Reviewerly/exHarmony huggingface dataset:
Aspect Sentiment Classification

Following the natural progression of chapters, this chapter should focus on expression-level (word or phrase) sentiment classification, as the last two chapters were about document-level and sentence-level classifications. However, we leave that topic to Chapter 7. In this and the next chapter, we focus on aspect-based sentiment analysis (or opinion mining) to deal with the full sentiment analysis problem as defined in Section 2.1 – that is, classifying sentiments and extracting sentiment or opinion targets (entities and aspects).
Language and Its Study

The vernacular language was a topic of great interest across Italy throughout the sixteenth century; most were interested in setting standards of good writing based on the models of Petrarch and Boccaccio as promoted by Pietro Bembo. Florentines differed especially in that they came to distinguish literary style from the language itself, and focused on the study of language. They modified the tools of humanistic study, developed for Latin and Greek, by distinguishing between the study of living languages and dead ones, since living languages constantly undergo change and development. They also used a biological model, arguing that a language is like a living thing with its own life cycle. Most Florentine writings from the 1540s and 1550s came from a circle of friends who often collaborated with one another: Pierfrancesco Giambullari, Giovan Battista Gelli, Cosimo Bartoli, Carlo Lenzoni, and Giovanni Norchiati. Although they are now best known for their arguments about the history of the language, in fact they devoted many years to the study of modern language practice.
References

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Introduction

Machine learning has become a prevalent tool in many computing applications. With the rise ofmachine learning techniques, however, comes a concomitant risk. Adversaries may attempt to exploit a learning mechanism either to cause it to misbehave or to extract or misuse information.
The Basics of Justification Logic

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Asymptotic VB Theory of Other Latent Variable Models

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Analysis of Educator Readiness in the Online Teaching Learning Process Using NaÃ¯ve Bayes

This study discusses the readiness of educators in the online teaching and learning process. Samples of data were taken randomly as many as 100 (one hundred) people who were carried out using a questionnaire for educators at the junior high school level in the city of Medan. The variables used in the research are human resources, facilities and infrastructure, skills in applying technology, time management in online learning, the assessment process. Data processing and data analysis using nave Bayes algorithm. This algorithm is very well used for the process of classifying large amounts of data. The reason for using the nave Bayes algorithm in processing and analyzing data is because the way this algorithm works uses statistical and probability methods in predicting future results. The results of calculations using the nave Bayes algorithm based on the specified training data show that educators at the junior high school level are ready for the online learning process.
Introduction

This chapter starts with basic definitions such as types of machine learning (supervised vs. unsupervised learning, classifiers vs. regressors), types of features (binary, categorical, discrete, continuos), metrics (precision, recall, f-measure, accuracy, overfitting), and raw data and then defines the machine learning cycle and the feature engineering cycle. The feature engineering cycle hinges on two types of analysis: exploratory data analysis, at the beginning of the cycle and error analysis at the end of each feature engineering cycle. Domain modelling and feature construction concludes the chapter with particular emphasis on feature ideation techniques.
Markov Chain Monte Carlo Methods

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Communication as Bayesian Inference

In this chapter, we consider a computational-level theory of communication as Bayesian inference. We again illustrate the use of classical complexity analysis to assess the theory’s intractability. In addition, we show that parameterized complexity analysis can converge with theoretically informed intuitions about possible sources of this intractability, in this case based on the Gricean Maxims in pragmatic theories of communication.
Consolidating and Integrating the Dataset

The first step in consolidating the dataset is to gather and organize the data in one place. We recommend doing this from the very beginning, at least as soon as the first value-creation story comes in. We use a matrix format for this purpose, typically, a spreadsheet, as shown in the example of Figure 17.1. We use the columns to represent the value-creation cycles. The first row is for monitored indicators under the relevant cycles. The rest of the rows are for stories, with each step of the story captured in the column under the relevant value cycle.
Introduction

In this chapter we introduce the motivation for studying "cognition and intractability." We provide an intuitive introduction to the problem of intractability as it arises for models of cognition using an illustrative every day problem as running example: selecting toppings on a pizza. Next, we review relevant background information about the conceptual foundations of cognitive explanation, computability and tractability. At the end of this chapter the reader should have a good understanding of the conceptual foundations of the Tractable Cognition thesis, including its variants: The P-Cognition thesis and the FPT-Cognition thesis, which motivates diving into the technical concepts and proof techniques covered in Chapters 2–7.
Features, Expanded: Computable Features, Imputation and Kernels

This chapter deals with the topic of feature expansion and imputation with a particular emphasis on computable features. While poor domain modelling may result in too many features being added to the model, there are times when plenty of value can be gained by looking into generating features from existing ones. The excess features can then be removed using feature selection techniques (discussed in the next chapter). Computable Features will be particularly useful if we know the underlining ML model is unable to do certain operations over the features, like multiplying them (e.g., if the ML involves a simple, linear modelling). Another type of feature expansion involves calculating a best effort approximation of values missing in the data (Feature Imputation). The most straightforward expansion for features happens when the raw data contains multiple items of information under a single column (Decomposing Complex Features). The chapter concludes by borrowing ideas from a technique used in SVMs called the kernel trick. The type of projections that practitioners have found useful can lend themselves to be applied directly without the use of kernels.
(F<sub>2</sub> + <i>u</i>F<sub>2</sub>)(F<sub>4</sub> + <i>u</i>F<sub>4</sub>)-Additive Cyclic Codes

In this paper, we study some structural properties of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\mathbb {F}_{2}+u\mathbb {F}_{2})(\mathbb {F}_{4}+u\mathbb {F}_{4})$ </tex-math></inline-formula> -additive cyclic codes, including the generator polynomials, the minimum generating sets and their dual codes. Structural properties of a map from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\mathbb {F}_{2}+u\mathbb {F}_{2})(\mathbb {F}_{4}+u\mathbb {F}_{4})$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{2}+u\mathbb {F}_{2}$ </tex-math></inline-formula> are also investigated.
MFS Risk Monitoring

Everything we do while managing a software development project should be aimed at dealing with the risks of not meeting deadlines, overspending resources, developing the wrong product that is required.The objectives of project risk management are to reduce the likelihood of occurrence and / or the significance of the impact of adverse events for the project.
Temporalization in Causal Modeling

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Vector Calculus

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Computer Vision

The use of biometrics in the field of security applications has been increasing rapidly due to their uniqueness, reliability, universality, wide acceptability, confidentiality, and highly precise performance. With the latest advancements in technology, various techniques and devices have been developed to read, understand, visualize, and record different physical and non-physical biometric features of the human body. The results obtained from such devices are processed and analysed through different complex mathematical functions and well-developed algorithms, which helps the researchers and concerned organizations to decode the valuable information from them with more precision and accuracy using deep learning and to boost the efficiency and response time by using powerful processing units. Some of the most acceptable and usable biometrics include fingerprint recognition, facial recognition, DNA matching, iris recognition, voice matching, etc. All the biometrics involve the use of different techniques and processes so as to be able to read, process, understand, store, visualize, and decode. This chapter primarily focuses on the latest algorithms and techniques developed in order to capture and analyse the facial biometrics of the human body. The use of facial biometrics has been widely used and preferred due to its high reliability, security, lower complexity, and more efficient performance. Facial biometrics, in the field of computer vision, involves two different concepts, facial detection and facial recognition,which are also briefly elaborated in the chapter. Face detection techniques involve the use of different machine learning and deep learning concepts in order to detect the human faces present in an image, whereas facial recognition systems involve more complex algorithms by adding an extra layer of identifying and labelling the human faces present in the image with the help of their facial landmarks and characteristics. The chapter also includes brief but clear, precise, and descriptive information as well as features of some of the well-known facial detection techniques along with the comparative analysis of their respective results and supporting graphics and visualizations. Also, the chapter focuses on the monitoring, security, and surveillance applications of facial detection systems along with the other relevant fields.
Textual Data

Extending the dataset from chapter 6 with the full Wikipedia page for each settlement, this chapter exemplifies natural language processing techniques to work with textual data. Textual problems are a domain that involves large number of correlated features, with feature frequencies strongly biased by a power law. Such behaviour is very common for many naturally occurring phenomena besides text. The very nature of dealing with sequences means this domain also involves variable length feature vectors. A central theme in this chapter is context and how to supply it within the enhanced feature vector to increase the signal-to-ratio available to the ML. As many times the target information (population) appears within the target page (as high as 53% of the cases in exact form), this problem is closely related to the NLP problem of Information Extraction. The chapter showcases different ways to approach the problem using words-as-features in the bag-of-words paradigm, then proceeding to do heavy feature selection using mutual information, stemming, modelling context with bigrams and skip-bigrams. More advanced topics include feature hashing and embeddings using word2vec.
Introduction

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The Problem of Sentiment Analysis

In this chapter, we define an abstraction of the sentiment analysis problem. This abstraction gives us a statement of the problem and enables us to see a rich set of interrelated subproblems. It is often said that if we cannot structure a problem, we probably do not understand the problem. The objective of the definitions is thus to abstract a structure from the complex and intimidating unstructured natural language text. This structure serves as a common framework to unify various existing research directions and enable researchers to design more robust and accurate solution techniques by exploiting the interrelationships of the subproblems. From a practical application point of view, the definitions let practitioners see which subproblems need to be solved in building a sentiment analysis system, how the subproblems are related, and what output should be produced.
Beyond GNNs: More Deep Models on Graphs

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Density Estimation with Gaussian Mixture Models

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Implication of emotions in the educational field: EEQ and ecq validation

The article that is presented below shows a detailed investigation about the relationship between competence in emotional education and the educational style —from both agents, teacher and student—. Once a thorough bibliographic search was carried out, a series of unknowns were compiled that, not only as researchers but also as teachers, we were committed to offering the scientific community a tool that not only offered ease of completion appropriate for use in mass but, in addition, it was reliable and easy to use, so that experimental death is minimized as much as possible. To do this, two questionnaires (EEQ and ECQ) were prepared, which underwent an exhaustive review by a committee of experts.
Foundations for Stochastic Spectral Analysis

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Formulaic Language

Chapter 1 starts off by introducing the reader to the concept of formulaic language and its significance. In the second section, an up-to-date view of this phenomenon is presented, and a fresh way of making sense of its complexities. The term multi-word expression (MWE) is then introduced in Section 3 to describe the phenomena of formulaic language that will be the subject of analysis. The reader is also introduced to some of the various types of evidence for the importance of formulaic language and its significance to both linguistic knowledge and language use (Section 4). The chapter ends by highlighting the reasons why looking at language and culture from the prism of formulaic language is particularly insightful.
TEACHING VOCABULARY IN TRANSACTIONAL TEXT USING ROTE LEARNING

The aim of this research is to improve students’ vocabulary in the transactional text by using Rote Learning for the seventh grade students of MTs Negeri 02 Pontianak in the academic year 2019/2020. The method used in this research is classroom action research which was conducted in two cycles. The subject of the research is 36 students of VII E class, consisting of 17 females and 19 males. In collecting the data, the researcher used observation checklist, field note, and achievement test. It was found that students’ vocabulary improved from cycle 1 to cycle 2. In the first cycle, 14.2% equal to 6 students passed the standard minimum score and it increased up to 91% or 32 students in the second cycle. By using Rote Learning in teaching vocabulary, the students could easily memorize new vocabulary. The students were actively engaged during the learning process. In short, Rote Learning is an appropriate method for the improvement of students’ vocabulary mastery.
Bibliography

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.
Attacking a Hypersphere Learner

In the second part of this book, we elaborate on Causative attacks, in which an adversary actively mistrains a learner by influencing the training data. We begin in this chapter by considering a simple adversarial learning game that can be theoretically analyzed. In particular, we examine the effect of malicious data in the learning task of anomaly (or outlier) detection. Anomaly detectors are often employed for identifying novel malicious activities such as sending virus-laden email or misusing network-based resources. Because anomaly detectors often serve a role as a component of learning-based detection systems, they are a probable target for attacks. Here we analyze potential attacks specifically against hypersphere-based anomaly detectors, for which a learned hypersphere is used to define the region of normal data and all data that lies outside of this hypersphere's boundary are considered to be anomalous. Hypersphere detectors are used for anomaly detection because they provide an intuitive notion for capturing a subspace of normal points. These detectors are simple to train, and learning algorithms for hypersphere detectors can be kernelized, that is implicitly extended into higher dimensional spaces via a kernel function (Forrest et al. 1996; Rieck & Laskov 2006; Rieck & Laskov 2007; Wang & Stolfo 2004; Wang et al. 2006; Warrender et al. 1999). For our purposes in this chapter, hypersphere models provide a theoretical basis for understanding the types of attacks that can occur and their potential impact in a variety of different settings. The results we present in this chapter provide intriguing insights into the threat of causative attacks. Then, in Chapter 5 and 6, we proceed to describe practical studies of causative attacks motivated by real-world applications of machine learning algorithms.
Privacy-Preserving Mechanisms for SVM Learning

High-profile privacy breaches have trained the spotlight of public attention on data privacy. Until recently privacy, a relative laggard within computer security, could be enhanced only weakly by available technologies, when releasing aggregate statistics on sensitive data: until the mid-2000s definitions of privacy were merely syntactic. Contrast this with the state of affairs within cryptography that has long offered provably strong guarantees on maintaining the secrecy of encrypted information, based on computational limitations of attackers. Proposed as an answer to the challenge of bringing privacy on equal footing with cryptography, differential privacy (Dwork et al. 2006; Dwork & Roth 2014) has quickly grown in stature due to its formal nature and guarantees against powerful attackers. This chapter continues the discussion begun in Section 3.7, including a case study on the release of trained support vector machine (SVM) classifiers while preserving training data privacy. This chapter builds on (Rubinstein, Bartlett, Huang, & Taft 2012).
AESTHETIC HERMENEUTICS: PARADIGMAL NETWORK

In the situation of the emergence of new and activation of existing Humanities, the interaction between them changes. So, today, when there is an intensive development of the science of culture, the relationship between aesthetics and art studies is changing. The science of art, having reached the status of “normal science”, seeks toexpand the concept of its subject. Such a science as cultural studies,as well as such philosophical areas as hermeneutics and phenomenology, meet this need.
Electrical Flow Algorithms

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Physical Magnitude Statements and Sparsity

This chapter suggests classic indispensability worries about nominalizing scientific theories can be answered by adding some cheap tricks to the modal if-thenist paraphrase strategy of Chapter 12.