Abstract

Given a monocular color image of a warehouse rack, we aim to predict the bird's-eye view layout for each shelf in the rack, which we term as' multi-layer'layout prediction. To this end, we present RackLay, a deep neural network for real-time shelf layout estimation from a single image. Unlike previous layout estimation methods which provide a single layout for the dominant ground plane alone, RackLay estimates the top-viewand front-view layout for each shelf in the considered rack populated with objects. RackLay's architecture and its variants are versatile and estimate accurate layouts for diverse scenes characterized by varying number of visible shelves in an image, large range in shelf occupancy factor and varied background clutter. Given the extreme paucity of datasets in this space and the difficulty involved in acquiring real data from warehouses, we additionally release a flexible synthetic dataset generation …

Abstract

Assessing the number of street trees is essential for evaluating urban greenery and can help municipalities employ solutions to identify tree-starved streets. It can also help identify roads with different levels of deforestation and afforestation over time. Yet, there has been little work in the area of street trees quantification. This work first explains a data collection setup carefully designed for counting roadside trees. We then describe a unique annotation procedure aimed at robustly detecting and quantifying trees. We work on a dataset of around 1300 Indian road scenes annotated with over 2500 street trees. We additionally use the five held-out videos covering 25 km of roads for counting trees. We finally propose a street tree detection, counting, and visualization framework using current object detectors and a novel yet simple counting algorithm owing to the thoughtful collection setup. We find that the high-level …

Abstract

Sentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspectbased sentiment analysis aims to extract the aspects of the given target entities and their respective sentiments. Prior works formulate this as a sequence tagging problem or solve this task using a span-based extract-thenclassify framework where first all the opinion targets are extracted from the sentence, and then with the help of span representations, the targets are classified as positive, negative, or neutral. The sequence tagging problem suffers from issues like sentiment inconsistency and colossal search space. Whereas, Span-based extract-then-classify framework suffers from issues such as half-word coverage and overlapping spans. To overcome this, we propose a similar span-based extract-then-classify framework with a novel and improved heuristic. Experiments on the three benchmark datasets (Restaurant14, Laptop14, Restaurant15) show our model consistently outperforms the current state-of-the-art. Moreover, we also present a novel supervised movie reviews dataset (Movie20) and a pseudo-labeled movie reviews dataset (moviesLarge) made explicitly for this task in English language and report the results on the novel Movie20 dataset as well.

Abstract

Human-robot interaction, particularly in wheeled mobile robots that can autonomously assist humans to traverse dynamically changing environments is a field of active research. Integrated motion planning and obstacle-avoidance pose a considerable challenge for an autonomous person-following robot (PFR). And, scenarios with intersections and occlusions along the path only increase the complexity in sustained tracking. In this paper, we use model predictive control (MPC) with earlyrelocation (ER) strategy to formulate a prioritized tracking scheme and implement it for a differential-drive system. Our approach ensures that the target person stays within the field of view (FOV) of the PFR consistently, even while it maneuvers intersections or crowded spots, by adding new locations to its updated path. As trajectory generation in such cases must be incremental to accommodate new information, the use of efficient representations is key. To that end, we build this social representation of following a person directly into the controller itself. MPC can naturally handle such state and input limitations as constraints to solve an on-line optimization at each time step. A non-linear MPC with ER is thus devised and tested with increasing levels of complexity arising from occlusions due to the map and its dynamic actors. By using 2D simulations, we show that for slow and medium walking speeds of the target person, the controller can plan maneuvers with an adequate margin of over 20 Hz apt for achieving a near real-time personfollowing behaviour.

Abstract

This paper presents the design of an in-pipe climbing robot that operates using a novel ‘Three-output open differential’(3-OOD) mechanism to traverse complex networks of pipes. Conventional wheeled/tracked in-pipe climbing robots are prone to slip and drag while traversing in pipe bends. The 3-OOD mechanism helps in achieving the novel result of eliminating slip and drag in the robot tracks during motion. The proposed differential realizes the functional abilities of the traditional two-output differential, which is achieved the first time for a differential with three outputs. The 3-OOD mechanism mechanically modulates the track speeds of the robot based on the forces exerted on each track inside the pipe network, by eliminating the need for any active control. The simulation of the robot traversing in the pipe network in different orientations and in pipe-bends without slip shows the proposed design’s effectiveness.

Abstract

This paper proposes a novel method for reactive multiagent collision avoidance by characterizing the longitudinal and lateral intent uncertainty along a trajectory as a closed-form probability density function. Intent uncertainty is considered as the set of reachable velocities in a planning interval and distributed as a Gaussian distribution over the robot’s instantaneous velocity. We utilize the Time Scaled Collision Cone(TSCC) approach, which characterizes the space of instantaneous collision avoidance velocities available to the egoagent. We introduce intent uncertainty into the characteristic equation of the TSCC to derive the closed-form probability density function, which allows the collision avoidance problem to be rewritten as a deterministic optimization procedure. The formulation also allows the flexibility for the inclusion of confidence intervals for collision avoidance. We thus demonstrate the results and ablation studies of this derived collision avoidance formulation on various confidence intervals

Abstract

Coronavirus 2019, called COVID-19, is a transmissible disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It earlier impacted the citizens of China alone. However, it has rapidly spread all over the world. The COVID-19 supply chain system aims to facilitate access to several critical items, such as personal protective equipment (PPE), biomedical equipment, diagnostics supplies, and vaccines. In this article, we discuss a robust security framework for vaccine distribution and tracking in an Internet of Medical Things (IoMT)-based cloud-assisted COVID-19 environment by considering both intra-country and inter-country scenarios. Various transactions related to vaccine requests, orders, distribution, and tracking are put into the blockchain in the form of blocks. Since blockchain technology offers immutability, transparency, and decentralization, the security of the proposed framework has been improved significantly. The proposed framework also supports artificial intelligence(AI)-based big data analytics on the information stored into the blocks in the blockchain. Furthermore, a practical demonstration of the proposed framework has been done through a blockchain simulation study.

Abstract

—In an Internet of Things (IoT) enabled e-health system, smart health devices sense the health data continuously and share the collected data with the neighboring controller device (i.e., personal server) via some wireless communication mechanism (i.e., bluetooth and zigbee), and finally the data is stored on some health server (i.e., a server over the cloud). The health data is then accessible to healthcare service providers (for example, doctors, nursing staff, relatives of patient) for tracking and monitoring of health conditions of the patients for their better treatment at the earliest. In an IoT enabled health system, the smart healthcare devices communicate over public channel, which causes various types of threats and attacks on the ongoing communication. Therefore, we need a powerful privacypreserving security mechanism to secure the communication happens in an IoT enabled e-health system as the health data is strictly private and confidential. In this paper, we propose a new privacy-preserving access control and key management scheme for the secure communication of IoT enabled e-health system (SPCS-IoTEH). We also conduct informal security analysis of the proposed SPCS-IoTEH to show its robustness against various types of active and passive attacks. The performance of SPCSIoTEH is also shown to be better than other existing competing schemes.

Abstract

In recent years, the Internet of Things (IoT) enabled drones, also called as unmanned aerial vehicles (UAVs), are widely used in many applications ranging from military to civilian applications, such as wildlife monitoring. Since the drones provide a risk-free as well as low-cost facility in order to quickly and persistently monitor natural circumstances at high spacial temporal resolution, they help in wildlife monitoring research. Due to wireless communication nature, the communication among the deployed drones in their respective flying zones and the IoT smart devices installed in animal bodies, and also among the drones and their respective ground station server (GSS), is susceptible to various passive and active attacks. To mitigate these issues, we propose a public blockchain based access control implementation for wild-life monitoring purpose. The application of both access control and blockchain at the same time not only protects various attacks, but also maintains immutability, transparency as well as decentralization properties. Next, we simulate the proposed security framework for the blockchain part to measure the total computational time needed to add a varied number of blocks in a blockchain and also a varied number of transactions per block. Finally, a practical testbed experiment has been implemented to show the feasibility of the proposed framework. Index Terms—Wildlife monitoring, blockchain, consensus, access control, security, testbed experiments.

Abstract

— The effect of adjusting damping factor α and tolerance τ on iterations needed for PageRank computation is studied here. Relative performance of PageRank computation with L1, L2, and L∞ norms used as convergence check, are also compared with six possible mean ratios. It is observed that increasing the damping factor α linearly increases the iterations needed almost exponentially. On the other hand, decreasing the tolerance τ exponentially decreases the iterations needed almost exponentially. On average, PageRank with L∞ norm as convergence check is the fastest, quickly followed by L2 norm, and then L1 norm. For large graphs, above certain tolerance τ values, convergence can occur in a single iteration. On the contrary, below certain tolerance τ values, sensitivity issues can begin to appear, causing computation to halt at maximum iteration limit without convergence. The six mean ratios for relative performance comparison are based on arithmetic, geometric, and harmonic mean, as well as the order of ratio calculation. Among them GM-RATIO, geometric mean followed by ratio calculation, is found to be most stable, followed by AM-RATIO.