Reviewerly/exHarmony huggingface dataset:
“The pedagogical expert witness: teaching complex science in the courtroom”

The search for the truth can be a daunting task, but without knowing what happened and who is responsible, there can be no attempt at achieving any measure of justice. Forensic scientists often play a pivotal role in determining significant facts and interpreting those facts for the court to consider. When called to testify, expert witnesses have a special duty to the court to provide fair, objective, and non-partisan assistance. The courtroom becomes the classroom as the expert witness takes on pedagogical duties of teaching the underlying science, methodology, and the nuances of forensic opinions. Depending on the area of expertise and evidence, the learning curve for the trier of fact can be steep. The didactic presentation of complex data requires planning and skill. Drawing upon my Courtroom Testimony for Expert Witnesses course and my PhD research, I focus on the search for truth, expert witness bias, and the challenge of teaching complex science to the trier of fact. There will be specific emphasis on the incorporation of visual evidence to explain complex concepts in a more concise and memorable manner to aid in comprehension. The expert witness is a teacher and how the expert teaches makes a difference.
Research on path planning of mobile robot based on ROS and improved A* algorithm

This study suggests an enhanced A* algorithm to address the issues of numerous traversal nodes, numerous inflection points, and huge turning angles in the path planning of the traditional A* algorithm. The traditional A* algorithm is first improved in this paper's heuristic function and search efficiency using dynamic weighted averaging; next, the path planning of the traditional A* algorithm is smoothed using Bezier curves; and finally, the effect of the improved A* algorithm is tested in the built scene using gazebo simulation software and ROS system. The outcome demonstrates that the modified A* algorithm has superior path planning capabilities compared to the traditional A* algorithm.
Ewa Jonsson and Tove Larsson (eds.), Voices past and present – Studies of involved, speech-related and spoken texts. In honor of Merja Kytö. Amsterdam: Benjamins, 2020. Pp. xiii + 348. ISBN 9789027207654.

Ewa Jonsson and Tove Larsson (eds.), Voices past and present – Studies of involved, speech-related and spoken texts. In honor of Merja Kytö. Amsterdam: Benjamins, 2020. Pp. xiii + 348. ISBN 9789027207654. - Volume 27 Issue 1
Explainable Artificial Intelligence-based framework for medical decision support systems

The rise in death tolls due to increased infectious diseases has become one of the most severe health problems and the largest source of death globally. Artificial Intelligence (AI)-based models have emerged and developed to assist medical experts in decision-making, thus reducing the mortality and morbidity rate. However, the most prominent weakness of these algorithms is the lack of interpretations for their results. In other words, the end-user is unfamiliar with the fundamental logic that supports the prediction. Hence, due to their black-box nature, physicians struggle to understand these models; thus, they often do not attract the confidence of the medical practitioners and, in most cases, are not permitted in medical practice. Therefore, this chapter reviews the most substantial reasons for and against explainable AI (XAI) in medical Decision Support Systems (MDSS) with future prospects. The chapter proposes a framework to address the above-mentioned issue in AL-based models using a deep Shapley additive explanations (DeepSHAP) for predicting various diseases. The framework relies on deep neural network architecture enabled with a feature selection method for disease prediction with an explanation. The proposed framework will provide medical experts with more accurate and personalized results for disease prediction and facilitate improved decision-making.
Statistical test with differential privacy for medical decision support systems

Several statistical testing methods have been employed to offer accessible analysis regarding medical data for medical decision support systems (MDSSs), with the Chi-squared test among the most widely used option. Critics have noted, however, that presenting such data risks exposing individual attribute values. This chapter will demonstrate how the findings of statistical analysis can inadvertently reveal individual attribute values. It will then show how advanced differential privacy systems, such as those utilized by companies including Google and Apple, can be used to protect individual attribute values while conducting extremely precise Chi-squared tests.
Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer’s

The average life expectancy is increasing globally due to advancements in medical technology, preventive health care, and a growing emphasis on gerontological health. Therefore, developing technologies that detect and track aging-associated disease in cognitive function among older adult populations is imperative. In particular, research related to automatic detection and evaluation of Alzheimer's disease (AD) is critical given the disease's prevalence and the cost of current methods. As AD impacts the acoustics of speech and vocabulary, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of ten linear regression models for predicting Mini-Mental Status Exam scores on the ADReSS challenge dataset. We extracted 13000+ handcrafted and learned features that capture linguistic and acoustic phenomena. Using a subset of 54 top features selected by two methods: (1) recursive elimination and (2) correlation scores, we outperform a state-of-the-art baseline for the same task. Upon scoring and evaluating the statistical significance of each of the selected subset of features for each model, we find that, for the given task, handcrafted linguistic features are more significant than acoustic and learned features.
Session Introduction: Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare

Precision medicine requires a deep understanding of complex biomedical and healthcare data, which is being generated at exponential rates and increasingly made available through public biobanks, electronic medical record systems and biomedical databases and knowledgebases. The complexity and sheer amount of data prohibit manual manipulation. Instead, the field depends on artificial intelligence approaches to parse, annotate, evaluate and interpret the data to enable applications to patient healthcare At the 2023 Pacific Symposium on Biocomputing (PSB) session entitled "Precision Medicine: Using Artificial Intelligence (AI) to improve diagnostics and healthcare", we spotlight research that develops and applies computational methodologies to solve biomedical problems.
Post-quantum and UC-Secure Oblivious Transfer from SPHF with Grey Zone

Oblivious Transfer (OT) is a major primitive for secure multi-party computation. Indeed, combined with symmetric primitives along with garbled circuits, it allows any secure function evaluation between two parties. In this paper, we propose a new approach to build OT protocols. Interestingly, our new paradigm features a security analysis in the Universal Composability (UC) framework and may be instantiated from post-quantum primitives. In order to do so, we define a new primitive named Smooth Projective Hash Function with Grey Zone (SPHFwGZ) which can be seen as a relaxation of the classical Smooth Projective Hash Functions, with a subset of the words for which one cannot claim correctness nor smoothness: the grey zone. As a concrete application, we provide two instantiations of SPHFwGZ respectively based on the Diffie-Hellman and the Learning With Errors (LWE) problems. Hence, we propose a quantum-resistant OT protocol with UC-security in the random oracle model.
Digital Archives for Indian Literatures and Cultures

The physical archives in India, in the contexts of financial constraints and global general evasiveness, face an existential challenge. This chapter tries to understand the potential of digital archives in India in curating Indian literatures and cultures in terms of collections, data visualization, and access with respect to dwindling interests and functions of the physical archives. It suggests few structural and curatorial policies and models for the Indian digital archives to respond to the proliferation of digital technology and Web 2.0 tools.
Navigating Contrasting Styles in the First Three Star Trek Film Scores

By the end of the 1970s, four styles of scoring science fiction films predominated: neoromantic orchestral, modernist orchestral, electronic atonal, and electronic popular. Each of these would influence the first three Star Trek films. Beginning with Star Trek: The Motion Picture (1979), the score by Jerry Goldsmith largely abandoned his experimental tendencies in lieu of a John Williams-style tonal sound, complete with sweeping string melodies. But the question of the representation of technology sits uncomfortably alongside this retro method, and a complex set of approaches results. Goldsmith used traditional orchestral and unusual “foreign” instruments in an elaborate web of signification. However, at key moments, Goldsmith also participates in the fashion for depicting the utopic future with electronic sounds. A similar approach was taken for the next two theatrical Star Trek films: James Horner's swashbuckling scores for The Wrath of Khan (1982) and The Search for Spock (1984) evoked Korngold's famous Seahawk score of 1940, while incorporating electronic sounds at carefully chosen moments. This chapter will explore how Star Trek's film composers reconciled these competing approaches to scoring. The singular place held by Star Trek in popular culture, and the distinctly unique configuration of cultural elements at play in the late 1970s and early 1980s, allowed Jerry Goldsmith and James Horner to draw liberally from the varied traditions of science fiction scoring in a way few other composers had been able to, a time capsule of a moment when the sonic representation of the future was fluid and in flux.
Statistics and data visualisation

This chapter introduces students to basic statistical and data visualisation techniques in linguistics. First, we provide a brief introduction to descriptive statistics with special focus on summarising and presenting data in a meaningful way. Second, we outline basic principles underlying inferential statistics and provide the first introductory steps into the most common techniques used in language research, such as t-test, ANOVA and correlations. Finally, we discuss effective visualisation techniques including boxplots, scatterplots and error bar plots. The goal of the chapter is to provide students with knowledge and skills to understand quantitative research reports and produce simple statistical measures themselves.
Into the Metaverse

Authors have seen that there is often a mistaken idea that VR is a new technology. From the moral panics around the technology to news about Big Tech acquisitions of smaller VR companies, there is often a huge amount of attention given to this emerging and developing technology. They saw how access to VR in education can be hampered by the digital divide but also the possibilities that it provides, opening up learning experiences for all with, for example, virtual field trips and safely learning in what could otherwise be harmful or dangerous environments. The immersive world opens up opportunities for escapism and exploration and by building it with our critical lens, the authors can hope that the dream-like environment envisaged by the early pioneers of VR wont become a nightmare.
The Virtual Space

This chapter explores some of the key areas around the production and consumption of VR experiences. Through reference to existing theoretical work and first-person testimony from content creators and educators, it explores the development of VR in relation to a set of themes that pave the way for a framework for VR literacy to be developed in the subsequent chapter.
Computational linguistics

Computational linguistic technologies, such as search engines, automatic speech recognition systems and machine translation technologies, are already commonplace in many corners of society. These technologies are the result of research and development efforts that have allowed the field to computationally model and manipulate natural language for all sorts of purposes. This chapter provides an insight into some of the key concepts involved in computational linguistics and outlines how some of these language technologies are built. It considers technologies that make use of speech data (e.g. phone calls and speech within video data) as well as text data (e.g. social media posts and emails). As well as explaining some of the inner mechanisms of computational linguistic systems, this chapter draws on a wide variety of their applications that are a result of innovations in computational linguistics. This chapter discusses some of the benefits these technologies bring to society, but it also considers the challenges they pose.
13. ‘Anorexia is Seen as a GOOD Thing When You’re Fat!’: Constructing ‘Eating Disorders’ in Fat Acceptance Blogs

Eating disorders remain little understood by the public, and sensationalist stories in the media have done little to dispel simplistic and reductionist perspectives. This edited volume uses a range of language-centred approaches to provide much needed critical in-depth analysis and interdisciplinary synthesis. The book
6. Experiencing Newspaper Representations of Eating Disorders: An Interpretative Phenomenological Study

Eating disorders remain little understood by the public, and sensationalist stories in the media have done little to dispel simplistic and reductionist perspectives. This edited volume uses a range of language-centred approaches to provide much needed critical in-depth analysis and interdisciplinary synthesis. The book
Introduction

Eating disorders remain little understood by the public, and sensationalist stories in the media have done little to dispel simplistic and reductionist perspectives. This edited volume uses a range of language-centred approaches to provide much needed critical in-depth analysis and interdisciplinary synthesis. The book
5. Representations of Eating Disorders in Turkish News Media

Eating disorders remain little understood by the public, and sensationalist stories in the media have done little to dispel simplistic and reductionist perspectives. This edited volume uses a range of language-centred approaches to provide much needed critical in-depth analysis and interdisciplinary synthesis. The book
The Effect of The Sequential Whispering Game on Listening Skill Teaching for Islamic Junior High School Students in Indonesia/أثر لعبة الهمس المتسلسل على تعليم مهارة الاستماع لدى طلاب المدرسة المتوسطة الإسلامية بتانجيرانج الجنوبية

The researcher chose this title because of problems in listening skills learning activities, such as students have not been able to receive information or messages contained from a speaker, which is caused by a lack of vocabulary,their inability to differentiate short and long sounds of letters in pairs.In addition, the students lack interest in learning, and the teacher chooses media and methods unsuitable for listening learning activities. This study aims to analyze the application of chain whispering games in teaching listening, analyze the effect of chain whispering on students' learning outcomes, and reveal the supporting and inhibiting factors in teaching this game. This quantitative research uses an experimental (pre-experimental) method with a one-group pretest-posttest design. The population in this study were students of class VIII at MTs Serpong, and researchers took research samples of 33 students in class VIII. Researchers used data collecting methods: pre- and post-tests, observation, interviews, and questionnaires. After the researcher gave the pretest and posttest to students, this study obtained an average pretest score of 53.48 and an average posttest score of 79.10, with the results of the effectiveness of the N-Gain pretest and posttest of language games on students' listening skills was 54.0 with the category enough. After the calculation, the table value is obtained at the 5% level, which is 0.943, and shows that the Zero Hypothesis ( 16HO" style="width: 15pt; height: 15pt;"&gt; ) and the Alternative Hypothesis ( 16Ha" style="width: 15pt; height: 15pt;"&gt; ) are accepted. It shows a significant effect between the series of whispering games on listening skills learning and student learning outcomes. Keywords: Learning Listening Skills, Language Games, Whispering Chains
Multiparameter Exponential Families

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UCS Security

In this chapter, we will be securing our UCS system through AAA for logins and hardening of the system.
What Is Java Programming

If you’ve decided to start learning how to program in Java and you’ve read this far, it’s probably a good idea to talk about what Java programming actually is. In this chapter, we’ll take a brief stroll down memory lane and talk, at a high level, about programming computers and what it means to program in Java. We’ll also talk about game programming and discuss the venerable main game loop.
Introduction

In this book, Introduction to Java Through Game Development, you will learn the fundamentals of the Java programming language through hands-on game development tasks. Through the completion of topic-specific coding challenges, focused on a particular aspect of one of the three games included with the text, you will gain experience and knowledge working with the Java programming language, the NetBeans IDE, a 2D game engine, and three different 2D games.
Corpus-based SLA research

In this afterword, we provide a brief overview of some of the main conceptual, theoretical, and methodological developments in the field of second language acquisition in service of situating corpus-based approaches as a promising avenue for second language acquisition research. Against this backdrop, we highlight some of the appealing features of ZAEBUC and the research studies presented in this volume, and offer suggestions for future research based on ZAEBUC. We close with some desiderata for future corpus compilation projects.
Can adult lexical diversity be measured bilingually? A proof-of-concept study

We report on the development of three new metrics designed to characterize bilingual lexical diversity extracted from the joint inspection of second language (L2) and first language (L1) texts by the same writers. We were motivated by insights from multi-competence in adult language learning (Cook, 1991) and conceptually scored vocabulary in bilingual child language acquisition (e.g., Pearson et al., 1993). First, we employed the Language-agnostic BERT Sentence Embeddings (LaBSE) model (Feng et al., 2020), a vector-based approach to cross-lingual word embeddings, and developed three measures: Total Vocabulary (TV), Conceptual Vocabulary (CV), and Bilingual Vocabulary (BV). Then, we applied them to a stratified random sample of 132 essays extracted from the Zayed Arabic-English Bilingual Undergraduate Corpus (ZAEBUC) (Palfreyman & Habash, 2021; Habash & Palfreyman, in process). Robust evidence of convergent validity was found for TV (i.e., verb, noun, and adjective lemmas summed up across L1 and L2) against the criterion measure of MTLD-W (Vidal & Jarvis, 2020) in each language. No predictive relationships were found for any of the measures and proficiency in either language, using CEFR ratings of the L2 and L1 essays. We discuss the limitations and implications of our approach to measuring L1-L2 lexical diversity jointly.
Integrating Artificial Neural Networks, Simulation and Optimisation Techniques in Ambulance Deployment for Heterogeneous Regions under Stochastic Environment

The paper focuses on the development of a strategy to integrate forecasting using artificial neural networks (ANN), simulation and optimisation techniques for ambulance deployment to predefined locations with heterogeneous demand patterns under stochastic environments. The metropolitan city of Bulawayo was used as a case study with high variability in call inter-arrival rates, response times, service times, and proportions of severity of emergencies by geographical zones covered by sub-stations. These stochastic environments complicate the decision-making process at strategic, tactical and operational level, in pursuit to achieve high levels of equality, efficiency and effectiveness in resource allocation and utilisation. This paper proposes an integrated simulation optimisation methodology that integrates future demand and allows for simultaneous evaluation of operational performances of deployment plans using multiple performance indicators such as average response time, total duration of a call-in system, number of calls in response queue, average queuing time, throughput ratios and ambulance utilisation levels. Increasing the number of ambulances influences the average response time below a certain threshold. Beyond this threshold, no significant changes occur in the performance measures. As the fleet size is increased, the ambulance utilisation levels decreased, hence there is always need to balance resource allocation and capacity utilisation to avoid idleness of essential equipment and human resources. Numerical experiments conducted to align the response time to international standards resulted in reduction in number of ambulances required for optimal deployment. For medical resources such as ambulances, deploying more resources do not always translate to better performance, hence there is need to simultaneously consider multiple performance measures. Decision makers in EMS must seriously consider ways of reducing the response time as it has significant bearing in reducing the required number of ambulances, a critical but scarce resource. Efforts must be directed towards digitisation of switch boards in the call center, training of the paramedics and provision of relevant modern equipment to the response teams as it will go a long way in reducing the pre-trip delay time, chute time and ultimately the response time. Based on the scientific evidence, management could lobby for de-congestion and resurfacing of old and dilapidated roads in order to increase access and speed when responding to emergency calls.
The Semantic Layer

In the two previous chapters, you learned how to take separate tables and join them together in a way that allows you to query data across multiple source data elements. You then saw how to add further elements to shape the data model to make it easier to use and maintain.