大学生是志愿服务活动的主要参与者,高校新生作为大学生群体的重要组成部分,探究其志愿精神现状存在的问题并提出培育对策具有重要的现实意义.采用量化研究和质性研究相结合的混合研究方法对高校新生的志愿精神现状进行调查研究,结果发现,高校新生的志愿精神在认知、情感方面表现良好,但行为倾向性缺乏.需要提升家庭教育功能、打造特色家庭志愿模式,促进思想政治工作与心理健康教育协同发展,从学生认知、情感、行为三个方面对其志愿精神进行引导和干预.为促进高校新生志愿精神培育工作的开展,必须坚持社会主义核心价值观教育,形成系统的志愿文化和崇德向善的社会价值导向,推动高校新生志愿精神培育模式的创新.

Deep learning technologies have been reshaping the research of image dehazing in recent years with superior performance. Due to the difficulty to obtain the real hazy and corresponding clear image pairs in the wild, most of the existed deep learning based methods utilize synthetic datasets for model training. As a result, the performance and robustness of those methods are compromised under the real-world complex scenarios. In this paper, we propose a novel image dehazing method for unpaired data via cycle-consistent adversarial networks with a multi-scale hybrid encoder-decoder and global correlation loss. The requirement of paired training data is eliminated by combining two generators and discriminators into a cycle-consistent adversarial network. Moreover, to further improve the feature representation capability of the network for degraded images, a multi-scale hybrid encoder-decoder structure is introduced into the generators and multiple residual and dense blocks are constructed. Furthermore, to preserve more details of color and structure in generated dehazed images, a global correlation loss function is proposed. The task-specific haze-line prior is reformulated in the form of color loss constraint and incorporates with adversarial loss, cycle consistency loss, identity mapping loss, and perceptual consistency loss into a unified framework. Comprehensive qualitative and quantitative experiments on both synthetic and real-world datasets demonstrate the favourable dehazing results of the proposed method compared with a number of state-of-the-art methods. The code will be made available on Github.

Studying in universities is a crucial development stage for students, whose thoughts, feelings, and actions are affected by interactions with their teachers and peers. This study explored the relationships between perceived teacher support and mental health as well as those between peer relationship and mental health among university students, and examined the mediating effects of reality and Internet altruistic behaviors on these relationships. Perceived teacher support questionnaire, peer relationship satisfaction questionnaire, self-reported altruism questionnaire, Internet altruistic behavior questionnaire, and general health questionnaire were administered to 553 university students. Results demonstrated that perceived teacher support and peer relationship positively predicted reality and Internet altruistic behaviors and positively predicted mental health. Reality and Internet altruistic behaviors positively predicted mental health and exerted significant mediating effects on the correlations between perceived teacher support and mental health as well as those between peer relationship and mental health. The male and female students differed insignificantly in the mediating effects of reality and Internet altruistic behaviors. Therefore, no matter for males or females, teachers should provide sufficient support for the students and establish favorable relationships with them. Friendly relationships, comfort, and active communication among peer students are also essential for creating a healthy and harmonious interaction environment. Those various factors of the school have impacts on the mental health of university students through their altruistic behaviors. This study suggests that further emphasis on teacher support and peer relationship is needed to promote the positive development of altruistic behaviors among university students, and ultimately provide a viable contribution to the university students' mental health interventions.

BACKGROUND:According to the ecological systems theory, the microsystems are important during the development process because they have direct effects on immediate and proximal factors that shape human development. The theory identifies the family as a microsystem that has profound influence on development since it is the immediate environment in which individuals live. This study explored the multiple mediation effect of perfectionism and altruistic behavior on the association between perceived parenting styles and mental health.METHODS:In this cross-sectional study, convenience cluster sampling was used, and the purpose was empirically examined by means of an online questionnaire survey. This study adopted the Demographic Questionnaire, short-form Egna Minnen Beträffande Uppfostran, Chinese Frost Multidimensional Perfectionism Scale, Self-Reported Altruism Scale, and General Health Questionnaire to conduct measurements in 525 university students.RESULTS:The results of the correlation analysis revealed that perceived parenting styles were significantly correlated to perfectionism, altruistic behavior, and mental health. In addition, perfectionism and altruistic behavior were significantly correlated to mental health, while negative perfectionism was not correlated to altruistic behavior. The results of the structural equation model analysis indicated that parental rejection and emotional warmth had direct and significant effects on children's mental health. Positive perfectionism and altruistic behavior not only played partial mediating roles between parental emotional warmth and children's mental health but also exerted a chain multiple mediation effect. Altruistic behavior played a partial mediating role between positive perfectionism and mental health.CONCLUSION:Therefore, parents should practice positive parenting styles such as parental emotional warmth toward their children to ensure that positive perfectionism and altruistic behavioral tendency improve mental health.

We linked self-determination theory and prosociality, and explored the mediating role of three dimensions of basic psychological needs satisfaction, namely, competence, autonomy, and relatedness, in the relationship between prosocial tendencies and subjective well-being. We explored these relationships using a cross-sectional research design with 1,106 Chinese adults. Results show that the public prosocial tendencies of men (vs. women) were higher, and competence, autonomy, and relatedness mediated the positive relationship between prosocial tendencies and subjective well-being. The indirect effect of relatedness was stronger than those of competence and autonomy, demonstrating the importance of relatedness in a collectivistic society like China. Our findings deepen understanding of the underlying mechanisms between prosociality and subjective well-being as mediated by basic psychological needs satisfaction, and may encourage people to engage in prosocial behavior.

Social e-commerce is an extension of the hidden economy to the digital realm. Through the convenience of social media platforms as a means of communication, content sharing and even payment between different users, black market transactions are now enabled by e-commerce. Tax authorities worldwide have voiced their concern over the difficulty to detect such transactions over the internet. This paper presents a machine learning based Regtech tool for international tax authorities to detect transaction-based tax evasion activities across social e-commerce. To build such a tool, we collected a dataset of 58,660 Instagram posts and manually labelled 2,041 sampled posts with multiple properties related to transaction-based tax evasion activities. Based on the dataset, we developed a multimodal deep neural network to automatically detect suspicious posts. The proposed model combines comments, hashtags and image modalities (including extracted from videos) to produce the final output. As shown by our experiments, the complementary combined model achieved sensitivity of 71.9 %, specificity of 87.5%, accuracy of 84.1 % and AUC of 0.837, outperforming any single modality models. This tool could help tax authorities to identify audit targets in an efficient and effective manner, and combat social e-commerce tax evasion in scale.

目的:探讨诱发情绪时情绪反应对状态共情与助人行为的影响,以及状态共情在诱发情绪时情绪反应与助人行为之间的中介作用.方法:通过视频诱发情绪,采用情绪自评问卷、共情反应量表、同伴评定量表和助人行为题目对98名大学生进行测量.结果:①通过视频诱发情绪有效,观看正性电影的个体其积极情绪反应显著高于观看负性电影的个体(F=24.17,P＜0.001),其负性情绪反应显著低于观看负性电影的个体(F=93.36,P＜0.001);②诱发情绪时情绪反应得分与状态共情呈显著正相关(P＜0.001),与助人时间呈边缘显著负相关(P＜0.08),与助人金钱相关不显著;③诱发情绪时情绪反应正向预测状态共情(β=10.80,P＜0.001),负向预测助人时间(β=-0.64,P＜0.05).状态共情在诱发情绪时情绪反应与助人行为之间的中介作用不显著.结论:个体消极情绪水平越高,越容易引起共情;个体积极情绪水平越高,越愿意花费时间帮助他人.状态共情在诱发情绪时情绪反应与助人行为之间不起中介作用.

目的:探讨集体主义对助人行为和活力的影响,以及基本心理需要满足在集体主义与助人行为和活力之间的中介作用.方法:采用自我构念量表、基本心理需要量表、主观活力量表和助人行为测量题目对432名大学生进行测查.结果:①相关分析显示,集体主义与基本心理需要满足和助人意愿、助人时间、助人金钱及活力呈显著正相关(r=0.28,0.17,0.18,0.12,0.19;P＜0.01),基本心理需要满足与助人意愿、助人时间、助人金钱及活力呈显著正相关(r=0.12,0.19,0.09,0.44;P＜0.05);②结构方程模型分析表明,集体主义可以显著正向预测基本心理需要满足(β=0.33,P＜0.001)和助人意愿、助人时间及助人金钱(β=0.14,P＜0.01;β=0.12,P＜0.05;β=0.09,P＜0.05),但不能显著预测活力(β=0.02,P＞0.05).基本心理需要满足可以显著正向预测助人时间和活力(β=0.18,P＜0.01 ;β=0.52,P＜0.001);③基本心理需要满足在集体主义与助人时间和活力之间的中介作用显著(Effect=0.11,SE=0.04,95％ CI=[0.04,0.19];Effect=0.22,SE=0.04,CI=[0.15,0.30]).结论:集体主义既可以直接预测助人意愿、助人时间、助人金钱及活力,还可以通过基本心理需要满足间接预测助人时间和活力.

In this chapter we use three concrete case studies in the areas of water utility infrastructure maintenance, smart parking and urban planning to demonstrate the power of cross-domain open urban data and its synergy with organisation-owned private data for supporting efficient urbanisation. On the one hand, we see the significant value of open urban data in the studies. On the other hand, there are still obstacles preventing organisations to make their own datasets open for public usage. We conclude the chapter with a discussion on the difficulties in making organisation-owned data open and the potential solutions to tackle them.

Social media platforms now serve billions of users by providing convenient means of communication, content sharing and even payment between different users. Due to such convenient and anarchic nature, they have also been used rampantly to promote and conduct business activities between unregistered market participants without paying taxes. Tax authorities worldwide face difficulties in regulating these hidden economy activities by traditional regulatory means. This paper presents a machine learning based Regtech tool for international tax authorities to detect transaction-based tax evasion activities on social media platforms. To build such a tool, we collected a dataset of 58,660 Instagram posts and manually labelled 2,081 sampled posts with multiple properties related to transaction-based tax evasion activities. Based on the dataset, we developed a multi-modal deep neural network to automatically detect suspicious posts. The proposed model combines comments, hashtags and image modalities to produce the final output. As shown by our experiments, the combined model achieved an AUC of 0.808 and F1 score of 0.762, outperforming any single modality models. This tool could help tax authorities to identify audit targets in an efficient and effective manner, and combat social e-commerce tax evasion in scale.

Retrieving videos with similar actions is an important task with many applications. Yet it is very challenging due to large variations across different videos. While the state-of-the-art approaches generally utilize the bag-of-visual-words representation with the dense trajectory feature, the spatial-temporal context among trajectories is overlooked. In this paper, we propose to incorporate such information into the descriptor coding and trajectory matching stages of the retrieval pipeline. Specifically, to capture the spatial-temporal correlations among trajectories, we develop a descriptor coding method based on the correlation between spatial-temporal and feature aspects of individual trajectories. To deal with the mis-alignments between dense trajectory segments, we develop an offset-aware distance measure for improved trajectory matching. Our comprehensive experimental results on two popular datasets indicate that the proposed method improves the performance of action video retrieval, especially on more dynamic actions with significant movements and cluttered backgrounds.

Drinking water pipe and waste water pipe networks are valuable urban infrastructure assets that are responsible for reliable water resource distributions and waste water collection. However, due to fast growing demand and aging assets, water utilities find it increasingly difficult to efficiently maintain their pipe networks. Pipe failures - drinking water pipe breaks and waste water pipe blockages - can cause significant economic and social costs, and hence have become the primary challenge to water utilities. Identifying key influential factors, e.g., pipes' physical attributes, environmental features, is critical for understanding pipe failure behaviours. The domain knowledge plays a significant role in this aspect. In this work, we propose a Bayesian nonparametric machine learning model with the support of domain knowledge for pipe failure prediction. It can forecast future high-risk pipes for physical condition assessment, thereby proactively preventing disastrous failures. Moreover, compared with traditional machine learning approaches, the proposed model considers domain expert knowledge and experience, which helps avoid the limit of traditional machine learning approaches - learning only from what it sees - and improves prediction performance.

Background: Omnipresent marketing of processed foods is a key driver of dietary choices and brand loyalty. Market data indicate a shift in food marketing expenditures to digital media, including social media. These platforms have greater potential to influence young people, given their unique peer-to-peer transmission and youths' susceptibility to social pressures. Objective: The aim of this study was to investigate the frequency of images and videos posted by the most popular, energy-dense, nutrient-poor food and beverage brands on Instagram and the marketing strategies used in these images, including any healthy choice claims. Methods: A content analysis of 15 accounts was conducted, using 12 months of Instagram posts from March 15, 2015, to March 15, 2016. A pre-established hierarchical coding guide was used to identify the primary marketing strategy of each post. Results: Each brand used 6 to 11 different marketing strategies in their Instagram accounts; however, they often adhered to an overall theme such as athleticism or relatable consumers. There was a high level of branding, although not necessarily product information on all accounts, and there were very few health claims. Conclusions: Brands are using social media platforms such as Instagram to market their products to a growing number of consumers, using a high frequency of targeted and curated posts that manipulate consumer emotions rather than present information about their products. Policy action is needed that better reflects the current media environment. Public health bodies also need to engage with emerging media platforms and develop compelling social counter-marketing campaigns.

Urbanization is a global trend that we have all witnessed in the past decades. It brings us both opportunities and challenges. On the one hand, urban system is one of the most sophisticated social-economic systems that is responsible for efficiently providing supplies meeting the demand of residents in various of domains, e.g., dwelling, education, entertainment, healthcare, etc. On the other hand, significant diversity and inequality exist in the development patterns of urban systems, which makes urban data analysis difficult. Different urban regions often exhibit diverse urbanization patterns and provide distinct urban functions, e.g., commercial and residential areas offer significantly different urban functions. It is desired to develop the data analytic capabilities for discovering the underlying cross-domain urbanization patterns, clustering urban regions based on their function similarity and predicting region popularity in specified domains. Previous studies in the urban data analysis area often just focus on individual domains and rarely consider cross-domain urban development patterns hidden in different urban regions. In this paper, we propose the infinite urbanization process (IUP) model for simultaneous urban region function discovery and region popularity prediction. The IUP model is a generative Bayesian nonparametric process that is capable of describing a potentially infinite number of urbanization patterns. It is developed within the supervised topic modelling framework and is supported by a novel hierarchical spatial distance dependent Bayesian nonparametric prior over the spatial region partition space. The empirical study conducted on the real-world datasets shows promising outcome compared with the state-of-the-art techniques.

Peer-to-peer networking offers a scalable solution for sharing multimedia data across the network.With a large amount of visual data distributed among different nodes, it is an important but challenging issue to perform content-based retrieval in peer-to-peer networks.While most of the existing methods focus on indexing high dimensional visual features and have limitations of scalability, in this paper we propose a scalable approach for content-based image retrieval in peer-to-peer networks by employing the bag-of-visual-words model.Compared with centralized environments, the key challenge is to efficiently obtain a global codebook, as images are distributed across the whole peer-to-peer network.In addition, a peer-to-peer network often evolves dynamically, which makes a static codebook less effective for retrieval tasks.Therefore, we propose a dynamic codebook updating method by optimizing the mutual information between the resultant codebook and relevance information, and the workload balance among nodes that manage different codewords.In order to further improve retrieval performance and reduce network cost, indexing pruning techniques are developed.Our comprehensive experimental results indicate that the proposed approach is scalable in evolving and distributed peer-to-peer networks, while achieving improved retrieval accuracy.

The bag-of-visual-words model has been widely utilized for content based image and video retrieval due to its scalability. In this paper, we extend this model for human action video retrieval. We adopt dense trajectory features which are able to achieve the state-of-the-art performance on action recognition, while most of the existing video retrieval methods utilize descriptors of local interest points. In order to improve similarity measurement between bag-of-visual-words model based representation, we propose to discover and incorporate spatial-temporal correlation (STC) among the trajectories in a given query video. The spatial-temporal correlation consists of spatial proximity and temporal consistence among trajectories, which is capable of strengthening discriminative power among visual words. Note that such query focused spatial-temporal correlation makes our method dynamic for different queries and is able to improve retrieval performance without significantly increasing the size of a visual vocabulary. The experimental results on an action video dataset demonstrate that our proposed method outperforms other similar methods.

Existing 3D modeling tools were designed to run on desktop computers with monitor, keyboard and mouse. To make 3D modeling possible with mouse and keyboard, many 3D interactions, such as point placement or translations of geometry, had to be mapped to the 2D parameter space of the mouse, possibly supported by mouse buttons or keyboard keys. We hypothesize that had the designers of these existing systems had been able to assume immersive virtual reality systems as their target platforms, they would have been able to design 3D interactions much more intuitively. In collaboration with professional architects, we created a simple, but complete 3D modeling tool for virtual environments from the ground up and use direct 3D interaction wherever possible and adequate. In this publication, we present our approaches for interactions for typical 3D modeling functions, such as geometry creation, modification of existing geometry, and assignment of surface materials. We also discuss preliminary user experiences with this system.

CaveCAD is our in-house developed 3D modeling tool, which runs in immersive virtual reality environments, such as CAVEs. We built it from the ground up, in collaboration with architects, to explore how immersive 3D interaction systems can support 3D modeling tasks. CaveCAD offers typical 3D modeling functions, such as geometry creation, modification of existing geometry, assignment of surface materials and textures, the use of libraries of 3D components, geographical placement functions, and shadows. CaveCAD goes beyond traditional 3D modeling tools by utilizing direct 3D interaction methods. We evaluated our modeling system by running a small pilot study with four participants: two novice users and two expert users were tasked to build Disney World's magic castle.

The multi-view/multi-modal features are commonly used in neuroimaging classification because they could provide complementary information to each other and thus result in better classification performance than single-view features. However, it is very challenging to effectively integrate such rich features, since straightforward concatenation or singleview spectral embedding methods rarely leads to physically meaningful integration. In this paper, we present a supervised multi-view/multi-modal spectral embedding method (SMSE) for neuroimaging classification. This method embeds the high dimensional multi-view features derived from multi-modal neuroimaging data into a low dimensional feature space and preserves the optimal local embeddings among different views. The proposed SMSE algorithm, validated using three groups of neuroimaging data, is able to achieve significant classification improvement over the state-of-the-art multi-view spectral embedding methods.