Abstract

Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)-based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML-based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road-map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

Abstract

We consider a setup where Alice is transmitting channel coded messages to Bob and intruder Willie is snooping over this communication. Willie does not know the channel code that Alice is using and wishes to identify it in order to decode the intercepted messages. This problem is termed as blind reconstruction of channel codes and has possible applications in cognitive radios and military. In this work, we consider the situation when Willie knows that Alice's channel code belongs to the family of binary cyclic codes and the underlying communication channel is the binary erasure channel. We present an algorithm to identify the parameters of the unknown channel code corresponding to the given data. The key step in our algorithm consists of distinguishing between the two situations when all of the assumed parameters are correct and when either of the parameter is incorrect. As part of analysis, we present a lower bound on probability of correctly distinguishing between these two situations.

Abstract

Maintaining the river water quality has become more prominent these days due to excess pollutants from various sources like industries and domestic sewage discharge. The assessment of river water quality is a significant task for the management and sustainability of the riverine health system for future generations. This study evaluates the water quality of the Bhadra river stretch in India using the water quality index and qual2k model. The water quality index (WQI) is estimated using Weighted Average Water Quality Index (WAWQI) method and water quality indicators like Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), Electrical Conductivity (Ec), Nitrate (NO3), pH, and Temperature are simulated using Qual2k model. The Qual2k model was calibrated and validated for a period of 91-months (April 2006 to October 2013). The estimated WQI values range from 92.35 to 112, and the quality classification ranges from very poor to unfit for consumption. It was observed that the quality status of the river water was very poor in upstream and downstream segments, while unfit for consumption in the middle segment indicating access to industrial and anthropogenic activities. To conserve river water from pollution, strict laws and regulations must be implemented to improve health and preserve water resources for future generations.

Abstract

After one learns to program in miniKanren, it is natural to want to understand how it is implemented. However, there is no resource to learn how to implement miniKanren with constraints such as =/=, symbolo, numbero, and absento. Furthermore, these are used to implement a wide class of interesting programs such as relational interpreters and relational type inferencers, making the need for such a resource all the more felt. This paper aims to be that resource. Additional Key Words and Phrases: miniKanren implementation, Racket, relational programming

Abstract

Investigation of microstates extracted from EEG signals of tip-pinch and wrist flexion/extension movement

Abstract

Homeostasis represents the idea that a feature may remain invariant despite changes in some external parameters. We establish a connection between homeostasis and injectivity for reaction network models. In particular, we show that a reaction network cannot exhibit homeostasis if a modified version of the network (which we call homeostasis-associated network) is injective. We provide examples of reaction networks which can or cannot exhibit homeostasis by analyzing the injectivity of their homeostasis-associated networks.

Abstract

Autocatalytic systems called hypercycles are very often incorporated in "origin of life" models. We investigate the dynamics of certain related models called bimolecular autocatalytic systems. In particular, we consider the dynamics corresponding to the relative populations in these networks, and show that it can be analyzed using well-chosen autonomous polynomial dynamical systems. Moreover, we use results from reaction network theory to prove persistence and permanence of several families of bimolecular autocatalytic systems called autocatalytic recombination systems. Keywords: Autocatalytic recombination networks; Origin of life; Permanence; Persistence. Copyright © 2022 Elsevier Inc. All rights reserved.

Abstract

Quadrotors are becoming more and more essen- tial for applications such as payload delivery, inspection and search-and-rescue. Such operations pose considerable control challenges, especially when various (a priori unbounded) state- dependent unknown dynamics arises from payload variations, aerodynamic effects and from reaction forces while operating close to the ground or in a confined space. However, existing adaptive control strategies for quadrotors cannot handle un- known state-dependent uncertainties. We address such unsolved control challenge in this work via a novel adaptive method for artificial time delay control, where unknown dynamics is robustly compensated by using input and state measurements collected at immediate past time instant (i.e., artificially delayed). Closed-loop stability is established via Lyapunov theory. The effectiveness of this controller is validated using experimental results

Abstract

Quadrotors are increasingly used nowadays as a mode of aerial transport during relief operations, payload delivery etc. However, in addition to higher inertia, large size payload may interfere with prop wash (displaced mass of air by the propeller), leading to unstable behaviour. On the other hand, using different quadrotors according to payload size is not feasible either economically or operationally. Therefore, in this work, we have developed a unique scissor-mechanism based reconfigurable quadrotor which can adapt to various sizes of payloads. A robust controller is also proposed to negotiate the parametric variations stemming from the reconfigurations in the system. The effectiveness of the proposed mechanism is studied extensively via comparative experiments.

Abstract

This work introduces a new single-stage adaptive controller for Euler-Lagrange systems with nonholonomic con- straints. The proposed mechanism provides a simpler design philosophy compared to double-stage mechanisms (that ad- dress kinematics and dynamics in two steps), while achieving analogous stability properties, i.e. stability of both original and internal states. Meanwhile, we do not require direct access to the internal states as required in state-of-the-art single-stage mechanisms. The proposed approach is studied via Lyapunov analysis, validated numerically on wheeled mobile robot dy- namics and compared to a standard double-stage approach