Reviewerly/exHarmony huggingface dataset:
Scalar Multiplication via Elliptic Net using Generalized Equivalent Sequences

Chord and tangent is a classical method to calculate the elliptic curve scalar multiplication. Alternatively, the scalar multiplication can be calculated by dividing polynomials over certain finite fields and the first elliptic net scalar multiplication was implemented on a short Weierstrass curve. The net was originated from non-linear recurrence sequences, namely as elliptic divisibility sequence. It is well known that the linear recurrence sequences have been applied in the cryptosystem as a cipher in the encryption and decryption process. From the perspective of cryptographic application, the elliptic divisibility sequence is used generally for integer factorization, solving elliptic curve discrete logarithm problem and computation of pairing or scalar multiplication. But there is a lack of contribution of these non-linear recurrence sequences in scalar multiplication. Therefore, this paper aims to discuss a generalization of the equivalent sequence of elliptic divisibility for computing scalar multiplication. The experimental results of scalar multiplication via the net and its coding in computer programming are presented. The future direction of scalar multiplication via the elliptic net is also discussed.
Electric Power Facility Patent Trend Analysis and a Study on Emerging Technology Trends

The electric power facility industry were started with all transmission and distribution devices, from power plants that produce electricity to consumers who use electricity. Major domestic power companies are concentrating on decarbonization, decentralization, digitalization, deregulation, and energy democratization as the policy direction to implement the decarbonization strategy. Although it seems a bit complicated, the reality is that the strategy of increasing the proportion of new and renewable power generation through the expansion of new and renewable business is the main issue. Therefore, as the main DC power source of renewable energy sources is connected to the transmission and distribution system, it is expected that the investment related to the safe system operation and DC facility will continue to increase. In this regard, the renewable and DC-related power facility industry is also expected to grow together. In this paper, based on patent information, the recent patent trends in the electric power facility field of IP5 countries in the United States, Europe, Japan, China, and Korea were analyzed quantitative and qualitative, respectively. The emerging technology trends were analyzed through activity and technology index analysis.
Demand for Competence-based Education for Worker of Public Health Care Service in Ulsan Metropolitan City

Introduction: In order to strengthen the core competencies of workers, systematic education tailored to their needs is necessary. In this study, a survey was conducted on workers in public health care service in Ulsan Metropolitan City (Ulsan) to investigate the demand for education according to core competencies.Methods: A total of 70 workers who work for public health care service in Ulsan participated in this online survey. The questionnaire consisted of socio-demographic factors, work ability, education demand, and preference of education form.Results: The core competency with a high level of work ability is ‘Expertise on health and disease’ (41, 58.6%). On the other hand, the core competency with a low level of work ability was found to be the ‘Evaluation-related theories of public health care service’ (57, 81.4%). The core competencies with the highest demand for education were “Resident-centered service implementation” and “Public health care service strategy development” (64, 91.4%), followed by ‘Public health care service cases review’ and ‘knowledge of public health service plan’ (63, 90.0%). The preferred form of education is offline education (49, 40.8%). The most important factor in education was ‘work utilization’ (Offline: 57, 81.4%; Online: 48, 68.6%), both online and offline.Conclusions: Through the research results, it was possible to find out education demand according to core competencies and preference of education form. Based on these results, we will develop a core competency education program tailored to actual demand. In the future, it is necessary to continuously conduct research on such education demand.
Simulating satellite quantum key distribution links: analytical model and software tool

We present a software tool able to simulate a given satellite-to-ground Quan- tum Key Distribution link. The software was designed according to a custom developed analytical model able to compute the secret key rate.
Escalas do tempo

Viral epidemics are processes in which temporality obviously constitutes an essential variable. But different time scales must be distinguished. To see the current pandemic as a singular event is but an illusion due to the “mesoscopic” timescale we are embracing. There is a microscopic scale — that of physiological processes —, a mesoscopic scale, which only allows to see the closest evidence, and a macroscopic scale, that of the ecological determinisms which explain the emergence of the disease in the history of the relationships between species. The article focuses on the mesoscopic level and highlights some semiotic specificities of today’s experience : a temporal suspension, the threat of pure, dramatic and final discontinuity, the behavior of a virus that appears to have “intentionality”, a strong intensity coupled with a long duration, a time of exception, drawn to a final end, and a victory which will only be achieved with great effort.
Multi-scene Classification of Blockchain Encrypted Traffic

The functions carried by the blockchain are gradually increasing, and the types of services are diverse. Users use blockchain by synchronizing block data, query balances or trade through the trading platform, use decentralized applications (DApps), which are deployed on different blockchains. Blockchain uses different proprietary encryption protocols to let users communicate with others. The trading platform and DApps adopt SSL/TLS encryption protocol, making blockchain’s encrypted traffic classification difficult. In this paper, we conduct a multi-scene classification of blockchain, including synchronizing block data, user behavior on DApps, and user behavior on the trading platform. We use deep learning and machine learning to solve different problems and verify the methods through the real traffic data because of the different encrypted protocols. The results show that our methods can perform well in the multi-scene classification of blockchain encrypted traffic.
Electronic Surveillance System for COVID-19 Virus Management in Palestine

This research investigated the performance of the electronic surveillance system of COVID-19 and assessed its key attributes. The research results for the overall system performance were good (82.81%). The highest attribute score was 100% for representativeness and data completeness and the lowest score was 75.30% for acceptability. The COVID-19 surveillance system is generally simple and accepted by users, although the instability of electricity and the internet, the benefit from the training on the system, and the lack of willingness to participate in the system at the health facility level had the greatest impact on simplicity and acceptability scores. The quality and completeness of the data enabled stakeholders to carry out the most effective prevention and control activities. System developers indicated that the system has achieved the desired benefit, due to the flexibility and stability of the system and comprehensiveness of geographical coverage.
Lightweight multiple scale-patch dehazing network for real-world hazy image

Image dehazing is an ill-posed problem which is far from being solved.Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets.The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements.Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry.To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper.Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network.Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multipatch structure was employed for MSP as an information supplement.In the algorithm image processing module, the image is segmented up and down for processed separately.Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets.Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing.The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s.Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the stateof-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.
Comparative Analysis of Different Disinfection Techniques Performances in Drinking Water Treatment Plant Using a Process Simulation Software Hayder Mohammed Issa

The performance of chlorination disinfection in drinking water treatment is facing challenges with the potentiality to produce disinfection by-products. Seeking safer drinking water quality is related to increasing demands by consumers who are looking for better assurances about the quality of the water they drink. In this study, a water treatment process simulator of WatPro 4.0 was used under certain influent conditions to evaluate possibilities for alternating an existing chlorination disinfection process in a drinking water treatment plant (DWTP-Efraz 2) in March 2014 at Erbil, North Iraq. The evaluation was made in terms of effluent water quality and disinfection by-products formation potentials. The studied disinfection processes are; chlorination, ozonation, and ultraviolet. In the chlorination process simulation, the predicted effluent water quality was verified by the analysis and testing results of the effluent treated water goes out from the plant (Efraz-2). Results showed that each of the studied disinfection processes has its own environmental and limitation pros and cons. Ultraviolet disinfection process meets the required standards of the lowest potential of disinfection by-products. Ozonation included a lower risk than chlorination in the formation of disinfection by-products from the dissolved organic matter. The other parameters of water quality showed close characteristics for all the three disinfection processes. The results obtained in this work gives further insight into the performance of water treatment plant design and encourage the application of water treatment simulators as tools for disinfection process development.
براہوئی گرامرٹی اسم مصدر نا لسانیاتی او تحقیقی مطالعہ ئس

Verb also has as much importance in Brahui language as in other languages of the world. Verb is a word class, traditionally defined as a 'doing' or 'action' word, which ends with "ing" in Brahui language. It is used as singular like "Makhing" (To laugh), if the second syllable "ing" is omitted, then "makh" (laugh.) remains, which clearly is used as imperative. By adding suffix or infix with imperative, interrogative and negative forms can be made. Verb is the reason of enhancement of Brahui dictionary. Due to the importance and status of verb in Brahui language, its linguistic and research study has been conducted with reference to Brahui grammar.
Regression Model Method for Analyze the Association Rules using Major Parameters

Using the data mining user can extract the information. Frequent itemsets is one of the popular task in data mining. Association Rule Analysis is the task of discovering association rules that occur frequently in a given large data set.The task is to find certain relationships among a set of itemsets in the database. There are two fundamental parameter(measurement) is Support and Confidence.Traditional association rule mining techniques employ predefined support and confidence values. But, it’s observed that specifying minimum support value of the minded rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system.This paper proposes a non-linear regression model using support, confidence and association rules. To predict the number of rules under the given explanatory variables say parameters. Use the R language for the Rules generations and also uses significance test to verify regression coefficients. Using the coefficient test and F-test verify the model
Report on Teaching Innovation Achievements of Pedagogy Foundation Course

Pedagogy foundation; Course; Teaching innovation achievements
Converging Artificial Intelligence in Indian Banking Business – An Overview

The endeavour of scientists to infuse human intelligence into machine, created artificial intelligence, that enables machine to think and act like human. This technology of artificial intelligence which primarily works on machine learning and deep learning is an extension to the computer science technology and is widely adopted in various avenues and banking sector is no exception. In this paper attempt is made to analyze the extent of artificial intelligence adopted in the Indian banking sector. The study is based on content analysis of disclosure made in annual report regarding application of artificial intelligence by the 29 public and private sector commercial banks in their different business activities. The study reveals virtual interaction with customers and detection and prevention of fraud and cyber security threats are the areas where artificial intelligence is largely adopted by the banking sector.
Introduction

In this introductory chapter, we provide an overview of machine learning towards enabling wireless systems. The current challenges in realizing machine learning-enabled wireless systems are also presented. Additionally, we present distributed machine learning schemes. Finally, an overview of federated learning and the organization of the book is presented.
PHISHING, TRUST AND HUMAN WELLBEING

The more time we spend online, the more our global connections grow and frighteningly, the more we are susceptible to cyberattacks.In particular, 2020/ 2021 has seen a significant increase in successful phishing attacks mainly due to the rise in human vulnerabilities caused by the global COVID-19 situation.In this paper, we consider the human factors of the phishing attack.We explore the evolution of the phishing attack with particular reference to its many different guises.Following that, the paper discusses the concept of trust and the human trust issues resulting from a phishing attack; it will focus on human vulnerability and the idea of phishing awareness.The findings from the study show that people feel that they can confidently recognize a phishing attack but it also highlights how personal attitudes and situational influences can make certain online users more susceptible than others.The findings also clearly detail how the phishing attack makes them and others feel and they assertively suggest ways to counter the attack.After portraying this picture, the question lies around the degree to which the everyday person is adequately equipped to psychologically deal with the sophisticated and contemporary phishing attack, and whether their thinking and methods of detection/ coping are so outdated that this (in itself) is creating an even greater risk.
Digital information marketing initiatives

Purpose: The purpose of this paper is to analyze the digital marketing initiatives taken up by the central universities of northeast India through their library websites to promote the use of their information products and services by their clienteles.Methodology: For the present study, websites of nine central universities of northeast India have been surveyed for evaluation. All features of the websites have been examined. A checklist has been designed for collecting the necessary data. To analyze the results, simple statistical calculations were used.Findings: It is found that the majority of the library websites (66.69%) have a direct link to the parent website, and they provide general information, such as library rules, library team/staff, about library, library staff directory, timing, library notices, news, and events. With regard to language, 77.77% of the websites used both English and Hindi languages. The majority of them also provide information about services such as OPAC, internet access, circulation, membership, current awareness service, reprographic service, anti-plagiarism, library orientation, and newsletter. However, the University Libraries under study are yet to exploit the full potential of the website as the clienteles are lagging behind the effective use of the same. The use of social networking sites by the libraries, as the study shows, is also not much encouraging.Research limitations: The paper is limited to nine central university libraries of northeast India. Researchers in the future can take up other problems associated with private and state-owned universities in this regard.Practical implications: Central universities of northeast India must come forward to improve their library websites featuring other portals and provisions to enhance in marketing their information products and other services in the digital environment to serve the community effectively.
Recent Progress in Coherent Ising Machines

In this talk we will discuss various recent results on coherent Ising machines (CIM): the quantum principles, new application as heuristic algorithms on current digital platform and energy-to-solution.
A Study on the Direction to Prepare the Korean-English Encyclopedia of Korean Culture

This study aimed to prepare a Korean-English Encyclopedia of Korean Culture that will contribute to building knowledge about Korea and expanding the base of Korean studies. As a primary reference material used in education academic research, an encyclopedia is a storage of basic information and general knowledge. However, while knowledge encyclopedias related to Korean culture are subdivided and compiled into specialized fields, those focused on universal knowledge are rare. Considering the diversity of domestic and foreign approaches to Korean studies, it is essential to prepare generalized basic data to increase the number of subsequent generations choosing Korean studies and public interest in the culture. This study aimed to present the theoretical paradigm, character, and principles to be internalized to create this encyclopedia. To set the direction of the knowledge about Korean culture that should be systematized, this study ensured that there is cultural essence in the historically accumulated symbolic meanings, and the systematization is consistent with the establishment of the Korean cultural tradition. Moreover, the encyclopedia should have general and core knowledge, step-by-step organization, and self-reference of synchrony and diachrony as basic characteristics. After revealing the theoretical basis and nature of the Korean–English Encyclopedia of Korean Culture, this study presented the principle of constructing the dictionary and writing the title. Korean– English Encyclopedia of Korean Culture does not materialize only by setting directions. However, this study is significant as it presented a blueprint for the basic book that will contribute to the quantitative and qualitative development of Korean studies by attempting to combine culture and humanities. Future studies should examine specific issues required in the execution of compilation.
Experimental enhancement of non-classical bosonic correlations via disordered Quantum Walk

We experimentally implemented a controllable inhomogenous Quantum Walk evolution of two photon states. Through the imposition of suitable disorder configurations, we observed an enhance-ment in quantum correlations of the system, compared to the ordered case.
Bayesian Inverse Problems

Many problems in science and engineering consist of estimating some unknown parameters of a physical system from limited and noisy observations. In practical conditions, these problems are often unstable; hence, they should be treated in an appropriate framework in the theory of inverse problems. The regularisation framework for inverse problems is concerned with the stabilisation of unstable problems. In this framework, the unknown is taken as a deterministic multi-dimensional quantity and the observation error is taken to be unknown without any probabilistic structure. In the Bayesian framework for inverse problems, which is the focus of this talk, the unknown and observation error are explicitly taken and modelled as jointly distributed random variables. One of the advantages of the Bayesian framework is that it can quantify the uncertainty of a point estimate for the unknown, unlike the deterministic framework. This framework can be used for many interesting inverse problems. A couple of them will be discussed in this short talk.
Human Factors in Model Interpretability

The previous chapters discussed properties and assessment criteria for interpretability methods; however, keep in mind that explanations are designed for humans. Hence, the human factor in designing explanations is very important.
A Novel Approach to Improve the Training of Deep Convolutional Networks

The deep convolutional networks have given a big leap towards classifying images. With the advent of GPUs and TPUs the training of large convolutional networks has become very simple. Due to this, the training time for classifying large set of images comes down from days to hours. However, a significant amount of research has taken place in improving network’s performance in view of test accuracy, training accuracy and validation accuracy. In this article, we are proposing a novel approach to improve the test accuracy and validation accuracy of deep convolutional networks. The classification task is the basic aim of any deep neural network. We do take some training samples where each sample is associated with a label and then we give a new training sample to predict the class label for it. This is known as supervised learning. The proposed idea simulates human intuition that children learn classifying objects easily in the order that are more contrasting in expose or outlook of the image and they find difficulty in learning in order of images that are most similar to each other. So, based on this intuition, we take the average of all images of class labels in training set for a specific class and we repeat this for finding average images for all classes. Then we form the order of training samples in the descending order of dissimilarity of classes. Now, the convolutional network is trained in this order of classes. We compared our approach with ascending order of dissimilarity of classes and random order of training samples. Results showed that training involved in descending order of dissimilarity of classes yields better testing accuracy compared to other two approaches.