False data injection attack (FDIA): An overview and new metrics for fair evaluation of its countermeasure

The concept of false data injection attack (FDIA) was introduced originally in the smart grid domain. While the term sounds common, it specifically means the case when an attacker compromises sensor readings in such tricky way that undetected errors are introduced into calculations of state variables and values. Due to the rapid growth of the Internet and associated complex adaptive systems, cyber attackers are interested in exploiting similar attacks in other application domains such as healthcare, finance, defense, governance, etc. In today’s increasingly perilous cyber world of complex adaptive systems, FDIA has become one of the top-priority issues to deal with. It is a necessity today for greater awareness and better mechanism to counter such attack in the cyberspace. Hence, this work presents an overview of the attack, identifies the impact of FDIA in critical domains, and talks about the countermeasures. A taxonomy of the existing countermeasures to defend against FDIA is provided. Unlike other works, we propose some evaluation metrics for FDIA detection and also highlight the scarcity of benchmark datasets to validate the performance of FDIA detection techniques. [Figure not available: see fulltext.] © 2020, The Author(s)

Autonomous reconnaissance mission: development of an algorithm for collaborative multi robot communication

A collaborative team of two resource constrained semi-autonomous hexapod robots have been developed that perform navigation tasks while satisfying communication constraints. Our approach is based on the use of a control structure where each hexapod performs elementary tasks, a behavior-based controller generates motion directives to achieve the collaborative tasks, and controller generates the actuator commands to follow the motion directives. The control technique has been developed for a mission where a target location spread across a static environment has to be visited once by the two hexapods while maintaining a relative given distance with wireless communication. Wireless communication under mobile ad-hoc networks are communication networks that do not rely on fixed, preinstalled communication devices like base stations or predefined communication cells. This wireless networks consist of mobile nodes which are characterized by their decentralized organization and the potentially high dynamics of the network structure, therefore ad-hoc network communication system has been the focus in this multi-robot communication. The ad-hoc network has to provide position data to support localization of the mobile robots, which might be of great importance to guide the robots to specific targets and locations. Communications standards considered for the ad-hoc network are Wireless LAN, Bluetooth and ZigBee. In this project Bluetooth and ZigBee are integrated on robots for real experiments

Autonomous biomimitic robot based multi-agent system for disaster management and rescue

This paper discusses the scope and feasibility of autonomous biomimitic robot based multi-agent systems for disaster management and rescue. Search and rescue operations in disastrous situations like earthquake, landslide, fire hazards, mineshaft breakdown etc. are still handled manually. Manual operations in these cases often fail due to complicated nature of the catastrophe. Especially in the case of human entrapment in areas inaccessible to either human or traditional rescue equipment. As such rescue operation suffers from improper strategy and even leads to unintentional further destruction due to lack of proper information along the rescue site. It is clear, proper information in and around the disaster can help successful handling of the catastrophe. Thus information like location of the survivor, state of the obstructions around him/her, state of injury, level of oxygen and hazardous gases are of crucial importance. To gather such widespread information from such difficult terrain, autonomous robots equipped with multiple sensors and capable to move inside difficult to access areas is a good choice. Autonomous biomimitic robot like Snake robot is meant to mimic motion of a natural snake, which does not possess any limb. Natural snakes can undergo wide range of motion and are able to move over rough terrains without the danger of entanglement. Slender structure of the snake body helps a snake to go inside narrow holes. Thus a snake robot able to mimic these features of a natural snake will be of extreme use in handling search and rescue operations. Snake robots equipped with multiple sensors and controlled under multiagent collaborative protocol are expected to bring about acceptable solution to disaster management and rescue. The other such biomimitic robots that can be considered in the autonomous robot team are flapping wing flyers and robot Monkeys. A team consisting of such robots will help in collecting information, distributing food and medicine in disastrous location

Hexagonal structure hexapod robot: developing a method for omni-directional navigation

In this paper, we propose a method that allows a hexapod robot to navigate omni-directionally with hexagonal structure. Typical body structures for hexapod robot are analyzed. Hexapod robot frequently navigates various directions over variety of surfaces. To enable locomotion in rough surface, hexapod must be able to stably move in any direction. A comparative study, based on different model of hexapod for omni-directional navigation, concludes that the hexagonal hexapod robot can be able to navigate omni-directionally on the complex surface. Finally, a method is developed for omni-directional navigation of the hexapod robot

Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8 %) F1-score, followed by gradient boost (84.3%), AdaBoost (78.9 %), and XGBoost (83.1 %). Second, it was revealed that during the time of the COVID-19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like

Liquid-Metal Synthesized Ultrathin SnS Layers for High-Performance Broadband Photodetectors

Atomically thin materials face an ongoing challenge of scalability, hampering practical deployment despite their fascinating properties. Tin monosulfide (SnS), a low-cost, naturally abundant layered material with a tunable bandgap, displays properties of superior carrier mobility and large absorption coefficient at atomic thicknesses, making it attractive for electronics and optoelectronics. However, the lack of successful synthesis techniques to prepare large-area and stoichiometric atomically thin SnS layers (mainly due to the strong interlayer interactions) has prevented exploration of these properties for versatile applications. Here, SnS layers are printed with thicknesses varying from a single unit cell (0.8 nm) to multiple stacked unit cells (approximate to 1.8 nm) synthesized from metallic liquid tin, with lateral dimensions on the millimeter scale. It is reveal that these large-area SnS layers exhibit a broadband spectral response ranging from deep-ultraviolet (UV) to near-infrared (NIR) wavelengths (i.e., 280-850 nm) with fast photodetection capabilities. For single-unit-cell-thick layered SnS, the photodetectors show upto three orders of magnitude higher responsivity (927 A W-1) than commercial photodetectors at a room-temperature operating wavelength of 660 nm. This study opens a new pathway to synthesize reproduceable nanosheets of large lateral sizes for broadband, high-performance photodetectors. It also provides important technological implications for scalable applications in integrated optoelectronic circuits, sensing, and biomedical imaging

Financing micro-entrepreneurs for poverty alleviation: a performance analysis of microfinance services offered by BRAC, ASA, and Proshika from Bangladesh

Microfinance services have emerged as an effective tool for financing microentrepreneurs to alleviate poverty. Since the 1970s, development theorists have considered non-governmental microfinance institutions (MFIs) as the leading practitioners of sustainable development through financing micro-entrepreneurial activities. This study evaluates the impact of micro-finance services provided by MFIs on poverty alleviation. In this vein, we examine whether microfinance services contribute to poverty alleviation, and also identify bottlenecks in micro-finance programs and operations. The results indicate that the micro-loans have a statistically significant positive impact on the poverty alleviation index and consequently improve the living standard of borrowers by increasing their level of income

The burden of unintentional drowning : global, regional and national estimates of mortality from the Global Burden of Disease 2017 Study

Background Drowning is a leading cause of injury-related mortality globally. Unintentional drowning (International Classification of Diseases (ICD) 10 codes W65-74 and ICD9 E910) is one of the 30 mutually exclusive and collectively exhaustive causes of injury-related mortality in the Global Burden of Disease (GBD) study. This study's objective is to describe unintentional drowning using GBD estimates from 1990 to 2017. Methods Unintentional drowning from GBD 2017 was estimated for cause-specific mortality and years of life lost (YLLs), age, sex, country, region, Socio-demographic Index (SDI) quintile, and trends from 1990 to 2017. GBD 2017 used standard GBD methods for estimating mortality from drowning. Results Globally, unintentional drowning mortality decreased by 44.5% between 1990 and 2017, from 531 956 (uncertainty interval (UI): 484 107 to 572 854) to 295 210 (284 493 to 306 187) deaths. Global age-standardised mortality rates decreased 57.4%, from 9.3 (8.5 to 10.0) in 1990 to 4.0 (3.8 to 4.1) per 100 000 per annum in 2017. Unintentional drowning-associated mortality was generally higher in children, males and in low-SDI to middle-SDI countries. China, India, Pakistan and Bangladesh accounted for 51.2% of all drowning deaths in 2017. Oceania was the region with the highest rate of age-standardised YLLs in 2017, with 45 434 (40 850 to 50 539) YLLs per 100 000 across both sexes. Conclusions There has been a decline in global drowning rates. This study shows that the decline was not consistent across countries. The results reinforce the need for continued and improved policy, prevention and research efforts, with a focus on low- and middle-income countries.Peer reviewe