Reviewerly/exHarmony huggingface dataset:
Dom Tree as the base for webpage content extraction: Review

Because of the fast advancement of internet technology in the last twenty years, which leads to a huge number of web pages that contain a massive amount of information in every domain, the volume of available information has been steadily expanding every minute, so the analyzing and extracting information from web pages is becoming increasingly crucial, add to that information in webpages in an unstructured or semi-structured format need to transform in a structured format. &#x0D; Since it is hard to collect the information manually, scientists have devised a variety of methods to help extract information from different domains in an automatic way. the main information in web pages is mixed with a significant amount of unrelated information (noise) like advertisements, boxes with links to relevant material, boxes with photos or other media, top and/or side navigation bars, animated commercials, etc., effect on the performance of information extraction and web content analysis technologies. to eliminate the noise by using the Document Object Model (DOM) that can easily reach every tag in the structure of the webpages to extract the information or delete the noise.&#x0D; This article explores in-depth DOM tree-based approaches, such as HTML tags and the DOM tree, by reviewing works from 2011 to 2021 and comparing numerous elements comprehensively, including classifier methods, contribution, limitation, and evaluation metrics.
Multiple approaches to Convert RNS to Decimal Numbers

Improving the performance and speed of encryption algorithms is an important issue in cryptography. One of these improvements is the use of the RNS system. In the research, the RNS system and how to retrieve numbers from the RNS system to WNS weight number system or (decimal number) where discussed in several ways, and after performing multiplication operations, giving illustrative examples and making comparisons between methods.
Direct side information learning for zero-shot regression

Zero-shot learning provides models for targets for which instances are not available, commonly called unobserved targets. The availability of target side information becomes crucial in this context in order to properly induce models for these targets. The literature is plenty of strategies to cope with this scenario, but specifically designed on the basis of a zero-shot classification scenario, mostly in computer vision and image classification, but they are either not applicable or easily extensible for a zero-shot regression framework for which a continuos value is required to be predicted rather than a label. In fact, there is a considerable lack of methods for zero-shot regression in the literature. Two approaches for zero-shot regression that work in a two-phase procedure were recently proposed. They first learn the observed target models through a classical regression learning ignoring the target side information. Then, they aggregate those observed target models afterwards exploiting the target side information and the models for the unobserved targets are induced. Despite both have shown quite good performance because of the different treatment they grant to the common features and to the side information, they exploit features and side information separately, avoiding a global optimization for providing the unobserved target models. The proposal of this paper is a novel method that jointly takes features and side information in a one-phase learning process, but treating side information properly and in a more deserving way than as common features. A specific kernel that properly merges features and side information is proposed for this purpose resulting in a novel approach that exhibits better performance over both artificial and real datasets.
Medical concept embedding of real-valued electronic health records with application to inflammatory bowel disease

Deep learning approaches are gradually being applied to electronic health record (EHR) data, but they fail to incorporate medical diagnosis codes and real-valued laboratory tests into a single input sequence for temporal modeling. Therefore, the modeling misses the existing medical interrelations among codes and lab test results that should be exploited to promote early disease detection. To find connections between past diagnoses, represented by medical codes, and real-valued laboratory tests, in order to exploit the full potential of the EHR in medical diagnosis, we present a novel method to embed the two sources of data into a recurrent neural network. Experimenting with a database of Crohn's disease (CD), a type of inflammatory bowel disease, patients and their controls (~1:2.2), we show that the introduction of lab test results improves the network's predictive performance more than the introduction of past diagnoses but also, surprisingly, more than when both are combined. In addition, using bootstrapping, we generalize the analysis of the imbalanced database to a medical condition that simulates real-life prevalence of a high-risk CD group of first-degree relatives with results that make our embedding method ready to screen this group in the population.
Uji Praktikalitas E-Modul Pembelajaran Project Based Learning Menggunakan Sigil Pada Mata Pelajaran Dasar-Dasar Kejuruan Siswa Kelas X Pengembangan Perangkat Lunak dan GIM (PPLG) di SMK N 1 Singkarak

This study aims to determine the level of validity, practicality, and effectiveness of the Development Of Project Based Learning (PjBL) Learning E-Modules Using Sigil In The Subjects Of Vocational Basics Of Students In Class X Software And Game Development (PPLG) At SMK 1 Singkarak Even Semester Year Teaching 2022/2023. This study uses the development model (ADDIE) with the following development steps: (1) Analyze, (2) Design, (3) Development, (4) Implementation and (5) Evalurate. The research subjects number of 31 people. The whole research on validator test On E-Modules Uses Sigil In The Basic Subjects Of Vocational Class X PPLG At SMKN 1 Singkarak by 92.73%, with very valid interpretation used. The research subjects number of 31 people. Overall practicality test research on E-Modules Using Sigil In The Basic Subjects Of Vocational Class X PPLG At SMKN 1 Singkarak by 93.81%, with very practical interpretation of use. The results of the overall effectiveness test of the effectiveness of the effectiveness of E-Modules Using Sigil In The Basic Subjects Of Vocational Class X PPLG In SMKN 1 Singkarak 89.29% are very effective. In the subjects of vocational basics, students of class X Software and Game Development (PPLG) at SMKN 1 Singkarak have been tested for feasibility, excellence, and can be used in learning activities in the subjects of vocational basics in class X PPLG in SMKN 1 Singkarak.
Philosophy of Education and Pedagogy: Peculiarities of Interaction

Aim. The aim of the article is to determine the status and purpose of philosophy of education, and to identify the peculiarities of its relationship with educational practice and pedagogical science. Concept. Philosophy of education and pedagogy explore different aspects of the same subject field – education. Realising different research functions, they do not act as competitors, but, on the contrary, have the potential for mutual complementarity and reciprocal strengthening of their cognitive capabilities. Results. Philosophy of education as a branch of applied philosophy is not an instrumental tool and cannot directly influence educational practice. It is designed to provide comprehension and understanding of the most important problems of the theory and practice of education. The role of mediator is played by another science of education – pedagogy. Conclusion. The most productive form of relationship between philosophy of education and pedagogical science is their interaction, which contributes to the mutual enrichment, building up and the fullest realisation of their special research capabilities. Pedagogy has a choice of philosophical and educational concepts to develop educational theories which are verified by experiment and directly implemented in practice. Philosophy of education realises its theoretical functions at all stages of educational activity. Cognitive value. The article presents a new perspective on the problem of the relationship between educational philosophy and educational practice. It is argued that the most efficient channel of transferring philosophical and educational ideas into practice is pedagogical science. Keywords: philosophy of education, educational practice, pedagogy, comprehension, understanding, interaction, functions of philosophy of education
Transductive Transfer Broad Learning Soft Sensor for Multimode Quality Prediction

In chemical processes, reliable soft sensors are generally established by enough labeled data. However, in most multimode processes, the collection of sufficient labeled data is difficult due to the high cost and complexity. In this work, transductive transfer broad learning (TTBL) is proposed for multimode quality prediction. By transferring the useful information from the related domain, unlabeled data in the prediction domain is utilized for modeling. First, the data feature is extracted by the feature and enhancement nodes. The similarity information of current and related domain data is captured by the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> nearest-neighbor graph. Then, label information in the related domain can be transferred and similar information in the same domain can be retained by the manifold regularization framework. Finally, the output weight can be effectively calculated by the ridge regression algorithm. Experimental results on continuous stirred tank reactor datasets show the superiority of TTBL, compared with several common methods.
THE INFLUENCE OF SYSTEM QUALITY, EDUCATOR, MOTIVATION ON SATISFACTION AND INTENTION TO CONTINUE ON CLASS X STUDENTS WHO USE E-LEARNING AT SMAK St. LOUIS 1 SURABAYA

Advances in technology and communication are expected to provide effective and efficient performance for educators in the current millennial era, especially in the conditions of a pandemic due to the COVID-19 outbreak that has infected the world, including Indonesia, especially in the city of Surabaya. The purpose of this study is to examine and analyze changes in the quality of the education system, and the challenges for educators in operating e-learning applications to deliver learning materials, to attract student interest in learning and bold interactions. Motivation becomes the driving force in students. They will judge the perceived usefulness by achieving satisfaction or not. Because it affects their intention to continue using the bold system, by still choosing the high school they chose switching to homeschooling or choosing not to continue school. The research method used consists of research design, variables, operational definitions, and data sources, research instruments, the population is class X students at SMAK St. Louis 1 Surabaya, samples taken were 334 respondents from class X students who were asked questions. The procedure for distributing questionnaire data through Google Forms, as well as data analysis techniques using SEM using Lisrel software. The reliability test shows (6) the reliability variable. This study obtained the results of hypothesis testing that had a positive and significant effect on the system quality, educator, and motivation variables on satisfaction, then the satisfaction variable on the intention to continue. Satisfaction mediates the relationship between system quality and the intention to continue has a positive and significant effect, while the satisfaction hypothesis mediates the relationship between motivation and intention to continue and has a positive and insignificant effect
Diversity recommendation based on multi-graph collaboration and graph convolution neural networks

Recommendation systems are the latest solution to solve the information surplus in the Internet era. However, the mainstream recommendation models have the problem of data sparsity and lack of diversity. Therefore, this paper proposes a diversity recommendation algorithm based on multi-graph collaboration and graph convolution neural networks (GCN) to solve the above problems. Specifically, we use the user-item bipartite graph to construct a multi-graph to mine the edge information of potential space, so as to improve the data sparsity problem and construct a diverse node neighbourhood, and we use the graph convolution neural networks to model the implicit characteristics of users and items, so as to learn users' diversity preferences. We conducted a series of experiments on real data sets, and the experimental results show the validity of our method (DR-MGCN).
PENGARUH LINGKUNGAN SOSIAL DAN POTENSI DIRI TERHADAP MINAT MELANJUTKAN PENDIDIKAN AKUNTANSI DI PERGURUAN TINGGI

This This study aims to examine and analyze the Social Environment and Self Potential for Interest in Continuing Accounting Education in Higher Education. This type of research is a survey, using the sampling technique of saturated sampling method. The sample in this study was 119 students of class XII, majoring in accounting at the 2nd State Vocational High School, Palu. Data collection technique by distributing questionnaires directly to respondents while the method of analysis used is multiple linear regression. The results of this study indicate that the social environment and self-potential simultaneously have a positive and significant effect on interest in continuing education of accounting in higher universities. The social environment partially has a positive and significant effect on interest in continuing education of accounting in higher universities. Self Potential partially has a positive and significant effect on interest in continuing education of accounting in higher universities.
Las olas de la creatividad en España

Taking advantage of the evolutionary process of Covid-19, through the different mutations that we have experienced over two years, I wanted to transfer this model of waves to the field of creativity, in order to know all the details of this human capacity. The waves of creativity have a regenerating effect on the mind and make you recover a sense of satisfaction and well-being that allows us to recharge energy to discover our essence and live with joy. What happens to creativity is what seems to have no limits to the sea. From my personal perspective, I have considered that in Spain we have had seven waves that I have precisely defined which are the key moments and their circumstances. At the same time, I wanted to pay a special tribute to the most relevant figure in the initial moments: Dr. Ricardo Marín Ibáñez, knew with his wisdom and love for education, to create a school, leave a mark and promote this universal capacity for the good of humanity. Finally, a visualization of the future of this unique attribute that defines us appears.
Low illumination image enhancement based on retinex theory

Aiming at the problem that low visible light and noise will not only reduce the visual beauty of images but also cause the loss of important information, an improved Retinex theory method is put forward to enhance low illumination images. More specifically, a light analytic model is applied before the original low illumination image to refine the original illumination image and construct an image with good parameters. Finally, color recovery factor is added to readjust the color distortion caused by contrast enhancement in local region of the image, so as to realize the corresponding image enhancement. Experiments on some challenging real-world low-light images revealed the effectiveness of our limiting images, and shows its better than SSR (Single Scale Retinex) and MSR (Multi–scale Retinex) and MSRCR (such as Multi– scale Retinex with Color Restoration) technology.
Decentralized Storage System to Store Data Using Blockchain Technology

In today's society, data storage is becoming more and more crucial. In the lives of customers, data reliability, secrecy, security, and accessibility have all been crucial factors. All web2.0 vulnerabilities are removed by blockchain in general by introducing new technology and structure. With the power of the blockchain, data storage problems might be resolved and customer privacy and security of their data could be guaranteed. This reduces the possibility of a single point of failure and encourages ideas like tokenization and incentivization to reward customers and storage providers for making data storage compatibility possible. As a result, we offer a decentralised storage system that use a blockchain network and smart contracts to store user data.
Python, ast, and redbaron

I recently had a use case at work where I wanted to check that file paths given in a Python script actually existed. These paths were in various GitHub repositories, so all I had to do was pull out the paths and check if they exist on GitHub.
Towards smaller single-point failure-resilient analog circuits by use of a genetic algorithm

Failure-resilient analog circuits are difficult to design, but artificial intelligence can help crawl the topology solution space. Us-ing evolutionary computation-based topology synthesis we evolve analog arcus tangent computational circuits, resilient to any rectifying diode or resistor high-impedance single failure or removal. We encode analog circuit topologies as individuals with an upper-triangular incident matrix. Circuits are evolved using a combined technique utilizing parts of NSGA-II and PSADE, based on a special three-dimensional robustness function. We show that topology size for a failure-resilient circuit can be classes smaller than hand-made component-redundancy-based solutions. Our best failure-resilient topology comprises six diodes, three resistors, and a voltage offset source.
Menyoal Keberimbangan dan Akurasi Berita Dalam Segmen Hard News RRI Pro-3 Periode Siar Mei-Juli 2021

RRI Pro-3 as a public broadcasting institution taking the task on conveying information in a neutral, independent, as well as non-commercial ways. With the purpose of monitoring the quality of RRI Pro-3 news content, then research on news content analysis was conducted. Research on news content analysis was carried out on the hard news segment of RRI Pro-3 which was broadcast at 07.00, 13.00 and 19.00. The stages of analysis in this news content analysis research were inter-coder reliability test, measuring instrument reliability test, data selection, data initial test, description of descriptive data processing, mean data processing, correlation test, and data validity test. The results of content analysis research on the reliability test between coders show a value of 96.29%, the reliability test of the measuring instrument is 0.834 with an alpha of 5%. The description of descriptive data processing gives results in the form of information on the distribution of aspects that become supporting information for the news. Mean data processing was obtained by measuring the average aspects of accuracy, balance and neutrality. Correlation test was conducted between news themes and aspects of accuracy, balance and neutrality. The data validity test was carried out to determine the validity of the processed data, the validity value was obtained at an accuracy of 0.868**, a balance of 0.680**, a neutrality of 0.2533** (negative question). The research results provide information that the quality of the RRI Pro-3 news is good, aspects that can be improved is the balance of information through the presence of expert resource persons. It can be concluded that the existence of expert resourcepersons as part of the balance of information will not only facilitate public understanding of listeners but also educate the listeners.
Poster: Exploring Synthetic Data Generation for Anomaly Detection in the 5G NWDAF Architecture

The transition of paradigm from non-standalone to standalone mode in 5G network creates an opportunity to utilize innovative machine learning and AI-based technology for network traffic analysis. In particular, NWDAF plays a key role in leveraging AI-based models to optimize and enhance the 5G core network functions, including anomaly detection and load balancing. However, it is challenging to ensure the sufficient performance of prediction algorithms under dynamic conditions in NWDAF without guaranteeing the quality and quantity of training data, but only a select group has access to the 5G dataset. To overcome this issue, this paper proposes an approach to generate high-quality synthetic 5G NWDAF data using CTGAN, a specialized generative model that creates synthetic output based on tabular input data. We provide preliminary results of leveraging CTGAN to generate 5G synthetic data and evaluate the synthesizing quality for anomaly detection.
An Improved Differential Evolution Algorithm for Numerical Optimization Problems

The differential evolution algorithm has gained popularity for solving complex optimization problems because of its simplicity and efficiency. However, it has several drawbacks, such as a slow convergence rate, high sensitivity to the values of control parameters, and the ease of getting trapped in local optima. In order to overcome these drawbacks, this paper integrates three novel strategies into the original differential evolution. First, a population improvement strategy based on a multi-level sampling mechanism is used to accelerate convergence and increase the diversity of the population. Second, a new self-adaptive mutation strategy balances the exploration and exploitation abilities of the algorithm by dynamically determining an appropriate value of the mutation parameters; this improves the search ability and helps the algorithm escape from local optima when it gets stuck. Third, a new selection strategy guides the search to avoid local optima. Twelve benchmark functions of different characteristics are used to validate the performance of the proposed algorithm. The experimental results show that the proposed algorithm performs significantly better than the original DE in terms of the ability to locate the global optimum, convergence speed, and scalability. In addition, the proposed algorithm is able to find the global optimal solutions on 8 out of 12 benchmark functions, while 7 other well-established metaheuristic algorithms, namely NBOLDE, ODE, DE, SaDE, JADE, PSO, and GA, can obtain only 6, 2, 1, 1, 1, 1, and 1 functions, respectively. Doi: 10.28991/HIJ-2023-04-02-014 Full Text: PDF
Using machine learning to unravel the intricacy of acute myeloid leukemia

Not available.
Analyzing Election Sentiments in Tweets with Gated Recurrent Units (GRU)

Sentiment analysis, a key task in natural language processing, is important for detecting the emotional tone portrayed in text. In this study, we focus on implementing a Gated Recurrent Unit (GRU) model to analyze attitudes within the 2020 Donald Trump Election tweets dataset. By setting the GRU model with carefully selected parameters, the aim of the study is to unveil the inherent sentiment patterns in the dataset. To develop the sentiment analysis model, the study devised a three phase methodology which that include data preprocessing, feature selection using correlation matrix, and lastly the implementation of GRU. Futhermore, we provided the outcomes of our experiment, evaluating the model's performance through important measures such as accuracy, precision, and recall. Notably, our data exhibit an exceptional accuracy rate of 93%, verifying the model's power to appropriately categorize attitudes. Additionally, both recall and precision receive outstanding ratings of 94% and 96%, indicating the model's skill in distinguishing both positive and negative attitudes. This inquiry emphases the effective usage of the GRU model in sentiment analysis, shedding light on the emotional nuances within the 2020 Donald Trump dataset and enriching our understanding of sentiments during the election period.