A privacy-preserving model based on differential approach for sensitive data in cloud environment

A large amount of data and applications need to be shared with various parties and stakeholders in the cloud environment for storage, computation, and data utilization. Since a third party operates the cloud platform, owners cannot fully trust this environment. However, it has become a challenge to ensure privacy preservation when sharing data effectively among different parties. This paper proposes a novel model that partitions data into sensitive and non-sensitive parts, injects the noise into sensitive data, and performs classification tasks using k-anonymization, differential privacy, and machine learning approaches. It allows multiple owners to share their data in the cloud environment for various purposes. The model specifies communication protocol among involved multiple untrusted parties to process owners’ data. The proposed model preserves actual data by providing a robust mechanism. The experiments are performed over Heart Disease, Arrhythmia, Hepatitis, Indian-liver-patient, and Framingham datasets for Support Vector Machine, K-Nearest Neighbor, Random Forest, Naive Bayes, and Artificial Neural Network classifiers to compute the efficiency in terms of accuracy, precision, recall, and F1-score of the proposed model. The achieved results provide high accuracy, precision, recall, and F1-score up to 93.75%, 94.11%, 100%, and 87.99% and improvement up to 16%, 29%, 12%, and 11%, respectively, compared to previous works.

     

Hybrid neural network model for reconstruction of occluded regions in multi-gait scenario

In real-time situations such as airports, railway stations, and shopping complexes, etc. people walk in a group, and such a group of walking persons termed as multi-gait (MG). In these situations, occlusion is a serious issue that affects gait recognition performance. This issue of occlusion of body regions affects the extraction of gait features for the correct recognition of an object. The objective of this article is to reconstruct occluded regions at the preprocessing stage, which can be used for human recognition in the MG scenario. The article is divided into two folds. Firstly, we segment five regions of interest such as ankle, knee, wrist, elbow, and shoulder. We propose a particle swarm optimization (PSO) based neural network (NN) called hybrid NN to solve this problem. The performance of the proposed model is validated on our constructed dataset (SMVDU-MG), considering two view directions i.e. lateral (left to right) and oblique (left to right diagonal). Experimental results show that the proposed model gives better performance compared to an artificial neural network and alternating least square (ALS) method based on mean square error (MSE) and mean absolute percentage error (MAPE) as a performance measure function.

     

Towards a super-resolution based approach for improved face recognition in low resolution environment

Multimedia Tools and Applications - The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object...     

A data hiding technique for digital videos using entropy-based blocks selection

Microsystem Technologies - The technique of hiding knowledge in certain details is steganography. One of the main trends of computer infrastructure and connectivity following the advent of the...     

Dual segment rectangular dielectric resonator antenna with metamaterial for improvement of bandwidth and gain

The simulation and experimental studies of an aperture‐coupled wideband dual segment rectangular dielectric resonator antenna with metamaterial for C‐band applications are presented in this paper. The antenna consists of Alumina (Al₂O₃) ceramic as upper segment and Teflon as lower segment. The combination of circular‐shaped coplanar split‐ring resonator and conducting strip has been used as metamaterial superstrate. With the use of metamaterial superstrate, the bandwidth of the antenna is increased by 48% through simulation and 22% experimentally. The broadside radiation pattern of the antenna is converted into directive radiation pattern with reduced beamwidth when metamaterial superstrate is used. The peak gain of the antenna is also enhanced by 33% through simulation and 31% experimentally with the use of metamaterial superstrate. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:646–655, 2014.     

An Online Algorithm for the Dynamic Maximal Dense Tree Problem

Abstract. The Online Maximal Dense Tree problem is as follows: given a weighted directed graph and a source node, users issue online requests for connection to the source node. A request can either be accepted or rejected (the admission control decision). If the connection request is accepted, it must be connected to the source or to a node previously connected to the source (the routing decision). The objective is to maximize the total number of connections while keeping the connection density , i.e. the ratio of accepted requests to the weight of the spanning tree, sufficiently high. The primary motivation for the Maximal Dense Tree problem is the Online Capacitated Multicast admission control and routing problem. In the Online Capacitated Multicast problem, we are given a communication network with limited link capacities and a set of signal source nodes. Users generate online requests for connection to the signal sources, and the network administrator has to make the admission control and routing decisions. The goal of the network administrator is to maximize the total number of users connected subject to the network capacity constraints. The Online Maximal Dense Tree problem is also faced by a cable TV operator who wishes to connect as many customers as possible while keeping down the amount of wiring per customer. Informally, the Online Maximal Dense Tree algorithm must ``gamble' on certain geographic areas, connecting nodes which are unprofitable to start with, in the hope that eventually enough requests will arrive in its vicinity to make the investment profitable. In this paper we present a randomized online algorithm for the Maximal Dense Tree problem that guarantees acceptance of a (1- ɛ) factor of the requests accepted by the optimum offline algorithm with the expectation of density being at most polylogarithmically lower than that of the offline algorithm. This yields an online capacitated multicast algorithm whose throughput is only poly-logarithmically lower than that of the optimum offline algorithm. Previous work on multicast routing and maximal dense tree problems either made probabilistic assumptions or resulted in linear performance gaps with the offline algorithm. Attempts to solve the Online Maximal Dense Tree problem have also lead to the development of the first polylogarithmic approximation algorithms for the k -MST and the Prize Collecting Salesman problems [AABV].     

Socio-ethics of interaction with intelligent interactive technologies

Socio-ethics covers the relation of the individual with the group and with society, as the individual acquires the skills for social life with others and the conduct of ‘normal responsible behaviour’ (Leal in AI Soc 9:29–32, 1995) that guides moral action. For a consideration of what it means to be socially skilled in everyday human interaction and the ethical issues arising from the new conditions of interaction that come with the integration of intelligent interactive artefacts, we will provide an analysis at multiple levels of these phenomena and draw on a variety of application domains, for example, healthcare and interactive media.     

Heterogeneous networking

In this article, security challenges related to a mobile heterogeneous networking environment, and the general access patterns are discussed. A novel, unified networking architecture that enables secure heterogeneous networking, both in terms of networks and user devices is discussed. A comprehensive security framework providing a generalized authentication scheme using the Extensible Authentication Protocol (EAP) is then presented, by taking into account existing methods for secure network and device access.     

Functional work breaks in a high-demanding work environment: an experimental field study

Work breaks are known to have positive effects on employees’ health, performance and safety. Using a sample of twelve employees working in a stressful and cognitively demanding working environment, this experimental field study examined how different types of work breaks (boxing, deep relaxation and usual breaks) affect participants’ mood, cognitive performance and neurophysiological state compared to a control condition without any break. In a repeated measures experimental design, cognitive performance was assessed using an auditory oddball test and a Movement Detection Test. Brain cortical activity was recorded using electroencephalography. Individual’s mood was analysed using a profile of mood state. Although neurophysiological data showed improved relaxation of cortical state after boxing (vs. ‘no break’ and ‘deep relaxation’), neither performance nor mood assessment showed similar results. It remains questionable whether there is a universal work break type that has beneficial effects for all individuals.

Practitioner Summary: Research on work breaks and their positive effects on employees’ health and performance often disregards break activities. This experimental field study in a stressful working environment investigated the effect of different work break activities. A universal work break type that is beneficial for this workplace could not be identified.     

Feedback linearization of differential-algebraic systems and force and position control of manipulators

The question of realization and feedback linearization of a class of differential-algebraic system is considered. Based on nonlinear inversion of an input-output map, an analytical expression for the constraint force vector satisfying the algebraic constraints is derived. In this derivation, certain requirements on the relative degree of the output variables are relaxed. Using a new representation of the system in an extended state space, a control law is derived for the independent control of the chosen output variables satisfying algebraic constraints. These results are applied for the position and force control of robotic manipulators. Simulation results are presented for a three-link robotic arm with revolute joints. It is shown that in the closed-loop system, precise position and force trajectory control is accomplished in spite of uncertainty in the robot parameters.