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

The disclosed embodiments illustrate methods and systems to recommend medical intervention activities for caregivers and/or human subjects. The method comprises receipt of medical data of a human subject and contextual data of a plurality of caregivers of the human subjects. The medical data includes periodic physiological data of the human subject received in real-time. A customized ontology is generated, from a pre-determined ontology related to a medical condition, based on the received medical data and the contextual data. Thereafter, a recommendation of medical intervention activities is generated for at least one of the plurality of caregivers and/or the human subject, based on analysis of the generated customized ontology, caregiver profiles, the received contextual data, and the periodic physiological data. The generated recommendation is transmitted to computing devices of the at least one …

Abstract

India is home to the largest under-25 demographic profile in the world, but lacks a job-ready educational system. It requires a wide-spread, skill-oriented educational model, equipping youth to thrive in highly dynamic job markets. As a response to the huge demand for technical education, a large private skill-tutoring ecosystem has sprung up in In-dia but remains geographically limited. This paper, drawn from a three-month ethnographic research conducted in Ameerpet (arguably India's largest IT skilling hub), probes the pedagogic style and characteristics of tutoring, and of-fers reasons why learners prefer to enroll into a physical model of classroom teaching over online courses. We make design suggestions for online learning platforms to attract students who are marginalized in the more formal and com-petitive education system, and opt for Ameerpet-like skill-hubs. Our primary offering is to suggest a shift in …

Abstract

The disclosed embodiments illustrate method and system for auto-generation of the sketch notes-based visual summary of the multimedia content. The method includes determining one or more segments, based on one or more transitions in the multimedia content. The method further includes generating a transcript based on audio content associated with each determined segment. The method further includes retrieving a set of images from an image repository based on each of the identified one or more keywords. The method further includes generating a sketch image of each of one or more of the retrieved set of images associated with each of the identified one or more keywords. The method further includes rendering the sketch notes-based visual summary of the multimedia content, generated based on at least generated one or more sketch images, on a user interface displayed on a display screen of the user …

Abstract

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.

Abstract

Recommender systems are a key component of music sharing platforms, which suggest musical recordings a user might like. People often have implicit preferences while listening to music, though these preferences might not always be the same while they listen to music at different times. For example, a user might be interested in listening to songs of only a particular artist at some time, and the same user might be interested in the top-rated songs of a genre at another time. In this paper we try to explicitly model the short term preferences of the user with the help of Last.fm tags of the songs the user has listened to. With a session defined as a period of activity surrounded by periods of inactivity, we introduce the concept of a subsession, which is that part of the session wherein the preference of the user does not change much. We assume the user preference might change within a session and a session might have multiple subsessions. We use our modelling of the user preferences to generate recommendations for the next song the user might listen to. Experiments on the user listening histories taken from Last.fm indicate that this approach beats the present methodologies in predicting the next recording a user might listen to.

Abstract

Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the average time a user spends on the platform often need to recommend songs which the user might want to listen to next at each point in time. This is different from recommendation systems which try to predict the item which might be of interest to the user at some point in the user lifetime but not necessarily in the very near future. Prediction of next song the user might like requires some kind of modeling of the user interests at the given point of time. Attentive neural networks have been exploiting the sequence in which the items were selected by the user to model the implicit short-term interests of the user for the task of next item prediction, however we feel that features of the songs occurring in the sequence could also convey some important information about the short-term user interest which only the items cannot. In this direction we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend next song to the user.