Miracle-dz/UniFAR-FTUs huggingface dataset:
Loneliness, regulatory focus, inter-personal competence, and online game addiction

Purpose The purpose of this paper is to investigate the predictors of game addiction based on loneliness, motivation and inter-personal competence using the samples of college students recruited from South Korea (n=251). Design/methodology/approach The authors examined the underlying mechanism of game addiction by testing a moderated mediation model, in which inter-personal competence moderated the mediation model of loneliness, regulatory focus and online game addiction. First, the authors clarified the relationship among loneliness, motivation and inter-personal competence, to understand the influences of loneliness on other variables in this study (mediation test). Second, the authors examined the underlying mechanism of game addiction by testing a moderated mediation model, in which inter-personal competence moderated the mediation model of loneliness, regulatory focus and online game addiction (moderated mediation). Findings Regulatory focus mediated the effect of loneliness on online game addiction. Moderated mediation analyses using PROCESS confirmed that inter-personal competence significantly buffered the indirect effect of loneliness (through regulatory focus) on online game addiction. The findings indicated that inter-personal competence accounted for significant differences in the mediation models. Originality/value This study bridges the gap in the online game addiction literature by explaining how loneliness is associated with online game addiction.
TempSeg-GAN: Segmenting Objects in Videos Adversarially using Temporal Information

This paper studies the problem of Video Object Segmentation which aims at segmenting objects of interest throughout entire videos, when provided with initial ground truth annotation. Although, variety of works in this field have been done utilizing Convolutional Neural Networks (CNNs), adversarial training techniques have not been used in spite of their effectiveness as a holistic approach. Our proposed architecture consists of a Generative Adversarial framework for the purpose of foreground object segmentation in videos coupled with Intersection-over-union and temporal information based loss functions for training the network. The main contribution of the paper lies in formulation of the two novel loss functions: (i) Inter-frame Temporal Symmetric Difference Loss (ITSDL) and (ii) Intra-frame Temporal Loss (IFTL), which not only enhance the segmentation quality of the predicted mask but also maintain the temporal consistency between the subsequent generated frames. Our end-to-end trainable network exhibits impressive performance gain compared to the state-of-the-art model when evaluated on three popular real-world Video Object Segmentation datasets viz. DAVIS 2016, SegTrack-v2 and YouTube-Objects dataset.
Socioeconomic status, health related behaviour, and self-rated health of children living in Roma settlements in Hungary.

OBJECTIVE The poor health of Roma is well documented, but there is only limited data regarding the health of Roma children. The aim of this study was to describe the socioeconomic status, health related behaviour, and health of children living in segregated Roma settlements, and to compare the data with that of non-Roma children. METHODS In March-April of 2011, a cross-sectional questionnaire-based survey among 11-year-old (211 boys and 252 girls) and 13-year-old (205 boys and 247 girls) children living in Roma settlements was performed (response rate: 91.5%). These data were compared with data from the Health Behaviour in School-Aged Children (HBSC) survey carried out in 2009/2010. RESULTS The parents of Roma children were substantially less educated and less likely to be actively employed, and Roma children reported lower material welfare than non-Roma ones. The prevalence of consuming sweets and soft drinks at least 5 times per week was 1.5-2 times higher among Roma children. The prevalence of regular intense physical activity was higher at the age of 13 years among Roma boys, while physical inactivity was substantially higher in both age groups among Roma girls. Almost one quarter of Roma children and approximately 14% of non-Roma children had tried smoking at the age of 11. More Roma boys tried alcohol at the age of 11 than non-Roma ones. One in ten Roma children was obese in both age groups. The self-rated health status of Roma children was worse than that of non-Roma children. CONCLUSIONS Children living in Roma settlements reported poorer socioeconomic conditions, higher consumption of sweets and soft drinks, earlier smoking and alcohol initiation, and worse self-rated health, but with some exceptions do not differ in fruit or vegetable consumption and BMI from general child population. To promote health of children living in Roma settlements, a multi-sector approach, special health education, plus social and health promotion programmes are needed.
Identity-based encryption resilient to continuous key leakage

Leakage of private information has become a threat to the security of computing systems. It has become a common security requirement that a cryptography scheme should withstand various leakage attacks, even the continuous leakage attacks. However, in the current constructions on the (continuous) leakage-resilient identity-based encryption (CLR-IBE) scheme, the leakage parameter is a fixed value. Aiming to solve these problems, in this study, the authors show how to construct the CLR-IBE scheme, and the adaptive chosen-ciphertext attacks security of proposed construction can be proved in the standard model. To further improve the practicability of CLR-IBE scheme, they design an improved IBE scheme with continuous leakage amplified property, and the leakage parameter has an arbitrary length.
Semantic Segmentation of Non-linear Multimodal Images for Disease Grading of Inflammatory Bowel Disease: A SegNet-based Application

Non-linear multimodal imaging, the combination of coherent anti-stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), has shown its potential to assist the diagnosis of different inflammatory bowel diseases (IBDs). This label-free imaging technique can support the ‘gold-standard’ techniques such as colonoscopy and histopathology to ensure an IBD diagnosis in clinical environment. Moreover, non-linear multimodal imaging can measure biomolecular changes in different tissue regions such as crypt and mucosa region, which serve as a predictive marker for IBD severity. To achieve a real-time assessment of IBD severity, an automatic segmentation of the crypt and mucosa regions is needed. In this paper, we semantically segment the crypt and mucosa region using a deep neural network. We utilized the SegNet architecture (Badrinarayanan et al., 2015) and compared its results with a classical machine learning approach. Our trained SegNet model achieved an overall F1 score of 0.75. This model outperformed the classical machine learning approach for the segmentation of the crypt and mucosa region in our study.
Boosting 3D Shape Classification with Global Verification and Redundancy-free Codebooks

We present a competitive approach for 3D data classification that is related to Implicit Shape Models and Naive-Bayes Nearest Neighbor algorithms. Based on this approach we investigate methods to reduce the amount of data stored in the extracted codebook with the goal to eliminate redundant and ambiguous feature descriptors. The codebook is significantly reduced in size and is combined with a novel global verification approach. We evaluate our algorithms on typical 3D data benchmarks and achieve competitive results despite the reduced codebook. The presented algorithm can be run efficiently on a mobile computer making it suitable for mobile robotics applications. The source code of the developed methods is made publicly available to contribute to point cloud processing, the Point Cloud Library (PCL) and 3D classification software in general.
Genetic mapping in Diversity Outbred mice identifies a Trpa1 variant influencing late phase formalin response

Identification of genetic variants that influence susceptibility to chronic pain is key to identifying molecular mechanisms and targets for effective and safe therapeutic alternatives to opioids. To identify genes and variants associated with chronic pain, we measured late phase response to formalin injection in 275 male and female Diversity Outbred (DO) mice genotyped for over 70 thousand SNPs. One quantitative trait locus (QTL) reached genome-wide significance on chromosome 1 with a support interval of 3.1 Mb. This locus, Nociq4 (nociceptive sensitivity inflammatory QTL 4; MGI:5661503), harbors the well-known pain gene Trpa1 (transient receptor potential cation channel, subfamily A, member 1). Trpa1 is a cation channel known to play an important role in acute and chronic pain in both humans and mice. Analysis of DO founder strain allele effects revealed a significant effect of the CAST/EiJ allele at Trpa1, with CAST/EiJ carrier mice showing an early, but not late, response to formalin relative to carriers of the seven other inbred founder alleles (A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, NZO/HlLtJ, PWK/PhJ, and WSB/EiJ). We characterized possible functional consequences of sequence variants in Trpa1 by assessing channel conductance, Trpa1/Trpv1 interactions, and isoform expression. The phenotypic differences observed in CAST/EiJ relative to C57BL/6J carriers were best explained by Trpa1 isoform expression differences, implicating a splice junction variant as the causal functional variant. This study demonstrates the utility of advanced, high-precision genetic mapping populations in resolving specific molecular mechanisms of variation in pain sensitivity.
Changes in the Expression of Aquaporin-3 in the Gastrointestinal Tract Affect Drug Absorption

Aquaporin-3 (AQP3) plays an important role in water transport in the gastrointestinal (GI) tract. In this study, we conducted a Caco-2 cell permeability assay to examine how changes in the expression and function of AQP3 affect the rate at which a drug is absorbed via passive transport in the GI tract. When the function of AQP3 was inhibited by mercuric chloride or phloretin, there was no change in warfarin permeability. In contrast, when the expression of AQP3 protein was decreased by prostaglandin E2 (PGE2) treatment, warfarin permeability increased to approximately twice the control level, and membrane fluidity increased by 15%. In addition, warfarin permeability increased to an extent comparable to that after PGE2 treatment when cell membrane fluidity was increased by 10% via boric acid/EDTA treatment. These findings suggest the possibility that the increased drug absorption under decreased AQP3 expression was attributable to increased membrane fluidity. The results of this study demonstrate that the rate of water transport has little effect on drug absorption. However, our findings also indicate that although AQP3 and other similar transmembrane proteins do not themselves transport drugs, changes in their expression levels can cause changes in cell membrane fluidity, thus affecting drug absorption rates.
Association between serum uric acid and large-nerve fiber dysfunction in type 2 diabetes: a cross-sectional study

Background Large-nerve fiber dysfunction, as assessed by vibration perception threshold (VPT) predicts risks of ulceration, amputation, and mortality in diabetes. Serum uric acid (UA) is closely associated with various metabolic disorders, especially diabetes. Thus, we sought to investigate the clinical relevance of UA to large-nerve fiber dysfunction, among patients with type 2 diabetes (T2D). Methods Medical records of consecutive patients with T2D who were admitted to Beijing Friendship Hospital Pinggu Campus between May 2014 and December 2016 were collected. Data for the 824 eligible patients included in the final analysis were extracted using a structured form. A VPT value ≥15 in either foot was defined as abnormal. We compared the clinical characteristics between patients with abnormal VPT and those with normal VPT (VPT value <15 in both feet) in the overall population and in gender subgroups. Logistic regression analysis was performed to explore the association of abnormal VPT with UA level. One-way analysis of variance was used to compare VPT values across four UA quartiles. Results UA levels were significantly lower in T2D patients with abnormal VPT than in those with normal VPT (294.5 ± 84.0 vs. 314.9 ± 92.8 &mgr;mol/L, P < 0.01), especially among male patients (311.7 ± 85.2 vs. 336.9 ± 89.6 &mgr;mol/L, P < 0.01). From the logistic regression analysis, hyperuricemia (males >420 &mgr;mol/L; females >360 &mgr;mol/L) was associated with a reduced risk of abnormal VPT (odds ratio [OR], 0.60; 95% confidence interval [CI], 0.39–0.91; P < 0.05). This association was robust in male patients (OR, 0.43; 95% CI, 0.24–0.76; P < 0.01) but not in female patients (OR, 0.92; 95% CI, 0.47–1.82; P = 0.816), even after adjustment for confounding factors. For the younger male subgroup (age <65 years), VPT values decreased as the UA level increased (P for trend = 0.002), but this trend was not significant in older male subgroup (age ≥65 years; P for trend = 0.400). Conclusions Low serum UA levels showed a significant association with an increased risk of large-nerve fiber dysfunction in male patients with T2D, but not in female patients with T2D. In addition, in only the younger subgroup of male patients (<65 years), lower levels of UA also correlated with higher VPT values.
Computation of cause and effect relationship for acceptance of autonomous mobile robots in industries

Abstract Autonomous mobile robot (AMR) is a programmed machine capable of maneuvering through obstacle free path to reach destination thereby reducing human labor. Prerequisite for an AMR is to self-navigate through arduous and unknown waypoints in dynamic or real-time situations over a shortest path which demands software and hardware development and interaction, using abundant electronic and mechanical components. AMRs are necessary for industries working in 24-7 work schedule to alleviate user’s performance and curb wear and tear of components in industries. Several factors govern acceptance of AMR in industries based on user feedback. In this work, authors have analyzed computationally user preferences for AMR acceptance in industries, and demonstrated their cause and effect relationship using fuzzified DEMATEL and priority ranking through TOPSIS method which has not been done previously. The results provide novel and better understanding of user behavior and attitude towards AMRs. Findings, thus, provide implications for predicting user behavior and suggest directives to increase willingness of AMR usage.
Mechanical and Biological Advantages of a Tri-Oval Implant Design

Of all geometric shapes, a tri-oval one may be the strongest because of its capacity to bear large loads with neither rotation nor deformation. Here, we modified the external shape of a dental implant from circular to tri-oval, aiming to create a combination of high strain and low strain peri-implant environment that would ensure both primary implant stability and rapid osseointegration, respectively. Using in vivo mouse models, we tested the effects of this geometric alteration on implant survival and osseointegration over time. The maxima regions of tri-oval implants provided superior primary stability without increasing insertion torque. The minima regions of tri-oval implants presented low compressive strain and significantly less osteocyte apoptosis, which led to minimal bone resorption compared to the round implants. The rate of new bone accrual was also faster around the tri-oval implants. We further subjected both round and tri-oval implants to occlusal loading immediately after placement. In contrast to the round implants that exhibited a significant dip in stability that eventually led to their failure, the tri-oval implants maintained their stability throughout the osseointegration period. Collectively, these multiscale biomechanical analyses demonstrated the superior in vivo performance of the tri-oval implant design.
The Gaussian Process Autoregressive Regression Model (GPAR)

Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have limited representational power. We present the Gaussian Process Autoregressive Regression (GPAR) model, a scalable multi-output GP model that is able to capture nonlinear, possibly input-varying, dependencies between outputs in a simple and tractable way: the product rule is used to decompose the joint distribution over the outputs into a set of conditionals, each of which is modelled by a standard GP. GPAR's efficacy is demonstrated on a variety of synthetic and real-world problems, outperforming existing GP models and achieving state-of-the-art performance on established benchmarks.
The Effects of Using Representations in Elementary Mathematics: Meta-Analysis of Research

The current study provides a meta-analysis of global research on using representations to support the learning of mathematics in Pre-K through Grade 5. A total of 13 primary studies encompassing 1,941 subjects was analyzed. The weighted mean effect size for the 13 studies was reported to be ES = 0.53 (SE = 0.05). A 95% confidence interval around the overall mean – Clower = 0.42 and Cupper = 0.63 – proved its statistical significance and its relative precision. The calculated effect size signifies strong, robust support for the use of representations in PreK through Grade 5 mathematics classes and highlights the importance of providing students with opportunities to construct and explore transitioning between various forms of representations. Moderator analysis revealed differences among the effects due to a different type of representation, grade levels, and concepts taught. A synthesis of moderator effects allowed for a formulation of a general way of applying representations that produces maximum learning effects and that the teachers can adopt in their school practice. While the effect sizes provided a means of determining the most effective ways of applying representations, questions about how to develop students’ transitioning from one representation to another remain unsolved. A further discussion of the impact of the study findings beyond the boundaries of elementary mathematics classrooms follows.
Adaptive RBFNN Strategy for Fault Tolerant Control: Application to DSIM under Broken Rotor Bars Fault

—This paper presents a fault tolerant control (FTC) based on Radial Base Function Neural Network (RBFNN) using an adaptive control law for double star induction machine (DSIM) under broken rotor bars (BRB) fault in a squirrel-cage in order to improve its reliability and availability. The proposed FTC is designed to compensate for the default effect by maintaining acceptable performance in case of BRB. The sufficient condition for
Chance-Constrained Optimization in D2D-Based Vehicular Communication Network

In this paper, a novel scheme is proposed to optimize the effectiveness of the device-to-device (D2D) enabled vehicular communications (D2D-V), which are underlaid in cellular networks, where the uplink channel allocated to one cellular user (CU) is reused by the multiple D2D-V links. To develop a well-functioning D2D-V system, the sum rate of all D2D-V links is necessary to be maximized, and also the reliability of the co-channel CU to infrastructure has to be guaranteed with the CU interference constraint. Unlike the traditional statical D2D systems, the D2D-V system suffers with high mobility and channel uncertainty. Therefore, the CU interference constraint is formulated as a probabilistic function by the Bernstein approximation, the constraint attempts to address the mobile channel fluctuations. Besides, the tractable approximate constraint is reformulated as two separable structure, this reformulated constraint attempts to determine the near-optimal solutions more efficiently. Since the objective function with a logarithmic form is nonconvex, successive convex approximation is applied to transform the nonconvex problem into the convex problem. The dual decomposition is used to determine the near-optimal solutions. After the problem is reformulated, a distributed robust power control algorithm is proposed to perform the chance-constrained optimization. Numerical simulations are used to evaluate the performance of the proposed scheme. The simulation results show the impacts of system performance when applying the proposed scheme in vehicular environments with high mobility. The proposed optimization schemes are further verified by comparing with the existing methods.
Model-based Region of Interest Segmentation for Remote Photoplethysmography

Remote photoplethysmography (rPPG) is a non-contact technique for measuring vital physiological signs, such as heart rate (HR) and respiratory rate (RR). HR is a medical index which is widely used in health monitoring and emotion detection applications. Therefore, HR measurement with rPPG methods offers a convenient and non-invasive method for these applications. The selection of Region Of Interest (ROI) is a critical first step of many rPPG techniques to obtain reliable pulse signals. The ROI should contain as many skin pixels as possible with a minimum of non-skin pixels. Moreover, it has been shown that rPPG signal is not distributed homogeneously on skin. Some skin regions contain more rPPG signal than others, mainly for physiological reasons. In this paper, we propose to explicitly favor areas where the information is more predominant using a spatially weighted average of skin pixels based on a trained model. The proposed method has been compared to several state of the art ROI segmentation methods using a public database, namely the UBFC-RPPG dataset (Bobbia et al., 2017). We have shown that this modification in how the spatial averaging of the ROI pixels is calculated can significantly increase the final performance of heart rate estimate.
Novel Method to Generate and Optimize Reticulated Structures of a Non Convex Conception Domain

Reticulated structures are increasingly represented in industry. They are lighter than traditional structures which allows weight saving. So they can be the best choice when material gain offsets the production cost which is an optimization purpose. In this paper, we propose an algorithm to generate automatically a reticulated structure. We particularly study non-convex conception domains which can be decomposed into a finite number of convex sub-domains, specially polygons. This solution generates a structure with an optimum form based on an algorithm that works on convex design domains.
Algorithm for Point Cloud Compression based on Geometrical Features

As a common and important form, point cloud data exists in computer graphics, especially for 3D visualization. However, with the development of 3D scanning technology, huge data sets have become a main burden in the data processing of point clouds. Therefore, the technology of point cloud compressing is a key content in data pre-processing. This paper provides a new algorithm to compress the point cloud data set. The compressing algorithm can be carried out based on the feature of measure objects. In order to find the data feature, we firstly introduce a point cloud compressing model based on conicoid according to the measure objects. Secondly, for the comparison of the features between the model and the point cloud, we provide a shape operator and a contour operator based on the estimation of geometrical features. Then, according to the value of the shape operator and the contour operator, we provide a matching model. The compressing data algorithm can be created through the matching computation of geometrical features. At last, we use the experiment to prove the feasibility of compressing algorithm, and compare the result of the proposed algorithm and the result of other algorithms in terms of the running time and the compressing effect.