Reviewerly/exHarmony huggingface dataset:
THE EFFECTIVENESS OF MEANS TO INCREASE MOTIVATION TO LEARN ENGLISH

The proposed article is devoted to the study of fictional texts as a means of increasing motivation in the process of learning English. The topic of the article is determined by the search of ways of motivation formation in the process of learning a foreign language on the basis of the consideration of what motivation in general and what ways of its increasing exist in modern methods. The article analyzes the role of motivation in activities and identifies that motivation is one of the most important tools for managing students’ learning activities. The theoretical foundations of formation of motivation in learning a foreign language are characterized. The main stages of scientific ideas in the sphere of pedagogy are reflected in the given work. Such concepts as “motivation”, “learning motivation” are considered and also the samples of creative text tasks as an effective way of increasing motivation in learning English are analyzed and their analysis for developing motivation in learning English is done. The system analysis of available data on the problem of motivation is conducted. Modern techniques of foreign language and their meaning for the formation of students’ motivation are distinguished and described. The complex of exercises contributing to the increase of motivation is found out. The complex analysis of pedagogical, psychological and linguistic knowledge on the problem of research is established. The monitoring of the level of formation of students’ motivation to learn a foreign language, observation, and questionnaire survey was revealed. The methodological principles of studying motivation have been analyzed. The regularities of motivation development as the highest form of regulation of mental processes and the driving force of human activity are highlighted and analyzed. The correspondence of the main components of learning activity organization is established; the leading motives of schoolchildren in learning a foreign language are grouped based on the given theories. The types of learning motivation of modern times are presented. The reasons that prevent students from learning a language are identified. The most motivating factors in language learning are presented.
Filter-banks and wavelet compression

Wavelets are mathematical functions that can be used as an alternative to transforms such as the discrete cosine transform (DCT) to split a signal into different frequency bands (subbands) prior to processing and quantization. Wavelets are usually implemented as banks of filters that enable each subband to be analyzed at a resolution matched to its scale. This, as we will see later, is normally achieved by combining filtering operations with subsampling, which also has the benefit of maintaining a constant overall sample rate at all stages in the decomposition. This approach has significant advantages over Fourier-based methods, especially when signals are not harmonically related, are of short duration with discontinuities, or are nonstationary. It also has benefits in terms of subband coefficient quantization since the artifacts produced tend to be perceptually less annoying than the blocking artifacts associated with the DCT. This chapter firstly examines the basic two-channel filter-bank structure and then investigates the filter properties that enable perfect reconstruction in the absence of quantization. We then go on to consider the more general case of multirate filtering, showing how, through the appropriate combination of filters and up- and downsampling operations, we can achieve critical sampling. We next examine specific cases of useful subband and wavelet filters and then go on to extend the basic architecture to the case of a multistage (multiscale) decomposition and to two dimensions as required for image compression applications. Finally, we examine bit allocation strategies and scalability in the context of the JPEG2000 still image coding standard.
Changing Expectations of Academic Libraries

The digital age has been presented as a stark contrast to everything that libraries have had to deal with prior to now. While it is true that academic libraries have had to change to stay relevant in the digital age, the changes are not as severe as was once thought. When libraries embrace a few simple changes and start thinking outside the box when it comes to their employees, their resources, and their mission statements, they begin to meet the challenges that the digital age presents, and will continue to thrive in the new world presented by electronic materials, while remaining true to their spirit of information exchange and knowledge sharing.
An Attack Path Prediction Method based on The Attack-Defense Game Theory in CBTC

With the widespread application of communication based train control (CBTC) technology, the level of informatization and automation of urban rail transit has been improved. However, the application of most general computers, communication and control technology have made the information security problem of the CBTC prominent increasingly. Based on the architectural features and operating mechanism of the CBTC, this paper analyzes the vulnerability of the system and constructs a CBTC attack path library based on the penetration attack graph. We introduce the attack and defense game theory to establish a non-cooperative and incomplete information static game model for the attack and defense process of CBTC. The system can predict the attack path based on the min-max criterion. The simulation results of the ZC subsystem show that the attack prediction model can predict the attack path and give key defense suggestions.
Methodology for the Evaluation of the Accessibility of the Web Pages

In the previous chapters, the authors focused on the description of common problems that people with dyslexia encounter while using the internet. The authors also tried to describe ways to simplify the usage of the internet for people with dyslexia. In this chapter, the authors focus on creating a relevant methodology for evaluation of web pages based on their accessibility for people with dyslexia. It will serve as a manual for researchers on how to adapt their tools for wider accessibility.
Visual Semantic Planning for Service Robot via Natural Language Instructions

The interactive instruction following task requires intelligent robots to complete natural language instructions by interacting with the surrounding environment through visual perception and mechanical manipulation. The most representative benchmark is the ALFRED challenge task, which aims to perform restricted sequential actions to accomplish daily housework in a virtual environment. However, recently proposed multi-modal task planning approaches that combine vision and language do not perform well on ALFRED task. In this work, we utilize natural language instructions to build a single-mode model, and translate the task into a sequential decision problem, which focuses on generating continuous high-level action sequences directly from the instructions. We demonstrate that our K-PLM (knowledge enabled pre-trained language model) may successfully generate concrete visual semantic plans in 31.4% tasks on unseen scenarios without visual cues, where 62.2% can be generated if the model merges some visual cues, i.e. the location of the first object in the scene. The results show that our model provides outstanding visual semantic plans for the embodied agent to perform tasks and outperforms prior works.
Exploring the design co-teaching model from the perspective of user experience

The teaching mode of collaborative teaching and group cooperation is accepted by many schools, yet there is not enough analysis and discussion on this teaching mode. Therefore, based on design collaborative teaching theory, this paper constructs a basic idea of applying user experience theory to design collaborative teaching, and forms a teaching model with knowledge collaboration, value collaboration, and organizational collaboration as the core; and designs a teaching objective oriented to group-based, integrated problem analysis and team collaborative design. Through the teaching implementation of the graduate course "Research on Art and Design Methods" at Wuhan University of Science and Technology, student feedback was collected and interviews were conducted. The results show that the design collaborative teaching model can effectively cultivate students’ problem analysis ability and improve their collaborative and innovative thinking ability and teamwork ability.
Research on the Influence of the Internet Technology on the Self-identity of College Students Based on Support Vector Machine Optimization Alogrithm

The college students’ self-identity refers to the process of self-exploration and self-affirmation formed. Internet technology has triggered the contradiction between real-world communication based on physical identities and virtual social communication. College students’ desire for careness and the "identity absence" in the virtual world are contradictory, causing a crisis of self-identity among college students. Meanwhile, Support Vector Machine (SVM) as one of the machine learning methods is of importance in statistical learning technology. Because of its high efficiency, good performance and wide applications in classification, forecasting, and estimation, the SVM has become the most recognized method in learning machines. Therefore, this paper applies the Support Vector Machine optimization algorithm in Matlab language to quantify the relationship between college students’ online time and identity, seeking to find a balance between virtual and real life for college students. Through the study of questionnaires, we find three characteristics of college students’ self-identity. We take these three data as the independent variables, and the average hours of weekly online time of college students as the dependent variable. By the SVM alogrithm, the model between self-identity and the hours of the network usage per week is created. Based on this, we can calculate the balance point, which means the healthy self- identity with surfing the Internet. The model shows that 40 hours should be the best hours for college students spending time online per week. Exceeding these hours will have a negative impact on the self-identity of college students. Also, it is necessary to lead college students to improve their comprehensive ability, and finally overcome the challenge of Internet technology to college students’ self-identity.
Forensic Technical Process by E-Mail

Forensic mail network were based more on the e-mail message structure, whereas email content was rarely used. In different security extension schemes, cyber criminals tend to make use of email protocols by multiple producers over the course of a year for illegal purposes, for example send emails have become the most important digital platform for communication, file transfer and transactions and use not only laptops but also many other electronic gadgets including mobile phones.In this context, a number of forensics open source resources were frequently used by professionals. Furthermore, the misuse of Internet infrastructure by denial of service, storage waste and computer resources costs every internet user directly or indirectly. E-mail forensic is used to the origin and the content of e-mail as evidence, to determine the actual sender, the recipient, and when and how it was sent, etc. These instruments were nevertheless developed in isolation rather than in cooperation. Since forensic email users have to know the utility of a method for forensics research in their own situations. This paper is intended to illustrate the email system architecture from a forensic perspective. It describes the email partners ' roles and responsibilities, the metadata in the email headers
Causal Discovery

This chapter covers the topic of learning causal models. A general introduction to causal discovery is presented, highlighting some of the challenges. Then the types of graphs used to represent partial models are introduced. Several algorithms for causal discovery from observational data are explained, including score-based and constraint-based; as well as a technique for linear models. Finally, it includes an example of learning causal models from real world data about children with attention deficit hyperactivity disorder; and an application for discovering brain effective connectivity networks based on neuro-images.
End-User Flexibilities in Digital Copyright Law – An Empirical Analysis of End-user License Agreements

In the platform age, copyright-protected contents are primarily disseminated over the internet. This model poses various challenges to the copyright regime that was mainly designed in and for the analog age. One of these challenges is related to the fair balance between the interests of rightsholders and other members of society. Copyright norms try to guarantee this balance by granting a high level of protection for rightsholders and preserving some flexibility for end-users. Regulation by platforms’ end-user license agreements might also be relevant to preserve that balance.The present paper focused on how these private norms allow for or diminish the exercise of user flexibilities. We collected, analyzed, and compared seventeen private ordering practices of service providers grouped in four main categories.Our empirical examination demonstrated that intermediaries offer substantive flexibilities for their consumers, on the one hand, and they meaningfully limit the possibilities and decrease the expectations of end-users by restricting certain uses and providing limited access to contents, on the other hand. We measured the flexibility of the selected platforms, and we provided for the state of the art of platform flexibilities in the period preceding the implementation deadline of the EU’s CDSM Directive.
How to put into practice the Integration Methods

Till graduating bachelor's degree at University it is admittable fact that most graduators essentially practice only on receiving specific knowledge, resulted that the lack of Utilisation of educational intelligence that is way it is purposed to lead the fresh bachelors of pedagogical units of High Education getting on how to deliver their learners obtained substances. It is directed that analyzing of Integration that promptable comprehensive Methodology on Teaching English language. Furthermore, the strategies of the integrating skills and the techniques about how to present knowledge during the procedure of lesson at GES (general education schools). It is brilliant brief instruction not only for the new teachers who just starting working with learners but also for the experienced teachers.
Cyber-Insurance

This chapter focuses on cyber-insurance for Cyber-Physical Systems (CPS). We identify two types of networked systems: traditional Information Technology Systems (ITS) and CPS. They differ with respect to cyber-security. Security challenges of CPSs are driven by their particulars (physical features and capabilities, regulations, and attacker motivations). The factors complicating the advancement of CPS cyber-insurance ecosystem, including: extreme information scarcity; risk assessment problems, exacerbated by the growing complexity of CPS and the intricacies of risk prorogation. We conclude that without new government policies improving security information, cyber-insurance market for CPS may stall.
The Way Forward

This chapter presents a summary of this book. We also outline a number of future research agenda based on the failure and success gained during the two years of implementing experiential learning in both undergraduate level and graduate level digital forensic courses, with the hope that the findings reported in this book can be used for facilitating the practice-oriented digital forensic education in the future.
TEEM

In this article, the authors describe a client-server architecture, designed for supporting data recording and transmission during emergency patient transportation by ambulance. The clients are a set of mobile apps, interfaced to the monitoring devices in the ambulance, that automatically send all the recorded data to a server at the destination center. One additional app enables the travelling personnel to input and transmit further significant patient data, or comments. At the destination center, the specialist physician logs onto the server, receives the data in real time, and is allowed to plot/analyze them, assessing the patient's situation, and possibly sending immediate feedback to the operators in the ambulance. The system is currently under evaluation at the Neonatal Intensive Care Unit (NICU) of Alessandria Children Hospital, Italy. The system, by allowing real time data communication, is able to provide clear advantages from the organizational and economical viewpoint.
Three Different Continuous-Time GMDS-ZNN Models and Multiple-Instant Discrete-Time Ones for Time-Varying Matrix Inversion with Comparisons

Securing the inverse matrix of a matrix varied with time online plays a considerable role in many engineering and scientific applications. Lots of effort has been made in securing the inverse varied with time of an interesting matrix with high speed and high accuracy so far. With the rapid development of neural networks and neural-dynamic methods, Getz-Marsden dynamic system (GMDS) is propounded to secure the time-varying matrix inversion. Subsequently, Zhang et al. put forward Zhang neural network (ZNN, also zeroing neural network as termed) models and Zhang functions (ZFs, also termed zeroing functions), with several kinds of GMDS-ZNN (GZ) models constructed in real and complex domains by means of ZNN models and ZFs. In the meantime, many researches on the features of these GZ models are carried out. However, there is no comparison among these GZ models. Hence, we conduct numerical experiments to compare three GZ models, including GZ model 1 (also termed GMDS), GZ model 2, and GZ model 3. The results of numerical computer experiments substantiate that GZ model 2 and 3 are evidently better in two aspects (i.e., convergence accuracy and convergence rate), compared with GZ model 1. Besides, the numerical results synthesized by GZ model 2 are completely the same as those synthesized by GZ model 3.
Cross-Dataset Pose Estimation of Faces In The Wild

In this paper, we propose a domain generalization method for cross-dataset pose estimation of faces captured in wild conditions. Conventional methods mainly devote efforts on extracting discriminative features to reason the three-dimension pose. Due to the large distribution discrepancies between widely-used synthetic training and real-world testing data, it is challenging to seek a domain-generalized feature space especially for the new test samples in real-world applications. To alleviate the influence of dataset bias, our model aims to learn the domain-invariant features across different domains. In detail, a carefully-designed domain discriminator is plugged to the features extracted from different domains, meanwhile the feature encoder is trained to enforce features from different domains confused by game-theorem iterations. With the adversarial manner, our model learns a generalized pose-relevant feature space shared across different domains. Extensive experimental results on the standard benchmark under the cross-dataset setting indicate the superiority of our method in comparisons with most state of the arts.
Cost Snipper – Predicting Prices of Online Shopping Items based on Preceding Data

The variation of product prices in online shopping is high which makes it difficult to decide when to buy. The tremendous growth of e-commerce helps us to create the solution of price prediction. We used web scraping technique to get the price data from various online shopping retailers and process the data for each commodity to predict the price for the future which helps us to make decisions on buying online products. We automated the web scraping of data and price prediction daily to make the price available for the customer without any delay.
Semantic Analysis

The content in this chapter is about the meaning analysis of words and sentences divided into four sections. The first three sections explain essential techniques commonly used for the analysis of the meaning, including the Pattern parsing, Ontology, and Semantic patterns. The final section is a summary of how to apply these techniques in Thai Natural Language Processing.
Forensic Investigations in Cloud Computing

Cloud computing environments add an inherent layer of complication to a digital forensic investigation. The content of this chapter explores current forensic acquisition processes, why current processes need to be modified for cloud investigations, and how new methods can help in an investigation. A section will be included that provides recommendations for more accurate evidence acquisition in investigations. A final section will include recommendations for additional areas of research in the area of investigating cloud computing environments and acquiring cloud computing-based evidence.
Big Data Security Framework for Distributed Cloud Data Centers

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment.
Thing Theory

As healthcare professionals and others embrace the Internet of Things (IoT) and smart environment paradigms, developers will bear the brunt of constructing the IT relationships within these, making sense of the big data produced as a result, and managing the relationships between people and technologies. This chapter explores how PolySocial Reality (PoSR), a framework for representing how people, devices and communication technologies interact, can be applied to developing use cases combining IoT and smart environment paradigms, giving special consideration to the nature of location-aware messaging from sensors and the resultant data collection in a healthcare environment. Based on this discussion, the authors suggest ways to enable more robust intra-sensor messaging through leveraging social awareness by software agents applied in carefully considered healthcare contexts.
Convolutional Neural Network for Image Classification Based on Satellite Imagery

Satellite image classification is a meaningful task, such as helping recognize the damaged houses. Traditional methods like ANN and BoW are not good at satellite image classification. CNN(Convolutional Neural Network) has been generally used to do image classification, and it can be applied to many daily situations like damage assessment. In this paper, we build a CNN model and discuss its advantage over other present models. Besides, to increase the robustness of our model, we add image enhancement to our training data. The preliminary model can reach an accuracy of 0.9706 in our experiment.