpolation algorithms and describe their applications in computer graphics. Although the course is focused on applying these techniques, we will introduce some of the underlying mathematical theory and briefly mention numerical considerations.
Ferroresonance phenomenon in PV system at LV side of three phase power transformer using of PSCAD simulation

Nowadays, most Photovoltaic installations are grid connected system. From distribution system point of view, the main point and concern related to PV grid-connected are overvoltage or overcurrent in the distribution network. This paper describes the simulation study which focuses on ferroresonance phenomenon of PV system on lower side of distribution transformer. PSCAD program is selected to simulate the ferroresonance phenomenon in this study. The example of process that creates ferroresonance by the part of PV system and ferroresonance effect will be fully described in detail.
Knowledge-guided Semantic Computing Network

It is very useful to integrate human knowledge and experience into traditional neural networks for faster learning speed, fewer training samples and better interpretability. However, due to the obscured and indescribable black box model of neural networks, it is very difficult to design its architecture, interpret its features and predict its performance. Inspired by human visual cognition process, we propose a knowledge-guided semantic computing network which includes two modules: a knowledge-guided semantic tree and a data-driven neural network. The semantic tree is pre-defined to describe the spatial structural relations of different semantics, which just corresponds to the tree-like description of objects based on human knowledge. The object recognition process through the semantic tree only needs simple forward computing without training. Besides, to enhance the recognition ability of the semantic tree in aspects of the diversity, randomicity and variability, we use the traditional neural network to aid the semantic tree to learn some indescribable features. Only in this case, the training process is needed. The experimental results on MNIST and GTSRB datasets show that compared with the traditional data-driven network, our proposed semantic computing network can achieve better performance with fewer training samples and lower computational complexity. Especially, Our model also has better adversarial robustness than traditional neural network with the help of human knowledge.
Strengthening the Case for Pair Programming

0 7 4 0 7 4 5 9 / 0 0 / $ 1 0 . 0 0 © 2 0 0 0 I E E E J u l y / A u g u s t 2 0 0 0 I E E E S O F T W A R E 19 design, algorithm, code, or test—does indeed improve software quality and reduce time to market. Additionally, student and professional programmers consistently find pair programming more enjoyable than working alone. Yet most who have not tried and tested pair programming reject the idea as a redundant, wasteful use of programming resources: “Why would I put two people on a job that just one can do? I can’t afford to do that!” But we have found, as Larry Constantine wrote, that “Two programmers in tandem is not redundancy; it’s a direct route to greater efficiency and better quality.”1 Our supportive evidence comes from professional programmers and from advanced undergraduate students who participated in a structured experiment. The experimental results show that programming pairs develop better code faster with only a minimal increase in prerelease programmer hours. These results apply to all levels of programming skill from novice to expert.
Elucidation of the metabolic pathway for dibenzothiophene desulphurization by Rhodococcus sp. strain IGTS8 (ATCC 53968).

Rhodococcus sp. strain IGTS8 (ATCC 53968) is able to utilize dibenzothiophene (DBT) as a sole source of sulphur. The carbon skeleton of DBT is not metabolized and is conserved as 2-hydroxybiphenyl (HBP), which accumulates in the medium. This phenotype is due to the expression of the plasmid-encoded DBT-desulphurization (dsz) operon, which encodes three proteins, DszA, B and C. In this paper it is shown, using [35S]DBT radiolabelling studies, that sulphur is released in the form of inorganic sulphite. The pathway of DBT desulphurization is described in detail. In summary, DszC catalyses the stepwise S-oxidation of DBT, first to dibenzothiophene 5-oxide (DBTO) and then to dibenzothiophene 5,5-dioxide (DBTO2); DszA catalyses the conversion of DBTO2 to 2-(2'-hydroxyphenyl)benzene sulphinate (HBPSi-) and DszB catalyses the desulphination of HBPSi- to give HBP and sulphite. Studies with cell-free extracts show that DszA and DszC, but not DszB, require NADH for activity. 18O2-labelling studies show that each incorporated oxygen atom is derived directly from molecular oxygen. These results are consistent with the role of DszC as a mono-oxygenase, of DszA as an apparently unique enzyme which catalyses the reductive hydroxylation of DBTO2 leading to cleavage of the thiophene ring, and of DszB as an aromatic sulphinic acid hydrolase.
CAPS: Context Aware Personalized POI Sequence Recommender System

The revolution of World Wide Web (WWW) and smart-phone technologies have been the key-factor behind remarkable success of social networks. With the ease of availability of check-in data, the location-based social networks (LBSN) (e.g., Facebook, etc.) have been heavily explored in the past decade for Point-of-Interest (POI) recommendation. Though many POI recommenders have been defined, most of them have focused on recommending a single location or an arbitrary list that is not contextually coherent. It has been cumbersome to rely on such systems when one needs a contextually coherent list of locations, that can be used for various day-to-day activities, for e.g., itinerary planning. This paper proposes a model termed as CAPS (Context Aware Personalized POI Sequence Recommender System) that generates contextually coherent POI sequences relevant to user preferences. To the best of our knowledge, CAPS is the first attempt to formulate the contextual POI sequence modeling by extending Recurrent Neural Network (RNN) and its variants. CAPS extends RNN by incorporating multiple contexts to the hidden layer and by incorporating global context (sequence features) to the hidden layers and output layer. It extends the variants of RNN (e.g., Long-short term memory (LSTM)) by incorporating multiple contexts and global features in the gate update relations. The major contributions of this paper are: (i) it models the contextual POI sequence problem by incorporating personalized user preferences through multiple constraints (e.g., categorical, social, temporal, etc.), (ii) it extends RNN to incorporate the contexts of individual item and that of whole sequence. It also extends the gated functionality of variants of RNN to incorporate the multiple contexts, and (iii) it evaluates the proposed models against two real-world data sets.
Coarse In-Building Localization with Smartphones

Geographic location of a person is important contextual information that can be used in a variety of scenarios like disaster relief, directional assistance, context-based advertisements, etc. GPS provides accurate localization outdoors but is not useful inside buildings. We propose an coarse indoor localization approach that exploits the ubiquity of smart phones with embedded sensors. GPS is used to find the building in which the user is present. The Accelerometers are used to recognize the user’s dynamic activities (going up or down stairs or an elevator) to determine his/her location within the building. We demonstrate the ability to estimate the floor-level of a user. We compare two techniques for activity classification, one is naive Bayes classifier and the other is based on dynamic time warping. The design and implementation of a localization application on the HTC G1 platform running Google Android is also presented.
EULAR recommendations for cardiovascular disease risk management in patients with rheumatoid arthritis and other forms of inflammatory joint disorders: 2015/2016 update.

Patients with rheumatoid arthritis (RA) and other inflammatory joint disorders (IJD) have increased cardiovascular disease (CVD) risk compared with the general population. In 2009, the European League Against Rheumatism (EULAR) taskforce recommended screening, identification of CVD risk factors and CVD risk management largely based on expert opinion. In view of substantial new evidence, an update was conducted with the aim of producing CVD risk management recommendations for patients with IJD that now incorporates an increasing evidence base. A multidisciplinary steering committee (representing 13 European countries) comprised 26 members including patient representatives, rheumatologists, cardiologists, internists, epidemiologists, a health professional and fellows. Systematic literature searches were performed and evidence was categorised according to standard guidelines. The evidence was discussed and summarised by the experts in the course of a consensus finding and voting process. Three overarching principles were defined. First, there is a higher risk for CVD in patients with RA, and this may also apply to ankylosing spondylitis and psoriatic arthritis. Second, the rheumatologist is responsible for CVD risk management in patients with IJD. Third, the use of non-steroidal anti-inflammatory drugs and corticosteroids should be in accordance with treatment-specific recommendations from EULAR and Assessment of Spondyloarthritis International Society. Ten recommendations were defined, of which one is new and six were changed compared with the 2009 recommendations. Each designated an appropriate evidence support level. The present update extends on the evidence that CVD risk in the whole spectrum of IJD is increased. This underscores the need for CVD risk management in these patients. These recommendations are defined to provide assistance in CVD risk management in IJD, based on expert opinion and scientific evidence.
Sport Activity for Health!! The Effects of Karate Participants’ Involvement, Perceived Value, and Leisure Benefits on Recommendation Intention

This study intends to discuss the effects of participants’ involvement, perceived value, and leisure benefits on recommendation intention in the sport of karate. The questionnaires were collected online by karate clubs on Facebook and included 369 valid participants. The research findings show that karate participants from different places of residence do not display significant differences in involvement, perceived value, leisure benefits, and recommendation intention. Furthermore, “attraction” in the involvement category reveals the highest mean, “paid spirit and energy being worthy” in perceived value appears as the highest mean, and “physiological benefits” in leisure benefits shows the highest mean. The Pearson correlation analysis result presents significant strong positive correlations between involvement, perceived value, leisure benefits, and recommendation intention. Finally, multiple regression analysis reveals that leisure benefits, except “physiological benefits”, show notably positive effects on recommendation intention. According to the research results, suggestions are proposed for the reference of karate teaching business managers, participants, and future research.
Over-current protection scheme for SiC power MOSFET DC circuit breaker

The electronic circuit breaker used in DC microgrids are required to work within their safe operating areas bounded by temperature, voltage and current limits. Traditional approach managed to protect these switches through rapid current cut-off operations at over-load or fault situations, but failing to avoid the disturbance induced by transient current surges or noises which are not harmful to the grid operations. Aiming to increase the quality of circuit breaker operations and furthermore improve its reliability, this paper proposed a SiC MOSFET based DC circuit breaker based on the variable time-delay protection scheme. The cutoff operations only take place after proper delay time, which are precisely catered according to the transient thermal properties of SiC devices and the properties of DC loads. The proposed scheme has been implemented with hardware prototype and experimentally verified under different fault situations.
Long short term memory recurrent neural network based encoding method for emotion recognition in video

Human emotion is a temporally dynamic event which can be inferred from both audio and video feature sequences. In this paper we investigate the long short term memory recurrent neural network (LSTM-RNN) based encoding method for category emotion recognition in the video. LSTM-RNN is able to incorporate knowledge about how emotion evolves over long range successive frames and emotion clues from isolated frame. After encoding, each video clip can be represented by a vector for each input feature sequence. The vectors contain both frame level and sequence level emotion information. These vectors are then concatenated and fed into support vector machine (SVM) to get the final prediction result. Extensive evaluations on Emotion Challenge in the Wild (EmotiW2015) dataset show the efficiency of the proposed encoding method and competitive results are obtained. The final recognition accuracy achieves 46.38% for audio-video emotion recognition sub-challenge, where the challenge baseline is 39.33%.
BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network

Facial makeup transfer aims to translate the makeup style from a given reference makeup face image to another non-makeup one while preserving face identity. Such an instance-level transfer problem is more challenging than conventional domain-level transfer tasks, especially when paired data is unavailable. Makeup style is also different from global styles (e.g., paintings) in that it consists of several local styles/cosmetics, including eye shadow, lipstick, foundation, and so on. Extracting and transferring such local and delicate makeup information is infeasible for existing style transfer methods. We address the issue by incorporating both global domain-level loss and local instance-level loss in an dual input/output Generative Adversarial Network, called BeautyGAN. Specifically, the domain-level transfer is ensured by discriminators that distinguish generated images from domains' real samples. The instance-level loss is calculated by pixel-level histogram loss on separate local facial regions. We further introduce perceptual loss and cycle consistency loss to generate high quality faces and preserve identity. The overall objective function enables the network to learn translation on instance-level through unsupervised adversarial learning. We also build up a new makeup dataset that consists of 3834 high-resolution face images. Extensive experiments show that BeautyGAN could generate visually pleasant makeup faces and accurate transferring results. Data and code are available at http://liusi-group.com/projects/BeautyGAN.
The thirty-fifth case of cutis laxa type II (Debre type).

A dysmorphic girl  was referred at the age of 19 months (late April 2008) because of slow growth (height, 75 cm; weight, 8.5 kg) and developmental delay. She had the classical phenotypic manifestation of type II recessive cutis laxa (Debre type) with redundant loose skin present from birth and especially evident on the limbs. She was small at birth. She was born as the first child of healthy parents of Arabian origin, who were second cousins. The girl was hypotonic and had an opened fontanel (1.5 cm in diameter); this allowed cranial ultrasonography, which revealed normal findings. Facially, the bridge of the nose was broad, the nose was prominent, and the eyes were wide set with down-slanting palpebral fissures. The palate was high and the right hip was dislocated. She could not stand or walk. She could sit, but could not crawl. She was a “bottom shuffler.” Her language was poorly developed and she said no meaningful words. Table 1 summarizes the presenting features.
A Measurement-based Study of the Skype Peer-to-Peer VoIP Performance

It has been increasingly popular to build voice-over-IP (VoIP) applications based on peer-to-peer (P2P) networks in the Internet. However, many such VoIP applications free-ride the network bandwidth of Internet Service Providers (ISPs). Thus their success may come at a cost to ISPs, especially those on the edge of the Internet. In this paper, we study the VoIP quality of Skype, a popular P2P-based VoIP application. Specifically, using large-scale end-toend measurements, we first conduct a systematic analysis of Skype supernode network. We then investigate the impacts of the access capacity constraint and the AS policy constraint on the VoIP quality of Skype. We show that even when free-riding is no longer possible for only 20% of supernodes that are located in stub ISPs, the overall VoIP quality of Skype degrades significantly, and a large percentage of VoIP sessions will have unacceptable quality. This result clearly demonstrates the potential danger of building VoIP applications based on P2P networks without taking into account operational models of the Internet. We also study using time diversity in traffic patterns to reduce the impacts of the preceding
Leaf area index estimation in vineyards using a ground-based LiDAR scanner

Estimation of grapevine vigour using mobile proximal sensors can provide an indirect method for determining grape yield and quality. Of the various indexes related to the characteristics of grapevine foliage, the leaf area index (LAI) is probably the most widely used in viticulture. To assess the feasibility of using light detection and ranging (LiDAR) sensors for predicting the LAI, several field trials were performed using a tractor-mounted LiDAR system. This system measured the crop in a transverse direction along the rows of vines and geometric and structural parameters were computed. The parameters evaluated were the height of the vines (H), the cross-sectional area (A), the canopy volume (V) and the tree area index (TAI). This last parameter was formulated as the ratio of the crop estimated area per unit ground area, using a local Poisson distribution to approximate the laser beam transmission probability within vines. In order to compare the calculated indexes with the actual values of LAI, the scanned vines were defoliated to obtain LAI values for different row sections. Linear regression analysis showed a good correlation (R 2 = 0.81) between canopy volume and the measured values of LAI for 1 m long sections. Nevertheless, the best estimation of the LAI was given by the TAI (R 2 = 0.92) for the same length, confirming LiDAR sensors as an interesting option for foliage characterization of grapevines. However, current limitations exist related to the complexity of data process and to the need to accumulate a sufficient number of scans to adequately estimate the LAI.
Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification

Video-based person re-identification matches video clips of people across non-overlapping cameras. Most existing methods tackle this problem by encoding each video frame in its entirety and computing an aggregate representation across all frames. In practice, people are often partially occluded, which can corrupt the extracted features. Instead, we propose a new spatiotemporal attention model that automatically discovers a diverse set of distinctive body parts. This allows useful information to be extracted from all frames without succumbing to occlusions and misalignments. The network learns multiple spatial attention models and employs a diversity regularization term to ensure multiple models do not discover the same body part. Features extracted from local image regions are organized by spatial attention model and are combined using temporal attention. As a result, the network learns latent representations of the face, torso and other body parts using the best available image patches from the entire video sequence. Extensive evaluations on three datasets show that our framework outperforms the state-of-the-art approaches by large margins on multiple metrics.
A Methodology for Evaluating and Selecting Data Mining Software

As data mining evolves and matures more and more businesses are incorporating this technology into their business practices. However, currently data mining and decision support software is expensive and selection of the wrong tools can be costly in many ways. This paper provides direction and decision-making information to the practicing professional. A framework for evaluating data mining tools is presented and a methodology for applying this framework is described. Finally a case study to demonstrate the method’s effectiveness is presented. This methodology represents the first-hand experience using many of the leading data mining tools against real business data at the Center for Data Insight (CDI) at Northern Arizona University (NAU). This is not a comprehensive review of commercial tools but instead provides a method and a point-of-reference for selecting the best software tool for a particular problem. Experience has shown that there is not one best data-mining tool for all purposes. This instrument is designed to accommodate differences in environments and problem domains. It is expected that this methodology will be used to publish tool comparisons and benchmarking results.
Magnesium, calcium and trace elements distribution in serum, erythrocytes, and hair of patients with chronic renal failure.

Disturbance of mineral status in patients with chronic renal failure (CRF) is one of many complications of this disease. Trace elements analysis in hair is sometime used by clinicians for a diagnosis of mineral status. In the present study concentration of magnesium and other trace elements was determined in serum, erythrocytes, and hair of patients with CRF undergoing hemodialysis (n = 31) and with impaired renal function but non-dialyzed (n = 15). Measurements of mineral content were performed by the atomic absorption spectrometry method (AAS). In serum of hemodialyzed patients as well as in erythrocytes and hair we found significantly increased levels of almost all tested elements, especially for Mg, Al, and Cr, compared to the control group. No significant differences were observed between these groups only in the Cd content in the examined samples. However, a significant correlation between its concentration in serum and erythrocytes was only found in the case of this element. Hair analysis reflected well the changes of mineral distribution in patients with CRF and may be used to diagnose these anomalies, in particular, with regard to Ca, Mg, Fe, and Cr. However, a strong variability of the concentration for these elements was found. In conclusion, our results confirm that renal failure as well as dialysis provoke imbalances of elemental status in physiological fluids and tissues, which should be monitored.