Reviewerly/exHarmony huggingface dataset:
Preserving Privacy of Face and Facial Expression in Computer Vision Data Collected in Learning Environments

Learners’ affective states are analyzed using their facial expressions obtained from image/video data. Deep learning architectures are the state-of-the-art affective state recognition methods used in the literature. Vast volumes of these data are used in the training, testing, and validation process. Now, sharing these data publicly for research purposes is a challenging task as it has privacy issues. The importance of creating a successful face de-identification algorithm for privacy protection cannot be overstated because the face is the single biometric feature that discloses the most recognizable qualities of a person in an image or a video frame. Existing approaches to face de-identification with facial expressions are not explored in the education domain. In this study, we design a methodology for a face de-identification technique that automatically creates a new face while maintaining the emotion and non-biometric facial characteristics of a target face from an input facial image. We consider a proxy set, which consists of a sizable number of synthetic faces produced by StyleGAN, and choose the proxy set face that most closely resembles the target face in terms of facial expression and position. Since the faces in the proxy settings were formed artificially, the face chosen from this collection is absolutely anonymous. We created a dataset of 10 students to test the methodology. The performance of StyleGAN was measured for standard parameters such as gender, emotion, age, etc., and the results show that the generated face preserved emotional attributes with a de-identified face.
Leave No One Behind - A Massive Online Learning Platform Free for Everyone

GCFGlobalLearning is a non-profit organization committed to creating life-changing opportunities through an innovative virtual education project that incorporates technological tools. Our platform offers free courses to learners worldwide, available in English, Spanish, and Portuguese. Since our launch, we have welcomed millions of learners. Our current focus is on incorporating artificial intelligence tools that will enhance our learners’ experience. To achieve this, we have developed a recommender system and a learning content search and organization tool that personalizes our learners’ learning journey. Even with limited information about our learners, we can enhance their experience through the use of these AI tools. In this paper, we introduce the platform’s primary components, detail how we overcome our limited learner information scenario, and share our vision of incorporating more artificial intelligence innovations in the future.
Criminal and legal protection of cyber virtual property in modern China

With the rapid development of Internet technologies, the term “virtual property” is increasingly attracting public attention. From game gold coins and game weapons that originally exist in online games, to the virtual currency represented by Tencent Q coins, also to the current bitcoin, etc. Their forms and functions are becoming more diverse, at the same time, their impact on people&amp;apos;s lives is also increasing. In recent years, there has been a gradual increase in cases of violations of virtual property rights in cyberspace. The Civil Code of the People’s Republic of China directly included virtual property in the sphere of protection, but there are no clear provisions on this in the criminal legislation of the country yet. New types of cybercrime cases create problems for the application of traditional elements of crimes. How exactly to apply criminal legislation to regulate violations of virtual property has become an urgent problem. In this article, in the aspects of Chinese legislation and judicial practice on the protection of virtual property, a comprehensive analysis of the relevant controversial issues in the field of criminal protection of cyber-virtual property is carried out. According to the author, the Civil Code of the People’s Republic of China contains only guidelines on virtual property and does not clearly give its legal nature. At the same time, gaps in the regulation of Chinese criminal legislation on the protection of virtual property have created a dilemma in judicial practice regarding the application of laws to violations of virtual property. After the Amendments to the Criminal Code of the People’s Republic of China No. 7 introduced into the criminal legislation the corpus delicti – “Illegal acquisition of computer information system data” (Part 2 of Article 285), the question of the application of the criminal law in relation to the illegal acquisition of virtual property caused a heated discussion. It seems that it is necessary to classify virtual property based on its content so that law enforcement officers can accurately understand the legal attributes of various virtual objects and provide them with appropriate criminal protection.
Error Analysis on Simple Past Tense Used in Short Story Made by EFL Students

Tenses are a crucial element in the English language. In writing or saying a sentence in English, people should consider what kind of tenses will be used. This study aims to find out what kind of errors are made by eleventh-grade students in writing a short story. The instrument used for the research is the writing test. This qualitative descriptive study involves thirty-one Senior High School students. To know the error made by the students, the researcher used a written test and then analyzed it qualitatively. There are one hundred twenty-eight errors made by the students and it is found seven omission types of error, sixteen errors for addition types, a hundred errors made in misformation, and five errors in misordering. It can be concluded that the students mostly made an error in the misformation type and the students have a low awareness of using verbs in the simple past tense while writing a short story.
Language Style Applied on Indonesian Beauty Products in Digital Advertisements

In modern today, people compete to beautify themselves since being beauty has become a primary needs for every woman, even for a man. Besides for personal reason such as taking care of themselves, very often because it is caused by demands of work that requires fine appearance. For now, there are lots of beauty products that are easily obtained from online or offline markets. To support each products, each company needs certain marketing technique to attract consumers. This research aims to investigate language style used in some advertisements for Indonesian beauty products. There were total of 10 advertisements of 5 Indonesian beauty brands investigated in this study. Researchers used note-taking as an instrument and applied purposive sampling technique. Result of this study reveals that from several language style found in those advertisements, hyperbole appeared the most with 33 times appearance. Hyperbole is a kind of figure of speech that conceive heightened impact through the using, or even repetition, of purposeful exaggeration. This language style is often boldly overstated claims to emphasize things. Thus, most of beauty brands by locals investigated in this study used hyperbolic technique to advertise their products.
Living in the Metaverse

As the quest to build Metaverses continues, many industries invest by entering existing metaverses or developing their own. There is substantial innovation and experimentation, from entertainment to commerce to real estate. Metaverse-provided services such as health, education, and government services are all under development. It will be in new “phygital” realms that Metazens (citizens of the Metaverse) will explore these new domains. “Phygital” is the use of technology to bridge the digital world with the physical world to provide unique interactive experiences for users. This chapter explores the sectors utilizing the Metaverse and issues for future growth, including infrastructure, user experience, adoption, and interoperability.
Blockchain Ecosystem

This is a relatively technical chapter designed to help readers understand some of the current blockchain technical challenges in more detail. In particular, it explores the blockchain scalability trilemma, protocol layers, and challenges in the blockchain ecosystem. The scalability trilemma prioritizes any two features of the blockchain (scalability, security, or decentralization) while compromising on one. The chapter outlines several solutions under development for solving the scalability trilemma. Developers will continue to solve these issues in years to come.
Improving Aspect and Compotency of Speaking Skill on EFL Students by Utilizing Zoom App

The purpose of this study is to investigate how to utilize zoom in improving students’ speaking aspect and speaking competency on EFL students IAIN Syekh Nurjati Cirebon. The type of this research was designated as online classroom action research. These actions were carried out in three cycles and each cycle distributed the test to measure the students’ speaking progress each circle. The sample of this research were 25 EFL students. The research method was implemented mix method which was qualitative and quantitative data. Qualitative data was acquired through to address questionnaires and observation. While quantitative data was acquired through speaking test each cycle. Based on the data obtained the researcher shown the progress of the students in each speaking aspect and speaking competency that proved by the result of speaking test. This research was also supported by the questionnaire that the students expressed their argument using zoom application in speaking online class which the result of this data shown the positive argument approximately the implementation zoom meeting in speaking class. Mostly the students excited implementing the zoom during learning prosses specially in speaking class. The students experienced the improvement of their speaking aspect such as grammar, pronunciation, fluency, comprehension and vocabulary and also the students adept positive progress of speaking competency such as knowledge, action and attitude. The speaking aspect and speaking competency of students is not separated because the implementation of the treatment can improve both and both are supported each other. Therefore, this research found the effectiveness of using zoom as one of the learning application choices to improve students’ speaking aspect and competency. Zoom has many features to supports lecturers and students in speaking class. By zoom the online class is most effective than using other learning application.
Subword Tokenization of Noisy Housing Defect Complaints for Named Entity Recognition

Subword Tokenization of Noisy Housing Defect Complaints for Named Entity Recognition Kahyun Jeon, Ghang Lee Pages 418-425 (2023 Proceedings of the 40th ISARC, Chennai, India, ISBN 978-0-6458322-0-4, ISSN 2413-5844) Abstract: In domain-specific named entity recognition (NER), the out-of-vocabulary (OOV) problem arises due to linguistic features and rare vocabulary. OOV problem is particularly challenging in agglutinative languages such as Korean. The irregular decomposition of morphemes makes it difficult to represent all of them in language model dictionaries, resulting in poor NER performance. Subword tokenization which segments a word into atomic tokens that are no longer divided can be one of the possible solutions. In the construction industry, existing NER methods do not effective on housing defect complaints which contain many rare words, including jargon, slang, and typos. To address this challenge, we propose subword tokenization algorithms that can mitigate OOV problems based on considering linguistic features and pre-trained language models (PLMs). The primary objective of this study is to identify the optimal NER performance by comparing different subword tokenization methods depending on the language models used. For domain-specific NER, we defined and used 23 defect-specific named entity tags for dataset labelling. We then experimented with a total of three state-of-the-art language models: one SentencePiece-based and two WordPiece-based subword tokenization models. The results demonstrate that the SentencePiece-based Korean Bidirectional Encoder Representations from Transformers (KoBERT) outperformed the two WordPiece-based language models (multilingual-BERT and Korean Efficiently Learning an Encoder that Classifies Token Replacements Accurately (KoELECTRA)) with an F1 score of 84.7%. The proposed method is expected to improve not only NER but also other downstream tasks that involve using Korean documents in the construction industry. Keywords: Out of vocabulary (OOV), Subword tokenization, WordPiece, SentencePiece, Named entity recognition (NER), Construction defect management DOI: https://doi.org/10.22260/ISARC2023/0057 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley Presentation Video: https://youtu.be/5n4syU3bMSI
Chapter 1: A linear algebra refresher

Data science can be approached from many different angles, and while different approaches have many similarities, the differences in the language used to describe them reflect the prevalent point of view mirroring the terminology differences, e.g., in statistics and applied mathematics. The algorithmic approach followed throughout this book relies strongly on linear algebra, which is the natural language of computations. This chapter presents a brief review of the principal linear algebra definitions and results that will be used in the algorithms introduced later. Readers with a solid background in linear algebra can skip this part, and those looking for a more in-depth treatment are referred to linear algebra textbooks, some of which are given at the end of the chapter.
Chapter 9: Image mining

In the previous chapter, we discussed methodology for classifying and analyzing data in text format. The crucial step there was to translate the text documents into a quantitative form by defining the term-document matrix which does not take into account the semantic contents or grammatical correctness of the documents: whether a text document is a literary or scientific masterpiece, or total gibberish, it will be treated in the same fashion, as a bag of words. A similar idea can be used for the classification of images, as the class of natural images is often too generic to be handled by general purpose data mining algorithms. In some applications the goal is to classify images according to the contents, such as identifying videos with cats, while in others the interest may be more in the texture of the images. We consider here only the latter question, which is relevant, for example, in medical imaging and remote sensing applications. Our approach to image data is similar to the text analysis of the previous chapter in the sense that the first step is a preprocessing of the data so as to represent them in a standardized form. Thus, in the end, a noise image or a Renaissance masterpiece of art are encoded in the same way, regardless of the contents.
Chapter 33: Order Statistics

In this chapter we are concerned with the distributions of order statistics. That is, if we take a random sample from a known distribution, then sort the sample, how are the sorted random variables distributed? We will be particularly interested in the distribution of the minimum and the maximum of the sample, but we can also find the distribution of the second largest, or third smallest, etc.
Chapter 8: Text mining

An increasingly important application of data science techniques is the exploration of text corpora that are objects of various studies, e.g., in linguistics or sociology. A corpus may consist of books, articles, internet sites, or social media entries. The most basic text exploration task is text querying, that is, to identify documents containing a given set of words or sentences. Querying methods are widely used by different search engines dealing with text data. In this chapter, we introduce some central concepts related to text analysis and discuss methods to identify, classify, or cluster documents based on the contents of the text in the document. The first task is to represent text documents of possibly very different form and length in a standard form suitable for the application of previously introduced algorithms.
Chapter 5: Probability

Probability is the mathematics of uncertainty, and it plays a key role in modeling the world around us. We focus more on modeling with these tools in Volume 3 (where we also treat probability in more depth), but probability is also central to the study of algorithms and optimization for several reasons. Probability is essential to analyzing algorithmic complexity, and it is useful in helping us construct better algorithms. It is also a source of many of the most important optimization problems.
Chapter 35: Hypothesis Tests for a Normal Population Parameter, Part I

Many statistical models involve random variables with normal distributions. For example, suppose that Y1, Y2,…, Yn are independent normal random variables all having mean μ and variance σ2. That is,For many applications, we say that Y1,Y2,…,Yn are a random sample from a population that is normally distributed. The sample is also referred to as “the data.”
Chapter 4: Conditional Probability, Independence, and Tree Diagrams

The conditional probability of an event is computed when we have information about another event in the sample space. Suppose we want to compute the probability of event B, given that event A has happened. In the figure below, we know that the outcome is in the set A (total shaded area), and we’re asking what is the probability that the event is also in B (dark shaded area).
Chapter 23: Jointly Distributed Discrete Random Variables

Often we need to consider two or more random variables at the same time, where the random variables might vary together. The joint distribution of two random variables describes the probabilities associated with pairs of values that the two can take.
Chapter 26: Conditional Distributions and Independent Random Variables

For continuous random variables Y1 and Y2, with joint density f (y1,y2), we define the conditional density for Y1, given Y2 = y2, aswhere the denominator is the marginal density for Y2. Even though the expression for the conditional density contains both y1 and y2, it’s important to keep in mind that the function f (y1 |y2) has only one argument, and y2 is a constant value. Conceptually, we fix a value of y2 and determine how Y1 is distributed, given that Y2 = y2.
Chapter 10: Tree-based classifiers

It is a common practice in decision theory to use branching algorithms consisting of simple yes/no questions, the answer determining the next question until the algorithm arrives at a decision. Branching algorithms are particularly appealing in fields like medical diagnostics where the idea of narrowing down the decision by exclusion is part of everyday practice. This constitutes a promising starting point for the development of automatic smart systems and artificial intelligence. The branching process provides a basis for a tree structure that turns out to be a useful tool in data science for, e.g., classification. In this chapter we discuss supervised learning using tree structures for classification purposes. These methods are often referred to by the acronym CART, which stands for classification and regression tree. Regression trees are briefly discussed at the end of the chapter.
Chapter 5: Self-organizing maps

A motivation for reducing the dimensionality of the data is to make it possible to visualize high-dimensional data, e.g., by projecting them onto a suitable lower-dimensional subspace. Successful data reduction and visualization by projection methods often require that an orthogonal projection onto a linear subspace capture salient features of the data, such as the presence of clusters. It is easy to find examples where the data are supported on or near a low-dimensional manifold, yet the intrinsic low dimensionality and the organization of the data are not correctly represented by any orthogonal projection onto a linear subspace. This is the case, for instance, when the data are distributed on or near the surface of a sphere in ℝ3; in fact, while the surface is clearly two-dimensional, a projection onto a two-dimensional plane may confound the organization of the data, since any pair of antipodal points along a ray orthogonal to the plane will be represented as being the same point, while when measured along the surface of the sphere, the points are distant.
Chapter 7: Classification algorithms

The driving theme of this chapter is to find rules to assign data not previously seen to one of given K classes based on a sample of data with known annotation. Here we approach the problem by making use of the algorithms that have been discussed so far, repurposing them for classification tasks. The design of a classification algorithm typically requires a set of annotated data, i.e., ordered pairs of data points and the class they belong to, referred to as training data. Because of the guidance provided via the training data, classification algorithms fall into the category of supervised learning, whereas algorithms that let the data reveal the underlying structure without external guidance fall under the header of unsupervised learning.
Chapter 2: Principal component analysis

Principal component analysis (PCA) is a very versatile mathematical tool in data science that can be used either alone or as a part of more complex algorithms. One of the appeals of PCA is its strong mathematical foundation, which removes the need for heuristics when it comes to making important algorithmic decisions. In this chapter we formally define PCA and outline some of its uses in data science, including dimension reduction and visualization of high-dimensional data sets. In many of the later chapters, PCA will appear in an ancillary role, often in the preprocessing and visualization phases. The mathematical foundation of PCA is the singular value decomposition (SVD), a brief review of which was presented in Chapter 1.
Chapter 11: Support vector machine

Binary classification of data is one of the most ubiquitous operations, arising in a wide variety of applications. The design of a binary classifier typically consists of finding a rule that partitions data points in two clusters so as to minimize classification error when tested on a set of annotated data. The support vector machine (SVM) algorithm, in particular its generalized kernel-based extension, has gained a lot of popularity as a binary classifiers. In this chapter we outline the mathematical principles leading to the SVM algorithm and discuss some aspects related to its implementation, without going too deeply into questions of optimization methods that are not a central topic in this book.
Increasing Trust in AI Using Explainable Artificial Intelligence for Histopathology – An Overview

Digital Pathology is an area that could benefit a lot from the automatic classification of scanned microscopic slides. One of the main problems with this is that the experts need to understand and trust the decisions of the system. This paper is an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts and ML engineers that work with histopathological images. This paper is an overview of the current state of the art methods used in the histopathological practice for explain. The search was performed using SCOPUS database and revealed that there are few applications of CNNs for digital pathology. The 4-term search yielded 99 results. This research sheds light on the main methods that can be used for histopathology classification and offers a good starting point for future works.