Chunming Hu

Enhancing reliability to boost the throughput over screen-camera links

With the rapid proliferation of camera-equipped smart devices (e.g., smartphones, pads, tablets), visible light communication (VLC) over screen-camera links emerges as a novel form of near-field communication. Such communication via smart devices is highly competitive for its user-friendliness, security, and infrastructure-less (i.e., no dependency on WiFi or cellular infrastructure). However, existing approaches mostly focus on improving the transmission speed and ignore the transmission reliability. Considering the interplay between the transmission speed and reliability towards effective end-to-end communication, in this paper, we aim to boost the throughput over screen-camera links by enhancing the transmission reliability. To this end, we propose RDCode, a robust dynamic barcode which enables a novel packet-frame-block structure. Based on the layered structure, we design different error correction schemes at three levels: intra-blocks, inter-blocks and inter-frames, in order to verify and recover the lost blocks and frames. Finally, we implement RDCode and experimentally show that RDCode reaches a high level of transmission reliability (e.g., reducing the error rate to 10%) and yields a at least doubled transmission rate, compared with the existing state-of-the-art approach COBRA.

Big graph search: challenges and techniques

On one hand, compared with traditional relational and XML models, graphs have more expressive power and are widely used today. On the other hand, various applications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. We first introduce the application of graph search in various scenarios. We then formalize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques towards big graph search: query techniques, data techniques and distributed computing techniques.

Big Graph Analyses: From Queries to Dependencies and Association Rules

AbstractThis position paper provides an overview of our recent advances in the study of big graphs, from theory to systems to applications. We introduce a theory of bounded evaluability, to query big graphs by accessing a bounded amount of the data. Based on this, we propose a framework to query big graphs with constrained resources. Beyond queries, we propose functional dependencies for graphs, to detect inconsistencies in knowledge bases and catch spams in social networks. As an example application of big graph analyses, we extend association rules from itemsets to graphs for social media marketing. We also identify open problems in connection with querying, cleaning and mining big graphs.

Extending Conditional Dependencies with Built-in Predicates

This paper proposes a natural extension of conditional functional dependencies (CFDs [1]) and conditional inclusion dependencies (CINDs [2]), denoted by CFD^ps and CIND^ps, respectively, by specifying patterns of data values with 6 ≠, <;,≤, >, and ≥ predicates. As data quality rules, CFD^ps and CIND^ps are able to capture errors that commonly arise in practice but cannot be detected by CFDs and CINDs. We establish two sets of results for central technical problems associated with CFD^ps and CIND^ps. (a) One concerns the satisfiability and implication problems for CFD^ps and CIND^ps, taken separately or together. These are important for, e.g. deciding whether data quality rules are dirty themselves, and for removing redundant rules. We show that despite the increased expressive power, the static analyses of CFD^ps and CIND^ps retain the same complexity as their CFDs and CINDs counterparts. (b) The other concerns validation of CFD^ps and CIND^ps. We show that given a set X of CFD^ps and CIND^ps on a database D, a set of SQL queries can be automatically generated that, when evaluated against D, return all tuples in D that violate some dependencies in Σ. We also experimentally verified the efficiency and effectiveness of our SQL based error detection techniques, using real-life data. This provides commercial DBMS with an immediate capability to detect errors based on CFD^ps and CIND^ps.

Discovering Graph Functional Dependencies

This paper studies discovery of GFDs, a class of functional dependencies defined on graphs. We investigate the fixed-parameter tractability of three fundamental problems related to GFD discovery. We show that the implication and satisfiability problems are fixed-parameter tractable, but the validation problem is co-W[1]-hard. We introduce notions of reduced GFDs and their topological support, and formalize the discovery problem for GFDs. We develop algorithms for discovering GFDs and computing their covers. Moreover, we show that GFD discovery is feasible over large-scale graphs, by providing parallel scalable algorithms for discovering GFDs that guarantee to reduce running time when more processors are used. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms.

Demo: a robust barcode system for data transmissions over screen-camera links

Visible light communication (VLC) over screen-camera links emerges as a novel form of near-field communication, and it offers a user-friendly, infrastructure-less and secure communication, which is highly competitive for one-time file transfer [1 - 4]. However, the limitations of smart devices and the uncertainty of user behaviors seriously impair the transmission reliability and hinder its applicability. Worse still, existing approaches [1, 2, 4]mostly focus on improving the transmission speed and ignore the transmission reliability. Hence, RDCode is proposed to boost the throughput over screen-camera links, by making use of a novel barcode design and several effective techniques to enhance the transmission reliability. In this demo, we show that our RDCode prototype system addresses many practical challenges. A short video on our prototype system is accessible from http://mashuai.buaa.edu.cn/demo/RDCode.mp4.

Incorporating User Grouping into Retweeting Behavior Modeling.

The variety among massive users makes it difficult to model their retweeting activities. Obviously, it is not suitable to cover the overall users by a single model. Meanwhile, building one model per user is not practical. To this end, this paper presents a novel solution, of which the principle is to model the retweeting behavior over user groups. Our system, GruBa, consists of three key components for extracting user based features, clustering users into groups, and modeling upon each group. Particularly, we look into the user interest from different perspectives including long-term/short-term interests and explicit/implicit interests. We have evaluated the performance of GruBa using datasets of real-world social networking applications, showcasing its benefits.

A probabilistic framework of preference discovery from folksonomy corpus

The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered.