Reviewerly/exHarmony huggingface dataset:
Unpacking Central Bank Digital Currency (CBDC) Implementation Challenges and Risks

This chapter outlines the risks and challenges involved in implementing central bank digital currency (CBDC). Interest in CBDC is currently growing, with a number of central banks paying it serious consideration, and a number of countries are implementing or piloting CBDC. Although CBDC holds a great deal of promise, its implementation is not without difficulty. This chapter highlights the major implementation issues, such as CBDC contributing to financial exclusion, technology risks, CBDC's inability to work in an offline environment, lack of privacy, and confidential consideration, since anonymity is difficult to achieve. The chapter concludes by highlighting the need to deploy CBDC with greater attention paid to societal, economic, and political factors instead of a purely technocratic approach.
Digital, Programmable Euro

The financial system has been changing rapidly with the recent advancements in technologies underlying the money and payment systems. With the increase in the involvement of technology in the financial world and the emergence of new technologies like distributed ledger technologies (DLT), there has been a rising trend of creating private cryptocurrencies and stablecoins to replace existing currencies and assets. The increasing influence of big-tech companies in the financial systems and the usage of unregulated cryptocurrencies and stablecoins pose many challenges for financial authorities worldwide, generating the need for government intervention. As a result, numerous central banks worldwide are looking for possibilities to change their monetary systems by including central bank digital currencies (CBDCs) like the digital euro in their offerings. However, it is uncertain how these CBDCs and other digital money initiatives will impact the existing systems and provide solutions to existing problems and benefit the citizens and other stakeholders.
Design of Ubiquitous Learning (U-Learning) on Thesis Writing Training and Final Project Assisted Massive Open Online Courses

This study aims to produce a ubiquoitous learning (u-learning) training design for students who are compiling thesis and final project using massive open online courses (MOOC).The research method uses research and development of the ADDIE model.Data collection was obtained from expert assessments of the acceptability of product content that met accuracy, usability, convenience and attractiveness of the product.Data analysis is then carried out with Interrater agreement to get the value of the expert agreement.The results of data analysis show the conclusion that the content developed in the ubiquotus learning (u-learning) training design had very high acceptance with aspects of accuracy, usability, convenience and attractiveness so that it was feasible to use.
Assessment of Projection Pursuit Index for Classifying High Dimension Low Sample Size Data in R

Analyzing “large p small n” data is becoming increasingly paramount in a wide range of application fields. As a projection pursuit index, the Penalized Discriminant Analysis ($\mathrm{PDA}$) index, built upon the Linear Discriminant Analysis ($\mathrm{LDA}$) index, is devised in Lee and Cook (2010) to classify high-dimensional data with promising results. Yet, there is little information available about its performance compared with the popular Support Vector Machine ($\mathrm{SVM}$). This paper conducts extensive numerical studies to compare the performance of the $\mathrm{PDA}$ index with the $\mathrm{LDA}$ index and $\mathrm{SVM}$, demonstrating that the $\mathrm{PDA}$ index is robust to outliers and able to handle high-dimensional datasets with extremely small sample sizes, few important variables, and multiple classes. Analyses of several motivating real-world datasets reveal the practical advantages and limitations of individual methods, suggesting that the $\mathrm{PDA}$ index provides a useful alternative tool for classifying complex high-dimensional data. These new insights, along with the hands-on implementation of the $\mathrm{PDA}$ index functions in the R package classPP, facilitate statisticians and data scientists to make effective use of both sets of classification tools.
Application of Machine Learning to Sentiment Analysis

The Internet is a vast communication tool that has become an indispensable tool for the exchange of information both at personal and professional level; and that allows access to data of all kinds, texts, photos, music, videos, through encryption and universal coding. Due to the rapid increase in the amount of data generated by users on social media platforms such as Twitter, several opportunities and new open doors have been created for organizations that strive to track customer opinions on their products. Sentiment analysis is one of the new challenges appeared in automatic language processing with the advent of social networks. Sentiment analysis is a process that automatically determines whether a user-generated text expresses a positive, negative or neutral opinion about an entity (i.e., product, people, subject, event, etc.). The purpose of this document is to provide detailed steps in the process of analyzing feelings on a data set using machine learning.
Toward a Generative Chatbot for an OER Recommender System Designed for the Teaching Community: General Architecture and Technical Components

A chatbot (Chatting Robot) is a computer program that enables people to communicate with machines using everyday language. The use of chatbots evolved quickly in the last years for different domains, including education and teaching. This paper presents related research conducted in the past year to contextualize the research. Next, we present the general context of a chatbot, its different intrinsic components, and their general architecture to constitute a solid base for the proposed proper system. Furthermore, we present the architecture of the proposed Chatbot by describing, in-depth, the function of every component and detailing the system's scenario. This study is a solid foundation for the final generative Chatbot for Open Educational Resources (OER) Recommender System. This paper aims to present the first step of developing a OER Chatbot" Recommender designated to the teaching community, which is the general architecture of the global system's first component.
Correction of an Image for Colour Blindness Using the Fusion of Ishihara Filter and Histogram Equalization

Colour blindness is one of the minor disorders in individuals appearing due to a genetic mutation causing the intensity to few colours making the individual fail to discriminate specific colours. Concerning the nobility of colour recognition, the disorder ranges in differentiating monochromic colour to the most common form. This 'red-green' dissimilarity causes vibrant colours to override dull colours easily are easily seen, and the dull shades are difficult to perceive. Targeting the impacts of colour blindness, in our research work, we have proposed a designed colour filter built on the combination of the well-known Ishihara test t and histogram equalization. Ishihara filter alone is insufficient as the approach is not suited well to translate real-world images. To overcome this problem, the proposed work aimed at the modification Ishihara filter eradicating lighter shades and shifting them to the darker regions. The image obtained after applying histogram equalization could also be perceived by a colour-blind person. The research paper has also presented a comparative analysis of the two approaches for the elimination of colour blindness.
Shifting Left for Early Detection of Machine-Learning Bugs

Computational notebooks are widely used for machine learning (ML). However, notebooks raise new correctness concerns beyond those found in traditional programming environments. ML library APIs are easy to misuse, and the notebook execution model raises entirely new problems concerning reproducibility. It is common to use static analyses to detect bugs and enforce best practices in software applications. However, when configured with new types of rules tailored to notebooks, these analyses can also detect notebook-specific problems. We present our initial efforts in understanding how static analysis for notebooks differs from analysis of traditional application software. We created six new rules for the CodeGuru Reviewer based on discussions with ML practitioners. We ran the tool on close to 10,000 experimentation notebooks, resulting in an average of approximately 1 finding per 7 notebooks. Approximately 60% of the findings that we reviewed are real notebook defects. (Due to confidentiality limitations, we cannot disclose the exact number of notebook files and findings.)
Face recognition under large age gap using age face generation

Age invariant face recognition (AIFR) is a challenging problem in the area of the face recognition. To handle large age gap for face recognition, we proposed a robust approach based on deep learning for face recognition under a large age gap. The presented approach consists of four important steps. The pre-processing is done for face detection. Age face generation is processed with the help of modified age conditional generative adversarial network (acGAN). Generated age face images are mixed with train dataset and augmentation is applied to increase the size of training data for handling biasness of the deep learning models towards dataset size. A modified residual convolutional neural network is applied for training and testing of face images. The performance has been evaluated using two-fold cross-validation on standard and challenging LAG dataset. The proposed approach achieved the 92.5% recognition accuracy, which is better than the existing face recognition approaches for a large age gap.
The Role of a Chatbot Personality in the Attitude of Consumers Towards a Banking Brand

In the recent years, it has been seen that brands have opted to implement chatbots as part of their digital customer service channels, especially since they can be hosted on social networks such as Facebook Messenger and WhatsApp. Although the use of this has become widespread, users perceive them as inefficient and of little credibility. The design of the personality of a chatbot arises as an alternative for the brand to generate a more human interaction with the user and show its brand values to generate a positive experience. In addition, a projected growth of the business value of chatbots for brands of 3.9 trillion dollars is estimated by 2022. For this reason, knowing how brands can rely on the correct implementation of the personality of a chatbot creating a positive influence on its users is important in a growing industry. The present research seeks to know how the personality of the chatbot influences the formation of consumer attitudes towards a brand. This exploratory study used a qualitative methodology of phenomenological design with young adults from Lima. Subsequently, it was analysed through descriptive coding. It was found that the social dynamics, the social role and the physique of the chatbot are the aspects that influence the attitude towards the brand and generate a feeling of trust and closeness between the chatbot and the brand.
Conceptually-grounded mapping patterns for Virtual Knowledge Graphs

Virtual Knowledge Graphs (VKGs) constitute one of the most promising paradigms for integrating and accessing legacy data sources. A critical bottleneck in the integration process involves the definition, validation, and maintenance of mapping assertions that link data sources to a domain ontology. To support the management of mappings throughout their entire lifecycle, we identify a comprehensive catalog of sophisticated mapping patterns that emerge when linking databases to ontologies. To do so, we build on well-established methodologies and patterns studied in data management, data analysis, and conceptual modeling. These are extended and refined through the analysis of concrete VKG benchmarks and real-world use cases, and considering the inherent impedance mismatch between data sources and ontologies. We validate our catalog on the considered VKG scenarios, showing that it covers the vast majority of mappings present therein.
Case Study: Mapping an E-Voting Based Curriculum to CSEC2017

An electronic voting (E-voting) oriented cybersecurity curriculum, proposed by Hostler et al. [4] in 2021, leverages the rich security features of E-voting systems and E-voting process to teach essential concepts of cybersecurity. Existing curricular guidelines describe topics in computer security, but do not instantiate them with examples. This is because their goals are different. In this case study, we map the e-voting curriculum into the CSEC2017 curriculum guidelines, to demonstrate how such a mapping is done. Further, this enables teachers to select the parts of the e-voting curriculum most relevant to their classes, by basing the selection on the relevant CSEC2017 learning objectives. We conclude with a brief discussion on generalizing this mapping to other curricular guidelines.
Storyteller: Guiding Students Through Code Examples

This paper describes a tool to help instructors guide learners through code examples. The tool captures low-level changes made to a group of files using a popular code editor. Then it allows the programming session to be replayed so that an author can annotate the evolution of the code and explain the reasons behind important decisions. The author-supplied narrative can include text, code highlights, media (hand-drawn pictures, screenshots, videos, and audio files), and self-grading questions. The combination of the narrative and the evolving code is called a code 'playback'. Playbacks are viewable in a web browser.
Cyber Arena

Numerous institutions are developing cybersecurity education and training programs to supply the considerable global demand for cybersecurity professionals. However, these institutions face barriers in building realistic laboratory environments, commonly referred to as cyber ranges, needed for hands-on skills development. Cybersecurity labs differ from traditional computing labs in both size and complexity. They often require multiple distinct components to represent network configurations, adversarial computing, and defense mechanisms.
Inverted pyramid frame forward and backward prediction for distorted video by water waves

There has been much research on how to restore a single image from distorted video. Random water surface variation, an inability to model the surface, and multiple factors in the imaging processing leading to different geometric distortions in each frame are among the challenges. This paper proposes an inverted pyramid structure based on the cross optical flow registration approach and a multi-scale weight fusion method based on wavelet decomposition. The inverted pyramid based on the registration method is used to estimate the original pixel positions. A multi-scale image fusion method is applied to fuse the two inputs processed by optical flow and backward mapping, and two iterations are proposed to improve the accuracy and stability of the output video. The method is tested on several reference distorted videos and our videos, which were obtained through our experimental equipment. The obtained results exhibit significant improvements over other reference methods. The corrected videos obtained with our approach have a higher degree of sharpness, and the time required to restore the videos is significantly reduced.
An Optimised Bitcoin Mining Strategy

Stale blocks are not avoidable in blockchain, such as the Bitcoin network, when proof-of-work is used as the consensus protocol. However, as the economic loss to the miners and the security risk to the network cannot be ignored, research is needed to identify and analyse stale blocks. By analysing the factors influencing the generation of stale blocks, the authors propose a new machine learning model based on XGBoost. They propose a new data collection method for bitcoin nodes to obtain real data for training prediction model. Then, based on the model, they generate optimal mining strategies and analyse the economic benefits. The experimental data and application cases show that the real-time data detection and machine learning model that they propose can accurately identify and predict the generation of stale blocks and generate an economically optimal mining strategy in the Bitcoin network with the presence of stale blocks.
Mining Competitors and Finding Winning Plans Using Feature Scoring and Ranking-Based CMiner++ Algorithm

For a business to succeed, it is very important to make things speaking more to clients than to rivals. It is more critical to decide on the significant feature of an item which influences its competency. In spite of the works that have been done already, a few algorithms gained efficient solution. This paper proposes the CMiner++ Algorithm to assess the competitive relationship among items in unstructured dataset and finding the Top-K competitors of a given item. Definitively, the nature of the outcomes and the versatility of this methodology utilizing numerous datasets from various areas are assessed, and the efficiency and adaptability of this algorithm on various data sets are improved when compared to existing algorithms. In today's busy world, automatic recommendation systems are emerging because people are looking for the products best suited for them. So, it is very important to analyse the behaviour of people, make a review on large and large unstructured data sets, and make the fully automated deep learning system to ensure the accurate outcome.
Logistics, Affordances, and Evaluation of Build Programming

Computing students are expected to contribute to large unfamiliar codebases as they transition from university to industry settings. While computing courses provide students ample opportunities to write code independently or utilize abstract functionalities from standard libraries, students have fewer opportunities to read or extend codebases written by other programmers. This paper presents the logistics, affordances, and empirical evaluation of a novel instructional strategy, Build Programming, which is designed to promote code reading and extension in CS courses. In this strategy, a student (1) solves a programming problem, (2) is assigned a new codebase from a peer who solved the same problem, and (3) is asked to extend the assigned codebase to solve another problem. This allows a student to understand and extend an authentic codebase that is situated in a familiar context. In this paper, we shed light on the logistics of operationalizing this strategy in the context of an undergraduate Data Structures and Algorithms course (N=206). We also describe the affordances of this strategy through student experiences and evaluate the efficacy of one of these affordances, improving code quality through source code analysis. Most students (91%) proposed continuing Build Programming and students' code quality significantly improved after our strategy. Our findings underscore the benefits of Build Programming, and we hope that more instructors incorporate it in CS courses.
Design of a virtual data shelf to effectively explore a large database of 3D medical surface models in VR

Abstract Purpose Medical researchers deal with a large amount of patient data to improve future treatment decisions and come up with new hypotheses. To facilitate working with a large database containing many patients and parameters, we propose a virtual data shelf , displaying the 3D anatomical surface models in an immersive VR environment. Methods Thereby, different functionalities such as sorting, filtering and finding similar cases are included. To provide an appropriate layout and arrangement of 3D models that optimally supports working with the database, three layouts (flat, curved and spherical) and two distances are evaluated. A broad audience study with 61 participants was conducted to compare the different layouts based on their ease of interaction, to get an overview and to explore single cases. Medical experts additionally evaluated medical use cases. Results The study revealed that the flat layout with small distance is significantly faster in providing an overview. Applying the virtual data shelf to the medical use case intracranial aneurysms, qualitative expert feedback with two neuroradiologists and two neurosurgeons was gathered. Most of the surgeons preferred the curved and spherical layouts. Conclusion Our tool combines benefits of two data management metaphors, resulting in an efficient way to work with a large database of 3D models in VR. The evaluation gives insight into benefits of layouts as well as possible use cases in medical research.
Investigating Reflection in Undergraduate Software Development Teams

Metacognition is widely acknowledged as a key soft skill in collaborative software development. The ability to plan, monitor, and reflect on cognitive and team processes is crucial to the efficient and effective functioning of a software team. To explore students' use of reflection--one aspect of metacognition--in undergraduate team software projects, we analyzed the online chat channels of teams participating in agile software development projects in two undergraduate courses that took place exclusively online (n = 23 teams, 117 students, and 4,915 chat messages). Teams' online chats were dominated by discussions of work completed and to be done; just two percent of all chat messages showed evidence of reflection. A follow-up analysis of chat vignettes centered around reflection messages (n = 63) indicates that three-fourths of the those messages were prompted by a course requirement; just 14% arose organically within the context of teams' ongoing project work. Based on our findings, we identify opportunities for computing educators to increase, through pedagogical and technological interventions, teams' use of reflection in team software projects.
A cable health assessment method based on multi-agent and matter-element extension model

It is of great significance for the stable operation of the power system to accurately evaluate the health status of the cable, and then provide a reference for the implementation of condition maintenance. Based on the matter-element extension model, this paper divides different cable health levels, selects the important state quantity of the cable as the evaluation index, assigns different weights to it, and finally obtains the health level of the cable evaluation. The multi-agent plays its role to make the cable health assessment system run well. The calculation example results show that the health assessment model in this paper can effectively assess the health status of cables in different stages of operation, and the assessment results can be used as a reference for the formulation of on-site maintenance strategies. Compared with other assessment methods, the assessment results are more comprehensive and more reliable.