Abstract

In this paper, we present an algorithmic framework which computes the collision-free velocities for the robot in a human shared dynamic and uncertain environment. We extend the concept of Inverse Velocity Obstacle (IVO) to a probabilistic variant to handle the state estimation and motion uncertainties that arise due to the other participants of the environment. These uncertainties are modeled as non-parametric probability distributions. In our PIVO: Probabilistic Inverse Velocity Obstacle, we propose the collision-free navigation as an optimization problem by reformulating the velocity conditions of IVO as chance constraints that takes the uncertainty into account. The space of collision-free velocities that result from the presented optimization scheme are associated to a confidence measure as a specified probability. We demonstrate the efficacy of our PIVO through numerical simulations and demonstrating its ability to generate safe trajectories under highly uncertain environments.

Abstract

In IoT applications, sensors produce raw data, and smart solutions consume the processed data. IoT driven smart solutions typically use data that is aggregated, filtered and processed to drive the smart solution. Such aggregated data has different semantics in comparison to raw sensor data, and it needs to be customized simultaneously for different application requirements. There is a need for proper data logistics for the movement of data from the sensors producing raw data to smart solutions consuming solution relevant data. In this paper, we present a data logistics system that includes (i) architecture to support the movement of data from sensors to smart applications, and (ii) event-driven solution to trigger workflows of smart solution. Workflows in our solution supports the data fulfillment requirements for the enactment of smart solutions

Abstract

Visual Question Answering (VQA) has emerged as an important problem spanning Computer Vision, Natural Language Processing and Artificial Intelligence (AI). In conventional VQA, one may ask questions about an image which can be answered purely based on its content. For example, given an image with people in it, a typical VQA question may inquire about the number of people in the image. More recently, there is growing interest in answering questions which require commonsense knowledge involving common nouns (e.g., cats, dogs, microphones) present in the image. In spite of this progress, the important problem of answering questions requiring world knowledge about named entities (e.g., Barack Obama, White House, United Nations) in the image has not been addressed in prior research. We address this gap in this paper, and introduce KVQA – the first dataset for the task of (world) knowledge-aware VQA. KVQA consists of 183K question-answer pairs involving more than 18K named entities and 24K images. Questions in this dataset require multi-entity, multi-relation, and multi-hop reasoning over large Knowledge Graphs (KG) to arrive at an answer. To the best of our knowledge, KVQA is the largest dataset for exploring VQA over KG. Further, we also provide baseline performances using state-of-the-art methods on KVQA.

Abstract

This paper describes our system (Fermi) for Task 4: Hyper-partisan News detection of SemEval-2019. We use simple text classification algorithms by transforming the input features to a reduced feature set. We aim to find the right number of features useful for efficient classification and explore multiple training models to evaluate the performance of these text classification algorithms. Our team - Fermi’s model achieved an accuracy of 59.10% and an F1 score of 69.5% on the official test data set. In this paper, we provide a detailed description of the approach as well as the results obtained in the task.

Abstract

We present a data set of 1460 Hindi-English code-mixed tweets consisting of 20,949 tokens labelled with Proposition Bank labels marking their semantic roles. We created verb frames for complex predicates present in the corpus and formulated mappings from Paninian dependency labels to Proposition Bank labels. With the help of these mappings and the dependency tree, we propose a baseline rule based system for Semantic Role Labelling of Hindi-English code-mixed data. We obtain an accuracy of 96.74% for Argument Identification and are able to further classify 73.93% of the labels correctly. While there is relevant ongoing research on Semantic Role Labelling (SRL) and on building tools for code-mixed social media data, this is the first attempt at labelling semantic roles in Hindi-English codemixed data, to the best of our knowledge.

Abstract

We present a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets. We explore the issues posed by noisy, user generated code-mixed social media data. We also compare the individual effect of various linguistic features used in our system. Our proposed model is a 2-step system for automated labelling which gives an overall accuracy of 84% for Argument Classification, marking a 10% increase over the existing rulebased baseline model. This is the first attempt at building a statistical Semantic Role Labeller for Hindi-English code-mixed data, to the best of our knowledge

Abstract

Cuff-less blood pressure (BP) is essential for continuous health monitoring to prevent diseases such as hypertension. Due to the discomfort caused by inflation and deflation of the cuff, it is not possible to monitor continuously. Although pulse transit time (PTT) based approach is commonly used, other parameters also vary with BP. Multi-parameter models are developed using regression analysis, to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). Hence, the correlation of multiple extracted features with the blood pressure is proved. To achieve this, simultaneous electrocardiogram (ECG) and photoplethysmographic (PPG) along with respective BP data were collected. The developed algorithm uses wavelet transformation on ECG and PPG signals for detection of the occurrence of essential wave points precisely, even in the presence of artifacts. Pulse wave analysis (PWA) is performed to create a feature vector. From the experimental results, it is found that, the SBP model gives mean error of 0.4916 mmHg, with standard deviation of 6.3986 mmHg, whereas for DBP model, mean error is 0.2527 mmHg and standard deviation is 3.2835 mmHg which is acceptable as per the British Hypertension Society (BHS) and Association for the Advancement of Medical Instruments (AAMI) standards. After the removal of BP oddities, mean absolute error improves from 5.625 to 3.854 mmHg for SBP and from 2.564 to 2.144 mmHg for DBP.

Abstract

Directed Energy System (DES) is highly efficient to damage both static and dynamic targets. Each target demands a minimum lethal fluence to make engagement successful, depending on the material, thickness, coating and structural properties. DES provides the required lethal fluence on the target to reach the melting or burning temperature. The static target consumes approximately constant fluence, but for the dynamic target, the flux density requirement estimation varies within the dwell time, which is proportional to the distance between the DES and the target. In this paper, the initial power requirement of DES has been estimated based on target specification.

Abstract

Social dynamics are based on human needs for trust, support, resource sharing, irrespective of whether they operate in real life or in a virtual setting. Massively multiplayer online role-playing games (MMORPGS) serve as enablers of leisurely social activity and are important tools for social interactions. Past research has shown that socially dense gaming environments like MMORPGs can be used to study important social phenomena, which may operate in real life, too. We describe the process of social exploration to entail the following components 1) finding the balance between personal and social time 2) making choice between a large number of weak ties or few strong social ties. 3) finding a social group. In general, these are the major determinants of an individual’s social life. This paper looks into the phenomenon of social exploration in an activity based online social environment. We study this process through the lens of the following research questions, 1) What are the different social behavior types? 2) Is there a change in a player’s social behavior over time? 3) Are certain social behaviors more stable than the others? 4) Can longitudinal research of player behavior help shed light on the social dynamics and processes in the network? We use an unsupervised machine learning approach to come up with 4 different social behavior types - Lone Wolf, Pack Wolf of Small Pack, Pack Wolf of a Large Pack and Social Butterfly. The types represent the degree of socialization of players in the game. Our research reveals that social behaviors change with time. While lone wolf and pack wolf of small pack are more stable social behaviors, pack wolf of large pack and social butterflies are more transient. We also observe that players progressively move from large groups with weak social ties to settle in small groups with stronger ties. Index Terms—Social Exploration, Social Behavior Typology, MMORPG, Clusterin

Abstract

Massively Multiplayer Online Role-Playing Games (MMORPGs) are persistent virtual environments where millions of players interact in an online manner. We study the problem of player churn and social contagion using MMORPG game logs by analyzing the impact of a node’s churn behavior on its immediate neighborhood or group. The two key research questions in this paper are - When an active node, ego, becomes dormant, what is the impact on the activity behavior of ego’s immediate neighbor, alter, 1) based on ego’s characteristics and ego’s relationship with alter and 2) based on the activity behavior of alter’s remaining neighbors. We use a supervised learning framework to study the impact of player churn and social contagion. Experimental results show that the classification models perform substantially better than random for both the research problems. Finally, we use a data-driven approach to propose a player typology based on degree of socialization and analyze churn behavior among these player types. Experimental results show that the loner player type is much more likely to churn than the socializer player types and as the degree of socialization decreases among socializers, the propensity to churn increases. Index Terms—churn, social contagion, supervised learning, clustering, player typology