Our speech research focuses on the detection of dialectal Most physiological assessments commonly used to determine variations by identifying speech language divergences the functional status of patients with Amyotrophic along a range of different dimensions. We borrow the notion lateral sclerosis (ALS) require trained clinical personnel to of divergence from the study of cross-linguistic variations administer and interpret the results. Speech impairments (Dorr, 1993) and apply it towards developing an assessment eventually affect 80-95% of patients with ALS (Beukelman, of bulbar function in patients with ALS, to improve 2011). Initial impairments include reduced speaking upon existing assessments (Green et al., 2013).

We present the simple idea that a graphical representation of subject-verb-object triples is useful for exploring blog texts, a preliminary implementation and a first level analysis of the positive and negative aspects of a naive implementation. We outline its potential for further development.

The growing use of social media has made it a critical component of disaster response and recovery efforts. Both in terms of preparedness and response, public health officials and first responders have turned to automated tools to assist with organizing and visualizing large streams of social media. In turn, this has spurred new research into algorithms for information extraction, event detection and organization, and information visualization. One challenge of these efforts has been the lack of a common corpus for disaster response on which researchers can compare and contrast their work. This paper describes the Hurricane Sandy Twitter Corpus: 6.5 million geotagged Twitter posts from the geographic area and time period of the 2012 Hurricane Sandy.

With the rapid development of mobile devices, global position system (GPS) and Web 2.0 technologies, location-based social networks (LBSNs) have attracted millions of users to share rich information, such as experiences and tips. Point-of-Interest (POI) recommender system plays an important role in LBSNs since it can help users explore attractive locations as well as help social network service providers design location-aware advertisements for Point-of-Interest. In this paper, we present a brief survey over the task of Point-of-Interest recommendation in LBSNs and discuss some research directions for Point-of-Interest recommendation. We first describe the unique characteristics of Point-of-Interest recommendation, which distinguish Point-of-Interest recommendation approaches from traditional recommendation approaches. Then, according to what type of additional information are integrated with check-in data by POI recommendation algorithms, we classify POI recommendation algorithms into four categories: pure check-in data based POI recommendation approaches, geographical influence enhanced POI recommendation approaches, social influence enhanced POI recommendation approaches and temporal influence enhanced POI recommendation approaches. Finally, we discuss future research directions for Point-of-Interest recommendation.

Causal structure discovery methods are investigated recently but none of them has taken possible time-varying structure into consideration. This paper uses a notion of causal time-varying dynamic Bayesian network (CTV-DBN) and define a causal boundary to govern cross-time information sharing. CTV-DBN is constructed by using asymmetric kernels to address sample scarcity and to adhere to causal principles; while maintaining good variance and bias trade-off. Upon satisfying causal Markov assumption, causal inference can be made based on manipulation rule. We explore trajectory data collected from taxis in Beijing which exhibit heterogeneous patterns, data sparseness and distribution skewness. Experiments show that by using casual structures and trajectory clustering, we can analyse the spatio-temporal behavior of the trajectory data.

Since semantic trajectories can discover more semantic meanings of a user’s interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home Restaurant Company Restaurant , but they are not similar, since Tom works at Restaurant , sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant , works at Company and has lunch at Restaurant . If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method.

While the number of women who choose to pursue computer science and engineering careers is growing, men continue to largely outnumber them. In this paper, we describe a data mining approach that relies on a large collection of scientific articles to identify differences in gender interests in this field. Our hope is that through a better understanding of the differences between male and female preferences, we can enable more effective outreach and retention, and consequently contribute to the growth of the number of women who choose to pursue careers in this field.

Homeless youth are prone to Human Immunodeficiency Virus (HIV) due to their engagement in high risk behavior such as unprotected sex, sex under influence of drugs, etc. Many non-profit agencies conduct interventions to educate and train a select group of homeless youth about HIV prevention and treatment practices and rely on word-of-mouth spread of information through their social network. Previous work in strategic selection of intervention participants does not handle uncertainties in the social network's structure and evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision support system to aid the agencies in this task. PSINET includes the following key novelties: (i) it handles uncertainties in network structure and evolving network state; (ii) it addresses these uncertainties by using POMDPs in influence maximization; and (iii) it provides algorithmic advances to allow high quality approximate solutions for such POMDPs. Simulations show that PSINET achieves around 60% more information spread over the current state-of-the-art. PSINET was developed in collaboration with My Friend's Place (a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.

We describe a multi-modal cargo routing (MMCR) domain for modelling military logistics planning problems. These are transport optimisation problems that feature timing constraints, concurrency, capacitated resources, and action costs. We have developed a PDDL domain model, and have released a collection of problem instances along with a software tool to aid in the design and generation of new problem instances. Small instances of this domain stretch the capabilities of existing automated planning procedures, and larger realistic instances are beyond the capabilities of existing automated planning systems. We anticipate that scalable solution procedures for this domain will follow in the footsteps of systems, such as OPTIC and TIMIPLAN, which combine heuristic search concepts with mathematical programming optimisation tools.

We consider an autonomous agent facing a partially observable, stochastic, multiagent environment where the unknown policies of other agents are represented as finite state controllers (FSCs). We show how an agent can (i) learn the FSCs of the other agents, and (ii) exploit these models during interactions. To separate the issues of off-line versus on-line learning we consider here an off-line two-phase approach. During the first phase the agent observes as the other player(s) are interacting with the environment (the observations may be imperfect and the learning agent is not taking part in the interaction.) The collected data is used to learn an ensemble of FSCs that explain the behavior of the other agent(s) using a Bayesian non-parametric (BNP) approach. We verify the quality of the learned models during the second phase by allowing the agent to compute its own optimal policy and interact with the observed agent. The optimal policy for the learning agent is obtained by solving an interactive POMDP in which the states are augmented by the other agent(s)' possible FSCs. The advantage of using the Bayesian nonparametric approach in the first phase is that the complexity (number of nodes) of the learned controllers is not bounded a priori. Our two-phase approach is preliminary and separates the learning using BNP from the complexities of learning on-line while the other agent may be modifying its policy (on-line approach is subject of our future work.) We describe our implementation and results in a multiagent Tiger domain. Our results show that learning improves the agent's performance, which increases with the amount of data collected during the learning phase.