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Cybercrimes have become as common as petty crimes and at every level we can face situations that make us vulnerable to cybercrimes through links, text messages on phones, scam phone calls etc. that demand to sensitive information and misuse them. The 2019-2021 Corona Virus (SARS-CoV-2) Pandemic has shown a drastic increase in cybercrimes. As use of technology, remote access work and communication rise, increase in digital process increase, it has also made people vulnerable to many different types of cyber-attacks such as phishing, malwares and social engineering. It has also resulted in commercial theft of intellectual property. This paper tries to throw light on economic loses that have occurred due to cybercrimes.

Cumulative screen exposure has been increased due to the explosion of digital technology ownership in the past decade for all people, including children who face exposure related risks such as obesity, eye problems, and disrupted sleep. Screen exposure is linked to physical and mental health risks among both children and adults. Current methods of screen exposure assessment have their limitations, mostly in the prospective of objectiveness, robustness, and invasiveness. In this paper, we propose a novel method to measure screen exposure time using a wearable sensor and computer vision technology. We use a customized, lightweight, wearable senor to capture egocentric images and use deep learning-based object detection module to identify the existence of electronic screens. The duration of screen exposure is further estimated using post-processing technology to filter consecutive frames regarding to the screen usage. Our method is non-invasive and robust, providing an objective and accurate means to screen exposure measurement. We conduct experiments on various environments to identify the existence of three types of screens and duration of screen exposure. The experimental results demonstrate the feasibility of automatically assessing screen time exposure and great potential to be applied in large scale experiments for behavioral study.

A huge opportunity for scribe automation is to design a system specifically to support visually impaired and blind students in the process of achieving high levels of independence while attending examinations, by automating their exam-taking process. The ability of students to control and independently answer questions through voice helps reduce their reliance on human assistance. The prime goal of the proposed application is to solve the problem of students being dependent on scribes to take examinations. Upon clicking particular keys on the keyboard, questions will be dictated by using the concept of speech synthesis, and answers can be given accordingly based on the concept of speech recognition. Hence, not only visually impaired students can benefit from this but it will prove to be useful for the other handicapped people as well.

Due to urbanization and population explosion in India, the number of vehicles on the road every day is increasing at an unprecedented rate. This indirectly calls for increase in rate of road accidents and fatality rate. With budding economic trends and shape-shifting growth plot of urban world, the need to improve consumer safety all together has been a major issue in hand for the transportation magnet industries. Inefficient transport system, meagre congestion management and reckless drivers with no regard to human life have been a major stake around the epicenter of all snags of road accidents. Thus, there is a crucial need to inculcate technological signatures to uplift the current backlogs transportation industry is facing, by making it more accessible, environmentally friendly, faster, comfortable, safe and affordable without compromising the future needs. Wrong way driving on busy roads and highways can have catastrophic outcomes. If a wrong way driver is not detected and dealt with instantly, they will be endangering the lives of themselves and other road users. This article therefore attempts to detect perilous conditions such as vehicles approaching in oncoming lane traffic and slow-moving vehicles and inform and aware the drivers who is forthcoming in the same lane.

The recently evolved research on neuromorphic computing which is considered as the hardware acceleration of brain-inspired computing, which has been one of the best adaptable intelligences and is being widely used in various areas of applications ranging from basic neuroscience research, to pattern recognition and artificial intelligence. Although, computing power is technologically progressing in astonishing rate, the need to push computing power further always has been the dire necessity in the modern world. The paradigm shift from traditional convectional computing to neuromorphic computing can be the next generation of computing offering high computational speeds and greater efficiency when compared to the traditional way of information processing and computing. This invited paper, starts off with the background introduction of Moore’s law followed by neuromorphic computing and end with recent innovations in the field and conclude with technological challenges and applications of neuromorphic computing in day to day life.