In disease mapping, the spatial scan statistic is used to detect spatial regions where population is exposed to a significantly higher disease risk than expected. In this important application, the current residence is typically used to define the location of individuals from the population. Considering the mobility of humans at various temporal and spatial scales, using only information about the current residence may be an insufficiently informative proxy because it ignores a multitude of exposures that may occur away from home, or which had occurred at previous residences. In this paper, we propose a spatial scan statistic that is appropriate for disease mapping on mobile populations. We formulate a computationally efficient algorithm that uses the proposed statistic to find significant high-risk regions from mobile population's disease status data. The algorithm is applicable on large populations and over dense spatial grids. The experimental results demonstrate that the proposed algorithm is computationally efficient and outperforms the traditional disease clustering approaches at discovering high-risk regions in mobile populations.

We report a novel approach to addressing the Raven’s Progressive Matrices (RPM) tests, one based upon purely visual representations. Our technique introduces the calculation of confidence in an answer and the automatic adjustment of level of resolution if that confidence is insufficient. We first describe the nature of the visual analogies found on the RPM. We then exhibit our algorithm and work through a detailed example. Finally, we present the performance of our algorithm on the four major variants of the RPM tests, illustrating the impact of confidence. This is the first such account of any computational model against the entirety of the Raven’s.

This paper presents an agent-based model that studies the emergence and evolution of a language system of logical constructions, i.e. a vocabulary and a set of grammatical constructions that allow the expression of logical combinations of categories. The model assumes the agents have a common vocabulary for basic categories, the ability to construct logical combinations of categories using Boolean functions, and some general purpose cognitive capacities for invention, adoption, induction and adaptation. But it does not assume the agents have a vocabulary for Boolean functions nor grammatical constructions for expressing such logical combinations of categories through language. The results of the experiments we have performed show that a language system of logical constructions emerges as a result of a process of self-organisation of the individual agents' interactions when these agents adapt their preferences for vocabulary and grammatical constructions to those they observe are used more often by the rest of the population, and that such a language system is transmitted from one generation to the next.

Efficient codes have been used effectively in both computer science and neuroscience to better understand the information processing in visual and auditory encoding and discrimination tasks. In this paper, we explore the use of efficient codes for representing information relevant to human movements during locomotion. Specifically, we apply motion capture data to a physical model of the human skeleton to compute joint angles (inverse kinematics) and joint torques (inverse dynamics); then, by treating the resulting paired dataset as a supervised regression problem, we investigate the effect of sparsity in mapping from angles to torques. The results of our investigation suggest that sparse codes can indeed represent salient features of both the kinematic and dynamic views of human locomotion movements. However, sparsity appears to be only one parameter in building a model of inverse dynamics; we also show that the "encoding" process benefits significantly by integrating with the "regression" process for this task. In addition, we show that, for this task, simple coding and decoding methods are not sufficient to model the extremely complex inverse dynamics mapping. Finally, we use our results to argue that representations of movement are critical to modeling and understanding these movements.

Agency - the capacity to plan and act - and experience - the capacity to sense and feel - are two critical aspects that determine whether people will perceive non-human entities, such as autonomous agents, to have a mind. There is evidence that the absence of either can reduce cooperation. We present an experiment that tests the necessity of both for cooperation with agents. In this experiment we manipulated people's perceptions about the cognitive and affective abilities of agents, when engaging in the ultimatum game. The results indicated that people offered more money to agents that were perceived to make decisions according to their intentions (high agency), rather than randomly (low agency). Additionally, the results showed that people offered more money to agents that expressed emotion (high experience), when compared to agents that did not (low experience). We discuss the implications of this agency-experience theoretical framework for the design of artificially intelligent decision makers.

For datasets in Collaborative Filtering (CF) recommendations, even if the identifier is deleted and some trivial perturbation operations are applied to ratings before they are released, there are research results claiming that the adversary could discriminate the individual's identity with a little bit of information. In this paper, we propose $k$-coRating, a novel privacy-preserving model, to retain data privacy by replacing some null ratings with "well-predicted" scores. They do not only mask the original ratings such that a $k$-anonymity-like data privacy is preserved, but also enhance the data utility (measured by prediction accuracy in this paper), which shows that the traditional assumption that accuracy and privacy are two goals in conflict is not necessarily correct. We show that the optimal $k$-coRated mapping is an NP-hard problem and design a naive but efficient algorithm to achieve $k$-coRating. All claims are verified by experimental results.

Diabetes complications often afflict diabetes patients seriously: over 68% of diabetes-related mortality is caused by diabetes complications. In this paper, we study the problem of automatically diagnosing diabetes complications from patients' lab test results. The objective problem has two main challenges: 1) feature sparseness: a patient only undergoes 1.26% lab tests on average, and 65.5% types of lab tests are performed on samples from less than 10 patients; 2) knowledge skewness: it lacks comprehensive detailed domain knowledge of the association between diabetes complications and lab tests. To address these challenges, we propose a novel probabilistic model called Sparse Factor Graph Model (SparseFGM). SparseFGM projects sparse features onto a lower-dimensional latent space, which alleviates the problem of sparseness. SparseFGM is also able to capture the associations between complications and lab tests, which help handle the knowledge skewness. We evaluate the proposed model on a large collections of real medical records. SparseFGM outperforms (+20% by F1) baselines significantly and gives detailed associations between diabetes complications and lab tests.

The emergence of location based social network (LBSN) services makes it possible to study individuals’ mobility patterns at a fine-grained level and to see how they are impacted by social factors. In this study we analyze the check-in patterns in LBSN and observe significant temporal clustering of check-in activities. We explore how self-reinforcing behaviors, social factors, and exogenous effects contribute to this clustering and introduce a framework to distinguish these effects at the level of individual check-ins for both users and venues. Using check-in data from three major cities, we show not only that our model can improve prediction of future check-ins, but also that disentangling of different factors allows us to infer meaningful properties of different venues.

Online social networks have been used for a variety of rich activities in recent years, such as investigating potential employees and seeking recommendations of high quality services and service providers. In such activities, trust is one of the most critical factors for the decision-making of users. In the literature, the state-of-the-art trust prediction approaches focus on either dispositional trust tendency and propagated trust of the pair-wise trust relationships along a path or the similarity of trust rating values. However, there are other influential factors that should be taken into account, such as the similarity of the trust rating distributions. In addition, tendency, propagated trust and similarity are of different types, as either personal properties or interpersonal properties. But the difference has been neglected in existing models. Therefore, in trust prediction, it is necessary to take all the above factors into consideration in modeling, and process them separately and differently. In this paper we propose a new trust prediction model based on trust decomposition and matrix factorization, considering all the above influential factors and differentiating both personal and interpersonal properties. In this model, we first decompose trust into trust tendency and tendency-reduced trust. Then, based on tendency-reduced trust ratings, matrix factorization with a regularization term is leveraged to predict the tendency-reduced values of missing trust ratings, incorporating both propagated trust and the similarity of users' rating habits. In the end, the missing trust ratings are composed with predicted tendency-reduced values and trust tendency values. Experiments conducted on a real-world dataset illustrate significant improvement delivered by our approach in trust prediction accuracy over the state-of-the-art approaches.

As an effective technology for navigating a large number of images, image summarization is becoming a promising task with the rapid development of image sharing sites and social networks. Most existing summarization approaches use the visual-based features for image representation without considering tag information.In this paper, we propose a novel framework, named JOINT, which employs both image content and tag information to summarize images. Our model generates the summary images which can best reconstruct the original collection. Based on the assumption that an image with representative content should also have typical tags, we introduce a similarity-inducing regularizer to our model. Furthermore, we impose the lasso penalty on the objective function to yield a concise summary set. Extensive experiments demonstrate our model outperforms the state-of-the-art approaches.