nils-herrmann/clean_scirepeval huggingface dataset:
Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard

An enterprise resource planning system (ERP) is the information backbone of a company that integrates and automates all business operations. It is a critical issue to select the suitable ERP system which meets all the business strategies and the goals of the company. This study presents an approach to select a suitable ERP system for textile industry. Textile companies have some difficulties to implement ERP systems such as variant structure of products, production variety and unqualified human resources. At first, the vision and the strategies of the organization are checked by using balanced scorecard. According to the company’s vision, strategies and KPIs, we can prepare a request for proposal. Then ERP packages that do not meet the requirements of the company are eliminated. After strategic management phase, the proposed methodology gives advice before ERP selection. The criteria were determined and then compared according to their importance. The rest ERP system solutions were selected to evaluate. An external evaluation team consisting of ERP consultants was assigned to select one of these solutions according to the predetermined criteria. In this study, the fuzzy analytic hierarchy process, a fuzzy extension of the multi-criteria decision-making technique AHP, was used to compare these ERP system solutions. The methodology was applied for a textile manufacturing company.
Towards a social and context-aware mobile recommendation system for tourism

Loyalty in tourism is one of the main concerns for tourist organizations and researchers alike. Recently, technology in general and CRM and social networks in particular have been identified as important enablers for loyalty in tourism. This paper presents POST-VIA 360, a platform devoted to support the whole life-cycle of tourism loyalty after the first visit. The system is designed to collect data from the initial visit by means of pervasive approaches. Once data is analysed, POST-VIA 360 produces accurate after visit data and, once returned, is able to offer relevant recommendations based on positioning and bio-inspired recommender systems. To validate the system, a case study comparing recommendations from the POST-VIA 360 and a group of experts was conducted. Results show that the accuracy of system ́s recommendations is remarkable compared to previous efforts in the field.
Dissecting Tendermint

In this paper we analyze Tendermint proposed in , one of the most popular blockchains based on PBFT Consensus. The current paper dissects Tendermint under various system communication models and Byzantine adversaries. Our methodology consists in identifying the algorithmic principles of Tendermint necessary for a specific combination of communication model adversary. This methodology allowed to identify bugs  in preliminary versions of the protocol ([19], ) and to prove its correctness under the most adversarial conditions: an eventually synchronous communication model and asymmetric Byzantine faults.
A spatial scan statistic for ordinal data.

Spatial scan statistics are widely used for count data to detect geographical disease clusters of high or low incidence, mortality or prevalence and to evaluate their statistical significance. Some data are ordinal or continuous in nature, however, so that it is necessary to dichotomize the data to use a traditional scan statistic for count data. There is then a loss of information and the choice of cut-off point is often arbitrary. In this paper, we propose a spatial scan statistic for ordinal data, which allows us to analyse such data incorporating the ordinal structure without making any further assumptions. The test statistic is based on a likelihood ratio test and evaluated using Monte Carlo hypothesis testing. The proposed method is illustrated using prostate cancer grade and stage data from the Maryland Cancer Registry. The statistical power, sensitivity and positive predicted value of the test are examined through a simulation study.
The Emergence of Spectral Universality in Deep Networks

Recent work has shown that tight concentration of the entire spectrum of singular values of a deep network’s input-output Jacobian around one at initialization can speed up learning by orders of magnitude. Therefore, to guide important design choices, it is important to build a full theoretical understanding of the spectra of Jacobians at initialization. To this end, we leverage powerful tools from free probability theory to provide a detailed analytic understanding of how a deep network’s Jacobian spectrum depends on various hyperparameters including the nonlinearity, the weight and bias distributions, and the depth. For a variety of nonlinearities, our work reveals the emergence of new universal limiting spectral distributions that remain concentrated around one even as the depth goes to infinity.
Improving Text-to-SQL Evaluation Methodology

New resources to make these evaluations easier • New Advising dataset, plus 7 existing-text-to-SQL datasets cleaned, variablized, and put into a single, standard format, with tools for easy use. • Scan above or visit https://github.com/jkkummerfeld/text2sql-data Evaluations should measure how well systems generalize to realistic unseen data. Yet standard train/test splits, which ensure that no English question is in both train and test, permit the same SQL query to appear in both. Using a simple classifier with a slot-filler as a basline, we show how the standard question-based split fails to evaluate a system’s generalizability. In addition, by analyzing properties of human-generated and automatically generated text-to-SQL datasets, we show the need to evaluate on more than one dataset to ensure systems perform well on realistic data. And we release improved resources to facilitate such evaluations.
Prevalence and Predictors of Video Game Addiction: A Study Based on a National Representative Sample of Gamers

Video gaming has become a popular leisure activity in many parts of the world, and an increasing number of empirical studies examine the small minority that appears to develop problems as a result of excessive gaming. This study investigated prevalence rates and predictors of video game addiction in a sample of gamers, randomly selected from the National Population Registry of Norway (N = 3389). Results showed there were 1.4 % addicted gamers, 7.3 % problem gamers, 3.9 % engaged gamers, and 87.4 % normal gamers. Gender (being male) and age group (being young) were positively associated with addicted-, problem-, and engaged gamers. Place of birth (Africa, Asia, South- and Middle America) were positively associated with addicted- and problem gamers. Video game addiction was negatively associated with conscientiousness and positively associated with neuroticism. Poor psychosomatic health was positively associated with problem- and engaged gaming. These factors provide insight into the field of video game addiction, and may help to provide guidance as to how individuals that are at risk of becoming addicted gamers can be identified.
A Dual Source-Filter Model of Snore Audio for Snorer Group Classification

Snoring is a common symptom of serious chronic disease known as obstructive sleep apnea (OSA). Knowledge about the location of obstruction site (VVelum, OOropharyngeal lateral walls, T-Tongue, E-Epiglottis) in the upper airways is necessary for proper surgical treatment. In this paper we propose a dual source-filter model similar to the source-filter model of speech to approximate the generation process of snore audio. The first filter models the vocal tract from lungs to the point of obstruction with white noise excitation from the lungs. The second filter models the vocal tract from the obstruction point to the lips/nose with impulse train excitation which represents vibrations at the point of obstruction. The filter coefficients are estimated using the closed and open phases of the snore beat cycle. VOTE classification is done by using SVM classifier and filter coefficients as features. The classification experiments are performed on the development set (283 snore audios) of the MUNICH-PASSAU SNORE SOUND CORPUS (MPSSC). We obtain an unweighted average recall (UAR) of 49.58%, which is higher than the INTERSPEECH-2017 snoring sub-challenge baseline technique by ∼3% (absolute).
Granger-Causality-based air quality estimation with spatio-temporal (S-T) heterogeneous big data

This paper considers city-wide air quality estimation with limited available monitoring stations which are geographically sparse. Since air pollution is highly spatio-temporal (S-T) dependent and considerably influenced by urban dynamics (e.g., meteorology and traffic), we can infer the air quality not covered by monitoring stations with S-T heterogeneous urban big data. However, estimating air quality using S-T heterogeneous big data poses two challenges. The first challenge is due to with the data diversity, i.e., there are different categories of urban dynamics and some may be useless and even detrimental for the estimation. To overcome this, we first propose an S-T extended Granger causality model to analyze all the causalities among urban dynamics in a consistent manner. Then by implementing non-causality test, we rule out the urban dynamics that do not “Granger” cause air pollution. The second challenge is due to the time complexity when processing the massive volume of data. We propose to discover the region of influence (ROI) by selecting data with the highest causality levels spatially and temporally. Results show that we achieve higher accuracy using “part” of the data than “all” of the data. This may be explained by the most influential data eliminating errors induced by redundant or noisy data. The causality model observation and the city-wide air quality map are illustrated and visualized using data from Shenzhen, China.
A Deep Learning Approach for Multimodal Deception Detection

Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multi-modal neural model for deception detection. By combining features from different modalities such as video, audio, and text along with Micro-Expression features, we show that detecting deception in real life videos can be more accurate. Experimental results on a dataset of real-life deception videos show that our model outperforms existing techniques for deception detection with an accuracy of 96.14% and ROC-AUC of 0.9799.
Smart Grids: A Cyber–Physical Systems Perspective

Smart grids are electric networks that employ advanced monitoring, control, and communication technologies to deliver reliable and secure energy supply, enhance operation efficiency for generators and distributors, and provide flexible choices for prosumers. Smart grids are a combination of complex physical network systems and cyber systems that face many technological challenges. In this paper, we will first present an overview of these challenges in the context of cyber-physical systems. We will then outline potential contributions that cyber-physical systems can make to smart grids, as well as the challenges that smart grids present to cyber-physical systems. Finally, implications of current technological advances to smart grids are outlined.
An analysis of legal warnings after drug approval in Thailand.

Drug risk management has many tools for minimizing risk and black-boxed warnings (BBWs) are one of those tools. Some serious adverse drug reactions (ADRs) emerge only after a drug is marketed and used in a larger population. In Thailand, additional legal warnings after drug approval, in the form of black-boxed warnings, may be applied. Review of their characteristics can assist in the development of effective risk mitigation. This study was a cross sectional review of all legal warnings imposed in Thailand after drug approval (2003-2012). Any boxed warnings for biological products and revised warnings which were not related to safety were excluded. Nine legal warnings were evaluated. Seven related to drugs classes and two to individual drugs. The warnings involved four main types of predictable ADRs: drug-disease interactions, side effects, overdose and drug-drug interactions. The average time from first ADRs reported to legal warnings implementation was 12 years. The triggers were from both safety signals in Thailand and regulatory measures in other countries outside Thailand.
Bad for Data, Good for the Brain : Knowledge-First Axioms For Visualization Design

Traditionally, visualization design assumes that the e↵ectiveness of visualizations is based on how much, and how clearly, data are presented. We argue that visualization requires a more nuanced perspective. Data are not ends in themselves, but means to an end (such as generating knowledge or assisting in decision-making). Focusing on the presentation of data per se can result in situations where these higher goals are ignored. This is especially the case for situations where cognitive or perceptual biases make the presentation of “just” the data as misleading as willful distortion. We argue that we need to de-sanctify data, and occasionally promote designs which distort or obscure data in service of understanding. We discuss examples of beneficial embellishment, distortion, and obfuscation in visualization, and argue that these examples are representative of a wider class of techniques for going beyond simplistic presentations of data.
A wideband dual-polarized dipole linear array with resistive loading

A dual-polarized, tightly-coupled dipole linear array, delivering 5.7:1 bandwidth, is presented. Most of previous work on tightly-coupled array is focused on two-dimensional array. This communication presents a dual-polarized, tightly-coupled dipole linear array, which has much more potential in application. The linear array incorporates a resistive loop as the substrate load to broaden the bandwidth of both polarization. For vertical polarized dipole of linear array, lacking tightly-coupled environment, there are sever methods implemented to broaden the bandwidth, such as gradient slot and folded arms. Simulated results show that the linear array can achieve 5.7:1 bandwidth while scan to ±50°, for VSWR<;3(sever frequency <;3.5), and the radiation efficiency maintain above 50%.
Learning analytics as a tool for closing the assessment loop in higher education

This paper examines learning and academic analytics and its relevance to distance education in undergraduate and graduate programs as it impacts students and teaching faculty, and also academic institutions. The focus is to explore the measurement, collection, analysis, and reporting of data as predictors of student success and drivers of departmental process and program curriculum. Learning and academic analytics in higher education is used to predict student success by examining how and what students learn and how success is supported by academic programs and institutions. The paper examines what is being done to support students, whether or not it is effective, and if not why, and what educators can do. The paper also examines how these data can be used to create new metrics and inform a continuous cycle of improvement. It presents examples of working models from a sample of institutions of higher education: The Graduate School of Medicine at the University of Wollongong, the University of Michigan, Purdue University, and the University of Maryland, Baltimore County. Finally, the paper identifies considerations and recommendations for using analytics and offer suggestions for future research.
Sesamoids and accessory ossicles of the foot: anatomical variability and related pathology

Sesamoids and accessory ossicles seen in the foot vary widely in their prevalence and appearance. Occasionally, these bones may be associated with painful syndromes, due to various pathologies, including trauma, infection, inflammation, degeneration and others. However, symptomatic accessory and sesamoid bones are rare, and search for additional pathology should be performed. Although the clinical significance of these osseous structures is probably minor, clinicians very commonly ask about these bones, which may originate an unnecessary work-up. Therefore, knowledge of their presence and morphological variations is important to prevent misinterpreting them as fractures-a common error. Finally, it may be very difficult to distinguish between incidental variants and truly symptomatic ones. Radiological studies provide insight regarding the presence and pathology involving these bones. This review describes an overview of the anatomy of sesamoids and accessory ossicles in the foot, and provides a pictorial review of their pathological conditions, including trauma, sesamoiditis, osteomyelitis, osteoarthritis and pain syndromes. Radiological studies including radiography, ultrasound, scintigraphy, computed tomography (CT) and magnetic resonance imaging (MRI) provide useful information which should be used in concert with clinical findings to guide patient management. Teaching points • Sesamoids and accessory ossicles seen in the foot vary widely in their prevalence and appearance. • Pathology of these bones includes trauma, sesamoiditis, infection, osteoarthritis and pain syndromes. • Radiography, ultrasound, scintigraphy, CT and MRI provide information regarding the pathology of these bones.
T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data

Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods which employ dimensionality reduction to two or three dimensions are often inefficient and/or ineffective for these datasets. This paper introduces T-SNE-CUDA, a GPU-accelerated implementation of t-distributed Symmetric Neighbour Embedding (t-SNE) for visualizing datasets and models. T-SNE-CUDA significantly outperforms current implementations with 50-700x speedups on the CIFAR-10 and MNIST datasets. These speedups enable, for the first time, visualization of the neural network activations on the entire ImageNet dataset - a feat that was previously computationally intractable. We also demonstrate visualization performance in the NLP domain by visualizing the GloVe embedding vectors. From these visualizations, we can draw interesting conclusions about using the L2 metric in these embedding spaces. T-SNE-CUDA is publicly available at https://github.com/CannyLab/tsne-cuda.
Idiopathic scrotal calcinosis: a case report of a rare condition

Scrotal calcinosis is a rarely seen benign disease in urological practice. It was first described by Lewinsky in 1883. The etiology is considered to be idiopathic and it is not known exactly. Scrotal calcinosis is usually asymptomatic. Patients live with their disease for a long time until they start to mind their appearances. Scrotal skin lesions can be solitary or multiple and usually are not associated with hormonal or metabolic abnormalities. Histologically, scrotal calcinosis is characterized by the presence of calcium deposits in the dermis, often surrounded by a granulomatous reaction. In this case report, we present a rare scrotal calcinosis case of a 28-year-old man who presented with cosmetic symptoms causing scrotal nodules with no history of metabolic, systemic, neoplastic, or autoimmune diseases.
Semi-parametric Image Inpainting

This paper introduces a semi-parametric approach to image inpainting for irregular holes. The nonparametric part consists of an external image database. During test time database is used to retrieve a supplementary image, similar to the input masked picture, and utilize it as auxiliary information for the deep neural network. Further, we propose a novel method of generating masks with irregular holes and present public dataset with such masks. Experiments on CelebA-HQ dataset show that our semiparametric method yields more realistic results than previous approaches, which is confirmed by the user study.
Model-Based Reinforcement Learning for Sepsis Treatment

Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continuous state-space model-based reinforcement learning (RL) to discover high-quality treatment policies for sepsis patients. Our quantitative evaluation reveals that by blending the treatment strategy discovered with RL with what clinicians follow, we can obtain improved policies, potentially allowing for better medical treatment for sepsis.
Aligning IT To Business In Complex Multinational Corporations: The Case Of The U.N. Secretariat

Business-IT strategic alignment grows in importance as organizations strive to link business and technology in light of the internationalization of their businesses. This positivistic research method uses a field survey design to examine (a) the role of knowledge management processes in the relationship between contextual factors and alignment in the U.N. Secretariat, and (b) the role of IT projects in the relationship between alignment and the performance and effectiveness of the U.N. Secretariat. Structural equation modeling techniques are conducted to analyze data collected through a sample of 166 IT managers and 97 business managers from 50 offices in the U.N. Secretariat. The measurement model exhibited a fairly good fit. The results of the study have at least four implications to leaders in the U.N. Secretariat and in multinational corporations (MNCs). A theoretical and practical perspective of business-IT strategic alignment in the U.N. Secretariat is provided. The study draws upon the strategic alignment model and the typology of MNCs to propose and test an IT strategic alignment model for MNCs (mSAM). The business-IT strategic alignment implementation model for MNCs (mSAIM) is the model for application proposed as the critical recommendation of the research study.
The role of orienting in vibrissal touch sensing

Rodents, such as rats and mice, are strongly tactile animals who explore the environment with their long mobile facial whiskers, or macrovibrissae, and orient to explore objects further with their shorter, more densely packed, microvibrissae. Although whisker motion (whisking) has been extensively studied, less is known about how rodents orient their vibrissal system to investigate unexpected stimuli. We describe two studies that address this question. In the first we seek to characterize how adult rats orient toward unexpected macrovibrissal contacts with objects and examine the microvibrissal exploration behavior following such contacts. We show that rats orient to the nearest macrovibrissal contact on an unexpected object, progressively homing in on the nearest contact point on the object in each subsequent whisk. Following contact, rats "dab" against the object with their microvibrissae at an average rate of approximately 8 Hz, which suggests synchronization of microvibrissal dabbing with macrovibrissal motion, and an amplitude of 5 mm. In study two, we examine the role of orienting to tactile contacts in developing rat pups for maintaining aggregations (huddles). We show that young pups are able to orient to contacts with nearby conspecifics before their eyes open implying an important role for the macrovibrissae, which are present from birth, in maintaining contact with conspecifics. Overall, these data suggest that orienting to tactile cues, detected by the vibrissal system, plays a crucial role throughout the life of a rat.