In this paper we address the difficulty of clipping articles from mobile apps. We propose a service called UniClip that allows a user to save the full content of an article by snapping a screenshot part of it. UniClip leverages a huge amount of indexed web data to mine the article by starting with a snapped screenshot. We propose approaches to solve three challenges: (1) how to represent a screenshot; (2) how to formulate effective queries for retrieving a full article; and (3) how to rank the best URL at the top from multiple search result lists. Experimental results indicate that our approach is effective in achieving as high an \(F_1\) measure as 0.905, which outperforms the best of three baseline methods by 18 points.

Abstract Nowadays, the message diffusion links among users or Web sites drive the development of countless innovative applications. However, in reality, it is easier for us to observe the time stamps when different nodes in the network react on a message, while the connections empowering the diffusion of the message remain hidden. This motivates recent extensive studies on the network inference problem : unveiling the edges from the records of messages disseminated through them. Existing solutions are computationally expensive, which motivates us to develop an efficient two-step general framework, Clustering Embedded Network Inference (CENI). CENI integrates clustering strategies to improve the efficiency of network inference. By clustering nodes directly on the time lines of messages, we propose two naive implementations of CENI: Infection-centric CENI and Cascade-centric CENI . Additionally, we point out the critical dimension problem of CENI: Instead of one-dimensional time lines, we need to first project the nodes to an Euclidean space of certain dimension before clustering. A CENI adopting clustering method on the projected space can better preserve the structure hidden in the cascades and generate more accurately inferred links. By addressing the critical dimension problem, we propose the third implementation of the CENI framework: Projection-based CENI . Through extensive experiments on two real datasets, we show that the three CENI models only need around 20–50 % of the running time of state-of-the-art methods. Moreover, the inferred edges of Projection-based CENI preserve or even outperform the effectiveness of state-of-the-art methods.

Abstract This paper explores the challenges raised by big data in privacy-preserving data management. First, we examine the conflicts raised by big data with respect to preexisting concepts of private data management, such as consent, purpose limitation, transparency and individual rights of access, rectification and erasure. Anonymization appears as the best tool to mitigate such conflicts, and it is best implemented by adhering to a privacy model with precise privacy guarantees. For this reason, we evaluate how well the two main privacy models used in anonymization (k-anonymity and $$\varepsilon $$ ε -differential privacy) meet the requirements of big data, namely composability, low computational cost and linkability.