Reviewerly/exHarmony huggingface dataset:
Implementasi Algoritma Apriori terhadap Kombinasi Pola Penjualan Kuliner Sidamanik Square

Data mining is an artificial intelligence that is used to process a lot of data. In data mining, there is one algorithm, namely the Apriori algorithm, which is the most famous algorithm for finding high frequency patterns. For this reason, this algorithm can combine items or items sold at Sidamanik Square Culinary Tour. The resulting apriori algorithm will be implemented and tested with the Tanagra application. This research was conducted to obtain a combination pattern of purchases at Sidamanik Square which is useful for sellers to find out patterns that affect sales and merchandise inventory, namely Penyet Chicken + Rice and Bakso Bakar, namely 60% and Chicken Satay and Bakso Bakar, namely 55.56%.
Modular Platform for Detecting and Classifying Phishing Websites Using Cyber Threat Intelligence

Phishing attacks are deceptive types of social engineering techniques that attackers use to imitate genuine websites in order to steal the login credentials and private data of the end-users. The continued success of these attacks is heavily attributed to the prolific adoption of online services and the lack of proper training to foster a security awareness mindset of online users. In addition to the financial and reputational damages caused by data breaches of individual users and businesses, cyber adversaries can further use the leaked data for various malicious purposes. In this work, a modular platform was introduced that facilitates accurate detection and automatic evaluation of websites visited by employees of a company or organization. The basis for this approach is a preceding website analysis, which is essential when hunting for potential threats from proxy logs. The platform contains three modules. Characterization of suspicious websites relies on a set of pre-defined features and a multi-stage threat intelligence technique, the functionality of which has been ascertained in initial tests on real data sets
Upaya Meningkatkan Keterampilan Membaca Anak Kelas 1 SD Muhammadiyah 26 dengan Metode Quantum Learning dalam Pembelajaran Daring

The corona virus pandemic in Indonesia has greatly affected the learning education system. People are being forced to switch to online learning. Many obstacles in the process of students absorbing the knowledge, especially kids who have not been able to concentrate on a long time facing the screen because their age is happy to move actively. The purpose of this study is how to improve students' reading skills through the quantum teaching method while studying online. This classroom action research was held at SD Muhammadiyah 26 Surabaya with 27 students in class 1C. Data collection method is using classroom action research on online learning through meetings using the Teams. The implementation of cycle I the average value of the students' reading ability 51,85% students do not receive standart value. After cyclus II the average value contains 82% students succed and only 18% students don’t receive standart value and. Based on the data analysis, the use of the quantum teaching method can improve reading skills in IC class students of SD Muhammadiyah 26 Surabaya because the atmosphere when reading is designed more fun and memorable for children and reading activities will increase the brain's working power and improve their skill reading.
The Audience: Developers

The target audience of developers is of course one of the main differentiators of Developer Relations. Identifying the right target for your program and engaging developers throughout their journey is your challenge. Success requires you and your team to be experts in their field as well as having the knowledge and appreciation of developers.
Timbral Heuristics and the Sonorous Presence of Steven Jesse Bernstein

In recent years, music analysts have grappled with the sonic strategies from popular expressions that evade traditional notation. Their approaches often rely on harmonic spectrographs or various textual tools to decode the creative mechanics of these art forms. But for many practices with innate musicality—such as spoken-word poetry—these common techniques make limited explanatory headway. This article proposes an alternate path to fill the gaps: Adopt an analytic perspective, grounded in phenomenology, that listens for the musical subject’s negotiation of embodiment through their calculated treatment of timbre in the voice. Here, the analyst traces their perception of the subject’s bodily resonance through diagrams called timbral maps. And through these maps, two key concepts are discovered that structure the creator’s interior logic: timbral surfaces and timbral moments. Surfaces and moments are built into recognizable patterns, which in turn disclose the methods of these artists as lucid on their own terms. This “surface-moment” model is prototyped using a recorded performance of “This Clouded Heart” by the grunge-era Seattle poet and performance artist Steven Jesse Bernstein. The model reveals several stylistic tactics honed by Bernstein through his play with resonant shifts, but more significantly, argues for recasting timbre in analytic contexts: first, as a sustained and winding musical dimension, able to unfurl like other large-scale organizing principles; and second, as a heuristic capable of engaging listeners in an empathetic web between themselves and the subject through the mimetic connection of their bodies.
UKT (Single Tuition) Classification Prediction uses MKNN (K-Nearest Neighbor Modification) algorithm

Islamic University of Sultan Syarif Kasim (UIN SUSKA) Riau Province has used an information system, namely the Sistem Registrasi (SIREG) to facilitate the registration of prospective students and also SIREG also provides a decision on determining the UKT of students who have been declared graduated. But there has never been an evaluation of SIREG's accuracy in determining the UKT. From these problems, a model is needed to be implemented to facilitate SIREG officers in conducting classifications to establish UKT new students. Using the MKNN method and supported by the K-Fold Cross Validation validation method, the classification results get an accuracy value of 71%
Amidst the Global Pandemic: Determining the Relationship ofAwareness to E-Learning and Academic Interest among Senior High School Students in St. Mary’s College of Bansalan, Incorporated

This study was conducted to measure the level of awareness to e-learning and academic interest of senior high school students in St. Mary’s College of Bansalan, Inc. (SMCBI), and to determine the significant relationship present between the two variables. This study used descriptive correlational method as research design with the statistical tools of relative frequency, mean, ANOVA, and Pearson correlation coefficient r. Two adapted questionnaires were utilized during the data collection. The gathered information were analysed and interpreted by a statistician and explained further by the researchers. The survey was virtually administered to 151 senior high school students of SMCBI using Google Forms software. The survey results showed that the levels of awareness to e-learning and academic interest are moderate. There is no significant difference on the level of both awareness to e-learning and academic interest according to gender, age group, and SHS program; but results showed a significant difference on both variables according to year level. Awareness to e-learning and academic interest were found to have a strong positive correlation. Utilizing the findings of the present research study, recommendations were also discussed.
The Governance or Otherwise of Cryptocurrency in the Sales of Goods Act, 1893

There have been controversies on whether cryptocurrency constitutes a form of money; and if yes, can it be used to purchase goods. This question further proceeds to whether or not the goods purchases can be governed by the Sales of Goods, Act. In this article, it posits that cryptocurency is a money, albeit digital.
“Vehicular Steganography”?: Opportunities and Challenges

What if an autonomous vehicle (AV) could secretly warn of potential threats? “Steganography”, the hiding of messages, is a vital way for vulnerable populations to communicate securely and get help. Here, we shine light on the concept of vehicular steganography (VS) using a speculative approach: We identify some key scenarios, highlighting unique challenges that arise from indirect perception, message generation, and effects of perspective–as well as potential carrier signals and message generation considerations. One observation is that, despite challenges to transmission rates and robustness, physical signals such as locomotion or sound could offer a complementary, currently-unused alternative to traditional methods. The immediate implication is that VS could help to mitigate some costly safety problems–suggesting the benefit of further discussion and ideation.
Spam Audit Detection Through Content Analysis

Nowadays e-commerce has gained recognition in day to day life; hence network became tremendous source for gathering customer reviews/opinions by marketplace analyzers. The count of user reviews that merchandise receives is increasing at high velocity. Opinions being posted on social media differ significantly in superiority. The client needs to essentially go through all reviews regardless of their superiority and decide whether to purchase or not purchase the manufactured goods. The main difficulty in obtainable study on opinion assessment is that all opinions are considered regardless of the implication of each of them. Hence categorization of opinions depending on implication is an essential job. In this article, attempt is made for opinion assessment depending on its superiority, and help buyer make a proper buying judgment. A web mining technique that is narrative and effective is used to assess the consumer opinion for manufactured goods depending on marked allocation are anticipated. The superiority consideration of consumer reviews are classified as genuine, near duplicate, and duplicate opinion. It is carried out in three steps: (1) Recognize opinion regions to take out opinions. (2) Take out and separate features of reviews by quartile compute and assign weights to the features that belong to each group. (3) Consider the feature weights and group belongingness to assess the reviews. Investigational output demonstrates the usefulness of the proposed method which measures the quality of review and assesses it in view of that. The efficiency of client opinion summarization task is probably improved by recognizing and discarding irrelevant opinions
“Aprendo en casa”: una nueva estrategia para la práctica docente

ObjectiveCheck that the Learning at Home strategy is ideal for improving pre professional practice. This allows us to appreciate a proper application of the strategy Learning at Home that allows the student of the pre professional practice, a correct selection and application of a didactic that emphasizes the management of the virtual environments, which will be decisive in ensuring the effectiveness of teaching practice.MethodThe study was carried out in a sample of 54 students from the 10th semester of the Initial Education Study Program, for which the survey technique and the questionnaire were applied as an instrument with 10 duly validated items and the reliability of Cronbach’s Alpha.ResultsThe results show that the students show an optimal management of the strategy learning at home about their teaching practice, so there is a great adaptation to the changes that generate the fortuitous situations that arise, with 95% confidence.ConclusionThe study reveals that the achievement of professional competencies in students in the 10th semester of the Initial Education Study Program, are at the pace of technological advancement and strategies that include its use.
AN ANALYSIS OF TEACHERS’ CLASSROOM MANAGEMENT IN ENGLISH LANGUAGE TEACHING (A SURVEY AT JUNIOR HIGH SCHOOLS IN BANDA ACEH)

ABSTRACT Name : Rabitah Student Number : 1509200080054 Department/Program : English Education/Graduate Title :.An Analysis of Teachers’ Classroom Management in English Language Teaching (A Survey of Three Junior High Schools in Banda Aceh) Keywords: Classroom Management, English Language Teaching, Lesson Plans This research was conducted to investigate the nature of the English language teaching classroom management at junior high schools in Banda Aceh and to find out whether the classroom management is in line with the lesson plans written by the English teachers at the junior high schools. This is qualitative survey research using observation, interview, and document analysis as the methods of data collection. Three junior high schools in Banda Aceh were selected as the research locations, SMPN 1, SMPN 2, and SMP Islam YPUI Darul Ulum Banda Aceh. The instruments The data were analyzed using qualitative analysis. The findings show that four strategies were used by the teachers at these junior high schools in teaching English, individual learning, contextual teaching learning, problem based learning and student-centered approach. The teachers modify the strategies in their teaching depending on the material being covered. They also connect them with students‟ experience, prior knowledge and the context of the students. The findings also show that in general the application of classroom management is in line with the lesson plans prepared by the teachers. The teaching materials were developed by the teachers to motivate students with reference to the syllabus and lesson plans. The teachers also prepare learning instruments such as student worksheet to keep the students engaged and written and oral test to evaluate the students‟ achievement.
Deep-learning-based membranous nephropathy classification and Monte-Carlo dropout uncertainty estimation

Membranous Nephropathy (MN) is one of the most common glomerular diseases that cause adult nephrotic syndrome. To assist pathologists on MN classiﬁcation, we evaluated three deep-learning-based architectures, namely, ResNet-18, DenseNet and Wide-ResNet. In addition, to accomplish more reliable results, we applied Monte-Carlo Dropout for uncertainty estimation. We achieved average F1-Scores above 92% for all models, with Wide-ResNet obtaining the highest average F1-Score (93.2%). For uncertainty estimation on Wide-ResNet, the uncertainty scores showed high relation with incorrect classiﬁcations, proving that these uncertainty estimates can support pathologists on the analysis of model predictions.
Development of an intelligent control system for the process of preparation and water transfer in the cooling circuit of an ammonia station

Introduction.In the modern socio-economic and geopolitical development of Russia, the development of industry comes to the fore.Among the many industries, ammonia stations play the most important role.The main regularities of the process of pumping and preparing water.The process consists of six stages, this article discusses the automation of stages 1 and 2: for water treatment and pumping it out with pumps H1 and H2 from the tank P2.Products in the form of purified water are the most important criteria for subsequent production at an ammonia plant, therefore, increased requirements are imposed on the quality of finished products, including the quality of purification of the water used with the help of nanofilters.The required quality cannot be achieved without control the process in an automated mode.Development of a neural network.To control the converters frequency values during the preparation and pumping of water, an artificial neural network must be used.Its development was carried out in the Matlab environment in the Neural Network Toolbox package, input and output data were defined for this, data processing and preparation were performed, as well as the choice of the type and architecture of the neural network.The architecture of the Layer Recurrent neural network, the process of its construction and training in Matlab is described.Testing of neural networks.During testing of the Layer Recurrent network for the degree of their training, the smallest error was obtained for 30 neurons in the hidden layer.The proximity to the set values indicates the applicability of the network for controlling the parameters of frequency converters.Development of the neural network controller model in the Simulink package.The simulation of the control system in the Simulink package using a neural network controller with the Layer Recurrent architecture is performed.Checking the frequencies of the frequency converters H1 and H2 in Simulink for the level parameters in the tanks and in the tank LT 1_вх , LT 2_вх , LT 3_вх showed that the object model works correctly, thus, the simulation of the neural network showed that the training was successful.Conclusion.As a result of the conducted research, an artificial neural network was developed to control the process of preparing and pumping water in the Matlab environment and a simulation of a neural network in the Simulink package.
The Implementasi Kubernetes Cluster Menggunakan KVM

Rapid technological development requires services that can be accessed at any time. The increasing number of client requests will result in the server being down due to more workloads. Therefore, clusters are present to handle increased server workloads and evenly divide each server. However, to run a cluster requires cluster orchestration software that is Kubernetes. Kubernetes is used for container management. The virtualization technology used in Kubernetes is Kernel-Based Virtual Machine (KVM) virtualization. In this research, we compare latency, CPU usage, and memory usage when opening web pages, between conventional technology and Kubernetes cluster. The results of the highest latency test are 16614 ms by Kubernetes cluster and the lowest latency is 1522 ms by conventional technology. In conventional systems, the response from the user is directly received by the web server, processed and then sent. In Kubernetes clusters, there are nodes running on top of virtual machines, so it takes longer to respond. Good latency is low latency because it can open web pages faster. In the standby state, the lowest CPU usage and memory usage occurred in conventional were 0.07% and 28% because there were no nodes running on the virtual machine. In a busy state, the lowest CPU usage occurs on the Kubernetes cluster server is 1.06% due to the division of workload on virtual machines. The lowest memory usage occurs on a conventional server is 57% because there are no nodes running on a virtual machine.
Forecasting COVID-19 pandemic using an echo state neural network-based framework

Forecasts can help in the decision-making process. Epidemiological forecasts are no different, they can help to evaluate the scenario and possible direction of disease spread, for guiding possible interventions. In this work, Echo State Networks (ESNs) are evaluated for COVID-19 (Coronavirus Disease 2019) cases and deaths forecasting ten days ahead. The chosen locations for the experiment are five states in Brazil, namely Sao Paulo (SP), Bahia (BA), Minas Gerais (MG), Rio de Janeiro (RJ), and Ceara (CE), the states with the most COVID-19 cases as of December 31, 2020. The results are evaluated using performance indexes RMSE (Root-mean-square error), MAE (Mean absolute error), and MAPE (Mean absolute percentage error). Results are compared with a common forecasting technique called ARIMA (Autoregressive Integrated Moving Average). The error signals are compared using Wilcoxon Signed-Rank Test, to evaluate the difference statistically. ESNs presented overall good results for a ten day horizon forecast regarding used performance metrics, but for the number of cases, ARIMA outperformed ESNs regarding RMSE, MAE, and MAPE in all but one state. For the number of deaths however, ESNs outperformed ARIMA in most states when the MAE is taken into account. ESNs are shown to be a solid forecasting model when compared with ARIMA, presenting comparable results and in some cases outperforming it.
Development of Examination Framework for Cyber Security Professional Competency Certification

Talent development in the area of cyber security rapidly evolve due to the dramatic changes in cyber threats and attacks. The needs of professional certification in cybersecurity industry have been addressed by many organizations throughout the world. It was reported by many sources, there is an exponential growth in the demand for cybersecurity professional and special treatment for employee with professional certification. Malaysia encouraged cybersecurity graduates to obtain professional certification for better employment. Malaysia Higher Education Blueprint stated that future ready curriculum includes certificate ready academic program. It is believed that this model can increase competency, knowledge and skills among university graduates. Therefore, rapid growth of cybersecurity professional examinations at the global level which are product-oriented scheme can be seen. There were limited studies that explored the advantages in obtaining certified cybersecurity professional. To our knowledge, none of the previous research shared the best practices for assessment procedure in professional cybersecurity competency modules. This article presents the method in handling examination for Cybersecurity Professional Examination by adopting ISMS generic pillars known as People, Process and Technology. Our framework consists five (5) main components structured in a loop. The five modules are examination question development, examination system, examination conduct, results coordination and manuscript management. As a conclusion, professional examinations must undergo a proper process to make sure it complies with international standards and penetrate the global market.
Comprehensive Review of Content based Image Retrieval

Since the last decade, Content-Based Image Retrieval was a most exciting and fastest growing research area.The computational complexity and theretrieval accuracy are the main problems that CBIR systems have to avoid.To avoid these problems, this paper proposes a new content-based image retrieval method that uses color, texture and edge direction feature .Color features are the fundamental characteristics of the content of images.Color feature is one of the most widely used features in low level feature.Texture provides the measures of properties such as smoothness, coarseness, and regularity.The edge of the image is another important feature that represented the content of the image.Using color,texture and edge direction feature to describe the image and use them for image retrieval is more accurate than using one of them. .