Reviewerly/exHarmony huggingface dataset:
Board 58: WIP: Enhancing Workforce Development of Data Science Skills within Domain-Specific Programs

Abstract In 2018, The National Academies of Sciences, Engineering, and Medicine identified a need for undergraduate students to have access to critical data science skills development opportunities. Over the next several decades, the world's reliance on cloud computing and big data will continuously increase, and new data-centric technologies and engineering approaches will be developed. Due to this rapidly developing field, there is a need to track these trends and incorporate the corresponding developments into our current science and engineering curriculum. Besides data science skills already taught in traditional engineering curricula, such as mathematical, computational, and statistical foundations, the National Academies guide discusses that key concepts in developing data acumen include domain-specific considerations and ethical problem-solving. This work-in-progress (WIP) paper will highlight the foundation of a comprehensive study to explore data science education in two domain-specific programs: material science and engineering and architectural engineering. This project is broken down into the following objectives: 1) facilitate data science education and workforce development for engineering and related topics, 2) provide opportunities for students to participate in practical experiences where they can learn new skills through opportunities in new settings to transform data science education, and 3) expand the data science talent pool by enabling the participation of undergraduate students with diverse backgrounds, experiences, skills, and technical maturity. The paper will focus on the topics, deployment strategies within courses and curricula, establishing data sets, representative examples of work-in-progress efforts and their success.
WIP: Replication of a 1/5th-Scale Autonomous Vehicle to Facilitate Curriculum Improvement in Cyber Engineering

Abstract To respond to the industry trend and the recent nationwide initiative for producing engineering professionals in the cyber domain, our university launched an undergraduate degree program in cyber engineering three years ago. Cyber engineering combines the fundamentals of computer engineering, cryptography, and cybersecurity techniques to design, incorporate, and secure systems across the digital landscape. This includes, but is not limited to, embedded technology, autonomous technology, edge and end-point technologies. Compared to cybersecurity, in general however, cyber engineering still requires further refinement in its curriculum coverage. The current curriculum for the cyber engineering program at our university is centered on cyber physical systems (CPS) and their security including device-level security, boot security, and attack-resilient hardware/middleware. As an engineering curriculum, cyber engineering requires a variety of hands-on laboratory-based training as well. To help hands-on training in a curricular setting, we have been developing a 1/5th-scale autonomous vehicle as a framework of cyber physical systems for a set of cyber engineering courses. For the 1/5th-scale autonomous vehicle, we adopted an existing platform known as AutoRally which was initially developed at Georgia Tech as a high-performance testbed for self-driving vehicle research and funded by DARPA in mid-2010. In this paper, we present technical details of our development effort including technical challenges in replicating an "AutoRally" years after the DARPA project was completed, as well as out intended mapping of technical content between the CPS platform and core courses in the cyber engineering curriculum. The primary technical challenges arose from the fact that some of the key components of the vehicle became obsolete and/or discounted. As such, part of the work done for the self-driving features of the original AutoRally required revision with a great deal of effort. Giving details of our work, we hope that the information presented in this paper is useful to educators in cyber engineering in general as well as those in embedded and cyber physical systems for refining a curriculum in the cyber engineering field.
Investigating Students' Intention to Use M-Learning

Mobile learning (M-learning) has become a crucial tool for both students and educators. The unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM), and outside factors were all included in this study's integrative review approach to examine the variables affecting university students' intention to use M-learning and mobile effectiveness. A study at King Faisal University examined the viewpoints of 364 undergraduate and graduate students using a random sampling technique to have better understand the impact of such technological innovation on teaching. Participants in the study were invited to complete a survey created expressly for this study in order to find out if they would still be open to using M-learning to advance the cause of sustainable education. The paradigm of the study was evaluated using a structural equation modeling (SEM) approach and was based on the UTAUT and TAM technology adoption models. The findings demonstrated that each element of the research model had a positive influence on learners' behavioral intention to use M-learning (BIM); it also demonstrated the impact of that intention on long-term educational sustainability. The link between independent characteristics and users' enjoyment and adoption of M-learning is moderated by mobile usability and behavioral control to utilize M-learning. The results also showed that BIM and the utility of mobile devices increased the degree to which M-learning is liked. Mobile usability and BIM both favorably affect sustainable education in terms of user satisfaction and M-learning adaptation. These findings suggest that mobile usability and behavioral intention to utilize M-learning are the main factors influencing the adoption of M-learning in Saudi Arabia's higher education.
Conveyance of feature gradation semantics in modern Turkish

The aim of the article is to study the Turkish adjective from the point of view of a complex and systematic description of the means that serve to indicate the semantics of feature gradation. Having analyzed the works of Ukrainian Turkologists and representatives of various grammar schools in Turkey and in European countries, which focus on the adjective, we determined that most of them study certain features of the adjective structure or functioning in modern Turkish. Besides, means of denoting the feature manifestation intensity, particularly those having diminutive semantics, especially word-forming affixes that realize this meaning, also became the subject of the scientists’ research. At the same time, we did not record any work that deals with a holisticdescription of linguistic (grammatical, word-forming) means as a system that serves to denote the semantics of feature gradation, which determines the relevance of our research. In the present work, we set the task to carry out such a systematic description of these means and determine the features of their interaction within this system. Along with this, the paper discusses the general problems of qualification of the adjective as part of speech in the Turkish language and differentiation of its lexical and semantic classes. Within the framework of the study, we offer the description of the degrees of comparison of adjectives, the intensive adjective forms, the polyfunctional affix -ca/-ce, the word-forming affixes with diminutive and augmentative semantics as well as the pair words (lexical repetitions) and syntactic models of the implementation of feature gradation semantics. The methodological prerequisite of the study is the functional and semantic approach.
Deciphering blockchain's role in Danish decision-making: evaluating opportunities and challenges through the prism of due process

This paper explores the benefits and pitfalls associated with integrating a common blockchain model within a distinct administrative environment, focusing particularly on its effects on decision-making processes. The study is grounded in the doctrine of procedural due process, a holistic concept encompassing intertwined administrative rules and principles, designed to promote fairness and justice in decision-making, which can be succinctly distilled into three core principles: accuracy, integrity, and transparency. On initial examination, these principles seem to correspond with the fundamental characteristics of blockchain technology, which are identified as authenticity, integrity, and transparency. In our analysis, we measure the extent to which the attributes of blockchain technology align with the principles of the due process doctrine. An essential component of this assessment includes a meticulous examination of the practical merging of each characteristic within the decision-making procedure. The approach and practical applications explored in this study highlight the potential of blockchain technology to enhance adherence to due process, especially in areas where traditional trust-based systems have faltered in maintaining basic procedural safeguards. However, given the intricate complexity tied to its implementation, this technology should be regarded as a fallback option, strictly reserved for situations where the benefits of its utilisation unmistakably outweigh the numerous inherent risks. In the ensuing sections of this paper, we will delve deeper into these risks and propose actionable mitigation strategies.
Python

In this chapter, we introduce the Python programming language and some common libraries used throughout the book. This chapter ends with a discussion of the Python ecosystem, comparing Python to other languages and learning how it works. While this chapter will cover all of the Python you need for the rest of the book, there are topics we will skip or only briefly mention that will be covered in more depth in Chapters 3 and 5.
Software Engineering

In this chapter, we will introduce tools and methods beneficial for writing, maintaining, sharing, and expanding your code. While different tools may come in and out of popularity, the functionality they provide should be similar.
Credit Card Fraud Detection using ANN

Abstract: Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.
The SoS conductor: Orchestrating resources with iterative agent‐based reinforcement learning

Abstract We introduce a novel resource management approach for Systems of Systems (SoS), utilizing hierarchical deep reinforcement learning, iterating with agent‐based simulation. A key innovation of this method is its ability to balance top‐down SoS management with the autonomy of individual systems. This is achieved by dynamically allocating resources to each system, thereby modifying the range of options they can autonomously choose from. This dynamic option adjustment is a powerful approach to managing the trade‐off between centralized efficiency and decentralized autonomous actions of the systems, enabling the SoS to maintain the systems' autonomy while ensuring efficient SoS governance. The method, validated through a case study, not only demonstrates the potential and efficacy of the learning framework but also reveals how, using this method, minor performance sacrifices can lead to substantial improvements in resource efficiency.
Foreign language learning using augmented reality environments: a systematic review

Augmented Reality (AR) is an advancing technology that has drawn the attention of educational material designers across various academic fields. However, few studies document the successes and setbacks of AR research in the language education sector. This review delves into educational research that employs AR for language training, examining the existing literature on this topic for development trends, benefits, challenges, and success patterns to derive design principles from them. In doing so, the paper covers 40 studies published between 2016 and 2023. The findings suggest that AR is mainly used for vocabulary acquisition with a clear trend toward applying marker-based technology and mobile devices. The design principles derived indicate that the potential of AR lies primarily in contextual learning, and that the technology alone may not satisfy students’ needs in all aspects of language learning but should be used in combination with traditional teaching methods.
Sia-m7G: Predicting m7G Sites through the Siamese Neural Network with an Attention Mechanism

Background: The chemical modification of RNA plays a crucial role in many biological processes. N7-methylguanosine (m7G), being one of the most important epigenetic modifications, plays an important role in gene expression, processing metabolism, and protein synthesis. Detecting the exact location of m7G sites in the transcriptome is key to understanding their relevant mechanism in gene expression. On the basis of experimentally validated data, several machine learning or deep learning tools have been designed to identify internal m7G sites and have shown advantages over traditional experimental methods in terms of speed, cost-effectiveness and robustness. Aims: In this study, we aim to develop a computational model to help predict the exact location of m7G sites in humans. Objective: Simple and advanced encoding methods and deep learning networks are designed to achieve excellent m7G prediction efficiently. Methods: Three types of feature extractions and six classification algorithms were tested to identify m7G sites. Our final model, named Sia-m7G, adopts one-hot encoding and a delicate Siamese neural network with an attention mechanism. In addition, multiple 10-fold cross-validation tests were conducted to evaluate our predictor. Results: Sia-m7G achieved the highest sensitivity, specificity and accuracy on 10-fold crossvalidation tests compared with the other six m7G predictors. Nucleotide preference and model visualization analyses were conducted to strengthen the interpretability of Sia-m7G and provide a further understanding of m7G site fragments in genomic sequences. Conclusion: Sia-m7G has significant advantages over other classifiers and predictors, which proves the superiority of the Siamese neural network algorithm in identifying m7G sites.
OLCH: Online Label Consistent Hashing for streaming cross-modal retrieval

Cross-modal hashing has received growing interest to facilitating efficient retrieval across large-scale multi-modal data, and existing methods still face three challenges: 1) Most offline learning works are unsuitable for processing and training with streaming multi-modal data. 2) Current online learning methods rarely consider the potential interdependency between the label categories. 3) Existing supervised methods often utilize pairwise label similarities or adopt relaxation scheme to learn hash codes, which, respectively, require much computation time or accumulate large quantization loss during the learning process. To alleviate these challenges, this paper presents an efficient Online Label Consistent Hashing (OLCH) approach for streaming cross-modal retrieval. The proposed approach first exploits the relative similarity of semantic labels and utilizes the multi-class classification to derive the common semantic vector. Then, an online semantic representation learning framework is adaptively designed to preserve the semantic similarity across different modalities, and a mini-batch online gradient descent approach associated with forward–backward splitting is developed to discriminatively optimize the hash functions. Accordingly, the hash codes are incrementally learned with high discriminative capability, while avoiding high computation complexity to process the streaming data. Extensive experiments highlight the superiority of the proposed approach and show its very competitive performance in comparison with the state-of-the-arts.
Robust secret image sharing scheme with improved anti-noise capability

Secret image sharing (SIS) is a promising image protection technology boasting high security and loss tolerance. However, shadow images generated by SIS are susceptible to noise contamination during real-world transmission, storage, and processing, resulting in data loss when recovering the secret image. Therefore, improving the robustness of SIS systems against noise is crucial. Unfortunately, there is currently limited research on this topic, and existing schemes require improvement in the anti-noise performance. Under the premise of no pixel expansion, this paper proposes a robust SIS scheme that can effectively resist many types of shadow image noise, single or compound. We have achieved the organic fusion of SIS and Reed–Solomon coding technology by elaborately designing many critical factors, such as the shortened Reed–Solomon codes, the construction and utilization method of pixel-blocks, and other prerequisites. The proposed scheme is particularly effective in resisting burst noise, such as salt-and-pepper noise, with lossless recovery possible when the noise density d≤0.01 and high-quality recovery when d<0.06. Compared with existing schemes, it has many advantages, such as stronger anti-noise capability, various applicable noise types, stable anti-noise performance, and no pixel expansion. Analyses and illustrations are conducted to validate the proposed scheme.
Improving answer quality using image-text coherence on social Q&amp;A sites

There has been a significant rise in the use of social Q&A to get answers to a variety of queries. One common problem faced by most social Q&A is how to help unskillful answerers construct well-received answers. Prior studies in answer quality assessment usually focus on ranking candidate answers for the sake of askers but show little value for the answerers. Moreover, existing work employs textural aspects and image quantity to predict answer quality, but semantic information inherent in answer images is rarely considered. To bridge the research gap, we designed an artifact, answer advisor (AA), to help answerers produce well-received answers. Our AA uses an image-text coherence measure that is obtained by integrating topic modeling with a deep learning approach. On a real-world dataset, the proposed measure can reduce the prediction error of answer popularity before the answer is actually posted on the Q&A site by 38.12%.
RESEARCH OF VALUE ORIENTATION PECULIARITIES OF FUTURE FAMILY SPECIALISTS AS A MOTIVATIONAL COMPONENT OF THEIR PROFESSIONAL SELF-CONSCIOUSNESS DEVELOPMENT

Introduction. Modern researchers do not lose interest in the study of value and meaning spheres of human personality at different stages of its development. Understanding the motivation of students and the process of their professional formation requires a constant and comprehensive study of value orientations that contribute to the formation of self-consciousness of future specialists of different profiles. The prevalence or shortage of certain values at the initial stage of students' education can become a starting point for creating favorable conditions for their professional development. Improvement of the motivational component of professional training will allow to understand more deeply the processes of formation of professional self-consciousness, to consider the system of professional training in different profiles in a new way. Purpose of the study. To study the prevailing values (groups of values) of freshmen students studying in the profile "Psychological and pedagogical work with family" and to identify the features of their value orientations for the organization of quality professional training. The article presents a theoretical review of modern research on the problem of formation of value orientations, which gives an idea of the conflicting nature of motivational components and value structures of modern representatives of youth. The professional self-awareness of future specialists of helping professions undergoes a complex path of interiorization of professional knowledge and inclusion of this knowledge in their personal system. The article describes the main results obtained in the course of empirical research of values (groups of values) of the first-year students, which allow to determine their orientation to the helping profession, the ability to harmonious development of professionally significant values, readiness to become a representative of professional culture and norms, as well as to identify value deficiency that will hinder the formation of a professional.
Limitations of neural network training due to numerical instability of backpropagation

Abstract We study the training of deep neural networks by gradient descent where floating-point arithmetic is used to compute the gradients. In this framework and under realistic assumptions, we demonstrate that it is highly unlikely to find ReLU neural networks that maintain, in the course of training with gradient descent, superlinearly many affine pieces with respect to their number of layers. In virtually all approximation theoretical arguments which yield high order polynomial rates of approximation, sequences of ReLU neural networks with exponentially many affine pieces compared to their numbers of layers are used. As a consequence, we conclude that approximating sequences of ReLU neural networks resulting from gradient descent in practice differ substantially from theoretically constructed sequences. The assumptions and the theoretical results are compared to a numerical study, which yields concurring results.
Age Guessing: A Game to Introduce Fundamental Statistical Concepts

We develop a spreadsheet-based game to illustrate fundamental statistical concepts in the first class of an undergraduate Statistics course to motivate students about the topics that they will learn in upcoming classes. This game has been implemented by Google Forms and Google Sheets and can be played in both online and in-person classes of small and large sizes. Statistics is one of the most anxiety-inducing courses for undergraduate students, especially if mathematics is not the focus of their program. Negative anecdotes about the course, mathematics anxiety, and not knowing what the course is exactly about and how practical it can be are among the reasons that contribute to statistics anxiety. The first class provides a good opportunity for an instructor to mitigate these negative impressions and to set a positive attitude towards the course. A pre- and post-game group discussion that we have conducted systematically for six years suggests that the game addresses the students’ negative impression about the course and helps them gain a clearer understanding of the tools and skills they will learn in Statistics.