表象训练对高校足球运动员比赛中注意品质的影响研究

依据表象训练法的理论，在比赛前的运动员队伍尤其是双方实力相当的情况下，心理因素的影响占据了大部分，采用文献资料法、数理统计法、逻辑分析法等研究方法，证明表象训练对比赛当中注意力的品质具有积极地促进作用，进而促进比赛成绩。     

经济法视野下政府购买公共服务的激励分析

政府购买公共服务内含激励的模式,在政府市场化购买、社会组织的培育和契约化合作方面存有不同激励面,与经济法的理念相契合。实践层面的检视,可厘定政府购买公共服务的法律程式,而制度层面的反思,又指出当前制度设计的激励缺位。于是,激励性立法的制度设计颇为必要,宜架构合理的法权结构,并明晰具体制度设计的留意点。     

HGNN: Hyperedge-based graph neural network for MOOC Course Recommendation

Previous studies on Course Recommendation (CR) mainly focus on investigating the sequential relationships among courses (RNN is applied) and fail to learn the similarity relationships among learners. Moreover, existing RNN-based methods can only model courses’ short-term sequential patterns due to the inherent shortcomings of RNNs. In light of the above issues, we develop a hyperedge-based graph neural network, namely HGNN, for CR. Specifically, (1) to model the relationships among learners, we treat learners (i.e., hyperedges) as the sets of courses in a hypergraph, and convert the task of learning learners’ representations to induce the embeddings for hyperedges, where a hyperedge-based graph attention network is further proposed. (2) To simultaneously consider courses’ long-term and short-term sequential relationships, we first construct a course sequential graph across learners, and learn courses’ representations via a modified graph attention network. Then, we feed the learned representations into a GRU-based sequence encoder to infer their short-term patterns, and deem the last hidden state as the learned sequence-level learner embedding. After that, we obtain the learners’ final representations by a product pooling operation to retain features from different latent spaces, and optimize a cross-entropy loss to make recommendations. To evaluate our proposed solution HGNN, we conduct extensive experiments on two real-world datasets, XuetangX and MovieLens. We conduct experiments on MovieLens to prove the extensibility of our solution on other collections. From the experimental results, we can find that HGNN evidently outperforms other recent CR methods on both datasets, achieving 11.96% on P@20, 16.01% on NDCG@20, and 27.62% on MRR@20 on XuetangX, demonstrating the effectiveness of studying CR in a hypergraph, and the importance of considering both long-term and short-term sequential patterns of courses.     

Where have you been: Dual spatiotemporal-aware user mobility modeling for missing check-in POI identification

The prevalence of Location-based Social Networks (LBSNs) services makes next personalized Point-of-Interest (POI) predictions become a trending research topic. However, due to device failure or intention camouflage, geolocation information missing prevents existing POI-oriented researches for advanced user preference analysis. To this end, we propose a novel model named Bi-STAN, which fuses bi-direction spatiotemporal transition patterns and personalized dynamic preferences, to identify where the user has visited at a past specific time, namely missing check-in POI identification. Furthermore, to relieve data sparsity issues, Bi-STAN explicitly exploits spatiotemporal characteristics by doing bilateral traceback to search related items with high predictive power from user mobility traces. Specifically, Bi-STAN introduces (1) a temporal-aware attention semantic category encoder to unveil the latent semantic category transition patterns by modeling temporal periodicity and attenuation; (2) a spatial-aware attention POI encoder to capture the latent POI transition pattern by modeling spatial regularity and proximity; (3) a multitask-oriented decoder to incorporate personalized and temporal variance preference into learned transition patterns for missing check-in POI and category identification. Based on the complementarity and compatibility of multi-task learning, we further develop Bi-STAN with a self-adaptive learning rate for model optimization. Experimental results on two real-world datasets show the effectiveness of our proposed method. Significantly, Bi-STAN can also be adaptively applied to next POI prediction task with outstanding performances.     

微博用户关注兴趣的社会网络分析

以新浪微博为研究平台,随机抽取部分用户及其关注对象数据作为研究样本,通过共链关系建立社会网络结构,利用社会网络分析并辅以聚类分析方法对该样本进行整体网络分析、内部子结构分析和角色位置分析,进而从微博关注数据中挖掘用户关注对象的分布及对象间的关联性,并对改进目前的微博关注推荐提出一些建议。     

论体育教学中如何调节学生的注意力

体育教学由于其条件的特殊性,易受外界条件的干扰。要提高教学质量,达到增进学生体质的目的,就必须排除干扰提高学生的注意力。因此,教师要充分认识和利用人的注意规律,合理地运用各种教学手段,集中学生的注意力,激发其学习兴趣,从而达到提高教学效果的目的。     

Association of maternal developmental disorder traits with child mistreatment: A prospective study in Japan

Maternal mental disorders are known risk factors for child mistreatment. However, little is known about the involvement of maternal developmental disorder traits. The aim of this study was to examine maternal traits related to Pervasive Developmental Disorder (PDD) and Attention Deficit Hyperactivity Disorder (ADHD), and their possible association with child mistreatment. Maternal PDD and ADHD were assessed through a self-administered questionnaire (N = 846) during mid-pregnancy using the Pervasive Developmental Disorders Autism Society Japan Rating Scale (PARS) and Adult ADHD Self-Report Scale (ASRS). The mothers completed another questionnaire on child mistreatment when the offspring was approximately 18 months of age. The associations between maternal PDD and ADHD traits and child mistreatment score were analyzed using linear regression models adjusted for covariates. Mothers who exhibited stronger PDD traits showed significantly higher child mistreatment score, even after adjustment for maternal characteristics at baseline and ADHD traits. At the same time, ADHD traits were significantly associated with child mistreatment after adjustment of covariates, although the association became non-significant after adjustment of PDD traits. Mothers who showed PDD and ADHD traits during pregnancy were more likely to mistreat their children. It is essential to educate mothers with such traits with appropriate, easy-to-follow childcare instructions, preferably in simple language combined with pictorial aids.     

不同级别青少年拳击运动员注意网络的区别

目的:基于注意网络测试范式探讨不同级别青少年拳击运动员各注意子网络的差异。方法:选取黑龙江体工队拳击健将运动员十人,吉林体育学院二级运动员十人,对这二十名拳击运动员进行注意网络测试,在进行行为学测试的同时运用功能性近红外光谱技术监测运动员各脑区的血氧变化情况。结果:行为学结果显示健将运动员与二级运动员注意网络中警觉网络和执行控制网络效应具有显著性差异(P<0.05);影像学结果表示在警觉网络中,两组运动员在rIPL处血氧激活量具有显著性(P<0.05);在定向网络中血氧激活量在rIGF处差异显著(P<0.05);执行控制网络中血氧激活量在rIPL(P<0.01)和rDLPFC(P<0.05)处差异显著。结论:拳击运动员的级别越高,警觉网络效应和执行控制网络效应越强;背外侧前额叶激活区域越大,运动员的执行控制功能越强。     

Rhetoric and Materiality in the Museum Park at the North Carolina Museum of Art

The material rhetoric of physical locations like the Museum Park at the North Carolina Museum of Art creates “spaces of attention” wherein visitors are invited to experience the landscape around them as a series of enactments that identify the inside/outside components of sub/urban existence, as well as the regenerative/transformative possibilities of such existence. Such rhetorical enactments create innovative opportunities for individuals to attend to the human/nature interface. These rhetorical enactments also create and contain tensions that come to the fore when they are employed as authentic mediations of nature, when they function as tropes to promote development of natural space, and/or when they are translated into discursive environmental argument.