Learning to Fly by Crashing

How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. For supplementary video see: https://youtu.be/u151hJaGKU

CASSL: Curriculum Accelerated Self-Supervised Learning

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for high-dimensional control require either scaling up the data collection efforts or using a clever sampling strategy for training. We present a novel approach - Curriculum Accelerated Self-Supervised Learning (CASSL) - to train policies that map visual information to high-level, higher- dimensional action spaces. CASSL orders the sampling of training data based on control dimensions: the learning and sampling are focused on few control parameters before other parameters. The right curriculum for learning is suggested by variance-based global sensitivity analysis of the control space. We apply our CASSL framework to learning how to grasp using an adaptive, underactuated multi-fingered gripper, a challenging system to control. Our experimental results indicate that CASSL provides significant improvement and generalization compared to baseline methods such as staged curriculum learning (8% increase) and complete end-to-end learning with random exploration (14% improvement) tested on a set of novel objects

Secured Technique of AMOV and ESOV in the Clouds

The available hardware and technology for consumers and service providers today allow for advanced multimedia services over IP-based networks. Hence,the popularity of video and audio streaming services such as Video-on-Demand (VoD),The user demand for videos over the mobile devices through wireless links this wireless links capacity cannot be corporate with the traffic demand. As delay between traffic demand and link capacity, with link conditions, low ouput quality of service and sending data on this media result in buffering time . in this paper we propose a new secure mobile video streaming framework AMoV (adaptive mobile video streaming) and ESoV(efficient social video sharing) are the terms which are currently gaining the attention of variety of computer users and researchers. While enjoying the multimedia services like videos and images, the basic quandary faced by any individual is the progressive downloading or the buffering of the videos. As the researches are focusing on various technologies in said issue, very least focus is given on to the security issues present in these technologies. The basic idea behind this paper is to study and to survey the literature and to propose the security aspects in related field

Swoosh! Rattle! Thump! -- Actions that Sound

Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world. In robotics, we have seen tremendous progress in using visual and tactile perception; however, we have often ignored a key sense: sound. This is primarily due to the lack of data that captures the interplay of action and sound. In this work, we perform the first large-scale study of the interactions between sound and robotic action. To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot. By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information. Using this data, we explore the synergies between sound and action and present three key insights. First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench. Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied to the object. Finally, object representations derived from audio embeddings are indicative of implicit physical properties. We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings. Project videos and data are at https://dhiraj100892.github.io/swoosh/Comment: To be presented at Robotics: Science and Systems 202

Simple and Efficient Group Key Distribution Protocol using Matrices

Group Key Distribution (GKD) protocols are designed to distribute a group key to several users for establishing a secure communication over a public network. The central trusted authority, called the key distribution center (KDC) is in charge of distributing the group keys. For securing the communication, all the users share a common secret key in advance with KDC. In this paper, we propose a secure and efficient Group Authenticated Key Distribution (GAKD) protocol based on the simple idea of encryption in matrix rings. In this protocol, each user registers in private with the KDC, while all the other information can be transferred publicly. The scheme also supports authentication of group keys without assuming computational hard problems such as Integer Factorization Problem (IFP).The analysis of our GAKD protocol shows that the proposed protocol is resistant to reply, passive and impersonation attacks. Our construction leads to a secure, cost and computation- effective GAKD protocol

Integrating SDLC and ITSM to \u27Servitize\u27 Systems Development

IT Service Management (ITSM) is generating much interest in industry as the quality and reliability of IT services are increasingly recognized as a critical factor for business success. Academic researchers have been slower to respond to industry demand for IT service management research and coursework. This paper argues that academia has an important role to play in integrating ITSM concepts and skills into traditional information systems coursework, specifically system development. The core systems analysis and design course is used to illustrate how IT service management concepts and models can be introduced into existing coursework to support the focus on IT services required by industry today, eventually reducing the growing percentage of IT budgets currently attributed to the operation and maintenance stages of ‘nonservitized’ systems development projects