Reviewerly/exHarmony huggingface dataset:
Poetry, Counsel, and Coercion in Shakespeare’s Early History Plays

This chapter explores how the conciliar and coercive discourses of Renaissance historiography and lyric poetry intersect in Shakespeare’s early history plays. It focuses on scenes of textual composition, and other engagements with the construction and interpretation of literary language, in plays including Edward III, Arden of Faversham, Richard III, The First Part of the Contention, and Titus Andronicus, to show how they illuminate the uneasy overlap between political advice and gendered, sexual coercion during Elizabeth I’s reign. The plays’ treatment of the coercive properties of poetry cast monarch and subject in fluid, shifting roles, and highlight the awkward ethical implications of mobile rhetorical authority, ultimately troubling the distinction between good and bad political advice. Moreover, the chapter considers the centrality of collaboration to the production of meaning, not just between actors, printers, and playwrights, but also of genres and discourses, within this intensely self-referential cluster of Shakespeare’s co-authored early works.
AgentVis: Visual Analysis of Agent Behavior With Hierarchical Glyphs

Glyphs representing complex behavior provide a useful and common means of visualizing multivariate data. However, due to their complex shape, overlapping, and occlusion of glyphs is a common and prominent limitation. This limits the number of discreet data tuples that can be displayed in a given image. Using a real-world application, glyphs are used to depict agent behavior in a call center. However, many call centers feature thousands of agents. A standard approach representing thousands of agents with glyphs does not scale. To accommodate the visualization incorporating thousands of glyphs we develop clustering of overlapping glyphs into a single parent glyph. This hierarchical glyph represents the mean value of all child agent glyphs, removing overlap and reduTcing visual clutter. Multi-variate clustering techniques are explored and developed in collaboration with domain experts in the call center industry. We implement dynamic control of glyph clusters according to zoom level and customized distance metrics, to utilize image space with reduced overplotting and cluttering. We demonstrate our technique with examples and a usage scenario using real-world call-center data to visualize thousands of call center agents, revealing insight into their behavior and reporting feedback from expert call-center analysts.
Anne Golden, Scott Jarvis &amp; Kari Tenfjord (Eds.), <i>Crosslinguistic Influence and Distinctive Patterns of Language Learning: Findings and Insights from a Learner Corpus.</i>

Abstract This article reviews Crosslinguistic Influence and Distinctive Patterns of Language Learning: Findings and Insights from a Learner Corpus 9781783098767
The scheme of personal privacy protection based on data separation in Mobile identity authentication

Abstract To avoid privacy leaked in mobile identity authentication by telephone number, this paper proposes a scheme of data separation that separates telephone number from server of service provider and stores it on trusted third-party server. In the process, we put forward some ideas to solve the problems brought by this scheme, which includes a table introduced to associate real identity and network one, a way of access authorization. Finally, we analyze the performance of the scheme and discuss the time efficiency by experiment.
Examining Cyber-Bullying Behavior on Social Networking Sites in General

Bullying is defined as a form of violence and abuse directed at a person or group of persons. Cyber-bullying is the frequent and violent abuse of people through the Internet. The behavior is prevalent in social networking sites and aims at harming and threatening the target person/victim. Cyber bullying can reveal personal information about the victim (real name, home, work or school address) on websites/forums, or use impersonation (including fake sites, comments, accounts etc.). The form and extent of cyber-bullying is unknown in social networking sites. Therefore, this study aims at uncovering this information by study the cyber-bullying behavior online and addresses its prevalence in the Arab world. Web Crawler is used to collect all the data relate to bullying on social media and the result demonstrated a high connection between the utilized watchwords and the constructed corpus.
Bimodal Face Recognition Based on Liveness Detection

In this paper, we proposed a three-dimensional face recognition method based on liveness detection. Firstly, one liveness detection method based on the three-dimensional structure of the face was proposed. According to the face feature point localization algorithm combined with the RANSAC fitting algorithm and the SVM model training prediction method, the depth information was used to judge the reality of the face, and the detection of the photo and video attacker behavior were solved. In this paper, the traditional loss function and the new central loss function were used as the supervised signals in the face recognition process. We used model fine-tuning to train two independent convolutional neural networks, and then we used the score fusion approach to fuse facial feature matching. Finally, we evaluated the Eurecom and Vap public test sets based on the way of actual application. The experimental results showed that compared with the traditional in liveness detection, our algorithm made use of the advantages of three-dimensional information to realize the judgment of face reality, and the algorithm had better accuracy in three-dimensional face recognition section.
Comparative and Analysis of spatial domain and frequency domaindigital image watermarking algorithm

Abstract Digital image watermarking belongs to the branch of information hiding discipline, the study of the digital watermarking will play an invaluable role in the integrity of business transactions and the protection of personal privacy.This paper introduce the embedding process of digital watermarking technology and the extraction process of digital watermarking technology. We analyze the the characteristics of digital image watermarking and juxtapose the basic principle of the Lease Significant Bits algorithm and the basic principle of the Quaternion Fourier Transform algorithm. Finally, According to analysis and comparison, we summarize the advantages of the frequency domain method.
Towards Privacy-Aware Smart Surveillance

Cameras are rapidly becoming a daily part of our lives. Their constant streaming of information about people gives rise to different security and privacy concerns. Human analysis using cameras or surveillance footage has been an active field of research. Different methods have been introduced which showed success in both the detection and tracking of pedestrians. Once a human is detected and/or tracked, different motion analyses can be performed in order to better understand and model human behavior. A majority of these methods do not take user privacy or security into account, making security monitoring systems a significant threat to individuals’ privacy. This threat becomes more serious and evident when the security cameras are installed in places where vulnerable people (e.g. elders, children) frequently spend time such as day-cares, schools, retirement homes, or violated to serve independent interests. This work presents a model that is able to understand human motion, and deploys an anonymization technique that facilitates the preservation of an individual’s privacy and security.
Feature Selection Techniques to Choose the Best Features for Parkinsons Disease Predictions Based on Decision Tree

Abstract Parkinson is a disease that is caused by nerve cell damage in the brain and incurable. Knowing about Parkinson disease is very important so that medical action can be taken to prevent Parkinson’s getting worse. The dataset that uses to analysis for Parkinson disease using machine learning algorithms has many features. The dataset with many features can increase complexity, but not all features have a positive influence on the results of the analysis. Irrelevant features can reduce model performance. This research proposes to apply feature selection to choose features that have a positive effect so that the performance of the model does not decrease. The experiment results show that the application of feature selection can lead to better model performance.
Reference velocity optimization base on LMPC for mobile robot trajectory planning

In this paper, a reference velocity critical principle is proposed for mobile robot trajectory planning, which based on the requirements of mobile robotic machining. The mobile platform is required to reach the desired position within schedule time. Besides, it benefits that the mobile robot goes straight to the desired position in the predefined angle thus avoiding rotation in the last posture to adjust angle error. Therefore, the trajectory planning method is designed based on those characteristics. Technically, we designed a reference straight line through the desired position and established a virtual vehicle moving at uniform motion on the line. Then a linearized model predictive control is adopted to accomplish fast convergence to reference trajectory thus arriving at the target posture. However, the performance of the LMPC is highly depended on the coefficients in the cost function. Therefore, the best choice of the reference velocity depending on the given matrix Q is proposed. Numerical simulation results demonstrate that the selection of coefficients in Q will affect the convergent performance of the mobile robot.
Who Read What When?

The relationship of plays to their sources has always been important evidence of chronology, authorship, and the derivation of textual variants. Such evidence has been particularly important to studies of Shakespeare’s early plays. But for centuries source scholarship has been based on random anecdotes: a scholar reading one text notices something about it that reminds them of another text. We can now re-evaluate those anecdotal findings by testing them systematically against digital databases. Such tests establish that Margaret's long speech at the beginning of Scene 2 of The First Part of the Contention is based on a passage in Hall's chronicle, whereas the variants in the Folio text of 2 Henry VI instead draw upon Holinshed's chronicle. This evidence supports revision rather than memorial reconstruction. Likewise, the links between the Contention speech and Edward II are best explained by Marlowe's authorship of both.
Shakespeare’s Early Verse Style

This chapter explores the relationship between three stretches of text in which a hunt is described: the exchange between Tamora and Aaron in Titus Andronicus (2.3.10–50); several stanzas of Venus and Adonis (830–99); and Thomas Arden’s narrative of his nightmare in Arden of Faversham, in which he is transformed from a by-stander to the prey (6.6–34). The first two of these passages are indubitably by Shakespeare, and reasons are given for concluding that all three, which share complexes of words and images, are by a single author. The order in which the works were composed is discussed. These and samples from other early plays – including a further evocation of hunted deer in 1 Henry VI, 4.2 ߝ are analysed to illustrate distinctive aspects of Shakespeare’s early poetic style, such as the way that word-play generates imagery, giving his verse its vivid particularity.
Enlacement and Interlacement Shape Descriptors

We propose a novel approach to characterize complex 2D shapes based on enlacement and interlacement directional spatial relations. This new relational concept allows to assess in a polar space how the concave parts of objects are intertwined following a set of directions. In addition, such a spatial relationship has an interesting behavior considering the common properties in pattern recognition such as translation, rotation, scale and symmetry. A shape descriptor is defined by considering the enlacement of its own shape and the disk area that surrounds it. An experimental study carried out on two datasets of binary shapes highlights the discriminating ability of these new shape descriptors.
Gated Residual Connection for Nerual Machine Translation

The Transformer framework has shown its flexibility in parallel computation and the effectiveness of modeling word dependencies since it is proposed. Due to the exploding and vanishing gradient problem, Transformer adopted the residual connection among the layers in a stack. However, normally residual connection layer just simply element-added the input and output. In this work, we focus on improving the residual connection layer through regulating the appropriate probability of the input and output flow but not only simply added. To maintain the simplicity and flexibility of the Transformer framework, we leverage the internal representations of the output. The results of experiments on WMT14 English-German translation dataset demonstrate the effectiveness of our proposed method.
Content Bubbles

Personalisation has extended to our entire user experience in the digital world. In practical terms, almost all the online services promote us to create a user profile and from there, they offer access to personalised content and/or services. This leads us to the generation of Big Data associated with user profiles which the companies harvest through analytic and predictive algorithms, which they later use to recommend, filter, and provide the content we consume. Having more and more detailed data from user profiles allows for the platforms to detect tendencies in a global public and to create the content that has the greatest chance for success. This chapter examines the massive data management that occurs on platforms that distribute visual content on demand and its impact on content creation. We will focus on Netflix as a paradigm example.
Improving Golay Code Using Hashing Technique

Data are the representation of our world and our life. Data are increasing continuously, they come from different sources such as sensors, maps, climate informatics, smartphones, social media and/or medical data domains. Data are represented by different forms such as image, text, video and/or digital data. These incomprehensible data need an influential technique to be clustered and analyzed. This paper presents a hashing technique for the clustering process of unclassified and disorganized data. These clustered data are useful for decision-making process. The proposed technique is based on Golay error-correction code. The main concept is reversing the original Golay error-correction scheme and building Golay Code Addresses Hash Table (GCAHT). Simulation results stated that the proposed technique achieved high performance. Beta-CV, Dunn Index, C-index and Sum Square Error are used for measurements.
The Research Status and Hotspots in the Domain of Smart Education from 2003-2019

Abstract To explore the status and hotspots in the domain of smart education, 183 related articles form the Web of Science (WOS) were analyzed by the visualization software CiteSpace. The results show that: (1) the area of smart education stared in 2003 while there was no research in the next six years. There was an upward trend year by year form 2011 and the number of articles reached the historical peak in 2016. Bakken, Lee and Shi, China and Smart Innovation Systems and Technologies topped the lists of contributing authors, country and publication respectively. (2) “engineering education”, “smart city”, “pedagogical affordance”, “ICT” and “big data” were the top five largest clusters on the area of smart education from 2003-2019.
Study on the Design of Evaluation Task and Implementation of Teachers'Attitude Towards the Application of Information Technology

With the rapid development of information technology, the evaluation of teachers' information technology application competence has caused the attention of the relevant experts and scholars in this field all over the world. Based on this, this study was carried out empirical research on teachers' attitudes towards the application of information technology and explored the design method and implementation strategies for the task of emotional attitude assessment. First of all, based on the technology acceptance model, this study determines the dimensions of teachers' attitudes towards the application of information technology. Secondly, the design process and basic structure of the emotional attitude assessment tasks are determined. In the end, three dimensions of the implementation of the assessment strategies are determined, including Value Identity, Cost Identity, and Usage Intention. The results show that the assessment of teachers' attitudes towards IT application can be carried out by designing assessment tasks, determining assessment situations, and using relevant assessment platforms. This study is also expecting to provide some theoretical and practical reference for improving the assessment of teachers' attitudes toward information technology application in underdeveloped areas.
EXPERTISE IN EDUCATION: THEORETICAL ANALYSIS OF BASIC CONCEPTS

The article is devoted to theoretical analysis of basic concepts of the problem of expertise in the field of education. It is revealed that ambiguity in the definition of expertise is traced in the scientific literature, as well as the substitution of this concept by similar ones, such as monitoring, diagnostics, inspection, evaluation, audit, control, consulting, etc. It is noted that the above concepts differ in their essence, purpose, tasks, functions, purpose, objects, content, participants, application, technologies, methods, tools and output. The author specifies the essence of theoretical concepts of «expert activity in education» and «expert in the field of education».
Research on Abstractive Automatic Summarization Technology Based on Deep Learning

Automatic summarization technology is a method to obtain important information from documents, which can alleviate people's time and energy problems in the era of information explosion. This paper mainly studied the abstractive automatic summarization technology based on deep learning. Abstractive automatic summarization is consistent with the human habit of writing abstract, and has the characteristics of simplicity, flexibility and diversity. The experimental results based on English automatic summary data set (CNN/Daily Mail) and Chinese short text summary data set (LCSTS) showed that after the Attention Mechanism, Pointer Networks and Coverage Mechanism were added to the Seq2Seq model, the automatic summary Rouge evaluation index had an apparent improvement. In addition, comparative experiments were carried out from the neural network types (LSTM, GRU, SRU, etc.), the impact of Pointer Networks and Coverage Mechanism and the role of position features and Beam Search. After adding Batch Normalization and location features to the Point-Generator Network, there was a significant improvement in the Rouge1 and Rouge2 evaluation index.
Traffic sign detection and recognition in natural scene based on human visual perception mechanism

Traffic sign detection and recognition in natural scenes remains a challenging work for intelligence traffic system (ITS). Moreover, most traditional methods cannot achieve a better detection and recognition performance. For which some traditional methods cannot overcome the interference of complex scenes, illumination change, weather condition and rotation and scaling of traffic signs. Therefore, as the human vision perception and processing mechanisms are increasingly revealed, a traffic sign detection and recognition approach is proposed in this paper. There are mainly two parts in the proposed approach. The first part is traffic sign detection. Firstly, visual saliency detection is combined with contrast cue and center-bias cue, which is performed for detecting region of interests (ROIs) of natural scene. Moreover, the geometric structure constraint model is constructed to obtain the location of traffic signs from ROIs accurately. Next, biological-visual-based algorithm is performed in the second part, which is utilized for feature extraction of detected traffic sign. The extracted feature vector is used for traffic sign recognition through the 1-nearest neighbor. Additionally, several experiments are implemented based on Swedish traffic signs database. The recognition accuracy achieves 90%. In summary, the proposed method provides a new framework for traffic detection and recognition. For traffic sign detection it overcomes the interference of complex urbanization backgrounds. And the interference of illumination change and weather conditions for traffic signs is overcame by biological-visual-based algorithm. The contributions of this paper is significant in computational intelligence for the further work.
Use of augmented reality information and communication technologies in urban historical environment

Abstract In Samara, in the historical center of the city, the fragments of two wooden fortresses were found (the years of construction were: 1586 and 1706). The task is to find a way to demonstrate these architectural objects to a full extent against the background of the landscape of their historical location. The reconstruction of lost objects on the site where they were located is difficult because such areas are densely built up with newer houses. One of the solutions may be to use augmented reality technologies that allow combining, by means of superimposition, visual information (textual, graphic and three-dimensional), including the graphic reconstruction of the historical object, and the modern urban landscape as it looks.
Effective Garbage Data Filtering Algorithm for SNS Big Data Processing by Machine Learning

Recently, as the use of social network services (SNS) increases in daily modern life, the amount of SNS data generated has become very large. In addition, increasing efforts are being directed to extracting various pieces of information by collecting, processing and analyzing large amounts of SNS data. While various pieces of information can be extracted from SNS data through big data processing, this is a highly resource-intensive task. Therefore, in order to obtain information from SNS data, a lot of time and material resources are required. In this paper, we propose a data filtering algorithm that filters out garbage data that has no meaning as data among SNS data. The proposed algorithm improves the filtering accuracy by recursive learning based on the initial learning data. Experimental results show that the proposed algorithm has a filtering effect of over 70% on the experimental keywords.