nils-herrmann/clean_scirepeval huggingface dataset:
Social Media Addiction Scale-Student Form : The Reliability and Validity Study

The purpose of this study is to develop a valid and reliable measurement tool to determine the social media addictions of secondary school, high school and university students. 998 students participated in the study. 476 students from secondary schools, high schools and universities participated in the first application during which the exploratory factor analysis of the scale was conducted. 298 students participated in the second application during which the confirmatory factor analysis was carried out. Test-retest method was used to determine the consistency of the scale with the participation of 224 student. Expert opinion, exploratory factor analysis, confirmatory factor analysis, total item correlations, mean differences between upper and lower groups, internal consistency coefficient and test-retest correlation coefficients were calculated within the scope of assessing the validity and reliability of the research. According to the exploratory and confirmatory factor analysis, the scale has a 4-factor structure accounting for 53.16% of the total variance. Kaiser-Meyer-Olkin (KMO) coefficient and the Bartlett’s test were found significant respectively at .96 and χ2=12680.88 (p=.00). Internal consistency coefficient (Cronbach’s alpha coefficient) was found .93 for the whole scale and at values ranging from .81 to .86 for the sub-factors. Test-retest coefficient was found .94. In conclusion, SMA-SF is a 5-point Likert-type scale consisting of 29 items grouped under 4 factors (virtual tolerance, virtual communication, virtual problem and virtual information). The statistical analysis indicates that the scale is valid and reliable enough to be used in determining the social media addictions of secondary school, high school and university students.
NASARI: a Novel Approach to a Semantically-Aware Representation of Items

The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/.
Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience

This work evaluates the feasibility of a motor imagery-based optical brain-computer interface (BCI) for humanoid robot control. The functional near-infrared spectroscopy (fNIRS) based BCI-robot system developed in this study operates through a high-level control mechanism where user specifies a target action through the BCI and the robot performs the set of micro operations necessary to fulfill the identified goal. For the evaluation of the system, four motor imagery tasks (left hand, right hand, left foot, and right foot) were mapped to operational commands (turn left, turn right, walk forward, walk backward) that were sent to the robot in real time to direct the robot navigating a small room. An ecologically valid offline analysis with minimal preprocessing shows that seven subjects could achieve an average accuracy of 32.5 %. This was increased to 43.6 % just by including calibration data from the same day of the robot control using the same cap setup, indicating that day-of calibration following the initial training may be important for BCI control.
The LMC Skills, Confidence & Preparedness Index (SCPI): development and evaluation of a novel tool for assessing self-management in patients with diabetes

Optimal diabetes care requires a specific set of self-management behaviours. The purpose of this study was to present the development and initial psychometric evaluation of a new tool to measure three key aspects of a patient's diabetes self-management: knowledge of the skill, confidence in being able to perform the skill and preparedness to implement the skill.



A sequential exploratory mixed-methods design was used. A panel of educators, researchers and clinicians established a scale with items that would adequately capture skills, confidence and preparedness in seven core health behaviours central to diabetes care. The psychometric properties of the items were pilot tested on 120 participants with diabetes from a tertiary referral centre, and repeated 6 months later on 70 participants. Item selection was informed by factor analysis, item-total statistics and the need for brevity.



Twenty five items from a pool of 36 were retained, with an excellent overall intraclass correlation (ICC) of 0.94 (95% CI 0.92-0.99; p < 0.001). Internal consistency for the subscales (skills-9 items, confidence - 8 items, preparedness - 8 items) was very good (intraclass correlation between 0.83 and 0.88), and retest reliability after 6 months was also good (r = 0.48; p < 0.01). The scale was positively correlated to established scales that assess skill (Michigan Diabetes Knowledge Test) (r = 0.21;p = 0.01), and assess skill and confidence (Diabetes Empowerment Scale) (r = 0.28;p < 0.01).



The Skills, Confidence & Preparedness Index is a brief and easy to administer new scale that is more comprehensive than existing tools. It should be used to assess self-management in patients with diabetes, optimize the resources applied to each patient, and determine educational needs and direct clinical management. The scale should be further evaluated in a broader population of patients with diabetes.
Foundations of Rule Learning

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Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations

Detection of lexico-semantic relations is one of the central tasks of computational semantics. Although some fundamental relations (e.g., hypernymy) are asymmetric, most existing models account for asymmetry only implicitly and use the same concept representations to support detection of symmetric and asymmetric relations alike. In this work, we propose the Dual Tensor model, a neural architecture with which we explicitly model the asymmetry and capture the translation between unspecialized and specialized word embeddings via a pair of tensors. Although our Dual Tensor model needs only unspecialized embeddings as input, our experiments on hypernymy and meronymy detection suggest that it can outperform more complex and resource-intensive models. We further demonstrate that the model can account for polysemy and that it exhibits stable performance across languages.
Discriminative Multimetric Learning for Kinship Verification

In this paper, we propose a new discriminative multimetric learning method for kinship verification via facial image analysis. Given each face image, we first extract multiple features using different face descriptors to characterize face images from different aspects because different feature descriptors can provide complementary information. Then, we jointly learn multiple distance metrics with these extracted multiple features under which the probability of a pair of face image with a kinship relation having a smaller distance than that of the pair without a kinship relation is maximized, and the correlation of different features of the same face sample is maximized, simultaneously, so that complementary and discriminative information is exploited for verification. Experimental results on four face kinship data sets show the effectiveness of our proposed method over the existing single-metric and multimetric learning methods.
An Algorithmic Framework for Shape Formation Problems in Self-Organizing Particle Systems

This material is based on work in progress. Imagine that we had a piece of matter that can change its physical properties like shape, density, conductivity, or color in a programmable fashion based on either user input or autonomous sensing. This is the vision behind what is commonly known as programmable matter. Programmable matter is the subject of many recent novel distributed computing proposals --- ranging from DNA tiles, shape-changing molecules, and synthetic cells, to reconfigurable modular robotics --- each pursuing solutions for specific application scenarios with their own, special capabilities and constraints.
Assessing vineyard condition with hyperspectral indices : Leaf and canopy reflectance simulation in a row-structured discontinuous canopy

Methods for chlorosis detection and physiological condition monitoring in Vitis vinifera L. through accurate chlorophyll a and b content (Cab) estimation at leaf and canopy levels are presented in this manuscript. A total of 24 vineyards were identified for field and airborne data collection with the Compact Airborne Spectrographic Imager (CASI), the Reflective Optics System Imaging Spectrometer (ROSIS) and the Digital Airborne Imaging Spectrometer (DAIS-7915) hyperspectral sensors in 2002 and 2003 in northern Spain, comprising 103 study areas of 10 10 m in size, with a total of 1467 leaves collected for determination of pigment concentration. A subsample of 605 leaves was used for measuring the optical properties of reflectance and transmittance with a Li-Cor 1800-12 Integrating Sphere coupled by a 200 Am diameter single mode fiber to an Ocean Optics model USB2000 spectrometer. Several narrow-band vegetation indices were calculated from leaf reflectance spectra, and the PROSPECT leaf optical model was used for inversion using the extensive database of leaf optical properties. Results showed that the best indicators for chlorophyll content estimation in V. vinifera L. leaves were narrow-band hyperspectral indices calculated in the 700–750 nm spectral region (r ranging between 0.8 and 0.9), with poor performance of traditional indices such as the Normalized Difference Vegetation Index (NDVI). Results for other biochemicals indicated that the Structure Insensitive Pigment Index (SIPI) and the Photochemical Reflectance Index (PRI) were more sensitive to carotenoids Cx+c and chlorophyll–carotenoid ratios Cab /Cx+c than to chlorophyll content Cab. Chlorophyll a and b estimation by inversion of the PROSPECT leaf model on V. vinifera L. spectra was successful, yielding a determination coefficient of r=0.95, with an RMSE=5.3 Ag/cm. The validity of leaf-level indices for chlorophyll content estimation at the canopy level in V. vinifera L. was studied using the scaling-up approach that links PROSPECT and rowMCRM canopy reflectance simulation to account for the effects of vineyard structure, vine dimensions, row orientation and soil and shadow effects on the canopy reflectance. The index calculated as a combination of the Transformed Chlorophyll Absorption in Reflectance Index (TCARI), and the Optimized Soil-Adjusted Vegetation Index (OSAVI) in the form TCARI/OSAVI was the most consistent index for estimating Cab on aggregated and pure vine pixels extracted from 1 m CASI and ROSIS hyperspectral imagery. Predictive relationships were developed with PROSPECT–rowMCRM model between Cab and TCARI/OSAVI as function of LAI, using fieldmeasured vine dimensions and image-extracted soil background, row-orientation and viewing geometry values. Prediction relationships for Cab content with TCARI/OSAVI were successfully applied to the 103 study sites imaged on 24 fields by ROSIS and CASI airborne sensors, yielding r=0.67 and RMSE=11.5 Ag/cm. D 2005 Elsevier Inc. All rights reserved.
Compact Elongated Mushroom (EM)-EBG Structure for Enhancement of Patch Antenna Array Performances

A compact elongated mushroom electromagnetic band-gap (EM-EBG) structure, exploiting the thickness of the substrate to achieve higher isolation compared to the case of the conventional mushroom EBG (CM-EBG), is proposed for the enhancement of the performances of patch antenna arrays. Guidelines, based on fairly accurate formulas, are provided for the design of the stop-band of the structure. The compactness of the EM-EBG is investigated and shown to be superior to that of the CM-EBG in practical cases. The superior reduction of mutual coupling of the EM-EBG is demonstrated by full-wave and experimental results both for a pair of patches and for a 4-element patch array. Finally, the benefits of the EM-EBG in terms of array processing, specifically side-lobe level (SLL) control and direction of arrival (DOA) estimation, are presented.
Overview of on-chip electrostatic discharge protection design with SCR-based devices in CMOS integrated circuits

An overview on the electrostatic discharge (ESD) protection circuits by using the silicon controlled rectifier (SCR)-based devices in CMOS ICs is presented. The history and evolution of SCR device used for on-chip ESD protection is introduced. Moreover, two practical problems (higher switching voltage and transient-induced latchup issue) limiting the use of SCR-based devices in on-chip ESD protection are reported. Some modified device structures and trigger-assist circuit techniques to reduce the switching voltage of SCR-based devices are discussed. The solutions to overcome latchup issue in the SCR-based devices are also discussed to safely apply the SCR-based devices for on-chip ESD protection in CMOS IC products.
Using fast weights to improve persistent contrastive divergence

The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few iterations to get a cheap, low variance estimate of the sufficient statistics under the model. Tieleman (2008) showed that better learning can be achieved by estimating the model's statistics using a small set of persistent "fantasy particles" that are not reinitialized to data points after each weight update. With sufficiently small weight updates, the fantasy particles represent the equilibrium distribution accurately but to explain why the method works with much larger weight updates it is necessary to consider the interaction between the weight updates and the Markov chain. We show that the weight updates force the Markov chain to mix fast, and using this insight we develop an even faster mixing chain that uses an auxiliary set of "fast weights" to implement a temporary overlay on the energy landscape. The fast weights learn rapidly but also decay rapidly and do not contribute to the normal energy landscape that defines the model.
Data deduplication techniques

With the information and network technology, rapid development, rapid increase in the size of the data center, energy consumption in the proportion of IT spending rising. In the great green environment many companies are eyeing the green store, hoping thereby to reduce the energy storage system. Data de-duplication technology to optimize the storage system can greatly reduce the amount of data, thereby reducing energy consumption and reduce heat emission. Data compression can reduce the number of disks used in the operation to reduce disk energy consumption costs. By studying the data de-duplication strategy, processes, and implementations for the following further lay the foundation of the work.
Human gut microbiota in obesity and after gastric bypass.

Recent evidence suggests that the microbial community in the human intestine may play an important role in the pathogenesis of obesity. We examined 184,094 sequences of microbial 16S rRNA genes from PCR amplicons by using the 454 pyrosequencing technology to compare the microbial community structures of 9 individuals, 3 in each of the categories of normal weight, morbidly obese, and post-gastric-bypass surgery. Phylogenetic analysis demonstrated that although the Bacteria in the human intestinal community were highly diverse, they fell mainly into 6 bacterial divisions that had distinct differences in the 3 study groups. Specifically, Firmicutes were dominant in normal-weight and obese individuals but significantly decreased in post-gastric-bypass individuals, who had a proportional increase of Gammaproteobacteria. Numbers of the H(2)-producing Prevotellaceae were highly enriched in the obese individuals. Unlike the highly diverse Bacteria, the Archaea comprised mainly members of the order Methanobacteriales, which are H(2)-oxidizing methanogens. Using real-time PCR, we detected significantly higher numbers of H(2)-utilizing methanogenic Archaea in obese individuals than in normal-weight or post-gastric-bypass individuals. The coexistence of H(2)-producing bacteria with relatively high numbers of H(2)-utilizing methanogenic Archaea in the gastrointestinal tract of obese individuals leads to the hypothesis that interspecies H(2) transfer between bacterial and archaeal species is an important mechanism for increasing energy uptake by the human large intestine in obese persons. The large bacterial population shift seen in the post-gastric-bypass individuals may reflect the double impact of the gut alteration caused by the surgical procedure and the consequent changes in food ingestion and digestion.
Reliability Issues in Photovoltaic Power Processing Systems

Power processing systems will be a key factor of future photovoltaic (PV) applications. They will play a central role in transferring, to the load and/or to the grid, the electric power produced by the high-efficiency PV cells of the next generation. In order to come up the expectations related to the use of solar energy for producing electrical energy, such systems must ensure high efficiency, modularity, and, particularly, high reliability. The goal of this paper is to provide an overview of the open problems related to PV power processing systems and to focus the attention of researchers and industries on present and future challenges in this field.
SLAM-based automatic extrinsic calibration of a multi-camera rig

Cameras are often a good choice as the primary outward-looking sensor for mobile robots, and a wide field of view is usually desirable for responsive and accurate navigation, SLAMand relocalisation. While this can potentially be provided by a single omnidirectional camera, it can also be flexibly achieved by multiple cameras with standard optics mounted around the robot. However, such setups are difficult to calibrate. Here we present a general method for fully automatic extrinsic auto-calibration of a fixed multi camera rig, with no requirement for calibration patterns or other infrastructure, which works even in the case where the cameras have completely non-overlapping views. The robot is placed in a natural environment and makes a set of programmed movements including a full horizontal rotation and captures a synchronized image sequence from each camera. These sequences are processed individually with a monocular visual SLAM algorithm. The resulting maps are matched and fused robustly based on corresponding invariant features, and then all estimates are optimised full joint bundle adjustment, where we constrain the relative poses of the cameras to be fixed. We present results showing accurate performance of the method for various two and four camera configurations.
Prevalence of kidney stones in the United States.

The last nationally representative assessment of kidney stone prevalence in the United States occurred in 1994. After a 13-yr hiatus, the National Health and Nutrition Examination Survey (NHANES) reinitiated data collection regarding kidney stone history.



Describe the current prevalence of stone disease in the United States, and identify factors associated with a history of kidney stones.



A cross-sectional analysis of responses to the 2007-2010 NHANES (n=12 110).


OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
Self-reported history of kidney stones. Percent prevalence was calculated and multivariable models were used to identify factors associated with a history of kidney stones.



The prevalence of kidney stones was 8.8% (95% confidence interval [CI], 8.1-9.5). Among men, the prevalence of stones was 10.6% (95% CI, 9.4-11.9), compared with 7.1% (95% CI, 6.4-7.8) among women. Kidney stones were more common among obese than normal-weight individuals (11.2% [95% CI, 10.0-12.3] compared with 6.1% [95% CI, 4.8-7.4], respectively; p<0.001). Black, non-Hispanic and Hispanic individuals were less likely to report a history of stone disease than were white, non-Hispanic individuals (black, non-Hispanic: odds ratio [OR]: 0.37 [95% CI, 0.28-0.49], p<0.001;  0.60 [95% CI, 0.49-0.73], p<0.001). Obesity and diabetes were strongly associated with a history of kidney stones in multivariable models. The cross-sectional survey design limits causal inference regarding potential risk factors for kidney stones.



Kidney stones affect approximately 1 in 11 people in the United States. These data represent a marked increase in stone disease compared with the NHANES III cohort, particularly in black, non-Hispanic and Hispanic individuals. Diet and lifestyle factors likely play an important role in the changing epidemiology of kidney stones.
Concentration of Measure for the Analysis of Randomized Algorithms

We have observed that the cost of the search is equal to the number of tosses of a coin of bias p that are necessary until we obtain H successes. That is, we flip the coin repeatedly and stop as soon as we observe H successes. The difficulty here is that the random variable we are studying is the sum of geometrically distributed random variables. The distribution of this random variable is called negative binomial and some of its properties are explored in the problem section. Here, we take a different approach. To fix ideas, let p := 1 2 . Suppose that we toss the coin L times where
Classification of ECG signals using machine learning techniques: A survey

Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of heart diseases. An accurate ECG classification is a challenging problem. This paper presents a survey of ECG classification into arrhythmia types. Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. Different classifiers are available for ECG classification. Amongst all classifiers, artificial neural networks (ANNs) have become very popular and most widely used for ECG classification. This paper discusses the issues involved in ECG classification and presents a detailed survey of preprocessing techniques, ECG databases, feature extraction techniques, ANN based classifiers, and performance measures to address the mentioned issues. Furthermore, for each surveyed paper, our paper also presents detailed analysis of input beat selection and output of the classifiers.