Abstract

Existing methods to manage privileges in smart home systems have not considered allocating privileges to users based on (i) the relationship of the user with the device, (ii) the location and risk of the device and (iii) the current environment. In this work, we take a multi perspective view on the problem of sharing fine-grained privileges of IoT devices among multiple users in a smart home. We propose the concepts of user role (subset of privileges specific to each device), tasks and security levels (labels for each privilege) to allot right privileges to users. Thereby, limiting the exploitation of privileges assigned to legitimate insiders of the house. Thus, our work matches the aspirations of previous surveys [8] on building a comprehensive access control system to manage privileges in a shared smart home. CCS CONCEPTS

Abstract

Existing methods to manage privileges in smart home systems have not considered allocating privileges to users based on (i) the relationship of the user with the device,(ii) the location and risk of the device and (iii) the current environment. In this work, we take a multi perspective view on the problem of sharing fine-grained privileges of IoT devices among multiple users in a smart home. We propose the concepts of user role (subset of privileges specific to each device), tasks and security levels (labels for each privilege) to allot right privileges to users. Thereby, limiting the exploitation of privileges assigned to legitimate insiders of the house. Thus, our work matches the aspirations of previous surveys on building a comprehensive access control system to manage privileges in a shared smart home.

Abstract

We present a decentralized motion planning and collision avoidance algorithm for multi-robot payload transport systems (PTS). A PTS is a formation of loosely coupled non-holonomic robots that cooperatively transport a deformable payload. Each PTS is constrained to navigate safely in a dynamic environment by inter-formation, environmental, and intra-formation collision avoidance. Real-time collision avoidance for such systems is challenging due to the deformability of formations and high dimensional multi-robot non-convex workspace. We resolve the above challenges by embedding workspaces defined by a multi-robot collision avoidance algorithm and multi-scale repulsive potential fields as constraints within a decentralized convex optimization problem. Specifically, we present two main steps to plan the motion of each formation. First, we compute collision-free multi-scale convex workspaces over a planning

Abstract

Conformance checking of treatment plans in discharge summary data would facilitate the development of clinical decision support system, treatment plan quality assurance, and new treatment plan discovery. Conformance checking requires extraction of medical entities and relationships among them to form a computable representation of the treatment plan present in the discharge summary. We propose a workflow representation of patient’s discharge summary that is referred to as workflow instance. We employ a multi-layer perceptron neural network to extract relationships between medical entities to construct the workflow instance. The aim of this work is to check the conformance of the workflow instance against standard treatment plan. Standard treatment plans are extracted from the treatment guidelines provided on healthcare websites such as WebMD, Mayo Clinic, and Johns Hopkins. For each disease …

Abstract

In this paper, we present a machine learning approach to move a group of robots in a formation. We model the problem as a multi-agent reinforcement learning problem. Our aim is to design a control policy for maintaining a desired formation among a number of agents (robots) while moving towards a desired goal. This is achieved by training our agents to track two agents of the group and maintain the formation with respect to those agents. We consider all agents to be homogeneous and model them as unicycle [1]. In contrast to the leader-follower approach, where each agent has an independent goal, our approach aims to train the agents to be cooperative and work towards the common goal. Our motivation to use this method is to make a fully decentralized multi-agent formation system and scalable for a number of agents.

Abstract

Consider a closed environment with static obstacles and mobile agents moving around. There are hider agents that hide from the seeker agents. The seeker has a limited visibility range, and if a hider comes into the visibility region of a seeker, it is considered caught. The practical applications range from gaming to security. In this work, we focus on deterministic capture of hiders, even if they are guided by an Oracle which knows the future positions of seekers. We develop strategies for seekers, having limited visibility ranges, to catch all hiders and establish minimum bounds on the number of seekers required to catch the hiders, on a per strategy basis. We use spatio-temporal graph models and reasoning to formulate and address the problem.

Abstract

Conformance checking of treatment plans in discharge summary data would facilitate the development of clinical decision support system, treatment plan quality assurance, and new treatment plan discovery. Conformance checking requires extraction of medical entities and relationships among them to form a computable representation of the treatment plan present in the discharge summary. We propose a workflow representation of patient’s discharge summary that is referred to as workflow instance. We employ a multi-layer perceptron neural network to extract relationships between medical entities to construct the workflow instance. The aim of this work is to check the conformance of the workflow instance against standard treatment plan. Standard treatment plans are extracted from the treatment guidelines provided on healthcare websites such as WebMD, Mayo Clinic, and Johns Hopkins. For each disease, these guidelines are curated, aggregated, and represented as a workflow specification. We commend multiple measures to compute the conformance of workflow instance with workflow specification. We validate our conformance checking methodology using discharge summary data of three diseases, namely colon cancer, coronary artery disease, and brain tumor, collected from THYME corpus and MIMIC III clinical database. Our approach and the solution can be used by hospitals and patients to determine adherence, gaps, and additions to standard treatment plans. Further, our work can facilitate to identify common errors and goodness in actual enactment of treatment plans, which can further lead to refinement of standard treatment plans.

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

Literary novels are a mine of information stored in a narrative. Information Extraction (IE) on these novels can be directed by a specific aim to derive knowledge of interest. In this paper, we explore the problem of extracting evidence summaries for all characters in a detective novel. But in this domain, there is no standard annotated text which provides the ground truth for evidence and there exists no fixed metric of evaluation. For extracting summaries, we explore unsupervised methods of learning. We first assume that all characters are equally likely to be culprits and build evidence against each character. Given this evidence, we assess the likelihood of each character being the culprit, eventually arriving at the true culprit(s). Our contribution is the formulation of evidence summaries for characters in determining the culprit. Our experiments are based on a corpus of detective novels spanning the last hundred years. We evaluate these summaries for comprehension with the help of human readers and broadly categorize novels into three groups.

Abstract

In this work, we present an algorithm for robot replacement to increase the operational time of a multi-robot payload transport system. Our system comprises a group of non-holonomic wheeled mobile robots traversing on a known trajectory. We design a multi-robot system with loosely coupled robots that ensures the system lasts much longer than the battery life of an individual robot. A system level optimization is presented, to decide on the operational state (charging or discharging) of each robot in the system. The charging state implies that the robot is not in a formation and is kept on charge whereas the discharging state implies that the robot is a part of the formation. Robot battery recharge hubs are present along the trajectory. Robots in the formation can be replaced at these hub locations with charged robots using a replacement mechanism. We showcase the efficacy of the proposed scheduling framework through simulations and experiments with real robots.