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In networks, multiple contagions, such as information and purchasing behaviors, may interact with each other as they spread simultaneously. However, most of the existing information diffusion models are built on the assumption that each individual contagion spreads independently, regardless of their interactions. Gaining insights into such interaction is crucial to understand the contagion adoption behaviors, and thus can make better predictions. In this paper, we study the contagion adoption behavior under a set of interactions, specifically, the interactions among users, contagions' contents, and sentiments, which are learned from social network structures and texts. We develop an effective and efficient interaction-aware diffusion (IAD) framework, incorporating these interactions into a unified model. We also present a generative process to distinguish user roles, a co-training method to determine contagions' categories and a new topic model to obtain topic-specific sentiments. Evaluation on the large-scale Weibo dataset demonstrates that our proposal can learn how different users, contagion categories, and sentiments interact with each other efficiently. With these interactions, we can make a more accurate prediction than the state-of-art baselines. Moreover, we can better understand how the interactions influence the propagation process and thus can suggest useful directions for information promotion or suppression in viral marketing. [ABSTRACT FROM AUTHOR]

As an efficient sampling algorithm for latent dirichlet allocation SparseLDA uses cache strategy to improve the time and space efficiency of its standard gibbs sampling algorithm (StdGibbs) by recycling previous computation. However, SparseLDA cannot further improve the time-efficiency of StdGibbs, since the amount of recycled computation is limited. This is because the word types of two adjacent tokens are usually different and the previous computation cannot be further recycled easily. To solve this problem, in this paper we propose a new algorithm named Efficient SparseLDA (ESparseLDA) based on SparseLDA. The main idea of ESparseLDA is to first rearrange the tokens within one text according to the word types so that the tokens of the same word type are aggregated together and then recycle more computation while making no approximation and ensuring the exactness. In this paper, we make detailed theoretical explanations and comparative experimental analyses on the correctness, exactness and time-efficiency of ESparseLDA. In detail, the statistical significance tests on perplexities strictly show that ESparseLDA is correct and exact. In addition, the running time results show that the time-efficiency of ESparseLDA is the higher than SparseLDA in varying degrees from 5.06% to 31.85% on the different datasets used in experiments. [ABSTRACT FROM AUTHOR]

Human activity recognition is an important area of ubiquitous computing. Most current researches in activity recognition mainly focus on simple activities, e.g., sitting, running, walking, and standing. Compared with simple activities, complex activities are more complicated with high-level semantics, e.g., working, commuting, and having a meal. This paper presents a hierarchical model to recognize complex activities as mixtures of simple activities and multiple actions. We generate the components of complex activities using a clustering algorithm, represent and recognize complex activities by applying a topic model on these components. It is a data-driven method that can retain effective information for representing and recognizing complex activities. In addition, acceleration and physiological signals are fused in classifier level to ensure the overall performance of complex activity recognition. The results of experiments show that our method has ability to represent and recognize complex activities effectively. [ABSTRACT FROM AUTHOR]

Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. Although topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, however, tend to be semantically richer due to careful selection of words that define the concepts, but they may not span the themes in a data set exhaustively. In this study, we review a new probabilistic framework for combining a hierarchy of human-defined semantic concepts with a statistical topic model to seek the best of both worlds. Results indicate that this combination leads to systematic improvements in generalization performance as well as enabling new techniques for inferring and visualizing the content of a document. [ABSTRACT FROM AUTHOR]