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It is of significant importance to detect and manage stress before it turns into severe problems. However, existing stress detection methods usually rely on psychological scales or physiological devices, making the detection complicated and costly. In this paper, we explore to automatically detect individuals' psychological stress via social media. Employing real online micro-blog data, we first investigate the correlations between users' stress and their tweeting content, social engagement and behavior patterns. Then we define two types of stress-related attributes: 1) low-level content attributes from a single tweet, including text, images and social interactions; 2) user-scope statistical attributes through their weekly micro-blog postings, leveraging information of tweeting time, tweeting types and linguistic styles. To combine content attributes with statistical attributes, we further design a convolutional neural network (CNN) with cross autoencoders to generate user-scope content attributes from low-level content attributes. Finally, we propose a deep neural network (DNN) model to incorporate the two types of userscope attributes to detect users' psychological stress. We test the trained model on four different datasets from major micro-blog platforms including Sina Weibo, Tencent Weibo and Twitter. Experimental results show that the proposed model is effective and efficient on detecting psychological stress from micro-blog data. We believe our model would be useful in developing stress detection tools for mental health agencies and individuals.

Most of the previous studies on mining association rules are on mining intratransaction associations, i.e., the associations among items within the same transaction where the notion of the transaction could be the items bought by the same customer, the events happened on the same day, etc. In this study, we break the barrier of transactions and extend the scope of mining association rules from traditional single-dimensional, intratransaction associations to multidimensional, intertransaction associations. An intertransaction association describes the association relationships among different transactions. In a database of stock price information, an example of such an association is "if (company) A's stock goes up on day one, B's stock will go down on day two but go up on day four." In this case, no matter whether we treat company or day as the unit of transaction, the associated items belong to different transactions. Moreover, such an intertransaction association can be extended to associate multiple properties in the same rule, so that multidimensional intertransaction associations can also be defined and discovered. Mining intertransaction associations pose more challenges on efficient processing than mining intratransaction associations because the number of potential association rules becomes extremely large after the boundary of transactions is broken. In this study, we introduce the notion of intertransaction association rule, define its measurements: support and confidence, and develop an efficient algorithm, FITI (an acronym for "First Intra Then Inter"), for mining intertransaction associations, which adopts two major ideas: 1) an intertransaction frequent itemset contains only the frequent itemsets of its corresponding intratransaction counterpart; and 2) a special data structure is built among intratransaction frequent itemsets for efficient mining of intertransaction frequent itemsets. We compare FITI with EH-Apriori, the best algorithm in our previous proposal, and demonstrate a substantial performance gain of FITI over EH-Apriori. Further extensions of the method and its implications are also discussed in the paper.

The eXtensible Markup Language (XML) is fast emerging as the dominant standard for describing and interchanging data among various systems and databases on the Internet. It offers the Document Type Definition (DTD) as a formalism for defining the syntax and structure of XML documents. The XML Schema definition language, as a replacement for the DTD, provides more rich facilities for defining and constraining the content of XML documents. However, it does not concentrate on the semantics that underlies these documents, representing a logical data model rather than a conceptual model. To enable efficient business application development in large-scale electronic commerce environments, it is necessary to describe and model real-world data semantics and their complex interrelationships. In this article, we describe a design methodology for XML documents. The aim is to enforce XML conceptual modeling power and bridge the gap between software development and XML document structures. The proposed methodology is comprised of two design levels: the semantic level and the schema level. The first level is based on a semantic network, which provides semantic modeling of XML through four major components: a set of atomic and complex nodes, representing real-world objects; a set of directed edges, representing semantic relationships between the objects; a set of labels denoting different types of semantic relationships, including aggregation, generalization, association, and of-property relationships; and finally a set of constraints defined over nodes and edges to constrain semantic relationships and object domains. The other level of the proposed methodology is concerned with detailed XML schema design, including element/attribute declarations and simple/complex type definitions. The mapping between the two design levels is proposed to transform the XML semantic model into the XML Schema, based on which XML documents can be systematically created, managed, and validated. • 391 Additional

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Long-term stress may lead to many severe physical and mental problems. Traditional psychological stress detection usually relies on the active individual participation, which makes the detection labor-consuming, time-costing and hysteretic. With the rapid development of social networks, people become more and more willing to share moods via microblog platforms. In this paper, we propose an automatic stress detection method from cross-media microblog data. We construct a three-level framework to formulate the problem. We first obtain a set of low-level features from the tweets. Then we define and extract middle-level representations based on psychological and art theories: linguistic attributes from tweets' texts, visual attributes from tweets' images, and social attributes from tweets' comments, retweets and favorites. Finally, a Deep Sparse Neural Network is designed to learn the stress categories incorporating the cross-media attributes. Experiment results show that the proposed method is effective and efficient on detecting psychological stress from microblog data.