This article aims to explore the potential benefits of a virtual reality platform in cooperative learning. The study involved 48 freshman students in the table tennis class of Xinxiang Medical University. They were divided into control groups (M = 19.48, SD = 0.37) and experimental groups (M = 19.68, SD = 0.51). The Holofit platform and the model of cooperative learning principles served as an influence. The research utilized the Chinese Sport Motivation Scale-II (CSMS-II) and five physical fitness assessment tests as primary instruments. Following the intervention, a discernible reduction of 2.08 in mean scores within the amotivation subscale was observed in contrast to the pre-test. Conversely, positive shifts were noted in external regulation (1.88), introjected regulation (2.20), identified regulation (1.28), integrated regulation (1.48), and intrinsic motivation (1.96) subscales. Intragroup analyses, comparing pre-and post-test results, unveiled significant improvements in physical fitness indicators over six months for both the intervention and control groups. However, upon intergroup comparisons, it was discerned that the intervention failed to yield a statistically significant enhancement in physical performance in comparison to the traditional approach. Statistically significant distinctions were evident between the intervention and control groups across all CSMS-II subscales, signifying a demonstrable positive influence of cooperative learning and virtual reality on motivation for sports participation.

Improving individuals’ low-carbon awareness has become an important research topic. Gamification design is regarded as a potential IT solution to this social problem. Although much research is concerned with the effect of low-carbon awareness to improve individuals’ behaviour, little attention has been paid to exploring how to improve low-carbon awareness, especially in the context of green gamification. To fill this research gap, our study uses the cognitive dissonance theory to build a theoretical model for exploring the impacts of green gamification factors on individuals’ low-carbon awareness. The model is also used to explore the mediating role of cognitive dissonance and the moderating effect of emotional dependence. Empirical results reveal that visibility of achievement, competition, and interactivity have positive impacts on users’ cognitive dissonance, which further positively affect users’ low-carbon awareness. In addition, users’ emotional dependence positively moderates the relationship between green gamification factors and users’ cognitive dissonance. These results extend the cognitive dissonance theory and the literature on green gamification. New insights and practical implications for the designers of gamification application and environmental protection organisations are also provided in our study.

This paper investigates the problem of outlier-resistant distributed fusion filtering (DFF) for a class of multi-sensor nonlinear singular systems (MSNSSs) under a dynamic event-triggered scheme (DETS). To relieve the effect of measurement outliers in data transmission, a self-adaptive saturation function is used. Moreover, to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization, a DETS is adopted to regulate the frequency of data transmission. For the addressed MSNSSs, our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS; the local upper bound (UB) on the filtering error covariance (FEC) is derived by solving the difference equations and minimized by designing proper filter gains. Furthermore, according to the local filters and their UBs, a DFF algorithm is presented in terms of the inverse covariance intersection fusion rule. As such, the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers, thereby improving the estimation performance. Moreover, the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is presented. Finally, the validity of the developed algorithm is checked using a simulation example.

With the rapidly growing of location-based social networks, point-of-interest (POI) recommendation has been attracting tremendous attentions. Previous works for POI recommendation usually use matrix factorization (MF)-based methods, which achieve promising performance. However, existing MF-based methods suffer from two critical limitations: (1) Privacy issues: all users’ sensitive data are collected to the centralized server which may leak on either the server side or during transmission. (2) Poor resource utilization and training efficiency: training on centralized server with potentially huge low-rank matrices is computational inefficient. In this paper, we propose a novel decentralized gradient-quantization based matrix factorization (DGMF) framework to address the above limitations in POI recommendation. Compared with the centralized MF methods which store all sensitive data and low-rank matrices during model training, DGMF treats each user’s device (e.g., phone) as an independent learner and keeps the sensitive data on each user’s end. Furthermore, a privacy-preserving and communication-efficient mechanism with gradient-quantization technique is presented to train the proposed model, which aims to handle the privacy problem and reduces the communication cost in the decentralized setting. Theoretical guarantees of the proposed algorithm and experimental studies on real-world datasets demonstrate the effectiveness of the proposed algorithm.

Sparse principal component analysis (SPCA) produces principal components with sparse loadings, which is very important for handling data with many irrelevant features and also critical to interpret the results. To deal with orthogonal constraints, most previous approaches address SPCA with several components using techniques such as deflation technique and convex relaxations. However, the deflation technique usually suffers from suboptimal solutions due to poor approximations. On the other hand, the convex relaxations are often computationally expensive. To address the above issues, in this paper, we propose to address SPCA over the Stiefel manifold directly, and develop a stabilized Alternating Direction Method of Multipliers (SADMM) to handle the nonconvex orthogonal constraints. Compared to traditional ADMM, the proposed SADMM method converges well with a wide range of parameters and obtains a better solution. We also theoretically study the convergence property of the proposed SADMM method. Furthermore, most existing methods ignore an inherent drawback of SPCA - the importance of different components is not considered when doing feature selection, which often makes the selected features nonoptimal. To address this, we further propose a two-stage method which considers the importance of different components to select the most important features. Empirical studies on both synthetic and real-world datasets show that the proposed algorithms achieve better performance compared to existing state-of-the-art methods.

Image-text matching is central to visual-semantic cross-modal retrieval and has been attracting extensive attention recently. Previous studies have been devoted to finding the latent correspondence between image regions and words, e.g., connecting key words to specific regions of salient objects. However, existing methods are usually committed to handle concrete objects, rather than abstract ones, e.g., a description of some action, which in fact are also ubiquitous in description texts of real-world. The main challenge in dealing with abstract objects is that there is no explicit connections between them, unlike their concrete counterparts. One therefore has to alternatively find the implicit and intrinsic connections between them. In this paper, we propose a relation-wise dual attention network (RDAN) for image-text matching. Specifically, we maintain an over-complete set that contains pairs of regions and words. Then built upon this set, we encode the local correlations and the global dependencies between regions and words by training a visual-semantic network. Then a dual pathway attention network is presented to infer the visual-semantic alignments and image-text similarity. Extensive experiments validate the efficacy of our method, by achieving the state-of-the-art performance on several public benchmark datasets.

BACKGROUND: Evidence suggests that common complex diseases may be partially due to SNP-SNP interactions, but such detection is yet to be fully established in a high-dimensional small-sample (small-n-large-p) study. A number of penalized regression techniques are gaining popularity within the statistical community, and are now being applied to detect interactions. These techniques tend to be over-fitting, and are prone to false positives. The recently developed stability least absolute shrinkage and selection operator (SLASSO) has been used to control family-wise error rate, but often at the expense of power (and thus false negative results). RESULTS: Here, we propose an alternative stability selection procedure known as stability smoothly clipped absolute deviation (SSCAD). Briefly, this method applies a smoothly clipped absolute deviation (SCAD) algorithm to multiple sub-samples, and then identifies cluster ensemble of interactions across the sub-samples. The proposed method was compared with SLASSO and two kinds of traditional penalized methods by intensive simulation. The simulation revealed higher power and lower false discovery rate (FDR) with SSCAD. An analysis using the new method on the previously published GWAS of lung cancer confirmed all significant interactions identified with SLASSO, and identified two additional interactions not reported with SLASSO analysis. CONCLUSIONS: Based on the results obtained in this study, SSCAD presents to be a powerful procedure for the detection of SNP-SNP interactions in large-scale genomic data.