Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence and Blockchain Driven Beyond 5G Networks: A Review

Swift evolution of novel computing and communication technologies in beyond 5G networks (B5G) opens up the possibilities for advanced techniques to tackle various issues which can not be solved by the existing frameworks. As the number of devices continuously increase, blockchain can provide a secure platform for communication among all the users in the network. Moreover, along with the security, blockchain requires low computation and also provides fast network response. Furthermore, artificial intelligence enhances the ability of devices to learn and construct knowledge about dynamic wireless environments. Recently, a lot of researchers have shown interest in integration of both the platforms to solve the complex problems of B5G networks. This work reviews the application of both the technologies in various networks related problems recently completed by the authors. The work also discusses various possible research issues that can be handled by the integration of both platforms.
PENERAPAN SISTEM INFORMASI PENJUALAN PADA UKM BILQIS DI DESA SANROBONE KECAMATAN SANROBONE KAB. TAKALAR

SMEs are a type of business that is carried out on a small to medium scale. Bilqis UKM is located in Sanrobone Village, Takalar Regency that focuses on sales, namely school clothes, and uniforms where sales are still conventional. With the rapid development of information technology today, we implement a website-based information system for UKM Bilqis as an online sales medium. In this service, we provide assistance in the use of sales information systems both from shop owners in this case Bilqis UKM, and from buyers taken from random samples. The results in this service are a questionnaire used for application assessment that we made with the result was good.
The Role of Female Human Capital in Sustainable Economic Development

Women are half of the world's human capital but it is one of the most underutilized human resources.Better utilization of Women's potential might increment economic growth, decrease poverty, improve social well-being, and guarantee sustainable development and economic growth in the world.Increasing female labour force and using their full potential and skills contribute in organizational and economic development.However, in Pakistan only 22 percent women are in the labour force.Majority of the women's potential is ignored or untapped.Therefore, this article discusses significance of developing female human capital in Pakistan and its relationship with economic development and presents recommendations for improving female labor force rate in Pakistan.
Learning ReLU networks to high uniform accuracy is intractable

Statistical learning theory provides bounds on the necessary number of training samples needed to reach a prescribed accuracy in a learning problem formulated over a given target class. This accuracy is typically measured in terms of a generalization error, that is, an expected value of a given loss function. However, for several applications -- for example in a security-critical context or for problems in the computational sciences -- accuracy in this sense is not sufficient. In such cases, one would like to have guarantees for high accuracy on every input value, that is, with respect to the uniform norm. In this paper we precisely quantify the number of training samples needed for any conceivable training algorithm to guarantee a given uniform accuracy on any learning problem formulated over target classes containing (or consisting of) ReLU neural networks of a prescribed architecture. We prove that, under very general assumptions, the minimal number of training samples for this task scales exponentially both in the depth and the input dimension of the network architecture.
Novel Projection Schemes for Graph-Based Light Field Coding

In light field compression, graph-based coding is powerful to exploit signal redundancy along irregular shapes and obtains good energy compaction. However, apart from high time complexity to process high dimensional graphs, their graph construction method is highly sensitive to the accuracy of disparity information between viewpoints. In real-world light field or synthetic light field generated by computer software, the use of disparity information for super-rays projection might suffer from inaccuracy due to vignetting effect and large disparity between views in the two types of light fields, respectively. This paper introduces two novel projection schemes resulting in less error in disparity information, in which one projection scheme can also significantly reduce computation time for both encoder and decoder. Experimental results show projection quality of super-pixels across views can be considerably enhanced using the proposals, along with rate-distortion performance when compared against original projection scheme and HEVC-based or JPEG Pleno-based coding approaches.
Komparasi Metode K-Nearest Neighbor dan Random Forest Dalam Prediksi Akurasi Klasifikasi Pengobatan Penyakit Kutil

Warts are a skin health problem, usually characterized by small, rough bumps on the surface of the skin caused by a virus, known as the human papillomavirus (HPV). The common way of treating warts is with immunotherapy, which is the treatment of warts by strengthening the body's immune system. In the process of predicting and diagnosing warts, it can be done by applying Machine Learning. This study focuses on the comparison of the K-Nearest Neighbor classification method with Random Forest to see the level of accuracy in predicting the success of the treatment of warts. Data for Immunotheraphy was obtained from the UCI Machine Learning Repository with a total of 90 data records, 7 attributes and 1 attribute class. Based on the results of testing the K-Nearest Neighbor and Random Forest methods to see the accuracy of the prediction of the success of the data being tested, the results obtained are the accuracy of the KNN method of 90.00% and the Random Forest method with an accuracy of 85.50%. From the results obtained from the tests that have been carried out, it is known that the Random Forest method is a better method than K-Nearest Neighbor in predicting accuracy in the Immunotheraphy Dataset.
Penerapan Algoritma Vertical Bit Rotation Dalam Pengamanan Data Sekolah

Cryptography is a science based on mathematical techniques related to security and information such as confidentiality, data integrity and entity authentication. In other words, it means to conduct the process of encoding libraries that act as ciphers, and converting the original text into illegal it. The implementation of encryption process requires a public and a secret key. One of the cryptography techniques applied in the research is the Algorithm of Vertical Bit Rotation (VBR). The VBR is a technique that implements an asymmetric key, which it has a same value in term of data encryption and decryption. The technique is also applied in the shape of an application designed in this research, then have ability to run encryption and decryption process in the format of text file, that providing data security to avoid eavesdropping and illegal access from irresponsible parties. The result done by using VBR is it may run well so have ability to restore the condition of the data in plaintext, then in addition information, encrypted data cannot be returned to its pure form without using a valid key
OTOMASI BAHAN PUSTAKA DI PERPUSTAKAAN TAMAN KANAK-KANAK

The main function of library automation is cataloging which can assist in various processes from creation, updating, copying, retrieval and record management. Library collection activities are one form of processing library collections. In this study, the main focus is on the process of automation of library materials for the Pertiwi 1 Kindergarten library using the c application. In addition, the author also describes the condition of the Pertiwi 1 Kindergarten library and also the use of the library for the development of the Pertiwi 1 Kindergarten children's reading interest. The research method used in this article is a descriptive qualitative method and the data collection techniques used are interviews and literature studies.
The Prediction of Hypertension Risk

Abstract This article presents an estimation of the hypertension risk based on a dataset on 1007 individuals. The application of a Tobit Model shows that “ Hypertension ” is positively associated to “ Age ”, “ BMI-Body Mass Index ”, and “ Heart Rate ”. The data show that the element that has the greatest impact in determining inflation risk is “ BMI-Body Mass Index ”. An analysis was then carried out using the fuzzy c-Means algorithm optimized with the use of the Silhouette coefficient. The result shows that the optimal number of clusters is 9. A comparison was then made between eight different machine-learning algorithms for predicting the value of the Hypertension Risk. The best performing algorithm is the Gradient Boosted Trees Regression according to the analyzed dataset. The results show that there are 37 individuals who have a predicted hypertension value greater than 0.75, 35 individuals who have a predicted hypertension value between 0.5 and 0.75, while 227 individuals have a hypertension value between 0.0 and 0.5 units.
Cryptocurrency and Its Impact on Different System

The fame of cryptocurrencies soars in 2017 because of a few consecutive months of the exponential development of their market capitalization. Even though machine learning has been fruitful in anticipating stock market costs through a large group of various time series models, its application in foreseeing cryptocurrency costs has been very prohibitive. The reason behind this is clear as the costs of cryptocurrencies rely upon a ton of factors like technological progress, internal competition, pressure on the markets to deliver, economic problems, security issues, political factors and so on Their high volatility prompts the incredible capability of high benefit if savvy designing systems are taken. Sadly, because of their absence of lists, cryptocurrencies are somewhat capricious contrasted with traditional financial predictions like stock market predictions. The proposed paper describes how Cryptocurrency works, its use, legal prospect, security and what is the technology behind it
The Effect of Short Story Extensive-Reading on EFL Reading Attitudes of the Secondary School Student

This study intends to investigate the effect of short story extensive reading on secondary school students’ EFL reading attitudes. This is an experimental study using a one-grouppretest-treatment-posttest design. The sample was 35 students, who were taken randomly from 355 Students of twelve classes in the second grade of SMAN 4 Samarinda. The researchers used a questionnaire as the instrument in this study. The questionnaire was used to obtain the data on students’ reading attitudes. To analyze the data, a series of paired t-tests and descriptive statistics were used. The results revealed that firstly, overall aspect, the short story-extensive reading gave an effect on the EFL secondary school students’ reading attitudes, t (34)= -1.229, p=0.003. Secondly, in detail per an aspect of attitude, the short story-extensive reading gave an effect on the comfort aspect in the students’ reading attitudes, t (34)= -3.610, p=0.001, the linguistic value aspect, t (34)= 0.463, p=0.646, and the anxiety aspect, t (34)= -3.762, p=0.001; In the meantime, the short story-extensive reading did not give an effect on the intellectual value aspect in the students’ reading attitudes, t (34)= -0.269, p=0.789 and the practical value aspect, t (34)= -0.179, p=0.859. Reading short stories brings about the students into a relaxed circumstance from which EFL secondary school students can browse ideas freely and excitingly. This study implies that such reading materials can be used by secondary school EFL teachers to give more reading input-rich circumstances, which finally becomes an enhancement of their reading attitudes.&#x0D; Keywords:EFL Reading attitude, Secondary school student, E-short story extensive-reading
Sequelas pós Covid - 19 em pacientes em internamento / Post- Covid - 19 sequels in hospitalized patients

Introduction: The Covid-19 pandemic took the entire population by surprise in 2020, which left the entire health system in chaos, causing hospital overcrowding and exhaustion of the health team, causing deaths and bringing comorbidities to the sick, the symptoms caused by this disease are numerous as well as the sequelae after hospitalization.Objective: This study aims to analyze the sequelae to which patients were affected after the period of hospitalization for covid-19.Methodology: This is an integrative literature review study that has a quantitative and descriptive character.Results and discussions: The covid-19 presented itself as a disease that affects the human being in the long term and that can leave sequels, such as: headaches, loss of taste and smell (temporary or lasting), difficulty breathing and even fibrosis in the lungs.Conclusion: the post-hospitalization sequelae caused by covid-19 can cause a low quality of
Multilingual Text Mining Based Open Source Emotional Intelligence

The purpose of this study is to learn how people who speak different languages interpret the same issues, and to compare the results obtained and show the difference between their perspectives. To learn this point of view, we must first turn to open source intelligence. In this execution, a sentiment analysis application was designed using the Python programming language and the Natural Language Processing algorithms in the texts, which were taken as a data set of comments in Azerbaijani, Turkish, Russian and English languages from social media. As the data set, the comments made on 4 subjects: the declaration of Hagia Sophia as a mosque, the objection events that started with the natural gas hike in Kazakhstan, the natural disasters in Turkey, the Ukraine crisis. After loading the texts in four languages from the network environment, after preprocessing, the text was divided into 8 different categories (neutral, fear, joy, anger, sadness, surprise, disgust, shame) by means of the application written in Python programming language based on Data Mining and Machine Learning topics. In the study, precision, sensitivity, accuracy and F1 score were obtained by using Random Decision Forests, K - Near Neighbor Algorithm, Decision Trees, Support Vector Machine, Naive Bayes Algorithm, Logistic Regression, which are machine learning methods. By comparing the results, it was determined that the Logistic Regression method obtained the highest result. A sentiment analysis model was created using the Logistic Regression method, and sentiment analysis was performed for each subject at separation and the results were compared.
Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer

Learning Bird's Eye View (BEV) representation from surrounding-view cameras is of great importance for autonomous driving. In this work, we propose a Geometry-guided Kernel Transformer (GKT), a novel 2D-to-BEV representation learning mechanism. GKT leverages the geometric priors to guide the transformer to focus on discriminative regions and unfolds kernel features to generate BEV representation. For fast inference, we further introduce a look-up table (LUT) indexing method to get rid of the camera's calibrated parameters at runtime. GKT can run at $72.3$ FPS on 3090 GPU / $45.6$ FPS on 2080ti GPU and is robust to the camera deviation and the predefined BEV height. And GKT achieves the state-of-the-art real-time segmentation results, i.e., 38.0 mIoU (100m$\times$100m perception range at a 0.5m resolution) on the nuScenes val set. Given the efficiency, effectiveness, and robustness, GKT has great practical values in autopilot scenarios, especially for real-time running systems. Code and models will be available at \url{https://github.com/hustvl/GKT}.
21st CENTURY SKILLS IN TEACHING

The modern concept of education implies that the teacher should possess not only the general knowledge on the subject and core pedagogical competencies, but also universal competencies, also called “21st century skills”. The article discusses communication, collaboration and leadership, reflectivity and creativity, and other professional competencies that are important and relevant in teaching. The most important task of preparing an effective teacher is not only personal development of a teacher, but also a fundamental condition for an effective teaching and educational process.
Promethee for Detecting Cancer in Wireless Body Area Network

Whenever thought about mass deaths of people, it was either from a flood, drought, or an explosion caused by a missile or bomb, but these days the significant risk to the human lives from the disease named “Cancer”. Cancer is largest cause of death in the world . To prevent the Cancer Disease from disseminate, regular identifying and monitoring of infected patients is needed. In this regard, Wireless Body Area Network (WBAN) can be used in conjunction with the Internet of Things (IoT) and Multi-Criteria Decision- Making Method (MCDM) for identifying and monitoring the human body for health-related information, which in turn can aid in early diagnosis of diseases. WBAN makes use of the biosensors in, on or around the body, to ensure the measurement of the biological specifications of the human body, which are designed for the healthcare application. This paper proposes a novel WBAN framework for detection of an early stage of Cancer which based on IoT to receive the biosensor signals from the patient and evaluate symptoms of disease and help healthcare professionals to improve and enhance cancer treatment . Due to resemblance of Cancer symptoms with several other diseases, this paper also presents the Promethee MCDM to help medical professionals identify the disease that the patient might be suffering from or suffer without Cancer. This methodology relies on collected information from the proposed WBAN framework, and it can deal with the complexity of imprecision and confusion arising from the relative priority scales of various disease symptoms. Keywords: Cancer; Wireless Body Area Network; Internet of Things; Multi-Criteria Decision-Making; Preference ranking organization method for Enrichment Evaluation
Augmented Reality and Education: Digital Transformation in Primary School

Technology is driving the aspects of our professional and personal applications through the use of devices and computers. It has impacted the way businesses operate and started changing the way education system works. During the pandemic technology has supported education online as well as disrupted the traditional way of teaching and learning. Education technology companies have provided multiple ways to deliver education. One of the education technologies that is driving digital transformation is augmented reality. Augmented Reality has been gaining popularity at different levels of education and used to explain information visually and in a 3D format. The strongest side of augmented reality is the visual representation of curricula and this can support different learning styles and pedagogical approaches. This study provides a literature overview on how augmented reality has created a digital transformation in the primary education sector through observations and analysist of existing peer to peer reviewed publications as well as provides further research suggestions.
Perturbative methods for mostly monotonic probabilistic satisfiability problems

The probabilistic satisfiability of a logical expression is a fundamental concept known as the partition function in statistical physics and field theory, an evaluation of a related graph's Tutte polynomial in mathematics, and the Moore-Shannon network reliability of that graph in engineering. It is the crucial element for decision-making under uncertainty. Not surprisingly, it is provably hard to compute exactly or even to approximate. Many of these applications are concerned only with a subset of problems for which the solutions are monotonic functions. Here we extend the weak- and strong-coupling methods of statistical physics to heterogeneous satisfiability problems and introduce a novel approach to constructing lower and upper bounds on the approximation error for monotonic problems. These bounds combine information from both perturbative analyses to produce bounds that are tight in the sense that they are saturated by some problem instance that is compatible with all the information contained in either approximation.
A Discrete Mathematical Model of the Variable State of the Pandemic

The paper describes and analyzes a discrete mathematical model of the variable state of the pandemic, which is important for determining production quantities of vaccines and antiviral drugs, predicting the number of infected persons, and the intensity of the process of disseminating information or new ideas to the public. According to the system of differential equations of the pandemic, a discrete mathematical model in vector-matrix form was developed and the equilibrium of the model in the space state was proved. As a result of the implementation of the pandemic model, the discrete dynamic curves of the variable state were obtained in a Matlab package.
Societal Impacts of Cyber Security in Academic Literature – Systematic Literature Review

The 2020 Allianz Risk Barometer, with 39% of responses, ranked cyber incidents as the number one risk threatening business continuity. Any organisation may face a number of challenges e.g. costly data breaches, ransomware incidents, and even litigation after an event. The Internet has, in many ways, changed society, transformed businesses, organisational communication and learning. People can now interact through social networking platforms. Modern society has become very technology driven, as ICT is now an integral component in peoples’ lives. However, besides the many benefits that the Internet and other ICT technology bring, there are also threats, such as cyber-attacks looking to exploit vulnerabilities in ICT applications and systems. This study is a systematic literature review that explores how societal impacts of cyber security in modern society are discussed in academic literature. The Introduction discusses the overall importance of cyber security in today’s society. The body of this paper presents the method in which the literature review was conducted, and a concise summary of the findings that answer the research question: How are societal impacts of cyber security discussed in academic literature? Six categories of investigation of societal impacts of cyber security are identified: 1) Impacts on Social and Societal Levels, 2) Detection of cyber-crime and incidents, 3) Critical infrastructures and services, 4) Impacts of incidents and individual technology, 5) Cybersecurity awareness, and 6) Cybersecurity and collaboration. Lastly, the conclusions, based on the research findings, address the feasibility, impact, strengths, weaknesses and possible ethical concerns of cybersecurity. This paper contributes to the overall understanding of current societal impacts of cyber security, and this understanding benefits the development of methods that assess societal impacts, as well as provides focus for future training and development of cyber and e-skills needed for better awareness of cyber threats, and to better address possible cyber incidents.