Reviewerly/exHarmony huggingface dataset:
Rwanda: International Bonds

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 58, Issue 8 p. 23557B-23557B Policy and Practice Rwanda: International Bonds First published: 11 October 2021 https://doi.org/10.1111/j.1467-6346.2021.10214.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume58, Issue8October 2021Pages 23557B-23557B RelatedInformation
Spatial-Temporal Regularized Correlation Filter with Precise State Estimation for Visual Tracking

Recently, the performances of discriminative correlation filter (CF) trackers are getting better and better in visual tracking. In this paper, we propose spatial-temporal regularization with precise state estimation based on discriminative correlation filter (STPSE) in order to achieve more significant tracking performance. First, we consider the continuous change of the object state, using the information from the previous two filters for training the correlation filter model. Here, we train the correlation filter model with the hand-crafted features. Second, we introduce update control in which average peak-to-correlation energy (APCE) and the distance between the object locations obtained by HOG features and hand-crafted features are utilized to detect abnormality of the state around the object. APCE and the distance indicate the reliability of the filter response, thus if abnormality is detected, the proposed method does not update the scale and the object location estimated by the filter response. In the experiment, our tracker (STPSE) achieves significant and real-time performance with only CPU for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).
Cyberspace and Women- Dimensions of Cybercrime against Women in India

The twenty-first century has been an era of inventions. Inventions that have greatly improved the quality of human life. Artificial intelligence's genesis and dominance have been witnessed. We have already entered the 5G era, which began with limited internet access. An alternate reality has emerged as a result of this unstoppable rise. An ethereal reality that promotes complete anonymity. With all of the benefits it provides, it has also proven to be lethal. With the rise of the online world came stalkers, hackers, scammers, and a slew of other miscreants and lawbreakers. As a result, society has become exposed to cybercrime. The researchers will focus on cybercrime perpetrated against women in this study. Women are easy prey for cybercriminals, and they are disproportionately victimized. Cyberbullying, voyeurism, sextortion, and stalking are all common online crimes against women. Women's privacy and security are in jeopardy as a result of the rise in cybercrime. The research's main goal is to examine the current state of cyber security in India and the need to enact specific legislation to protect women. The researchers would show how the laws are not being implemented throughout this study. The most important finding of this study is that more precise regulations and legislation against cybercrime are required. With the rise of social media networks and private websites, it is more important than ever for the government to enact special legislation for each type of crime perpetrated against women. Throughout the course of this study, an analysis of how many crimes is not reported due to traditional society and patriarchal attitudes will be offered. Researchers have also looked into the government's success in combating cybercrime and have come up with some useful suggestions for combating this threat. The researchers used a doctrinal research method and cited their sources using the bluebook method.
Deep Learning Models Evolution Applied to Biomedical Engineering

In the last chapter we studied the underlying principle of “ Deep Learning ” as the compositional nature of the “ neural network ” inspired by the biological elements that form the “ human brain ,” as a collection of “ nodes ” emulating “ brain neurons ” and their “ neuron synapses ” connections as primary elements based on combined primary elements, and their connections to form midlevel elements identified as “ Artificial Neural Networks ( ANN )” such as: “ Feed Forward Neural Network ,” “ Backpropagation Neural Networks ,” “ Shallow neural network ,” their applications using “ Transfer Learning from Pretrained Deep Learning Network and the design of custom “ ANN .” In this chapter we focus on “ Deep Learning Models Evolution ” that combined midlevel elements with different connections of “ ANN ” to form more complex networks types such as: “ Recurrent Neural Networks ,” “ Memory Augmented Neural Networks ,” “ Modular Neural Networks ,” and “ Evolutionary Neural Networks .” Many research examples are studied with regard to biomedical engineering solutions.
An Enhanced Approach to Detect Covid-19 Patients and Level of Infection using FSM

The present situation is this world is pathetic and very uncertain. The killer disease Covid-19 dramatically turn down the regular activities of human beings. The normal life procedure of people is impacted. The society is highly stressed. To identifying covid 19 patients and their level of infection is a challenging task. In this paper proposes a Finite State Machine based classification model to identify the covid 19 patients and their level of infections. This model will help the physicians to diagnose and treat the patients in well advance. The experimental results shows the efficiency of the proposed system.
ON PLASTICITY OF THE TERM ECOSYSTEM

The improvement of the scientific image of the world is closely connected with the development of scientific terminology, with the understanding of its cognitive content. In this process, due to the metaphorical reinterpretation of terms, which is ensured by their semantic plasticity, their reinterpretation or secondary terminologization occurs. Metaphors construct a conceptual system of ordinary and, especially, scientific thinking. The purpose of the study is to highlight the problem of linguistic and cognitive characteristics of terms as a factor in the implementation of interdisciplinary knowledge. The term ecosystem has entered the active circulation of the language of science, where the first element “eco” implies a conceptual connection with ecology. The purpose of the study is to consider the features of the linguocognitive aspect of the term ecosystem. The term ecosystem (écosystème) has a wide range of uses in various scientific fields of knowledge in different national literary languages. Due to its semantic plasticity, it participates in the formation of new term concepts, developing the synergy of scientific thinking. The meaning of a new term does not arise simply as the sum of the values of its term elements, although the meaning of each of them is preserved in it to a certain extent. Thus, the term concept ecosystem has become widespread in various spheres of human activity ((urban ecosystem, marine ecosystem, information ecosystem, entrepreneurship ecosystem, innovation ecosystem, museum ecosystem, narratology ecosystem, media space ecosystem, etc.). Terminology, thus, as a set of definitions of various aspects of scientific knowledge, becomes one of the important sections of cognitive linguistics. A comparative analysis of French and Russian terminology allows us to highlight the problems of not only interdisciplinary connections within one national image of the world, but also to see the peculiarities of the formulation of terms taking into account the specifics of different national languages.
Training Graph Neural Networks by Graphon Estimation

In this work, we propose to train a graph neural network via resampling from a graphon estimate obtained from the underlying network data. More specifically, the graphon or the link probability matrix of the underlying network is first obtained from which a new network will be resampled and used during the training process at each layer. Due to the uncertainty induced from the resampling, it helps mitigate the well-known issue of over-smoothing in a graph neural network (GNN) model. Our framework is general, computationally efficient, and conceptually simple. Another appealing feature of our method is that it requires minimal additional tuning during the training process. Extensive numerical results show that our approach is competitive with and in many cases outperform the other over-smoothing reducing GNN training methods.
Improving In-House Communication About Serials

Numerous departments are involved in the serials processing chain, with functions becoming more inter-related in the online environment. At the University of Maryland at College Park, no one person or individual acted as a central clearing house for planning, implementing, or disseminating information about serials processing. This workshop examines function and process of a continuous quality improvement (CQI) team which studied the issues surrounding processing of serials, as well as the team’s recommendations and their implementation. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
An evaluation of approaches for enhancing inductive learning with a transductive view

Abstract Availability of enough labeled data is a challenge for most inductive learners who try to generalize based on limited labeled dataset. A traditional semi-supervised approach for the same problem attempts to approach it by methods such as wrapping multiple inductive learners on derived pseudo-labels, unsupervised feature extraction or suitable modification of the objective function. In this work, a simple approach is adopted whereby an inductive learner is enhanced by suitably enabling it with a transductive view of the data. The experiments, though conducted on a small dataset, successfully provide few insights i.e. transductive view benefits an inductive learner, a transductive view that considers both attribute and relations is more effective than one that considers either attributes or relations and graph convolution based embedding algorithms effectively captures the information from transductive views compared to popular knowledge embedding approaches.
Deep RNN-based Traffic Analysis Scheme for Detecting Target Applications

Analysis of traffic data using deep learning can have tremendous advantages, such as application detection. In this research, a novel classification learning method is designed where input features and output labels are two-dimensional images with the traffic packet and target applications, respectively. After getting the data, the proposed scheme provides a fast and exact traffic analysis from the produced inferred function by analyzing the training data. The proposed scheme is implemented using a commercial deep long short-term memory system. Simulation-based experiments show that the proposed scheme achieves 99.82% accuracy performance with low complexity. This research can be applied to tools like Wireshark which are traditionally used for packet analysis and monitoring in any given network. With this research, the listener can comprehend the packets better.
Scientific and technical information's restoration in the open access environment through smart search engines

Open access is one of the topics that attracted the researchers interest as it is a turning point for the recovery of technical and scientific information’s recovery which requires a set of tools and technical skills. This study aims to discover the main problems of information’s recovery within open access in addition to the inventory of the most important smart search engines and to know the strategies of information’s recovery. The study adopted the descriptive analytical approach, and came out with a number of important conclusions, the most important are : Searching for scientific and technical information in the open access environment has become a very difficult and the researcher does not know which of them is more useful. Relying on the common ranking of sites, smart search engines in its work, depends on semantic web applications, most notably XML, RDF and ontology, users can quickly find specific search results through smart search engines without having to become experts in search engines or have a well-defined strategies for searching within the open access environment. The study also showed that the semantic scholar search engine deals with open sources more efficiently than traditional search engines through its ability to discover these sources and display them to the beneficiary in a distinctive way.
Bitcoin Price Co-Movements and Culture

This paper analyses the cultural drivers (tightness−looseness and individualism−collectivism) of Bitcoin prices co-movements and exchange shutdowns in 34 major countries around the world over the period 20 July 2010 – 5 February 2020. Under the assumption that investors prefer to use local or international Bitcoin exchanges accepting the local currency to trade, we find that Bitcoin prices co-move more in countries with tighter and more collectivistic cultures. However, greater connectivity between international Bitcoin exchanges in the form of financial openness reduces the impact of the cultural variables on the behaviour of investors and on Bitcoin price co-movements. Further, the probability of Bitcoin exchanges shutdowns is higher in tighter and more collectivistic cultures where investors are more risk-averse and thus more likely to exhibit herding behaviour.
Law reform and employee-invention patents in Nigeria

Research and development is the foundation of innovation. In Nigeria, most researches are embarked on in universities and research institutes, which are public-funded (by the government). This form of funding subjects the researchers to public service rules, resulting in an employment with statutory flavour (governed by statute). An employee is entitled to fair remuneration for inventions made in the course of employment, based on activities regarded as the process of inventing in the Patent and Designs Act (PDA) 1970. Regardless of the absence of a contract of employment on inventive activity, an employee remains reliant on the data and materials provided by the employer for inventions and in some situations the invention could be of exceptional importance. The aim of this work is to identify the relevant rules and regulations regarding inventions by employees reliant on public funds and discuss their impact on inventions in Nigeria. Extant provisions in the United Kingdom on employee-invention patents are considered. Recommendations on reform of patent law in Nigeria, specifically on employee invention in a way that it encourages innovation is provided.
Analysis of online learning during the Covid-19 pandemic in tourism education

The online learning process is considered as a new challenge in the era of the industrial revolution 4.0, especially in a pandemic like today. This relates to the current condition of Indonesia amid the Covid-19 virus outbreak where students are required to study at home. Digital-based learning models have been massively maximized almost throughout Indonesia at the time of the pandemic. The purpose of this research is to analyze digital-based learning models due to the Covid-19 pandemic in the online Tourism Vocational Class in Bandung. The research method used is a quantitative analysis of descriptive data analysis in the form of a questionnaire. The results of the study are expected to be able to answer the problem of this study, which covers the implementation of online learning during the Covid-19 virus pandemic.
Latency and Power Improvement of Hardware Sequences Using Collapse and Evolve Approach: Nature-Inspired Methodology

The main motivation of this work is from the extensive observation in the way tasks are implemented within hardware subsystems to achieve the desired functionality. In most of the scenarios for the completion of a particular computation a predetermined sequence is implemented. This in terminology of subsystem level implementation is called as hardware internal sequences. A lot of the steps which happen within the whole hardware sequence are redundant in nature. The target of optimization in this work is redundancy occurring in hardware sequences. To make the optimization methodology more compatible to the current trend of AI development, a nature-inspired methodology is proposed. The algorithm used here is called as collapse and evolve approach. The focus optimization parameters are latency and power consumed when the whole hardware sequence gets implemented. Nature or the process of evolution of nature is some hundreds of billion years old when compared to human evolution which is some tens of billion years old suggesting natural evolution to be a more advanced flow. So, focus is put on the natural evolution process to extract an efficient technique which can achieve our target optimization within the hardware sequences. The manner in which initial set of species survived and evolved frames the base of the hardware computational algorithm. The work suggested falls in the unison of a very interesting area of AI to hardware and nature-inspired computing which allows the outcomes of the work to be utilized in far more diversified areas. As a part of the study, the proposed algorithm is implemented on the power processing block of digital system.
Digital Place-based Learning

While present in the research literature for almost a decade prior, the release of Pokemon Go! brought place-based interaction to the forefront of the public eye. Spurred forward by a record 21 million players in the first week, place-based augmented reality games have continued to receive attention for both entertainment and learning purposes. The field of place-based learning represents a decade-long effort to situate learning experiences in the physical world while also augmenting that experience with semiotic resources, tasks, and prompts to facilitate learning. This chapter explores place-based language learning and synthesizes previous research done in the area. In addition, it draws on relevant design and implementation principles related to five key issues: (1) Interactivity, (2) Context, (3) Engagement, (4) Learning Features, and (5) Technological Affordances. The chapter concludes with a discussion of future directions for research and implementation.
Locus communis как приглашение к полилогу: реминисценция стихотворения И.-В. Гете «Kennst du das Land» А. К. Толстым и Е. К. Остен-Сакен

The article considers the functioning of locus communis in a universally symbolic topological model of idyll (locus amoenus, genius loci, motif Et in Arcadia ego) using formula constructs based on the material of Goethe’s poem «Kennst du das Land» and its reminiscences in the poetry of A. K. Tolstoy («You know the land where everything breathes abundantly») and E. K. Osten-Saken («You know the land, our blessed land»).
A Word Pair Dataset for Semantic Similarity and Relatedness in Korean Medical Vocabulary: Reference Development and Validation (Preprint)

<sec> <title>BACKGROUND</title> The fact that medical terms require special expertise and are becoming increasingly complex makes it difficult to employ natural language processing techniques in medical informatics. Several human-validated reference standards for medical terms have been developed to evaluate word embedding models using the semantic similarity and relatedness of medical word pairs. However, there are very few reference standards in non-English languages. In addition, because the existing reference standards were developed a long time ago, there is a need to develop an updated standard to represent recent findings in medical sciences. </sec> <sec> <title>OBJECTIVE</title> We propose a new Korean word pair reference set to verify embedding models. </sec> <sec> <title>METHODS</title> From January 2010 to December 2020, 518 medical textbooks, 72,844 health information news, and 15,698 medical research articles were collected, and the top 10,000 medical terms were selected to develop medical word pairs. Attending physicians (n=16) participated in the verification of the developed set with 607 word pairs. </sec> <sec> <title>RESULTS</title> The proportion of word pairs answered by all participants was 90.8% (551/607) for the similarity task and 86.5% (525/605) for the relatedness task. The similarity and relatedness of the word pair showed a high correlation (&lt;i&gt;ρ&lt;/i&gt;=0.70, &lt;i&gt;P&lt;/i&gt;&amp;lt;.001). The intraclass correlation coefficients to assess the interrater agreements of the word pair sets were 0.47 on the similarity task and 0.53 on the relatedness task. The final reference standard was 604 word pairs for the similarity task and 599 word pairs for relatedness, excluding word pairs with answers corresponding to outliers and word pairs that were answered by less than 50% of all the respondents. When FastText models were applied to the final reference standard word pair sets, the embedding models learning medical documents had a higher correlation between the calculated cosine similarity scores compared to human-judged similarity and relatedness scores (namu, &lt;i&gt;ρ&lt;/i&gt;=0.12 vs with medical text for the similarity task, &lt;i&gt;ρ&lt;/i&gt;=0.47; namu, &lt;i&gt;ρ&lt;/i&gt;=0.02 vs with medical text for the relatedness task, &lt;i&gt;ρ&lt;/i&gt;=0.30). </sec> <sec> <title>CONCLUSIONS</title> Korean medical word pair reference standard sets for semantic similarity and relatedness were developed based on medical documents from the past 10 years. It is expected that our word pair reference sets will be actively utilized in the development of medical and multilingual natural language processing technology in the future. </sec> <sec> <title>CLINICALTRIAL</title> <p /> </sec>
Finite-element modeling of vocal fold self-oscillations in interaction with vocal tract: Comparison of incompressible and compressible flow model

Finite-element modeling of self-sustained vocal fold oscillations during voice production has mostly considered the air as incompressible, due to numerical complexity. This study overcomes this limitation and studies the influence of air compressibility on phonatory pressures, flow and vocal fold vibratory characteristics. A two-dimensional finite-element model is used, which incorporates layered vocal fold structure, vocal fold collisions, large deformations of the vocal fold tissue, morphing the fluid mesh according to the vocal fold motion by the arbitrary Lagrangian-Eulerian approach and vocal tract model of Czech vowel [i:] based on data from magnetic resonance images. Unsteady viscous compressible or incompressible airflow is described by the Navier-Stokes equations. An explicit coupling scheme with separated solvers for structure and fluid domain was used for modeling the fluid-structure-acoustic interaction. Results of the simulations show clear differences in the glottal flow and vocal fold vibration waveforms between the incompressible and compressible fluid flow. These results provide the evidence on the existence of the coupling between the vocal tract acoustics and the glottal flow (Level 1 interactions), as well as between the vocal tract acoustics and the vocal fold vibrations (Level 2 interactions).