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Zhang, Y.; Xu, W.: Fast exact maximum likelihood estimation for mixture of language model (2008)
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- Abstract
- Language modeling is an effective and theoretically attractive probabilistic framework for text information retrieval. The basic idea of this approach is to estimate a language model of a given document (or document set), and then do retrieval or classification based on this model. A common language modeling approach assumes the data D is generated from a mixture of several language models. The core problem is to find the maximum likelihood estimation of one language model mixture, given the fixed mixture weights and the other language model mixture. The EM algorithm is usually used to find the solution. In this paper, we proof that an exact maximum likelihood estimation of the unknown mixture component exists and can be calculated using the new algorithm we proposed. We further improve the algorithm and provide an efficient algorithm of O(k) complexity to find the exact solution, where k is the number of words occurring at least once in data D. Furthermore, we proof the probabilities of many words are exactly zeros, and the MLE estimation is implemented as a feature selection technique explicitly.
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Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014)
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- Abstract
- In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval.
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Vishwanath, A.; Xu, W.; Ngoh, Z.: How people protect their privacy on facebook : a cost-benefit view (2018)
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- Abstract
- Realizing the many benefits from Facebook require users to share information reciprocally, which has overtime created trillions of bytes of information online-a treasure trove for cybercriminals. The sole protection for any user are three sets of privacy protections afforded by Facebook: settings that control information privacy (i.e., security of social media accounts and identity information), accessibility privacy or anonymity (i.e., manage who can connect with a user), and those that control expressive privacy (i.e., control who can see a user's posts and tag you). Using these settings, however, involves a trade-off between making oneself accessible and thereby vulnerable to potential attacks, or enacting stringent protections that could potentially make someone inaccessible thereby reducing the benefits that are accruable through social media. Using two theoretical frameworks, Uses and Gratifications (U&G) and Protection Motivation Theory (PMT), the research examined how individuals congitvely juxtaposed the cost of maintaining privacy through the use of these settings against the benefits of openness. The application of the U&G framework revealed that social need fulfillment was the single most significant benefit driving privacy management. From the cost standpoint, the PMT framework pointed to perceived severity impacting expressive and information privacy, and perceived susceptability influencing accessibility privacy.
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