Reviewerly/exHarmony huggingface dataset:
Digital Currency in Nigeria: The Next Big Thing

Advances in the global digital ecosystem are ever continuous. The use of digital devices is already ubiquitous. At one glance, the cascading effects of digitization have crystallized on the evolution of money as we know it. The arrival of Bitcoin on the global financial is a testament to this assertion. As national central banks around the world simmer the prospects of issuing their digital currency, all hands must be on deck and knowledge brought to the fore. The creation and regulation of a digital currency ecosystem is an uncharted destination unguarded even by the constellation of the stars in the galaxy. The Central Bank of Nigeria must steer the frontiers of digital currency innovation by leading the race in the ecosystem. This paper proposes three names for digital currency and a pathway for digital currency regulation while revealing the prospects and challenges in Nigeria.
A Fast Algorithm for Liquid Voting on Blockchain

Blockchain-based voting, including liquid voting, has been extensively studied in recent years. However, it remains challenging to implement liquid voting on blockchain using Ethereum smart contract. The challenge comes from the gas limit, which is that the number of instructions for processing a ballot cannot exceed a certain amount. This restricts the application scenario with respect to algorithms whose time complexity is linear to the number of voters, i.e., O(n). As the blockchain technology can well share and reuse the resources, we study a model of liquid voting on blockchain and propose a fast algorithm, named Flash, to eliminate the restriction. The key idea behind our algorithm is to shift some on-chain process to off-chain. In detail, we first construct a Merkle tree off-chain which contains all voters' properties. Second, we use Merkle proof and interval tree to process each ballot with O(log n) on-chain time complexity. Theoretically, the algorithm can support up to 21000 voters with respect to the current gas limit on Ethereum. Experimentally, the result implies that the consumed gas fee remains at a very low level when the number of voters increases. This means our algorithm makes liquid voting on blockchain practical even for massive voters.
Augmented Reality for Immersive Learning: Lessons Learnt from Educational Entrepreneurships

The study analyses the lessons learnt from two cases of entrepreneurship that launched Augmented Reality in educational contexts. The first one focuses on the enhancement of a compulsory book used by elementary-school students all over the country. In this case, the enterprise has a direct customer, the government. The second one is an independent effort to design from scratch a local book. In this case, the company expects a return on investment from selling the book. Both cases exemplify how to use Augmented Reality to improve immersive learning environments. Furthermore, both cases illustrate how to implement the concept of economies of scale to make feasible the implementation of immersive learning. The method of inquiry is the Extended-Case Method. Consequently, the chapter contributes by contrasting previous literature with new scenarios, update it, and remarking future perspectives, opportunities, and issues.
Fostering Learning Transfer by Employing a Learning App for Future Preschool Educators in Vocational Schools

This paper focuses on the development of a learning app in the area of STEM education for future professionals in the area of Early Childhood Education and Care (ECEC). We discuss how differing expectations of various actors have influenced the concept, structure, and content of the app. The app discussed here is part of a project that aims to raise future educators’ levels of awareness of STEM learning opportunities for kindergarten children. A central objective of the project is to facilitate the transfer from vocational schools to practice (and vice versa) as well as the application of the apprentices’ theoretical knowledge (disposition) in the kindergarten (performance). To identify expectations about the app, we conducted workshops and developed questionnaires aimed at users of the app, teachers employing the app in their lessons, experts in early STEM education, and experts in the area of digital media. The results indicate somewhat differing expectations toward a learning app, the need for expertise of many stakeholders, and the need for several rounds of coordination and adjustment of the app.
Research on Road-Network Traversal of Fixed-Wing Multi-UAV in Urban Environment

Road-network plays an important role in urban environment, so the reconnaissance along it should be attached much attention. However, the complexity of the road-network brings a lot of difficulties. In this paper, considering the constraints of road-network adjacent relationship and turn radius of fixed-wing UAV, we defined the problem model, including the road-network model with the description for different kinds of nodes. For road-network traversal problem, this paper proposed a method using improved depth-first search with angle constraint (DFS-AC) and genetic algorithm with double chromosome. The simulation results show that this method is able to solve road-network traversal problem both with and without infeasible nodes, at which fixed-wing UAV has no road to fly along.
The Use of Bézier Curves for Optimal Motion Planning of Autonomous Vehicles

There are a wide range of techniques available for cooperative motion planning of autonomous vehicles. This paper focuses on one particular type of methods, known as Direct Methods, that solve motion planning expressed as a continuous-time optimal control problem. Out of all of the Direct Methods that were investigated, methods based on Bernstein polynomials were shown to provide the best results, namely, trajectories that prevent inter-vehicular and environmental collisions while satisfying vehicle constraints and keeping energy of temporal-related costs close to optimal.
mcboost: Multi-Calibration Boosting for R

Given the increasing usage of automated prediction systems in the context of high-stakes decisions, a growing body of research focuses on methods for detecting and mitigating biases in algorithmic decision-making.One important framework to audit for and mitigate biases in predictions is that of Multi-Calibration, introduced by Hebert-Johnson et al. (2018).The underlying fairness notion, Multi-Calibration, promotes the idea of multi-group fairness and requires calibrated predictions not only for marginal populations, but also for subpopulations that may be defined by complex intersections of many attributes.A simpler variant of Multi-Calibration, referred to as Multi-Accuracy, requires unbiased predictions for large collections of subpopulations.Hebert-Johnson et al. ( 2018) proposed a boosting-style algorithm for learning multi-calibrated predictors.Kim et al. (2019) demonstrated how to turn this algorithm into a post-processing strategy to achieve multi-accuracy, demonstrating empirical effectiveness across various domains.This package provides a stable implementation of the multi-calibration algorithm, called MCBoost.In contrast to other Fair ML approaches, MC-Boost does not harm the overall utility of a prediction model, but rather aims at improving calibration and accuracy for large sets of subpopulations post-training.MCBoost comes with strong theoretical guarantees, which have been explored formally in Hebert-Johnson et al.
Improving Policy Implementation of the Conformity Assessment System for Radio and Telecommunication and Broadcasting Equipment

The purpose of a conformity assessment system is to facilitate the supply and sale of radio and telecommunication and broadcasting equipment in the market in compliance with technical standards to prevent electromagnetic interference with other equipment and harmful effects on human health. The conformity assessment system in South Korea is significantly oriented towards pre-regulation and constitutes a government-centered regulatory framework based on the grand principles of regulatory legislation, which mainly focus on minimal and post-regulatory processes. In this paper, the original purpose of the conformity assessment system is described and desirable policy alternatives are suggested to eliminate administrative inefficiency and to secure consumer interests and national competitiveness. In addition, it is stated that the National Radio Research Agency (RRA) should aim towards the overall management of radio spectrum policies, such as the recognition, management, and supervision of certification institutes, and follow-up collaborative governance between government and private sectors when the latter conduct certification and post-regulatory processes under the supervision of the RRA.
Research on Application of 5G Technology in Unmanned Combat System

In recent years, with the rapid development of 5G technology, its application research in the military field has been paid more and more attention by military powers. Firstly, On the basis of analyzing the communication capability of 5G technology, this paper discusses the military application prospect of 5G network. Secondly, starting from the communication requirements of unmanned combat system, the advantages and challenges of applying 5G technology to unmanned combat system are analyzed. Lastly, combined with the development of information technology, the typical application modes of "5G + Military Internet of Things" to build unmanned combat system, "5G + AI" to drive the intelligent development of unmanned combat system, and "5G + Edge Computing" to enable unmanned combat cluster are proposed, which provide ideas for promoting the application of 5G technology in unmanned combat system.
Building, Importing, and Exporting GEXF Graph Files with rgexf

First introduced in 2012, the rgexf package for the R programming language was the first effort to make the Graph Exchange XML Format (GEXF) (Heymann et al., 2009) specification available to the R world.With more than 500,000 downloads 1 , it is one of the most popular ways to incorporate GEXF files into the R programming language environment.Developed by the Gephi Core Group (Bastian et al., 2009), the GEXF specification is a flexible and widely used format to describe graphs.Although it has not been updated since 2009, the GEXF format has been introduced to several tools and programming environments.A few examples include:
Application of an Improved C4.5 Algorithm in Shopping Websites

Decision tree classification is an effective instance-based learning and data-mining method, which is applied to process the large continuous data to save the processing time. In this paper, on the one hand, a K-equal separation method is presented, which reduces the calculation amount and accelerates the tree building speed. On the other hand, the calculation formula of the information gain ratio in C4.5 algorithm is simplified. The improved C4.5 algorithm is applied to analyze users' purchasing behaviors, including helping recommend them the products that they are interested in, and accelerating them to quickly find those goods they want. Experimental results show that the advanced method can effectively improve the training speed of the model.
Learning representation of heterogeneous temporal graphs for recommendation

Heterogeneous temporal graphs are important abstractions for organising data in recommender systems, for which an effective representation learning method is presented in this paper. Specifically, an attention-based two-stage aggregation technique is adopted to aggregate the message passed over each edge. The aggregation stage first involves temporal aggregation for each group of neighbouring nodes connected by the same type of edges. Then a further aggregation is performed among the aggregated results of each edge type. The method can capture the temporal evolution patterns of heterogeneous temporal graphs in continuous time-space and model the graphs' heterogeneity without requiring predefined meta paths. Experimental results on public recommendation datasets demonstrate that the recommendation algorithm based on the representation learning method outperforms the state-of-the-art baselines.
Intelligent Self-Protection Solution Against COVID-19

With the ongoing crisis, it is important for every human to protect themselves from the virus infection. Even though the enemy is invisible and looks terrifying, it is very well possible to defend it with simple techniques. One among those techniques is to maintain a safe distance when we encounter another human. But, how do we always keep track of this distance of 6 feet apart can always seem like an illusion and there is a high possibility that we may lose track of our position. In this paper, we propose a system which will help in detecting when a human comes in contact with another person less than the recommended distance. As the solution demands advanced methodologies, the present paper proposes mask RCNN-based deep learning architecture in the computer vision domain. This algorithm is able to draw bounding boxes effectively whenever the person comes closer than the preferred social distance.
FreqCounter: Efficient Cacheability of Encryption and Integrity Tree Counters in Secure Processors

The data in the off-chip main memory can be potentially extracted or tampered by an adversary having physical access to the device and thus it becomes inevitable to secure the data present in the off-chip memory. The modern designs consider storing the counters on-chip to prevent replay attacks. However, these designs have significant overheads in terms of the on-chip storage used to store counters, and the additional execution time. In this paper, we propose a new mechanism FreqCounter that trims the on-chip storage by storing the counters blockwise in a tag array on-chip cache rather than in a page-wise manner as is conventionally done. The counters for only those blocks are maintained that are expected to miss frequently in the last level cache (LLC). We show that the FreqCounter reduces the space overheads compared to prior schemes by 53.02% keeping performance almost similar if not better. We further show that our design reduces the ED2 by 14.5% on an average compared to the recent competing scheme Morphable Counters without any compromise in the security.
The Concept of a Natural Object As an Element of the Forensic Databases

A natural object as an element of the system of accounting and registration activities has a dual nature, which determines the multidirectional functional purposes of its use. On the one hand, as an object of reference funds, it conveys a system of properties and attributes inherent in it as in an independent material object. On the other hand, a natural object acts as one of the forms of keeping forensic records, containing important information about a trace-forming object and the mechanism of trace formation. The currently existing theoretical approaches to the deﬁnition of a “natural object” do not fully consider its speciﬁcity and the variety of its constituent elements. The article addresses the etymological, subject, and functional approaches to the essence of a natural object, determining its position and status in the system of forensic databases. The analysed features determine the possibility of using this form of information storage as an object of identiﬁcation, diagnostic and situational expert research. The authors propose a “natural object” deﬁnition as a material source of primary (primary-reﬂected) objective information, containing a set of properties and features resistant to time factors that form derived information. The suggested deﬁnition provides a precise understanding of the considered analysed of a “natural object” for further use in departmental regulatory documents and scientiﬁc and practical works.
A Novel Pandemic Delivery Pattern for a New Digital User Experience Degree Apprenticeship

The teaching and study of User Experience (UX) is relatively new compared to more established fields. Additionally, the interdisciplinary nature of UX may make a consensus on teaching pedagogies and methods difficult to reach. To add to the complexity of teaching UX, is the emergence of the degree apprenticeship programme where apprentices study at a higher education university for 20% of their time while 80% is spent with their employer. This paper describes the process of launching a programme to respond to this unique and emerging context during a global pandemic. It focuses on reflections from lecturers on the challenges and reception of the programme and discusses the initial contributions this work can bring to conceptions of UX pedagogies, especially those delivered in an online delivery environment.
Golf Driving Posture Analysis Using Joint Comparison via Kinect V2 Sensor

This paper covers information about a system that detects the proper golf posture by showing the results in a graphical user interface after the testing which is a joint comparison method using a Kinect V2 as the main sensor. The researchers created a system that uses skeletal imaging to compare two frames in which one frame is the input and the other one is the video showing the depth skeletal image. After comparing the results, the computed correlation coefficient is 0.88 which means the statistical accuracy of the evaluation is approximately 88 percent.
Implementation and Performance Analysis of ECC-Based Text Encryption on Raspberry Pi 3

In recent years, elliptic curve cryptography (ECC) has gained a lot of popularity because it provides a high level of security with smaller key size than other cryptographic techniques as RSA. It has emerged as a solution to the problem of faster operation because of its less computational complexity. Generally, the texts are mapped to the affine or Lopez-Dahab (LD) coordinates. Still, using the hexadecimal values of the text in blocks, the situation could be improved in terms of computational complexity and need to share a standard lookup table between the communicating parties. The present work uses defined ASCII values, binary values, and decimal values of the message in blocks of fixed length, depending upon the chosen curve, to map the text and symbols as points on the elliptic curve for encryption and decryption. The deterministic approach adopted has contributed in making the system immune to many attacks. The technique was implemented on Raspberry Pi 3, and its performance analysis was done. The relation between text size and encryption/decryption time was found approximately linear, which can be interpreted as being more efficient for smaller files as compared to the relatively larger files.
Analysis on the Application of Cryptographic Technology in the Communication Security of Intelligent Networked Vehicles

Intelligent networked vehicles are rapidly developing in intelligence and networking. The communication architecture is becoming more complex, external interfaces are richer, and data types are more complex. Different from the information security of the traditional Internet of Things, the scenarios that need to be met for the security of the Internet of Vehicles are more diverse and the security needs to be more stable. Based on the security technology of traditional Internet of Things, password application is the main protection method to ensure the privacy and non-repudiation of data communication. This article mainly elaborates the application of security protection methods using password-related protection technologies in car-side scenarios and summarizes the security protection recommendations of contemporary connected vehicles in combination with the secure communication architecture of the Internet of Vehicles.
Research on Evaluation of Teaching Skills of Normal Students Based on Text Mining Technology —Take Information Technology as an Example

In the context of the rapid development of artificial intelligence technology, teachers' teaching methods, students' learning habits and behaviors are quietly changing. The application of artificial intelligence to the training of teaching skills for teachers has become a research topic in the training of teaching skills for teachers. one. This research is based on the teaching characteristics of the information technology subject, based on the ideas of grounded theory, combined with the training objectives, the nature of the information technology subject and the teaching rules, to construct a conceptual model of the video teaching behavior of normal students. The model is constructed on the basis of understanding the characteristics of the subject and the teaching rules, and can reflect the teacher's teaching philosophy and teaching implementation process. Based on this model, the teaching behavior tag database extracted from the simulated teaching videos of normal students has laid a certain foundation for subsequent research and AI applications. On this basis, this research uses text mining technology to construct four models: word vector model, behavior classification model, behavior effect classification model and overall effect prediction model to automatically evaluate the simulated teaching situation of normal students.
Lexicase Selection

tutorial Lexicase Selection Authors: Thomas Helmuth Hamilton College Hamilton CollegeView Profile , William La Cava University of Pennsylvania University of PennsylvaniaView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 839–855https://doi.org/10.1145/3449726.3461408Published:08 July 2021Publication History 0citation90DownloadsMetricsTotal Citations0Total Downloads90Last 12 Months26Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access