Reviewerly/exHarmony huggingface dataset:
The Use of ICT in Preschool Education While Teaching English Language

At the present stage of the development of society, the modernization of the content of education is closely connected with innovative processes in the organization of foreign language teaching. Therefore, the question of the use of new information technologies in the education system is very relevant. This is a new approach to the learning process, new forms and methods of teaching. The main purpose of teaching foreign languages is the formation and development of the communicative culture of preschool children, teaching practical mastery of the English language. Pre-school education provides a basic knowledge base of English for preschoolers.
Selection of Cybersecurity Safequards Portfolio

This chapter deals with the optimal selection of countermeasures in IT security planning to prevent or mitigate cyber-threats and a stochastic MIP approach is proposed for the decision-making. Given a set of potential threats and a set of available countermeasures, the decision maker needs to decide which countermeasure to implement under limited budget to minimize potential losses from successful cyber-attacks and mitigate the impact of disruptions caused by IT security incidents. The selection of countermeasures is based on their effectiveness of blocking different threats, implementation costs and probability of potential attack scenarios. The problem is formulated as a single- or bi-objective stochastic mixed integer program and a conditional value-at-risk approach combined with scenario-based analysis is applied to control the risk of high losses due to operational disruptions and optimize worst-case performance of an IT system. The bi-objective trade-off model provides the decision maker with a simple tool for balancing expected and worst-case losses and for shaping of the resulting cost distribution through the selection of optimal subset of countermeasures for implementation, i.e., the selection of optimal countermeasure portfolio. The selected portfolio explicitly depends on preferred confidence level and cost/risk preference of the decision maker. Numerical examples are presented and some computational results are reported to compare the risk-averse solutions that minimize conditional value-at-risk with the risk-neutral ones that minimize expected cost. The major managerial insights are provided at the end of this chapter.
The geology, mineralogy, and near surface characteristics of the Ashmore and Seppelt kimberlite clusters, north Kimberley Province, Australia

The Seppelt and Ashmore kimberlite clusters are situated in the north Kimberley region of Western Australia (figure 1).The emplacement of the kimberlites, which all intrude Proterozoic volcanic and sedimentary rocks of the Kimberley basin, is controlled by 30° trending structures.Rb/Sr dating on phlogopite suggest consistent emplacement ages of Neoproterozoic (774±279 Ma) (Wyatt et al., 1998) for both clusters.These kimberlite clusters occur 20km apart and form single to bifurcating pipe-like bodies with an associated outer, often collapsed and dipping, contact breccia (figure 2).
Geographic Potential

The problem of solar radiation harvesting using photovoltaic (PV) plants with fixed orientation is compared with PV plants with horizontal axis tracking and vertical axis tracking. Problem of bifacial panel and of albedo is also discussed. Land occupancy and geographic potential are analyzed. The advantages and potentiality of floating plants are outlined with reference to small basins and wastewater basins as well as to salty shallow water surfaces.
Implementation of Arithmetic unit for RNS using 2n-3 as Base

Residue Number System (RNS) is often used in Cryptographic applications. Choice of a unique base for RNS is an important factor in implementing RNS. Bit folding after multiplication is a commonly used method for implementing RNS. In this paper an architecture based on modulo 2 n -3 arithmetic is implemented. Each word of partial product is mapped once normal and then with one bit left shift with reference to the base number. The results are tabulated in terms of delay and area with Xilinx tool. Efficiency of implementation is compared with results available in literature.
Surveillance Computing

Modern computing has to face the hardware bottleneck at present. Nowadays, supercomputing including multithread, multicore, GPU, and FPGA technologies (Kilts, Advanced FPGA design. Wiley, Hoboken, 2007; Stallings, Operating systems: internals and design principles. Pearson Education Limited, New Jersey, 2015) are alleged for resolving the problems and overcoming the technical barriers (Sanders, Kandrot, CUDA by examples: an introduction to general-purpose GPU programming. Addison-Wesley, Upper Saddle River, 2011). In this chapter, we will dwell on how to use these cutting-edge technologies in digital surveillance and make intelligent computing much faster. There are several criteria in evaluating or choosing parallel programming models (Rauber, Runger, Parallel programming: for multicore and cluster systems. Springer, Berlin, 2010), a few well-known parallel programming models such as OpenMP, UPC, and CUDA have been adopted in practice (Sanders, Kandrot, CUDA by examples: an introduction to general-purpose GPU programming. Addison-Wesley, Upper Saddle River, 2011).
Trajectory planning of autonomous vehicle based on Frenét frame system

With the development of autonomous driving technology, the situations handled by autonomous vehicles are becoming more and more complex. Local path planning is one of the important technologies of intelligent vehicles in the face of complex situations. To integrate more factors into consideration for different scenarios, an optimal trajectory generation method based on Frenét frame system is presented in this paper for trajectory planning of autonomous vehicles. In the optimal trajectory generation method, path is divided into multiple segments along the reference line and the vehicle state is sampled in each path segment. The cost function to evaluate each sampled path point and the vehicle state is established by considering the trajectory smoothness, comfort, energy consumption and the deviation degree of the reference route, and the parameters of the cost function are adjusted according to the different influence degree of factors through different driving modes of the vehicle. The function of obstacle avoidance of the trajectory planning is realized by deleting the trajectory that is less than the safe distance from the obstacles. Simulation verification shows that this method can complete trajectory planning under static obstacles and dynamic obstacles to realize the functions of obstacles avoidance and overtaking.
Smart Contracts

Chapter 7 examines smart contracts’ ability to self-perform, self-enforce, and self-remedy and the remaining applicability of contract law and contract remedies. Smart contracts (coupled with blockchain technology) have created visions of self-executing, self-enforcing, and self-remedying contracts that eliminate the need for courts or arbitral tribunals to apply contract law to disputes. The theory goes that, since the possibility of breach is eliminated in such contracts, contract remedies become unnecessary.
Visual Analytics

An overview of the history, development, and applications of visual analytics is provided. Readers familiar with some of the research and development aspects in visual analytics should also benefit from this review of the field’s genesis and objectives. The principal drivers for the generation of the field are summarized and the initial research and development agenda that followed from these is reviewed. The distinctive aspects of visual analytics are discussed in relation to other forms of visualization. A wide variety of software for visual analytics is summarized and a methodology for effective comparison is proposed. The current trend toward large collaborative research and development projects across institutions and organizations, and between the academy and industry, is analyzed and reviewed in the context of visualization research. A number of typical applications in interactive data visualization are presented, while recognizing the limitations of presenting these solely in a written and visual format when their power is normally in real-time interactive exploration. Aspects of current research and development in visual analytics are presented and compared with those in scientific visualization and information visualization. The question of whether visual analytics has subsumed the areas of information visualization and scientific visualization is considered. Possible future directions for visual analytics are explored.
Motion Planning in Lane Change Scenario for Autonomous Vehicles

Motion planning is an important and complex research content in automatic driving system. This paper proposes a motion planning method in lane change scenario, which decouples the motion planning into two parts: longitudinal and lateral planning. This method takes the leading vehicle of each lane change gap as the following control vehicle, obtains the longitudinal trajectory of each gap by LQR algorism. Then the most appropriate lane change gap is selected through a certain strategy, which is developed on driving comfort and timeliness. After the gap selection, the host vehicle follows the leading vehicle of the selected gap to adjust its speed and position to meet the lane change condition. The lateral planning is initialized when the condition is met. The whole algorithm has a small amount of calculation and is convenient for practical engineering application. Finally, the validity and practicability of this method are verified by a real vehicle test.
Image Style Transfer Based on Deep Learning Network

Image style transfer is an intersection field of art and computer science, which uses deep learning tools to achieve style transfer between images. The objective of this review is to compare two popular image style transfer algorithms. Specifically, the paper first walks through the history and basic principles of these two algorithms and then discusses several existing drawbacks. After that, the paper introduces the person who proposes the solution to the drawbacks mentioning above. Finally, the paper compares the advantages and disadvantages of each algorithm and discusses the possible future improvements. The main contribution of this review paper is that it provides readers a clear walk-through of today’s popular algorithms of image style transfer as well as explores the potential combined fields of art and computer science by discussing the possible improvements of the existing popular algorithms.
Adaptive Clustering Ensemble Method Based on Uncertain Entropy Decision-Making

As an unsupervised data mining method, clustering can extract valuable information in complex and redundan-t network data analysis. However, the existing methods are sensitive to the selection of initial cluster centers, and cannot automatically determine the number of clusters, which fails to adapt to various types of network data. To solve these issues, this paper proposes a method of adaptive clustering ensemble based on uncertain entropy decision-making. Firstly, K-means is used as the base clustering algorithm of clustering ensemble, and several base clustering members are randomly generated according to different the number of clusters, and the members with high stability and quality are selected as clustering ensemble inputs. Furthermore, the uncertainty of clusters in the base clusterings are calculated based on the information entropy criterion, and then the co-association matrix is established. The obtained co-association matrix is transformed into a distance matrix by Bhattacharyya distance among data samples. Finally, we use the distance matrix as the input of the density peaks (DP) algorithm, and further calculate the final clustering result. The experimental results on real-world datasets illustrate that the proposed method has better performance than other clustering methods.
Effective Deep Learning-based Side-Channel Analyses Against ASCAD

Side-channel analysis (SCA) based on deep learning (DL) techniques have the benefit that they can disclose the secret key of protected block ciphers without preprocessing. But the size of convolutional neural network (CNN) architecture is so large that the training process is too time-consuming and the required number of traces for recovering secret key is too much. In this paper, we apply heatmap and SNR to reduce the number of parameters of our CNN architecture to 5,269,568 (i.e., one to tenth to the CNNbest). We also combine our CNN architecture with the multi-label classification and the transfer learning techniques to reduce the number of required traces. Consequently, two new CNN architectures are presented and validated on the public ASCAD dataset. The execution time of the training process approximates 15 minutes on average. The first CNN architecture can recover a key byte with only 30 synchronized traces. Combined with the transfer learning technique, the second CNN architecture requires 141 and 171 traces respectively in two different desynchronization cases. To our knowledge, for both synchronization and desynchronization cases, our analysis methods need the smallest amount of traces to extract a key byte.
Scientific and Methodological Approaches to Creating a System of Pedagogical Diagnostics of the Level of Training and Development of Primary School Graduates

The article discusses the problem of obtaining reliable information about the level of educational achievements of junior schoolchildren during their transition to the next stage of school education. The implementation of the development goals of students at the initial stage of schooling, set by the Federal State Educational Standard of Primary General Education, requires pedagogical diagnostics, which makes it possible to establish the cause of the difficulties that children have in the course of studying various subjects. This ensures the timely intervention of the teacher in the learning process to provide each younger student with pedagogical assistance and support, which makes it possible to eliminate the individual difficulties that arise in him.
Information Sharing and Sustainable Growth: Evidence from the US Mutual Fund Family

In this paper we investigate the information sharing within the organization and its consequences on the sustainable growth on performance. Using a sample from the US open-end mutual fund industry, we quantify information sharing insider the organization by using individual funds’ dependence on the fund family joint information set. Our findings suggest a positive relation between the degree of information sharing and the consequence performance. Such a finding is robust across difference performance evaluation metrics and various model specifications. We also information sharing positively affect sustainable growth in funds’ performance. We attribute such positive performance effect to quality information shared and information timely diffusion inside the fund family.
Introduction

Quantum key distribution addresses one of the society’s most pressing concerns for secret and authenticated communication. This is achieved by exploiting the principles of quantum theory to establish a secret key between two distant parties whose security is guaranteed by the laws of physics. In the past years, there has been tremendous progress with regard to the design of novel protocols, the development of sophisticated techniques for security proofs, and even in-field implementations of technological prototypes.
Data

The analyses in this book use a large quantity of data, all of which is publicly available from data resource consortia. The data is categorized into four types: single-author literary texts; large, multi-author corpora; miscellaneous data related to language; and corpora for analyzing different scripts. This chapter describes these datasets and the preprocessing applied to them.
Teaching Challenges and Opportunities in Higher Education during COVID-19

COVID-19 has forced higher educational institutions to make a fast change from face-to-face learning into online learning system. However, the latter provides many opportunities, but it encompasses many challenges. Thus, its implementation necessitates a careful flexible strategy based on a continuous evaluation. The purpose of this study is to explore the impact of COVID-19 on the learning system in Lebanese higher educational institutions. Based on a sample of 263 students and 48 instructors from 10 universities, the result of this study reveals that the implemented online system during COVID-19 has some advantages like time management and flexibility for both instructors and students. However, several disadvantages of online learning have been revealed during the pandemic period in Lebanon like absence of social interaction, logistic problems, pressure on instructors, lack of training on online learning and difficulty of know-how transfer from instructors to students. With the appearance of COVID-19 Delta Variant, Lebanese universities have to improve their online learning methods to balance between health care conditions, instructors’ competences and students’ motivation.
Automatic Detection of Dynamic and Static Activities of the Elderly using a Wearable Sensor and Support Vector Machines

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the elderly is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the elderly. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter —are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying static and dynamic activities of daily life in the elderly.
Customized Automated Email Response Bot Using Machine Learning and Robotic Process Automation

Email is one of the most reliable, dependable, authentic, genuine and frequently used modes of communication in corporate world. It is a common practice in corporate culture to send an automatic response to new incoming messages but it is very generic and limited in extent of its utility. A system can be designed to substitute the tedious task of replying to thousands of mails manually. This work proposes a system which in broader context automates the task of responding to a client. It aims to mitigate the laborious task of perusing through email manually by customizing the response as per the query of the user and fabricating a reply accordingly. The proposed system proves the effectiveness of the approach in its implementation.
Decoherence and Quantum Error Correction

One of the biggest problems in building a quantum computer is noise or decoherence. Qubits are coupled to other systems whether we want them to be or not, e.g. atoms couple to the electromagnetic field and spins couple to other spins via dipole-dipole interactions. These unwanted couplings can cause errors, and we need to protect quantum information against these errors.
Prediction of Remaining Useful Life of Bearing Based on Long Short-Term Memory Network Optimized by Slime Mould Algorithm

For resolve the trouble of low prediction accuracy and poor generalization ability of the neural network in the prediction of remaining useful life (RUL) of bearing, a prediction manner because of the combination of slime mould algorithm (SMA) and long short-term memory (LSTM) was proposed. The hyperparameters of the LSTM were automatically optimized by using the SMA with dynamic search ability. The trained SMA-LSTM model was adopted to calculate the RUL of bearing. The simulation effects indicate that the optimization effect of SMA is improving than the grey wolf optimizer and genetic algorithm. The SMA-LSTM model is improving than the SMA-SVR and SMA-BP model in bearing RUL prediction, which proves the capability of the put forward method.
Neural Network Based Automated System for Diagnosis of Cervical Cancer

Cervical Cancer is one of the most common cancers among women worldwide. Few concerns have arisen such as the shortage of skilled pathologists leading to increase in burden on them. This requires a need for efficient and accurate method that diagnoses cervical cancer without human intervention. In this paper, an automated system is developed for diagnosis of cervical cancer using image processing techniques and neural networks. The system is developed using Cytology images taken from Bangalore based cancer pathologist. MATLAB image processing toolbox is used to extract features from cytology images that are used for discriminating various stages of cervical cancer. The dominant features used for diagnosis are Nucleus to cytoplasm ratio, shape, and color intensity along with nucleus area, perimeter and eccentricity. These features are used to train the neural network using Back-propagation algorithm of supervised training method. The cytology cells were then successfully classified as non-cancerous, low- grade and high-grade cancer cells.
Morphing Attack Detection: A Fusion Approach

Face morphing attacks pose a serious threat to existing face recognition systems. As a number of studies have shown, existing face recognition systems and human experts can be fooled by morphed facial images. Based on these findings various approaches to morphing attack detection have been published. Automated morphing attack detection is still a young branch of research with many recent publications.Using features extracted by different feature extractors we develop a score-level based fusion approach. The scores are gen- erated by different classifiers with optimised hyperparameters. We use different approaches to determine the weights for the sum-rule: grid-search and random forests scoring function as well as normalised scores.We notice that a weighted score-level fusion can achieve improved results. Moreover, we observe that weights determined by grid-search might lead to better results when using fewer scores compared to those obtained by random forest while the former is more time consuming. However, both random forest and grid-search weights can significantly improve the morphing attack detection performance.
Infrared Deflectometry

This paper illustrate the use of deflectometry in the infrared spectrum to measure surface slopes on a plate deformed in bending.