Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.

Generating a coherent synopsis and revealing the development threads for news stories from the increasing amounts of news content remains aformidable challenge. In this paper, we proposed a hddCRP (hybird distant-dependent ChineseRestaurant Process) based HierARChical tOpic model for news Article cLustering, abbreviated as CHARCOAL. Given a bunch of news articles, the outcome of CHARCOAL is threefold: 1) it aggregates relevant new articles into clusters (i.e., stories); 2) it disentangles the chain links (i.e., storyline) between articles in their describing story; 3) it discerns the topics that each story is assigned (e.g., Malaysia Airlines Flight 370 story belongs to the aircraft accident topic and U.S presidential election stories belong to the politics topic). CHARCOAL completes this task by utilizing a hddCRP as prior, and the entities (e.g., names of persons, organizations, or locations) that appear in news articles as clues. Moveover, the adaptation of nonparametric nature in CHARCOAL makes our model can adaptively learn the appropriate number of stories and topics from news corpus. The experimental analysis and results demonstrate both interpretability and superiority of the proposed approach.

Rosetta is a European Space Agency (ESA) cornerstone mission that entered orbit around the comet 67P/Churyumov-Gerasimenko in August 2014 and will escort the comet for a 1.5 year nominal mission offering the most detailed study of a comet ever undertaken by humankind. The Rosetta orbiter has 11 scientific instruments (4 remote sensing) and the Philae lander to make complementary measurements of the comet nucleus, coma (gas and dust), and surrounding environment. The ESA Rosetta Science Ground Segment has developed a science scheduling system that includes an automated scheduling capability to assist in developing science plans for the Rosetta Orbiter. While automated scheduling is a small portion of the overall Science Ground Segment (SGS) as well as the overall scheduling system, this paper focuses on the automated and semi-automated scheduling software (called ASPEN-RSSC) and how this software is used.

Many electoral control and manipulation problems — which we will refer to in general as manipulative actions problems — are NP-hard in the general case.  Many of these problems fall into polynomial time if the electorate is single-peaked, i.e., is  polarized along some axis/issue. However, real-world electorates are not truly single-peaked — for example, there may be some maverick voters — and to take this into account, we study the complexity of manipulative-action algorithms for  the case of nearly single-peaked electorates.

Traditional methods which the features form cluster structures, as can be seen in often rely on R.I.P or its relaxed variants. However, many machine learning [Lehiste, 1976] and computer vision in real applications, features are often correlated problems [Lan et al., 2013; Lowe, 2004]. Due to the fact that to each other, which makes these assumptions many features extractors are similar to each others and they too strong to be useful. In this paper, we reflect the characteristics of the same image, vision features study the sparse recovery problem in which the feature are often correlated and have cluster structures. This correlation matrix is strictly non-R.I.P.. We prove that is even stronger in those systems that have thousands when features exhibit cluster structures, which often to millions of features [Lan et al., 2013; Gan et al., 2015a; happens in real applications, we are able to recover 2015b].

Tensor factorization methods provide a useful way to extract latent factors from complex multirelational data, and also for predicting missing data. Developing tensor factorization methods for massive tensors, especially when the data are binary- or count-valued (which is true of most real-world tensors), however, remains a challenge. We develop a scalable probabilistic tensor factorization framework that enables us to perform efficient factorization of massive binary and count tensor data. The framework is based on (i) the Polya-Gamma augmentation strategy which makes the model fully locally conjugate and allows closed-form parameter updates when data are binary- or count-valued; and (ii) an efficient online Expectation Maximization algorithm, which allows processing data in small mini-batches, and facilitates handling massive tensor data. Moreover, various types of constraints on the factor matrices (e.g., sparsity, non-negativity) can be incorporated under the proposed framework, providing good interpretability, which can be useful for qualitative analyses of the results. We apply the proposed framework on analyzing several binary- and count-valued real-world data sets.

We understand a sociotechnical system as a microsociety in which autonomous parties interact with and about technical objects. We define governance as the administration of such a system by its participants. We develop an approach for governance based on a computational representation of norms. Our approach has the benefit of capturing stakeholder needs precisely while yielding adaptive resource allocation in the face of changes both in stakeholder needs and the environment. We are currently extending this approach to address the problem of secure collaboration and to contribute to the emerging science of cybersecurity.

Recommender system has become an indispensable component in many e-commerce sites. One major challenge that largely remains open is the cold-start problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RaPare) to break this ice barrier. The center-piece of  our RaPare is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RaPare strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.

We study computational problems for two popular parliamentary voting procedures: the amendment procedure and the successive procedure. While finding successful manipulations or agenda controls is tractable for both procedures, our real-world experimental results indicate that most elections cannot be manipulated by a few voters and agenda control is typically impossible. If the voter preferences are incomplete, then finding possible winners is NP-hard for both procedures. Whereas finding necessary winners is coNP-hard for the amendment procedure, it is polynomial-time solvable for the successive one.

Abstractive summarization is an ideal form of summarization since it can synthesize information from multiple documents to create concise informative summaries. In this work, we aim at developing an abstractive summarizer. First, our proposed approach identifies the most important document in the multi-document set. The sentences in the most important document are aligned to sentences in other documents to generate clusters of similar sentences. Second, we generate K-shortest paths from the sentences in each cluster using a word-graph structure. Finally, we select sentences from the set of shortest paths generated from all the clusters employing a novel integer linear programming (ILP) model with the objective of maximizing information content and readability of the final summary. Our ILP model represents the shortest paths as binary variables and considers the length of the path, information score and linguistic quality score in the objective function. Experimental results on the DUC 2004 and 2005 multi-document summarization datasets show that our proposed approach outperforms all the baselines and state-of-the-art extractive summarizers as measured by the ROUGE scores. Our method also outperforms a recent abstractive summarization technique. In manual evaluation, our approach also achieves promising results on informativeness and readability.