Abstract

We estimate that a large number of news articles contain references to future. The reference is detected through the notion of predictive statements (phrases). Distinguishing such predictive statements from factual statements in news articles is important for most applications such as fact checking, opinion mining, future trend analysis, etc. In this paper, we approach the problem of automatically extracting futurerelated information by solving two sub-problems. The first sub-problem is labeling a sentence as predictive or factual. In addition to extracting the predictions, we address the tasks of clausal scope resolution and dis-embedding linguistic peripheral clauses with respect to the predictive clause in a sentence. To solve these problems, we extract all the clauses of a given sentence and classify each of the clauses as predictive or factual. We then use a machine learning based approach to disambiguate the clause labels by using the clausal dependency relations and label the sentence.

Abstract

Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.

Abstract

Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.

Abstract

In the game of hide and seek, one or more agents (hiders) can hide behind objects to prevent other agents (seekers) from seeing them through their line of sight (visibility). These hider agents can be eventually found by seekers who explore the environment. The hide and seek model serves as an abstraction of many real world scenarios such as police agents trying to capture intruders hiding in a warehouse, security/patrolling agents on the lookout for any suspicious activity, spying drones trying to prevent themselves from being caught. Medusa allows modeling such scenarios by supporting the simulation, visualization and analysis of hider and seeker agent strategies under different environments.

Abstract

Always-on agents are those agents which are like daemon programs that are always on and can do tasks as and when they arrive. These agents are idle when there is no task assigned to them. Further, those agents that work on a task, also wait for some event or task completion, and hence, are also idle for a short duration between execution of the tasks. The question arises what should the agent be doing when it is idle. In this paper, we conduct an empirical analysis to show improved decision-making capabilities by agents when they exploit their idle-time to analyze their past tasks in terms of decisions taken and their impact. The improvement of performance can be measured by considering an increase in success rate, avoiding strategies that may not work, quicker decision making, etc. Our always-on agent, while executing a task, stores some of the pertinent details of the task done in a database, such as decisions taken, paths of execution of the task, goodness of them, etc. The agent uses different strategies to use this stored knowledge. We present and evaluate three strategies that always-on agents can use (i) Frequent Decision Strategy (FDS) - the agent stores the prior executions and their frequency of success and failure, repeats the most frequent successful decision taken during prior executions of the task, (ii) Analyzed Decision Strategy (ADS) - the agent analyzes prior executions that were successful or not, stores in database the goodness of various alternatives and chooses the best alternative and (iii) Online analysis decision strategy (OADS) - the alwayson agent while executing its task, during its idle time analyzes the possible future situations and prepares the list of best possible decisions that can be taken in future. Note that the FDS and ADS are used when the agent is not doing any task and is off-line, whereas, OADS generates new mock tasks to consider possible alternative task execution situations to expand its decision-making scenarios while having a task at hand. We conduct our empirical study on always-on agents playing connect-4 games to check the viability and show improved decision making.

Abstract

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies nonconvex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies for different scenarios to showcase the convergence and efficacy of the proposed algorithm.

Abstract

High dimensional data is often challenging to cluster due to the curse of dimensionality leading to challenges in identifying clusters. The key challenge in high dimensional clustering is to develop a solution that identifies clusters which are as complete as they can be, while not merging well-separated clusters. We propose core points which represent local compact regions. The strongly connected component from the k-nearest neighbor graph of core points provides for a group of points that are strongly mutually connected. These mutually connected regions represent the core structure of the clusters. Our empirical analysis and experimental results present the rationale behind our solution and validate the goodness of the clusters against the state of the art high dimensional clustering algorithms.The novelty of our solution is to use the concept of reverse nearest neighbors to generate natural clusters in high dimensions.

Abstract

Being the ever-growing online encyclopedia, Wikipedia requires a keen investigation about which articles are to be included for it to maintain its indispensability. To prevent unnecessary articles from being included, official guidelines of Wikipedia demand these named entities to meet" notability" standards for their article inclusion. In this paper, we evaluate named entities for their notability by using reliability and entity salience features. Evaluations of our system provide evidence for the viability of our solution as an alternative to the manual decisions made by the reviewers for inclusion of an article using the notability rules.

Abstract

Networks have become ubiquitous in many real world appli-cations and to cluster similar networks is an important problem. There are various properties of graphs such as clustering coefficient (CC), den-sity, arboricity, etc. We introduce a measure, Clique Conversion Coeffi-cient (CCC), which captures the clique forming tendency of nodes in anundirected graph. CCC could either be used as a weighted average ofthe values in a vector or as the vector itself. Our experiments show thatCCC provides additional information about a graph in comparison torelated measures like CC and density. We cluster the real world graphsusing a combination of the features CCC, CC, and density and show thatwithout CCC as one of the features, graphs with similar clique formingtendencies are not clustered together. The clustering with the use ofCCC would have applications in the areas of Social Network Analysis,Protein-Protein Interaction Analysis, etc., where cliques have an impor-tant role. We perform the clustering of ego networks of the YOUTUBEnetwork using values in CCC vector as features. The quality of the clus-tering is analyzed by contrasting the frequent subgraphs in each cluster.The results highlight the utility of CCC in clustering subgraphs of alarge graph

Abstract

We propose an interactive visual analytics system for exploring spatio-temporal data in VAST 2017 Mini-Challenge-1. As part of this challenge, we are expected to determine repeating, seasonal and unusual cars' movements in Lekagul park dataset. We use varied visualizations such as heatmap, sequential sunburst and line plots. Further, we have developed a web deployable ad-hoc system for displaying spatio-temporal information on geographical map