A process model of cyberbullying in adolescence

Cyberbullying is an emerging form of aggression that utilizes information and communication technologies (ICTs). While cyberbullying incidents attract considerable attention, research on the causes and psychosocial predictors of cyberbullying is still limited. The present study used an integrated theoretical model incorporating empathy, moral disengagement, and social cognitions related to cyberbullying. Structured questionnaires were administered to 355 randomly selected adolescents (M = 14.7, SD = 1.20). Linear regression analysis showed that social norms, prototype similarity and situational self-efficacy directly predicted cyberbullying expectations. Multiple mediation modelling indicated that normative influences mediated the effects of moral disengagement and affective empathy on cyberbullying expectations. These findings provide valuable information regarding the effect of both distal and proximal risk factors for cyberbullying in adolescence, highlight the relationship between normative processes and moral self-regulation, and set the basis for related educational and preventive interventions.     

Local difference patterns for drunk person identification

In this work, facial thermal infrared images are employed for intoxicated person discrimination. Specifically, the region of the forehead of the face of the sober and the corresponding intoxicated person is used to test if the employed Local Difference Patterns (LDPs) constitute discriminative features. For an intoxicated person, vessels on the forehead become more active so that the intensity of the pixels in this region is affected accordingly. The LDPs employed ignore orientation of the pixels distribution and give emphasis on the first and second norms of the differences as well as the ordered values of the pixels in the employed kernels. The statistics of the LDPs for the drunk person are different from those of the sober one and accordingly drunkenness can be ascertained by comparing the thermal infrared image of the corresponding sober and intoxicated person. Six from the eight LDPs examined to be used as features for drunk identification were proved successful. Their classification success rate was over 73 and up to 85%. The proposed method can be incorporated into a non-invasive inspection commercial system to be used by the police as a first step for intoxicated person detection. Forty one participants in the experiment have contributed to the creation of the unique sober–drunk database which is available on the web and contains over 4.000 images.     

Effect of water aging on the mechanical properties of flax fiber/bio-based resin composites

Present work investigates the effect of hydrothermal aging of flax fiber-reinforced bio-based epoxy resin laminates on the mechanical and thermomechanical properties of the composites. Three different types of bio-based resins were used. Plates reinforced with eight layers plain weave flax fibers of 150 g/m², manufactured using Resin Transfer Molding (RTM), compression molding or autoclave technique depends on type of the resin. One dimensional Fickian behavior shows a good fitting to the experimental data derived from weight measurements. The water uptake at the equilibrium state in the case of 60 °C temperature was slightly greater than that at 40 °C. The mechanical properties after hydrothermal aging show a significant reduction and do not return to their initial values even after the drying process. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48787.     

Data hiding based on image texture classification

In this paper, a new pattern-based fragile, semi-blind, spatial domain data hiding scheme is proposed. The Local Binary Pattern texture classification approach is used, in order to transparently and securely embed secret data into an image. Pixel values are modified in such a way that the texture satisfies the message requirements. The method is thoroughly studied and compared to other techniques in spatial domain in terms of capacity and image quality. The scheme performs well in images with smooth areas and can be used for authentication, tamper proofing, and secret communications.     

OPC-SMS: a wireless gateway to OPC-based data sources

This paper describes OPC-SMS gateway, a platform that integrates IP networks with the Short Message Service (SMS), in order to deliver an integrated service for access to data sources conforming to Object Linking and Embedding for Process Control (OPC) standard specifications, through SMS-enabled mobile devices. The gateway supports pull and push services in order to support both request-based and alarm/scheduled-based notifications, respectively. The proposed architecture is based entirely on the ubiquitous HyperText Transport Protocol (HTTP), Simple Object Access Protocol (SOAP), Extensible Markup Language (XML) protocols, and the Global System for Mobile communication (GSM) network and thus exploits the network infrastructure already in place. The capability of accessing different types of OPC data sources (real-time and historical) by any SMS-enabled device consists of a highly flexible service, supporting mobility and event-based notification. A pilot implementation of our approach has been tested in a large-scale installation for accessing OPC data sources of several automation subsystems in a hospital.     

Input Feature Extraction for Multilayered Perceptrons Using Supervised Principal Component Analysis

A method is proposed for constructing salient features from a set of features that are given as input to a feedforward neural network used for supervised learning. Combinations of the original features are formed that maximize the sensitivity of the network's outputs with respect to variations of its inputs. The method exhibits some similarity to Principal Component Analysis, but also takes into account supervised character of the learning task. It is applied to classification problems leading to improved generalization ability originating from the alleviation of the curse of dimensionality problem.     

Skin color-based video segmentation under time-varying illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling, and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24 percent is observed in 17 out of 21 test sequences. In all but one case, the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.     

Aerosol lidar intercomparison in the framework of the EARLINET project. 3. Raman lidar algorithm for aerosol extinction, backscatter, and lidar ratio

An intercomparison of the algorithms used to retrieve aerosol extinction and backscatter starting from Raman lidar signals has been performed by 11 groups of lidar scientists involved in the European Aerosol Research Lidar Network (EARLINET). This intercomparison is part of an extended quality assurance program performed on aerosol lidars in the EARLINET. Lidar instruments and aerosol backscatter algorithms were tested separately. The Raman lidar algorithms were tested by use of synthetic lidar data, simulated at 355, 532, 386, and 607 nm, with realistic experimental and atmospheric conditions taken into account. The intercomparison demonstrates that the data-handling procedures used by all the lidar groups provide satisfactory results. Extinction profiles show mean deviations from the correct solution within 10% in the planetary boundary layer (PBL), and backscatter profiles, retrieved by use of algorithms based on the combined Raman elastic-backscatter lidar technique, show mean deviations from solutions within 20% up to 2 km. The intercomparison was also carried out for the lidar ratio and produced profiles that show a mean deviation from the solution within 20% in the PBL. The mean value of this parameter was also calculated within a lofted aerosol layer at higher altitudes that is representative of typical layers related to special events such as Saharan dust outbreaks, forest fires, and volcanic eruptions. Here deviations were within 15%.     

The Wave & Probe Communication Mechanisms

This paper is motivated by the modifications recently suggested to enhance TCP performance over wireless channels. We argue that TCP end-to-end error-control mechanism lacks the functionality to respond appropriately in situations where errors vary in nature, frequency, or duration. As a result, this mechanism could, under some circumstances, yield zero throughput achievements at high energy-cost, or degrade throughput performance without conserving energy. This incompetent behavior with respect to the energy/throughput tradeoff puts in question TCP's suitability as a universal, reliable transport protocol of choice, especially for battery-powered mobile devices for which energy is a critical resource and congestion is not the exclusive cause of errors.We propose Wave and Probing communication and control mechanisms that permit end-to-end protocols to detect congestion without necessarily experiencing packet drops, to distinguish random and burst errors from congestion, and, as conditions vary, to rapidly adjust the transmission window upwards or downwards depending on the nature of the error. We report extensively on the performance of these new mechanisms to demonstrate their energy-conserving and high-throughput capabilities.     

Context-based caching and routing for P2P web service discovery

In modern heterogeneous environments, such as mobile, pervasive and ad-hoc networks, architectures based on web services offer an attractive solution for effective communication and inter-operation. In such dynamic and rapidly evolving environments, efficient web service discovery is an important task. Usually this task is based on the input/output parameters or other functional attributes, however this does not guarantee the validity or successful utilization of retrieved web services. Instead, non-functional attributes, such as device power features, computational resources and connectivity status, that characterize the context of both service providers and consumers play an important role to the quality and usability of discovery results. In this paper we introduce context-awareness in web service discovery, enabling the provision of the most appropriate services at the right location and time. We focus on context-based caching and routing for improving web service discovery in a mobile peer-to-peer environment. We conducted a thorough experimental study, using our prototype implementation based on the JXTA framework, while simulations are employed for testing the scalability of the approach. We illustrate the advantages that this approach offers, both by evaluating the context-based cache performance and by comparing the efficiency of location-based routing to broadcast-based approaches. Recommended by: Zakaria Maamar