This article introduces a highly integrated passive wireless sensing system based on the extended electronic product code (EPC) Gen2 protocol, using sensor tags and a reader to support concurrent data streams from multiple sensors. Multiple subcarrier multiple access (MSMA) is used by assigning different subcarrier frequencies to the sensor tags for backscattering so that each tag has an independent channel for communication to achieve the simultaneous streaming of multisensor data. In order to improve the subcarrier frequency accuracy and further increase the number of communication channels, this article proposes a novel method of subcarrier frequency calibration based on the Gen2 protocol. This method enables a high-accuracy clock to be generated on-chip. At the same time, this article also proposes a method based on Gen2 protocol to start a group of tags and receive real-time data in parallel. The tag chip is fabricated in a$0.18{-}\mu \text{m}$CMOS process with a die size of$850\,\,\mu \text{m}\,\,\times 850\,\,\mu \text{m}$, and the reader prototype is built using software-defined radio equipment. The test results show that the sensing system proposed in this article can collect and recognize concurrent data streams from multiple sensor tags synchronously. The measured sensitivity of the sensor tag is −14.9 dBm with a 3-axis accelerating sensor, and the calibration accuracy of subcarrier frequency is better than${\pm }{\mathrm {0.385}}$kHz in the range of 40–640 kHz. With this accuracy, the tags can be allocated up to 78 subcarrier communication channels theoretically.

In this demonstration, we will showcase Myria, our novel cloud service for big data management and analytics designed to improve productivity. Myria's goal is for users to simply upload their data and for the system to help them be self-sufficient data science experts on their data -- self-serve analytics. Using a web browser, Myria users can upload data, author efficient queries to process and explore the data, and debug correctness and performance issues. Myria queries are executed on a scalable, parallel cluster that uses both state-of-the-art and novel methods for distributed query processing. Our interactive demonstration will guide visitors through an exploration of several key Myria features by interfacing with the live system to analyze big datasets over the web.

This paper is concerned with the problem of mining social emotions from text. Recently, with the fast development of web 2.0, more and more documents are assigned by social users with emotion labels such as happiness, sadness, and surprise. Such emotions can provide a new aspect for document categorization, and therefore help online users to select related documents based on their emotional preferences. Useful as it is, the ratio with manual emotion labels is still very tiny comparing to the huge amount of web/enterprise documents. In this paper, we aim to discover the connections between social emotions and affective terms and based on which predict the social emotion from text content automatically. More specifically, we propose a joint emotion-topic model by augmenting Latent Dirichlet Allocation with an additional layer for emotion modeling. It first generates a set of latent topics from emotions, followed by generating affective terms from each topic. Experimental results on an online news collection show that the proposed model can effectively identify meaningful latent topics for each emotion. Evaluation on emotion prediction further verifies the effectiveness of the proposed model.

Community discovery on large-scale linked document corpora has been a hot research topic for decades. There are two types of links. The first one, which we call d2d-link, indicates connectiveness among different documents, such as blog references and research paper citations. The other one, which we call u2u-link, represents co-occurrences or simultaneous participations of different users in one document and typically each document from u2u-link corpus has more than one user/author. Examples of u2u-link data covers email archives and research paper co-authorship networks. Community discovery in d2d-link data has achieved much success, while methods for that in u2u-link data either make no use of the textual content of the documents or make oversimplified assumptions about the users and the textual content. In this paper we propose a general approach of community discovery for u2u-link data, i.e., multiple user data, by placing topical variables on multiple authors' participations in documents. Experiments on a research proceeding co-authorship corpus and a New York Times news corpus show the effectiveness of our model.

This paper is concerned with community discovery in textual interaction graph, where the links between entities are indicated by textual documents. Specifically, we propose a Topical Link Model(TLM), which leverages Hierarchical Dirichlet Process(HDP) to introduce hidden topical variable of the links. Other than the use of links, TLM can look into the documents on the links in detail to recover sound communities. Moreover, TLM is a nonparametric model, which is able to learn the number of communities from the data. Extensive experiments on two real world corpora show TLM outperforms two state-of-the-art baseline models, which verify the effectiveness of TLM in determining the proper number of communities and generating sound communities.

This paper is concerned with the study of information retrieval (IR) on Accumulative Social Descriptions (ASDs). ASDs refer to Web texts that accumulated by many Web users describing certain Web resources, such as anchor texts, search logs and social annotations. There have been some studies working on leveraging ASDs for improving search performance in both internet and intranet. However, to the best of our knowledge, no prior study has concerned the specific generation features of ASDs, which are the focus point of this paper. Specifically, we consider the generation features from two perspectives, the generation processes and the generated distributions. Further, three probabilistic IR models are derived based on them. The three models are first demonstrated with one toy dataset and then empirically evaluated with two real datasets: an internet dataset consisting of 90,295 Web pages, with 25,845,818 social annotations crawled from Del.icio.us and 31,320,005 pieces of anchor texts crawled through Yahoo! API, and an intranet dataset consisting of 179,835 Web pages with 1,245,522 annotations dumped from the intranet tagging system in IBM, named as Dogear. Extensive experimental results show that the proposed methods, which fully leverage the generation features of ASDs, improve the performance of both internet and intranet search significantly.

This paper is concerned with the problem of social affective text mining, which aims to discover the connections between social emotions and affective terms based on user-generated emotion labels. We propose a joint emotion-topic model by augmenting latent Dirichlet allocation with an additional layer for emotion modeling. It first generates a set of latent topics from emotions, followed by generating affective terms from each topic. Experimental results on an online news collection show that the proposed model can effectively identify meaningful latent topics for each emotion. Evaluation on emotion prediction further verifies the effectiveness of the proposed model.

This paper is concerned with the problem of boosting social annotations using propagation, which is also called social propagation. In particular, we focus on propagating social annotations of web pages (e.g., annotations in Del.icio.us). Although social annotations are developing fast, they cover only a small proportion of Web pages on the World Wide Web. To alleviate the low coverage problem, a general propagation model based on Random Surfer is proposed. Specifically, four steps are included: basic propagation, multiple-annotation propagation, multiple-link-type propagation, and constraint-guided propagation. Experimental results show that the proposed model is very effective in increasing coverage of annotations as well as preserving property of social annotations.

The rapidly increasing popularity of community-based Question Answering (cQA) services, e.g. Yahoo! Answers, Baidu Zhidao, etc. have attracted great attention from both academia and industry. Besides the basic problems, like question searching and answer finding, it should be noted that the low participation rate of users in cQA service is the crucial problem which limits its development potential. In this paper, we focus on addressing this problem by recommending answer providers, in which a question is given as a query and a ranked list of users is returned according to the likelihood of answering the question. Based on the intuitive idea for recommendation, we try to introduce topic-level model to improve heuristic term-level methods, which are treated as the baselines. The proposed approach consists of two steps: (1) discovering latent topics in the content of questions and answers as well as latent interests of users to build user profiles; (2) recommending question answerers for new arrival questions based on latent topics and term-level model. Specifically, we develop a general generative model for questions and answers in cQA, which is then altered to obtain a novel computationally tractable Bayesian network model. Experiments are carried out on a real-world data crawled from Yahoo! Answers during Jun 12 2007 to Aug 04 2007, which consists of 118510 questions, 772962 answers and 150324 users. The experimental results reveal significant improvements over the baseline methods and validate the positive influence of topic-level information.

As a social service in Web 2.0, folksonomy provides the users the ability to save and organize their bookmarks online with "social annotations" or "tags". Social annotations are high quality descriptors of the web pages' topics as well as good indicators of web users' interests. We propose a personalized search framework to utilize folksonomy for personalized search. Specifically, three properties of folksonomy, namely the categorization, keyword, and structure property, are explored. In the framework, the rank of a web page is decided not only by the term matching between the query and the web page's content but also by the topic matching between the user's interests and the web page's topics. In the evaluation, we propose an automatic evaluation framework based on folksonomy data, which is able to help lighten the common high cost in personalized search evaluations. A series of experiments are conducted using two heterogeneous data sets, one crawled from Del.icio.us and the other from Dogear. Extensive experimental results show that our personalized search approach can significantly improve the search quality.

This poster is concerned with the problem of exploring the use of social annotations for improving language models for information retrieval (denoted as LMIR). Two properties of social annotations, namely keyword property and structure property are studied for this aim. The keyword property improves LMIR by concatenating all the annotations of a document to generate a summary of the document. The structure property can boost LMIR further when similarity among annotations and similarity among documents are taken into consideration simultaneously. The two properties of social annotations are leveraged for the use of language modeling with a mixture model named as "Language Annotation Model" (denoted as LAM). Evaluations using del.icio.us data show that LAM outperforms the traditional LMIR approaches significantly.