Energy Efficient Personalized Hand-Gesture Recognition with Neuromorphic Computing

Hand gestures are a form of non-verbal communication that is used in social interaction and it is therefore required for more natural human-robot interaction. Neuromorphic (brain-inspired) computing offers a low-power solution for Spiking neural networks (SNNs) that can be used for the classification and recognition of gestures. This article introduces the preliminary results of a novel methodology for training spiking convolutional neural networks for hand-gesture recognition so that a humanoid robot with integrated neuromorphic hardware will be able to personalise the interaction with a user according to the shown hand gesture. It also describes other approaches that could improve the overall performance of the model

Blockchain Technology, Technical Challenges and Countermeasures for Illegal Data Insertion

Blockchain is a decentralized transaction and data management technology. It was developed for the world’s first cryptocurrency known as Bitcoin in 2008. The reason behind its popularity was its properties which provide pseudonymity, security, and data integrity without third-party intervention.  Initially, most of the researches were focused on the Bitcoin system and its limitation, but later other applications of Blockchain e.g. smart contracts and licensing [1] also got famous. Blockchain technology has the potential to change the way how transactions are conducted in daily life. It is not limited to cryptocurrencies but could be possibly applied in various environments where any forms of transactions are done. This article presents a comprehensive overview of Blockchain technology, its development, applications, security issues, and their countermeasures. In particular, the security towards illegal data insertion and the countermeasures is focused. Our analysis of countermeasures of illegal data insertion can be combined for increased efficiency. After the introduction of the Blockchain and consensus algorithm, some famous Blockchain applications and expected future of Blockchain are deliberated. Then, the technical challenges of Blockchain are discussed, in which the main focus here is on the security and the data insertion in Blockchain. The review of the possible countermeasures to overcome the security issues related to data insertion are elaborated

Neuromorphic Computing for Interactive Robotics: A Systematic Review

Modelling functionalities of the brain in human-robot interaction contexts requires a real-time understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how they interact all together to accomplish complex behavioural tasks while interacting with the environment. Human brains are very efficient as they process the information using event-based impulses also known as spikes, which make living creatures very efficient and able to outperform current mainstream robotic systems in almost every task that requires real-time interaction. In recent years, combined efforts by neuroscientists, biologists, computer scientists and engineers make it possible to design biologically realistic hardware and models that can endow the robots with the required human-like processing capability based on neuromorphic computing and Spiking Neural Network (SNN). However, while some attempts have been made, a comprehensive combination of neuromorphic computing and robotics is still missing. In this article, we present a systematic review of neuromorphic computing applications for socially interactive robotics.We first introduce the basic principles, models and architectures of neuromorphic computation. The remaining articles are classified according to the applications they focus on. Finally, we identify the potential research topics for fully integrated socially interactive neuromorphic robots

Differential privacy made easy

Data privacy has been a significant issue for many decades. Several techniques have been developed to make sure individuals' privacy but still, the world has seen privacy failures. In 2006, Cynthia Dwork gave the idea of Differential Privacy which gave strong theoretical guarantees for data privacy. Many companies and research institutes developed differential privacy libraries, but in order to get differentially private results, users have to tune the privacy parameters. In this paper, we minimized these tunable parameters. The DP-framework is developed which compares the differentially private results of three Python based differential privacy libraries. We also introduced a new very simple DP library (GRAM - DP), so that people with no background in differential privacy can still secure the privacy of the individuals in the dataset while releasing statistical results in public