Human's cognition system prompts that context information provides potentially powerful clue while recognizing objects. However, for fine-grained image classification, the contribution of context may vary over different images, and sometimes the context even confuses the classification result. To alleviate this problem, in our work, we develop a novel approach, two-stream contextualized Convolutional Neural Network, which provides a simple but efficient context-content joint classification model under deep learning framework. The network merely requires the raw image and a coarse segmentation as input to extract both content and context features without need of human interaction. Moreover, our network adopts a weighted fusion scheme to combine the content and the context classifiers, while a subnetwork is introduced to adaptively determine the weight for each image. According to our experiments on public datasets, our approach achieves considerable high recognition accuracy without any tedious human's involvements, as compared with the state-of-the-art approaches.

With the development of Web 2.0, many users express their opinions online. This paper is concerned with the classification of social emotions on varied-scale datasets. Different from traditional models which weight training documents equally, the concept of emotional entropy is proposed to estimate the weight and tackle the issue of noisy documents. The topic assignment is also used to distinguish different emotional senses of the same word. Experimental evaluations using different data sets validate the effectiveness of the proposed social emotion classification model.

Privacy preserving in association mining is an important research topic in the database security field. This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. It aims at reducing undesirable side effects and increasing desirable side effects, while ensuring to conceal all sensitive rules. The candidate transactions are selected for sanitization based on their relations with border rules. Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals.

We report on a novel use of parallel coordinates as a pedagogical tool for illustrating the non-intuitive properties of high dimensional spaces with special emphasis on the phenomenon of Curse of Dimensionality. Also, we have collated what we believe to be a representative sample of diverse approaches that exist in literature to conceptualize the Curse of Dimensionality. We envisage that the paper will have pedagogical value in structuring the way Curse of Dimensionality is presented in classrooms and associated lab sessions.

Sensor selection has become an active topic aimed at energy saving, information overload prevention, and communication cost planning in sensor networks. In many real applications, often the sensors' observation regions have overlaps and thus the sensor network is inherently redundant. Therefore it is important to select proper sensors to avoid data redundancy. This paper focuses on how to incrementally select a subset of sensors in a streaming scenario to minimize information redundancy, and meanwhile meet the power consumption constraint. We propose to perform sensor selection in a multi-variate interpolation framework, such that the data sampled by the selected sensors can well predict those of the inactive sensors. Importantly, we incorporate sensors' spatial information as two regularizers, which leads to significantly better prediction performance. We also define a statistical variable to store sufficient information for incremental learning, and introduce a forgetting factor to track sensor streams' evolvement. Experiments on both synthetic and real datasets validate the effectiveness of the proposed method. Moreover, our method is over 10 times faster than the state-of-the-art sensor selection algorithm.

We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of roads post some disaster within a fixed budget such that the connectivity between a set of nodes is optimized. We show that our likelihood maximization approach using the EM algorithm scales well for this problem taking the form of message-passing among nodes of the graph, and provides significantly better quality solutions than a standard mixed-integer programming solver.

Sarcasm understandability or the ability to understand textual sarcasm depends upon readers' language proficiency, social knowledge, mental state and attentiveness. We introduce a novel method to predict the sarcasm understandability of a reader. Presence of incongruity in textual sarcasm often elicits distinctive eye-movement behavior by human readers. By recording and analyzing the eye-gaze data, we show that eye-movement patterns vary when sarcasm is understood vis-à-vis when it is not. Motivated by our observations, we propose a system for sarcasm understandability prediction using supervised machine learning. Our system relies on readers' eye-movement parameters and a few textual features, thence, is able to predict sarcasm understandability with an F-score of 93%, which demonstrates its efficacy. The availability of inexpensive embedded-eye-trackers on mobile devices creates avenues for applying such research which benefits web-content creators, review writers and social media analysts alike.

A simple Neural Network model is presented for end-to-end visual learning of arithmetic operations from pictures of numbers. The input consists of two pictures, each showing a 7-digit number. The output, also a picture, displays the number showing the result of an arithmetic operation (e.g., addition or subtraction) on the two input numbers. The concepts of a number, or of an operator, are not explicitly introduced. This indicates that addition is a simple cognitive task, which can be learned visually using a very small number of neurons. Other operations, e.g., multiplication, were not learnable using this architecture. Some tasks were not learnable end-to-end (e.g., addition with Roman numerals), but were easily learnable once broken into two separate sub-tasks: a perceptual Character Recognition and cognitive Arithmetic sub-tasks. This indicates that while some tasks may be easily learnable end-to-end, other may need to be broken into sub-tasks.

Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions. Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition. Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints. To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons. Experimental results on three human action recognition datasets consistently demonstrate the effectiveness of the proposed model.

Designing effective feature and handling the model drift problem are two important aspects for online visual tracking. For feature representation, gradient and color features are most widely used, but how to effectively combine them for visual tracking is still an open problem. In this paper, we propose a rich feature descriptor, MC-HOG, by leveraging rich gradient information across multiple color channels or spaces. Then MC-HOG features are embedded into the correlation tracking framework to estimate the state of the target. For handling the model drift problem caused by occlusion or distracter, we propose saliency proposals as prior information to provide candidates and reduce background interference. In addition to saliency proposals, a ranking strategy is proposed to determine the importance of these proposals by exploiting the learnt appearance filter, historical preserved object samples and the distracting proposals. In this way, the proposed approach could effectively explore the color-gradient characteristics and alleviate the model drift problem. Extensive evaluations performed on the benchmark dataset show the superiority of the proposed method.