Reviewerly/exHarmony huggingface dataset:
A Robust Approach to Document Skew Detection

This article presents a script-independent approach for the estimation of skew angle of the offline document image. It follows a certain divide and conquer strategy. A document image is segmented into several vertical strips of smaller and equal width and a simple strategy is applied to identify individual lines in each such vertical segment. Next, an association of horizontally adjacent lines in consecutive vertical strips takes place extracting individual text lines of the whole document image. Let $$P_{ij}$$ and $$P_{i(j+1)}$$ be the center points of the baselines of two adjacent segments of the i-th text line of the input document. The angle of inclination $$\theta _{ij}$$ of the straight line segment $$P_{ij}P_{i(j+1)}$$ with the positive direction of x-axis is computed. The average of all $$\theta _{ij}$$ ’s for the entire document image is used as the estimated skew of the document. This strategy of document image skew estimation has been tested on the DISEC’13 (ICDAR 2013 Document Image Skew Estimation Contest) database and its performance is comparable with the state-of-the-art results in terms of both accuracy and computation time. Additionally, this skew estimation scheme has been used on a wide variety of other documents of different scripts, mixed script documents, degraded quality documents, documents containing texts and doodles, etc. The results of skew correction in every such case have been observed to be satisfactory.
Life-seeking approaches in eco-artistic practices: A proposal for a radical cultural change through language

This article developed in three parts the precursory practice-based research, through a phenomenological framework, to the following yet-to-be-resolved core question: is it possible for the artistic (and ritualistic) object to be imbued with what we consider to be the essence of life? The research arrives at this question by exploring how silence and sound can contribute to the perception of life, highlighting language as a driver for cultural change and advancing the use of a new pronoun assigned to those who bear life.
ALP: An Arabic Linguistic Pipeline

This paper presents ALP, an entirely new linguistic pipeline for natural language processing of text in Modern Standard Arabic. In contrary to the conventional pipeline architecture, we solve common NLP operations of word segmentation, POS tagging, and named entity recognition as a single sequence labeling task. Based on this single component, we also introduce a new lemmatizer tool that combines machine-learning-based and dictionary-based approaches, the latter providing increased accuracy, robustness, and flexibility to the former. In addition, we present a base phrase chunking tool which is an essential tool in many NLP operations. The presented pipeline configuration results in a faster operation and is able to provide a solution to the challenges of processing Modern Standard Arabic, such as the rich morphology, agglutinative aspects, and lexical ambiguity due to the absence of short vowels.
Arabic Named Entity Recognition with a CRF Model Based on Transformer Architecture

Named Entity Recognition (NER) is one of the most important tasks of Natural Language Processing that has a very well-defined purpose. There are multiple methodologies suitable for identification of a named entity in a wider text, each with its own advantages. One methodology that has been demonstrated to be very effective involves local versions of deep learning algorithms based on BERT, where ARBERT/MARBERT and AraBERT represent some of the best known implementations in the Arabic language. In this chapter, we introduce a simple model capable of performing NER task with Arabic content. This model is composed of three separate layers—one based on Transformer architecture, one fully connected, and finally one based on the Conditional Random Field technique. This model performs quite well compared to other NER tools for Arabic, reaching F1-scores of nearly 90% on three relevant datasets (ANERCorp, AQMAR, and CANERCorp). We also evaluate our model on one of our customized datasets of Arabic legal textual content. The model achieved 85% which is the highest results of F1-macro score comparing to other related models.
Pedagogical Conditions for the Implementation of the Use of Digital Tools in Distance Learning

The article deals with the issue of practical application of Internet technologies in the educational process. The main directions of using computer networks in teaching students are indicated. Particular attention is paid to the use of distance learning systems based on the Internet.
Low-Complexity Quality Measurement for Real-Time Video Compression

A wide variety of video quality (VQ) metrics have been introduced over the past decades. VQ metrics are used in a range of applications, including video quality monitoring, encoder optimization, bitrate selection, and adaptive bitrate ladder configuration. While quality metrics have been developed that correlate well with human visual perception, they typically require high computational complexity. Other popular metrics are less complex, but are not accurate enough to base encoder decisions on. — In this paper, we discuss relevant quality metrics and their computational complexity. We address the difficulties around real-time VQ measurement, and determine which metrics can be used inside real-time encoders, to not only measure, but to actively control video quality. We include recent Machine Learning (ML) driven research, with a focus on the complexity introduced by ML techniques for video quality assessment. While these techniques provide benefits to improve VQ measurement and monitoring applications, we illustrate the fine line between accuracy of ML networks and the number of operations performed in these networks and their associated cost.
Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM

Social media is currently being used by many individuals online as a major source of information. However, not all information shared online is true, even photos and videos can be doctored. Deepfakes have recently risen with the rise of technological advancement and have allowed nefarious online users to replace one face with a computer generated face of anyone they would like, including important political and cultural figures. Deepfakes are now a tool to be able to spread mass misinformation. There is now an immense need to create models that are able to detect deepfakes and keep them from being spread as seemingly real images or videos. In this paper, we propose a new deepfake detection schema using two popular machine learning algorithms.
Preface

Extract Music unfolds on a vast playing field with a mathematical structure of great complexity and beauty. This book is a study of that structure. Mathematics as a tool for the study of music Music theory has in recent decades been enriched by an infusion of ideas from many other disciplines, among which mathematics has earned a particular preeminence. Mathematical techniques enhance our understanding of numerous aspects of music, from scale construction and rhythmic organization to triadic harmony and atonal pitch structure, sometimes building upon established music-analytical traditions but sometimes providing entirely new methodologies and paradigms. Today’s music theory journals publish many articles at least partly mathematical in nature; the Journal of Mathematics and Music began publication in 2007, and international conferences on “Mathematics and Computation in Music” are held biennially. It is not an exaggeration to say that mathematical music theory has blossomed into a sizable industry. The price of admission to the field, however, can seem exorbitant. A solid foundation in mathematics, traditionally expected of students in physics, engineering, and other disciplines that use mathematics, is rare among students of music. Important work in the field is scattered through countless articles and an armload of books, terminology and notation are not consistent, and much of this work is highly technical, not readily accessible to those with limited mathematical training, no matter their musical expertise. David Lewin’s Generalized Musical Intervals and Transformations (Lewin [1987] 2007), the pathbreaking volume that singlehandedly established the field of transformational music theory, has been a definitive resource for more than three decades, but it also remains a famously difficult read for music theorists who must contend from its first pages not only with abstract concepts such as semigroup congruences and homomorphisms but also with an uncompromisingly formal Definition-Theorem-Proof-Corollary-Remark style characteristic of advanced mathematics texts.1
Data Protection in Permissioned Blockchains using Privilege Separation

This paper concerns the Hyperledger Fabric permissioned blockchain system. This system is in popular use in several enterprise settings, where each participating corporate entity may have sensitive business-related data whose confidentiality it wishes to protect. Fabric provides the channel abstraction that ensures that channel data (e.g., data stored in that channel's ledger, or data transmitted via the network to members of that channel) are only accessible to members of that channel. Unfortunately, as we show in this paper, the channel abstraction only offers data protection under the implicit assumption that all system components in the permissioned blockchain are trustworthy. This assumption may not hold in the presence of compromised container nodes, on which several blockchain-related components execute, or malicious business users inside any one of the participating corporate entities. Under such situations, sensitive corporate data can be leaked to unauthorized entities. We present Aramid, which is an enhanced version of Fabric that offers data protection even in the presence of compromised blockchain components. Aramid uses a privilege-separated architecture in which blockchain components (such as peer or orderer nodes) that are members of multiple channels execute on different containers. Aramid is transparent to legacy Fabric applications, requiring no changes to their codebase. Through our prototype implementation, we show that Aramid robustly defends against a number of attacks possible on Fabric, and that it does so with performance comparable to Fabric.
Abstractive Text Summarization for English Language Using NLP and Machine Learning Approaches

A text summarizing system’s principal goal is to extract the most important information from a given text. We are building an abstractive text summarizer (ATS) to generate high-quality English summaries that is similar to human generated summaries. We have worked on three approaches in our 1st approach, we have used a bi-directional LSTM and encoding layer (with attention mechanism) and attention model in decoding layer, and to generate a summary, we have applied the sequence-to-sequence model. We have used the concept of embedding layers, tokenization, attention mechanism, stacked BiLSTM, and Seq2Seq model. Our 2nd approach incorporates RoBERTa which is robustly optimized BERT pre-training. In our 3rd approach, we have used the DistilRoBERTa model. RoBERTa and DistilRoBERTa optimize the training of BERT architecture in order to take less time during pre-training. The ROUGE score was used to test our model, and the results were compared. The DistilRoBERTa model does better than other models for text summarization problem.
Comparative Study Between Different Algorithms of Data Compression and Decompression Techniques

In the literature, many proposals have been made to improve the storage and transmission of data including cheaper, efficient and safe compression techniques. Thus, this paper proposes to make a comparative study of BID, Huffman, LZ77, three (3) powerful algorithms of data compression and decompression techniques in order to determine the best one. These algorithms each have impressive qualities. For this, we will focus on the quality of their ratios, the percentage of registration, the speed and their basic techniques. Some of these algorithms are already world references and have a certain undeniable popularity today. They are all three parts of the best algorithms in the world. But despite all these difficulties and obstacles to overcome, this paper will have to determine a clear classification and its share. This is why this hard and tedious work will be demonstrated and discussed in this document in an explicit way in order to eliminate all ambiguity (doubts). The importance of this manuscript will therefore be to provide comparative clarity between these three (3) different algorithms according to the defined criteria of sincere way.
[High capacity: social competences and cyberbullying].

High Intellectual Ability (HIA) is a differential manifestation of human intelligence with a neurobiological basis but which must express its high potential along the developmental trajectory through the covariation of endogenous (such as social competence) and exogenous modulating factors. The aim of the study is twofold: 1) to know, comparatively, the social competence of children with and without HIA, 2) to differentiate those social competences that could be protective or risk factors against the misuse of digital technologies. The Social Skills Improvement System-Rating Scales were administered to a sample of n = 70 learners (n = 35 with ICA, n = 35 with average intelligence) aged 11 to 16, analysing whether there are statistically significant differences in social skills and behavioural difficulties. Results show statistically significant differences in favour of participants with ICA in social skills (especially in: Responsibility, Co-operation and Self-Control) and better personal adjustment, with low incidence of internalising and externalising difficulties. We conclude and discuss the protective role of social skills in coping with complex interactive contexts such as the digital age and aggressions such as cyberbullying.La Alta Capacidad intelectual (ACI) es una manifestación diferencial de la inteligencia humana, de base neurobiológica, pero que debe expresar su alto potencial a lo largo del desarrollo de la persona que la posee, mediante la covariación de factores moduladores endógenos (como la competencia social) y exógenos. El objetivo del trabajo es doble: 1) conocer, comparativamente la competencia social de menores con y sin ACI, 2) diferenciar aquellas competencias sociales que podrían ser factores protectores o de riesgo frente al mal uso de las tecnologías digitales. Se administra la Social Skills Improvement System-Rating Scales a una muestra de n = 70 aprendices (n = 35 con ACI, n = 35 con inteligencia promedio) de 11 a 16 años, analizando si existen diferencias estadísticamente significativas en habilidades sociales y en dificultades de conducta. Los resultados muestran diferencias estadísticamente significativas, a favor de los participantes con ACI en habilidades sociales (especialmente en: Responsabilidad, Cooperación y Autocontrol) y mejor ajuste personal, con baja incidencia de dificultades internalizantes y externalizantes. Se concluye y discute el rol protector de las habilidades sociales para afrontar contextos interactivos complejos como el derivado de la era digital y agresiones como el cyberbullying.
Safety tech will aid online content moderation

Headline INT: Safety tech will aid online content moderation
The Role of Blockchain and Smart Contracts in International Relations

This chapter deals with the current and potential role of blockchain technology in the context of international relations. The development of blockchain technology spreads increasingly across various branches and sectors, from IT to financial and politics applications. The most important characteristic of this development is the implementation of a more efficient and more secure data verification—more efficient because it enables a decentralized storage within the network, and more secure because the data is secured by the proof of work concept, which is independent of a centralized server or entity.
Cryptocurrency Industry: A Review of Current and Future Trends

This chapter summarizes the current state of cryptocurrencies and expands their development trajectories to shed light on the industry outlook. The topics introduce the nature of each cryptocurrency, evaluate cryptocurrencies as financial assets, and assess cryptocurrencies as money. Each topic in this chapter starts from the current trend review and concludes with future trends in the specific area.
Cryptocurrencies, Blockchain, and Public Choice

Nobel Economist James M. Buchanan famously described Public Choice as “politics without romance” (Buchanan 1984) to show the political world not through the normative lens of the noble civic but through a more pragmatic and realistic view. For at least the last half-century, the public choice research program has worked to provide a greater understanding of the political world through the application of economic tools.
Evolution of immersive journalism research

This chapter presents a scientometric analysis on immersive journalism research over the past several decades from its origin to the current advancement. Based on a bibliometric data assessment using VOSviewer, this chapter (a) identifies the main concepts and foci of research on immersive journalism, (b) visualises the conceptual and thematic structure of research on immersive journalism, (c) presents key components that were revealed in immersive journalism research and (d) suggests potential agendas for future immersive journalism research. Data were collected from Web of Science throughout the month of September 2021, totalling 223 research articles. Results showed that the ‘label view’ for keywords was clustered into six groups: Virtual reality, immersive journalism, journalism, media, communication and gamification. Major keywords were journalism, virtual reality, credibility, engagement, social media, emotion, experience and empathy. These findings lead the author to suggest that future immersive journalism research may focus on technology adoption, news games, experiential documentaries, data visualisation and civic engagement.
Promises, pitfalls and potentials of immersive journalism

When moving the discussion from virtual reality (VR)-based interactive fiction to non-fiction genres such as immersive journalism, several issues of critical concern come to the fore: (1) how can the informants (or, indeed, the objects) of the immersive experiences implied by 360o video journalism be adequately protected and how can they be properly included in the projects realised in their life worlds? 2) which implicit understandings of realism are underlying the widespread presumption of VR-promoters to be able to offer unmitigated access to the lives of others? (3) is the triggering of emotional reactions really a task for journalists? if yes, what are the wider implications of this for the journalistic profession? and finally, (4) how can audiences be sufficiently made aware of the manipulative nature, constructed frames and potentially disturbing effects of the simulations they are immersed in? This chapter unpacks these questions, which continue to haunt current attempts to realise the potentials of immersive journalism. The chapter proposes how a genuinely ethical immersive journalism might look like and how the identified severe ethical and epistemological problems can be addressed in a productive and progressive manner
Author Index

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Automatic error calibration system for English semantic translation based on machine learning

The traditional English semantic translation error calibration system can not determine the optimal translation solution, which has the problems of high CPU utilisation, low translation accuracy and high calibration time-consuming. Before English semantic translation, English semantic features are decomposed to realise fuzzy mapping selection of English semantic translation. Then, English semantic translation decision function is obtained by constructing semantic ontology model, while English semantic translation error automatic calibration algorithm is realised by machine learning algorithm. Finally, the overall architecture and network topology of the system is designed, and the design of automatic proofreading system of English semantic translation errors is completed. The experimental results show that the running time of the proposed system is 1.5 s, the CPU occupancy rate of the designed system is only 0.9%, and the calibration accuracy is as high as 99%.
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms

Real-time video surveillance, through CCTV camera systems has become essential for ensuring public safety which is a priority today. Although CCTV cameras help a lot in increasing security, these systems require constant human interaction and monitoring. To eradicate this issue, intelligent surveillance systems can be built using deep learning video classification techniques that can help us automate surveillance systems to detect violence as it happens. In this research, we explore deep learning video classification techniques to detect violence as they are happening. Traditional image classification techniques fall short when it comes to classifying videos as they attempt to classify each frame separately for which the predictions start to flicker. Therefore, many researchers are coming up with video classification techniques that consider spatiotemporal features while classifying. However, deploying these deep learning models with methods such as skeleton points obtained through pose estimation and optical flow obtained through depth sensors, are not always practical in an IoT environment. Although these techniques ensure a higher accuracy score, they are computationally heavier. Keeping these constraints in mind, we experimented with various video classification and action recognition techniques such as ConvLSTM, LRCN (with both custom CNN layers and VGG-16 as feature extractor) CNNTransformer and C3D. We achieved a test accuracy of 80% on ConvLSTM, 83.33% on CNN-BiLSTM, 70% on VGG16-BiLstm ,76.76% on CNN-Transformer and 80% on C3D.
Modified Nanobentonites for Water Remediation

Bentonite is a natural clay, which consists mainly of montmorillonite (MMT), a phyllosilicate (sheet silicate minerals formed by parallel layers of silicate tetrahedra) composed of alternating tetrahedral and octahedral layers in a 2:1 ratio. MMT is a nanoclay with a layer thickness around 1 nm (Barakan and Aghazadeh 2021); the lattice has an excess negative charge, which is balanced in the interlayer space by hydrated inorganic cations, such as Na(I), Ca(II), etc. Therefore, MMT has a high Cation Exchange Capacity (CEC) and a large surface area, leading to high adsorption capacities for pollutants (Awad et al. 2019).