Reviewerly/exHarmony huggingface dataset:
Video based e-module development by using smartphone for programmable logic controllers practical learning in vocational high school

The purposes of this research are: (1) Develop an interactive e-module that contain videos by using smartphone; and (2) to find out the feasibility of the results of the development of an interactive video e-module by using smartphone for PLC practical learning in Vocational High School. Type of research conducted through is research and development referring to ADDIE development model according to Robert Maribe Branch. Research steps conducted are: Analyze, Design, Develop, Implement, and Evaluate. Instruments used are questionnaire and observation sheet. Data collected is analyzed descriptively. Research result indicates: (1) An interactive video e-module by using smartphone for PLC practical learning in Vocational High School has been produced, which installed in smartphone with the name of Basic PLC Module application; and (2) Video Based Interactive E-module by using smartphone has the value of 80,75% which means it can be considered feasible to be used in PLC practical learning in Vocational High School.
The impact of mobile phones applications in the educational process among students and faculty staff members at the College of Education- Female section, Jazan University

A systematic random sample of (110) college students (female) aged (18-22) year old, and (15) faculty staff members at the College of Education-female section, Jazan University, was chosen. The study conducted in the academic year (2018). Questionnaire and interviews were used for collecting data. The study sample were asked to express their attitudes towards the usage of mobile phone applications in education. All data were analyzed with the SPSS personal computer program. Appropriate statistics for description (frequencies, percentage, means, standard deviations, and Chi2) were used. The results showed that calculated Chi2 value is bigger than the value of Chi2 derived from statistical tables. Thus, the null hypotheses (H0) rejected. Rejection of the null hypotheses indicates that the difference is significant in favor of the usage of mobile phone applications in the educational process.
Pareto-Based Hybrid Algorithms for the Bicriteria Asymmetric Travelling Salesman Problem

We consider the bicriteria asymmetric travelling salesman problem (bi-ATSP): Given a complete directed graph where each arc is associated to a couple of positive weights, the aim is to find the Pareto set, consisting of all non-dominated Hamiltonian circuits. We propose new hybrid algorithms for the bi-ATSP using the adjacency-based representation of solutions and the operators that use the Pareto relation. Our algorithms are based on local search and evolutionary methods. The local search combines principles of the well-known Pareto Local Search procedures and Variable Neighborhood Search approach, realizing the search in width and depth. A genetic algorithm with NSGA-II scheme is applied to improve and extend a set of Pareto local optima by means of evolutionary processes. The experimental evaluation shows applicability of the algorithms to various structures of the bi-ATSP instances generated randomly and constructed from benchmark asymmetric instances with single objective.
A State-aware Proof of Stake Consensus Protocol for Power System Resilience

The resilience of the power grid is critical for the energy delivery system and the assurance of the energy distribution process. The integration of blockchain technology with the power grid can provide a consistent view of the system state and constant validation. However, there are still significant challenges in the integration of blockchain in current power system architecture. From the cyber physical perspective, the physical components in the power system are heterogeneous and there are limited communication capabilities among these components. In this work, we propose a two-layer architecture that enables a state-aware blockchain integration with power grid state estimation and still scales in terms of performance dealing with high volume data.
Multi-Task Learning for Semantic Relatedness and Textual Entailment

Recently, several deep learning models have been successfully proposed and have been applied to solve different Natural Language Processing (NLP) tasks.However, these models solve the problem based on single-task supervised learning and do not consider the correlation between the tasks.Based on this observation, in this paper, we implemented a multi-task learning model to joint learn two related NLP tasks simultaneously and conducted experiments to evaluate if learning these tasks jointly can improve the system performance compared with learning them individually.In addition, a comparison of our model with the state-of-the-art learning models, including multi-task learning, transfer learning, unsupervised learning and feature based traditional machine learning models is presented.This paper aims to 1) show the advantage of multi-task learning over single-task learning in training related NLP tasks, 2) illustrate the influence of various encoding structures to the proposed single-and multi-task learning models, and 3) compare the performance between multi-task learning and other learning models in literature on textual entailment task and semantic relatedness task.
Analysis of Job Scheduling Techniques in a HPC Cluster Deployed in a Public Cloud

In this paper has been presented an analyses of different scheduling techniques on AWS ParallelCluster, an Amazon Web Services supported, open source cluster management tool for the deployment and management of High Performance Computing (HPC) clusters in the AWS cloud.
Encryption Using Logistic Map and RSA Algorithm

Data collected by each individual is increasing day by day and so is the rate of transfer of that data. Hence, there is a need to protect this data while the transfer is being done. One simple solution to this problem of data security is cryptography. Our paper proposes a method to share images after being encrypted by logistic maps and Linear-Feedback Shift Register (LSFR) followed by the RSA Algorithm. Thereby, we provide extra layers of security over the existing encryption technique making it difficult to decrypt the images after data transfer. The image can be decrypted only with the help of a key.
Application of AUV Navigation Based on SVD Unscented Particle Filter

In order to solve the problems of low filtering accuracy and poor real-time performance of standard algorithm in strong nonlinearity, a SVD Unscented Particle Filter (SVDUPF) based on strong tracking and Kullback-Lerbler Distance (KLD) resampling is proposed. Firstly, the stability of covariance matrix is guaranteed by singular value decomposition. Then the multi-fading factor adaptive adjustment covariance matrix in the strong tracking theory is introduced. Finally, the number of particles required for the next filtering iteration is determined according to the KLD sampling principle. Eliminating unnecessary particles and using the minimum number of particles on the premise of ensuring accuracy can improve navigation accuracy, achieve powerful tracking and reduce computational complexity. By comparing the results of the simulation Extended Kalman Filter (EKF) algorithm, the feasibility and application of the Unscented Particle Filter (UPF) algorithm and SVDUPF in AUV navigation positioning algorithm are tested. The correctness of SVDUPF algorithm is proved.
Markerless Augmented Advertising for Sports Videos

Markerless augmented reality can be a challenging computer vision task, especially in live broadcast settings and in the absence of information related to the video capture such as the intrinsic camera parameters. This typically requires the assistance of a skilled artist, along with the use of advanced video editing tools in a post-production environment. We present an automated video augmentation pipeline that identifies textures of interest and overlays an advertisement onto these regions. We constrain the advertisement to be placed in a way that is aesthetic and natural. The aim is to augment the scene such that there is no longer a need for commercial breaks. In order to achieve seamless integration of the advertisement with the original video we build a 3D representation of the scene, place the advertisement in 3D, and then project it back onto the image plane. After successful placement in a single frame, we use homography-based, shape-preserving tracking such that the advertisement appears perspective correct for the duration of a video clip. The tracker is designed to handle smooth camera motion and shot boundaries.
Learning decomposed subspaces for supervised bidirectional image generation

This study addresses the task of supervised cross-domain image generation, which aims to translate an image from the source domain to the target domain, guided by a reference image from the latter. The key difference between the authors setting and the recently proposed domain guided photogeneration is that their image generation is bidirectional, i.e. images are generated in both domains. Thus, they call it bidirectional cross-domain image generation. This novel task poses new challenges as it requires the model learning to decouple personalised and shared semantics of the two domains. For this purpose, they propose a framework to learn a feature space, which breaks into three parts, namely, two personalised semantic subspaces, which encode patterns that are unique for each domain, and a shared semantic subspace that captures the common patterns. The three subspaces are automatically decoupled through end-to-end training. Extensive experiments on the modified shoe and handbag datasets show that their framework can generate high-quality images in both domains.
Design of Behavior Change Environment with Interactive Signage Having Active Talk Function

In this paper, we construct an interactive signage system, which is able to actively talk to users who are passing the signage system and at the same time tries to induce behavioral changes through visual and auditory stimulation. On top of our previous feasibility study, we re-design our proposed system based on the persuasive system design (PSD) model and the behavior change support system (BCSS) theory. The new design consists of 4 parts: recognizing (identifying and classifying users), executing (sending triggers and inducing behavior change), reviewing (recording users' reaction), and feedback (keeping or improving users' motivation). To track users' status and performance, we use the smartphone and the smartwatch to collect users' bio-data (e.g. heart rate, number of steps) and users' location. To send user triggers continuously without being interrupted by notifications from other applications, we set the interactive signage in the daily action line of users and present information to users when they pass the signage. In order to make the system more persuasive, we pick up eight features from the 28-feature list of PSD and apply them to our design.
C2Net: A Network-Efficient Approach to Collision Counting LSH Similarity Join(Extended Abstract)

Similarity join of two datasets P and Q is a primitive operation that is useful in many application domains. The operation involves identifying pairs (p, q), in the Cartesian product of P and Q such that (p, q) satisfies a stipulated similarity condition. In a high-dimensional space, an approximate similarity join based on locality-sensitive hashing (LSH) provides a good solution while reducing the processing cost with a predictable loss of accuracy. A distributed processing framework such as MapReduce allows the handling of large and high-dimensional datasets. However, network cost frequently turns into a bottleneck in a distributed processing environment, thus resulting in a challenge of achieving faster and more efficient similarity join [2]. This paper focuses on collision counting LSH-based similarity join in MapReduce and proposes a network-efficient solution called C2Net to improve the utilization of MapReduce combiners. The solution uses two graph partitioning schemes: (i) minimum spanning tree for organizing LSH buckets replication; and (ii) spectral clustering for runtime collision counting task scheduling. Experiments have shown that, in comparison to the state of the art, the proposed solution is able to achieve 20% data reduction and 50% reduction in shuffle time.
Research on Machine Translation Model Based on Neural Network

Machine Translation is an important part of Natural Language Processing. The model based on convolution neural network and attention mechanism (Fcnn model), which was proposed by Facebook in 2017, has been successful. We use this Fcnn model as the baseline model of this subject, and on the base of this model, we try to improve it by the combination of bytes pair encoding method, model ensemble method. In this issue, we use Bilingual Evaluation Understudy (BLEU) as a criterion to measure the quality of translation. After testing, these methods can improve the translation quality of the model. Finally, the overall translation quality increased from 0.28 of the baseline Fcnn model to 0.32.
초등학교 교사의 교권침해에 대한 심리적 경험과 극복요인

This study used the concept mapping method to confirm about the psychological experience and the ways to overcome by elementary school teachers who have experienced rights violations. As a result of conducting research on 12 elementary school teachers who have experienced rights violations within 3 ...
Doing Collaborative Digital Advertising Conceptualization

The development of digital advertising capabilities has drawn attention toward the benefits of advertising automation and digital consumer analytics, including the appropriate technical skills to support these capabilities. By focusing on constraint and provocation in otherwise innocuous requests from advertisers, I uncover collaborative creative practices undertaken by the digital advertising workers responding to those requests when conceptualizing creative digital branding campaigns. The findings highlight some of seen but unnoticed interpretive methods these workers use to marshal a variety of technical and non-technical resources to develop successful and creative campaign ideas and plans from these requests. Outcomes can be used to improve the technology used to plan and launch digital advertising campaigns by helping teams better consider the user and advertiser, and enable better communication of campaign differentiation.
Landscape analysis under measurement error

There are situations where the need for optimisation with a global precision tolerance arises - for example, due to measurement, numerical or evaluation errors in the objective function. In such situations, a global tolerance ϵ > 0 can be predefined such that two objective values are declared equal if the absolute difference between them is less than or equal to ϵ. This paper presents an overview of fitness landscape analysis under such conditions. We describe the formulation of common landscape categories in the presence of a global precision tolerance. We then proceed by discussing issues that can emerge as a result of using tolerance, such as the increase in the neutrality of the fitness landscape. To this end, we propose two methods to exhaustively explore plateaus in such application domains - one of which is point-based and the other of which is set-based.
From Feeling Vibrations to Building Audiovisual Scenes

This chapter explores the interactions between the richness of sonic environments, human ways of living in sound and audiovisual storytelling. It acknowledges the perceptual doubts, appreciates the delicate transmission of information performed by the human body and ponders about auditory technologies as sensory tools. Self-reflective experiences of our physical engagements with sound connect wonder and conscious imagination in ways that enrich methods of sharing sensations. A set of progressive exercises, provided in this chapter, associates sonic perception and sensory awareness with collaborative audio and visual storytelling.
A Birkhoff Connection Between Quantum Circuits and Linear Classical Reversible Circuits

Birkhoff’s theorem tells how any doubly stochastic matrix can be decomposed as a weighted sum of permutation matrices. Similar theorems on unitary matrices reveal a connection between quantum circuits and linear classical reversible circuits. It triggers the question whether a quantum computer can be regarded as a superposition of classical reversible computers.
Towards the Defuzzification Procedure in an ANFIS

The paper pays attention to particularities concerned with the defuzzification procedure in adaptive neural network based fuzzy inference systems. Specifically, the problem of constructing recursive parameter estimation algorithms is considered with regard to their convergence and stability. In the combination with various forms of the quadratic criterion, such an approach enables on to obtain strongly consistent estimation algorithms under essential generality of modeled system description; and the convergence properties are demonstrated in the comparison with conventional algorithms.
Improved Data Fusion for Multi-Sensor Tracking using a Reinforced Viterbi Algorithm

Employing multiple wide aperture radars with partially overlapping coverage to accurately track moving objects is becoming increasingly popular. However, identifying a common track across the radars can be challenging when each radar sensor obtains multiple measurements from different targets in its field of view. The presence of clutter and spurious measurements further complicates this problem. Data association and target tracking in this context can benefit from the combined processing of the sensor measurements. We adapt the well known single sensor Viterbi Data Association (VDA) algorithm to exchange information between multiple sensors, thereby reinforcing the target tracking performance. The proposed multi-sensor data fusion algorithm is demonstrated to have vastly improved performance over conventional single sensor techniques.
A Neural Framework for Joint Prediction on Intent Identification and Slot Filling

In task-oriented dialog systems, understanding of users’ queries (expressed in natural language) is a process of parsing users’ queries and converting them into some structure that machine can handle. The understanding usually consists of two parts, namely intent identification and slot filling. To address this problem, we propose a neural framework, named SI-LSTM, that combines two tasks and integrates CRF into LSTM network, where the slot information is extracted by using CRF, and the intent will be identified by using LSTM. In our approach, the slot information is used for determining the intent, while the intent type is used to rectify the slot filling deviation. Based on the dataset provided by NLPCC 2018, SI-LSTM achieved 90.71% on intent identification, slot filling and error correction in terms of accuracy.
Network Security Intelligence Centres for Information Security Incident Management

Intensive IT development is driving current information security (IS) trends and require sophisticated structures and adequate approached to manage IS for different businesses. The wide range of threats is constantly growing in modern intranets; they have become not only numerous and diverse but also more disruptive. In such circumstances, organizations realize that IS incidents’ timely detection and prevention in the future (what is more important) are not only possible but imperative. Any delay leaves only reactive actions to IS incidents, putting assets at risk as a result. A properly designed IS incident management system (ISIMS), operating as an integral part of the whole organization’s governance system, reduces IS incidents’ number and limits damage caused by them. To maximally automate IS incident management (ISIM) within one organization and to deepen its knowledge of IS level, this research proposes to unite together all advantages of a Security Intelligence Centre (SIC) and a Network Operations Centre (NOC) with their unique and joint toolkits and techniques in a unified Network SIC (NSIC). This paper presents the research, which is focused upon the designing and evaluating the concept of NSICs, and represents a novel advancement beyond existing concepts of security and network operations centres in current security monitoring scenarios. Key contributions are made in relation to underlying taxonomies of threats and attacks, leading to the requirements for NSICs, the related design, and then evaluation in a practical context and the implications arising from this (e.g. training requirements).
Speaker verification with fake intonation based on Neural Networks

Unlike authentication systems in which the user wants to be correctly identified, for forensics purposes it is very common that speaker utters incorrectly (i.e. with fake intonation) the message, and therefore the verification task is not correctly performed. Our proposal is to provide a system that overcomes this problem. Taking into account that a suspect will not voluntarily provide audio recordings for training, our solution uses indirect features to train and validate a machine learning model using neural networks (NN). The inputs of our system are not directly obtained from audio recordings, but from a quantitative comparison between a pair of recordings of the same speaker or different speakers, using several messages. Wavelet coherence matrix between each pair of audio recordings was used for training, cross validation and test. Neural Network (NN) is adjusted in terms of hidden neurons, learning rate, and number of iterations. According to the results, our system provides OA about 88.2%, Precision of 84.5%, Recall of 90%, F1 score of 87.2%, and AUC of 93.8%.
Semiotic knowledge about mass communication Umberto Eco and problems of comprehension of digital reality

Classical sociological theories have enormous potential for explaining social objects, phenomena, and processes, including those taking place in a modern informational society. Among these classics is owned Umberto Eco - an Italian scholar, literary critic, publicist and writer, is among those classics. Some aspects of the scientific heritage of Umberto Eco about semiotic analysis, signs and interpretation of their meanings, mass communication in relation to the tasks of studying virtual communications, the Internet, network society and the digital economy have been revealed. The phenomenon of “visual communication” has been considered in detail: in natural language, the value is predetermined, in the visual it is generated as the message is received. It is assumed that not all communicative phenomena can be explained using linguistic categories. Separately, a description of the methodological components of the concept of mass communication of the scientist has been made: it is argued that by means of mass culture a certain cultural code opposite to the transmitter code can be formed at the receiving instance. The results of the interpretation of the primary data of the sociological research project of the State University of Management have been presented, on the basis of which it can be concluded, that young people go beyond the important sociocultural norms of communication, which can cause distortion of signs and image codes of the virtual interlocutor and lead to a dangerous situation. The conclusion about the possibilities of using the scientific method of Umberto Eco in digital sociology for social diagnostics of the content and specificity of communications on the Internet, which allows to represent different aspects of your real or desired “I”, to create identities through many virtual characters, has been substantiated.