Reviewerly/exHarmony huggingface dataset:
DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer

In this paper, we propose a novel Discrete Cosine Transform (DCT)-based neural network layer which we call DCT-perceptron to replace the $3\times3$ Conv2D layers in the Residual neural Network (ResNet). Convolutional filtering operations are performed in the DCT domain using element-wise multiplications by taking advantage of the Fourier and DCT Convolution theorems. A trainable soft-thresholding layer is used as the nonlinearity in the DCT perceptron. Compared to ResNet's Conv2D layer which is spatial-agnostic and channel-specific, the proposed layer is location-specific and channel-specific. The DCT-perceptron layer reduces the number of parameters and multiplications significantly while maintaining comparable accuracy results of regular ResNets in CIFAR-10 and ImageNet-1K. Moreover, the DCT-perceptron layer can be inserted with a batch normalization layer before the global average pooling layer in the conventional ResNets as an additional layer to improve classification accuracy.
#maskUp: Selective Attribute Encryption for Sensitive Vocalization for English language on Social Media Platforms

Social media has become a platform for people to stand up and raise their voices against social and criminal acts. Vocalization of such information has allowed the investigation and identification of criminals. However, revealing such sensitive information may jeopardize the victim's safety. We propose #maskUp, a safe method for information communication in a secure fashion to the relevant authorities, discouraging potential bullying of the victim. This would ensure security by conserving their privacy through natural language processing supplemented with selective encryption for sensitive attribute masking. To our knowledge, this is the first work that aims to protect the privacy of the victims by masking their private details as well as emboldening them to come forward to report crimes. The use of masking technology allows only binding authorities to view/un-mask this data. We construct and evaluate the proposed methodology on continual learning tasks, allowing practical implementation of the same in a real-world scenario. #maskUp successfully demonstrates this integration on sample datasets validating the presented objective.
An Empirical Evaluation of the “Cognitive Complexity” Measure as a Predictor of Code Understandability

Code that is difficult to understand is also difficult to inspect and maintain and ultimately causes increased costs. Therefore, it would be greatly beneficial to have source code measures that are related to code understandability. Many “traditional” source code measures, including for instance Lines of Code and McCabe’s Cyclomatic Complexity, have been used to identify hard-to-understand code. In addition, the “Cognitive Complexity” measure was introduced in 2018 with the specific goal of improving the ability to evaluate code understandability.The goals of this paper are to assess whether (1) “Cognitive Complexity” is better correlated with code understandability than traditional measures, and 2) the availability of the “Cognitive Complexity” measure improves the performance (i.e., the accuracy) of code understandability prediction models.We carried out an empirical study, in which we reused code understandability measures used in several previous studies. We first built Support Vector Regression models of understandability vs. code measures,and we then compared the performance of models that use “Cognitive Complexity” against the performance of models that do not.“Cognitive Complexity” appears to be correlated to code understandability approximately as much as traditional measures, and the performance of models that use “Cognitive Complexity” is extremely close to the performance of models that use only traditional measures.The “Cognitive Complexity” measure does not appear to fulfill the promise of being a significant improvement over previously proposed
Reasons for non-confirmation of explored reserves at the Charmitan gold deposit

Errors arising in the exploration of ore deposits can be divided into two groups: errors in determining specific quantitative characteristics of mineralization (average thickness, grade, density) and errors in identifying the conditions of occurrence, shape and structure of ore clusters. This article discusses the reasons for the non-confirmation of explored reserves for individual blocks of ore bodies of the Charmitan gold deposit. The calculated parameters and ore and metal reserves are compared in the framework of proven reserves for four ore bodies with unconfirmed reserves. The main discrepancies in ore and metal reserves, non-confirmation of body thicknesses and gold grades, as well as a complex of factors are considered: the density of the exploration network, and statistical and spatial variability of useful components.
RANCANG BANGUN APLIKASI PEMBELAJARAN TENSES BAHASA INGGRIS BERBASIS ANDROID PADA SEKOLAH MENENGAH PERTAMA NEGERI 3 SUKAMAJU

This study aims to produce an Application for Learning English Tenses at The State Junior High School 3 Sukamaju so that it can make it easier for teachers and students. The data collection methods used in this study are observation, interviews, literature studies and documentation. In this study, the author used the R&amp;D (Research and Development) method with reference to the waterfall model. System design using UML (Unified Modeling Language) as visual modeling, as for making applications using the MIT App Inventor platform to design applications. Tests performed on the system using black box testing. The results of this study are that the existence of an English tenses learning application system can make it easier for schools and students to carry out learning
Quantum Network Intelligent Management System

Quantum network materializes the paradigm change caused by the depletion of classical computation. Quantum networks have been built gathering reliable quantum repeaters connected by optical fiber networks. The need to build robust and resilient networks against hacking attacks is fundamental in the design of the future quantum Internet, detecting structural security as the major issue in the current development of the technology. A network management method is proposed to achieve its real-time adaptation and to protect itself against sabotage or accidents that render part of the network or its nodes useless.
Design and Development of an Advancing Web Information Stockpiling for Engraved Ontology in User Contours

As a version for know-how description and formalization, ontologies are extensively used to represent person contours in Adapted net information gathering. However, whilst representing person contours, many fashions have utilized best expertise from either a Biosphere wide know-how base or a person nearby records. In this article, a customized ontology style is suggested pro expertise illustration and reasoning over user contours respectively. That version absorbs ontological user contours on or after each a biosphere know-how base and person local instance repositories. The ontology model is evaluated by way of evaluating it in opposition to benchmark fashions in internet data gathering. The consequences display that this ontology prototype is a hit.
Chapter 15. Studying Unknown Unknowns: Lessons from Critical Making on Twitter

The Practices
Pengaruh Strategi Pembelajaran Online dan Gaya Belajar terhadap Hasil Belajar Aplikasi Konsep

This study aims to determine the differences in the learning outcomes of the application of concepts to students who learn to use online strategies with face-to-face learning strategies, as well as to test whether there is a difference in the average learning outcomes of the application of concepts to students who have different learning styles and to test whether there is an interaction between learning strategies and learning outcomes. learning styles on the learning outcomes of student concept applications. This study used a quasi-experimental research design. 2x2 factorial design The variables of this study are the independent variables are online learning strategies and face-to-face learning, the control variables are learning styles and the dependent variable is the learning outcomes of concept application. This research instrument is a question of pretest and posttest which was developed in accordance with the research objectives of the lectures listed in the RPS. The hypothesis testing of this research uses 2-way Analysis of Variance (ANOVA) to test the hypothesis, in the form of the Univariate Test of Between-Subject Effects. The results of this study indicate (1) there are significant differences, including learning outcomes of student concept applications using online learning strategies with face-to-face learning strategies. The use of online learning strategies is significantly superior to face-to-face learning strategies., (2) there is a significant difference in the acquisition of concept application learning outcomes between students who have an active learning style and a reflective learning style, significantly students who have an active learning style are more superior in learning outcomes of concept applications compared to students who have reflective learning styles. (3) there is an interaction between online learning, learning strategies and learning styles on concept application learning outcomes. the influence between the interaction, the independent variable and the moderator variable on the dependent variable, both for the learning outcomes of the application of the concept.
Sosialisasi Dan Edukasi Software Accounting Dalam Pencatatan Laporan Keuangan Pada Bmt Dmi Kota Pekanbaru

Islamic financial institutions or can be called interest-free banks are finance/banking whose operations and products are developed based on the arguments of the Qur'an and the Hadith of the Prophet SAW. Islamic financial institutions are banks whose working mechanism uses a profit-sharing system. Currently IAI (Indonesian Accounting Associations) has issued a statement of financial accounting standards (PSAK) which regulates Islamic Financial Accounting. The purpose of this activity is to provide knowledge and insight to readers about the role of the importance of accounting software in sharia cooperatives in recording their financial statements in the era of all-technology at this time, the role of technology in each activity is greatly assisted, for that it is necessary to have good socialization related to software Accounting in Sharia cooperatives. And help the BMT DMI in Pekanbaru. As for the target, the members/managers of the sharia cooperative BMT DMI Pekanbaru City understand and understand Accounting Software which can facilitate managers in recording financial statements properly and correctly. This activity will greatly assist the management of BMT DMI to run the program and its financial system. The result is that Accounting Software can help BMT DMI in doing accounting
Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data

Bayesian inference is ubiquitous in science and widely used in biomedical research such as cell sorting or “omics” approaches, as well as in machine learning (ML), artificial neural networks, and “big data” applications. However, the calculation is not robust in regions of low evidence. In cases where one group has a lower mean but a higher variance than another group, new cases with larger values are implausibly assigned to the group with typically smaller values. An approach for a robust extension of Bayesian inference is proposed that proceeds in two main steps starting from the Bayesian posterior probabilities. First, cases with low evidence are labeled as “uncertain” class membership. The boundary for low probabilities of class assignment (threshold ε) is calculated using a computed ABC analysis as a data-based technique for item categorization. This leaves a number of cases with uncertain classification (p &lt; ε). Second, cases with uncertain class membership are relabeled based on the distance to neighboring classified cases based on Voronoi cells. The approach is demonstrated on biomedical data typically analyzed with Bayesian statistics, such as flow cytometric data sets or biomarkers used in medical diagnostics, where it increased the class assignment accuracy by 1–10% depending on the data set. The proposed extension of the Bayesian inference of class membership can be used to obtain robust and plausible class assignments even for data at the extremes of the distribution and/or for which evidence is weak.
PENGEMBANGAN RUBI WEB (RUMAH BIOLOGI WEB) DALAM KONSEP KEANEKARAGAMAN HAYATI THE DEVELOPMENT OF RUBI WEB (RUMAH BIOLOGI WEB) IN BIODIVERSITY CONCEPT

This study aims to develop a web-based learning media, namely RUBI WEB (House of Biology Web) in the concept of biodiversity. The research method used in this research is a modified development research method from Borg &amp; Gall whose stages consist of information gathering, planning, product development, and initial field trials. Aspects of the assessment of the material assessed include aspects of material and aspects of language, then aspects of media assessment that are assessed include aspects of software engineering, aspects of instructional design, and aspects of visual communication. The results of the material feasibility test by material experts obtained a percentage value of 80% and media feasibility tests by media experts obtained a percentage value of 83.4%. The results of the user response test by students obtained a percentage value of 86. Therefore, it can be interpreted that the RUBI WEB website learning media in the concept of biodiversity is included in the grade A+ category with a range of percentile ratings between 96-100%, best imaginable adjectives category, acceptable level of acceptance, and NPS (Net Promotore Score) in class promoter, i.e. users are satisfied and loyal to RUBI WEB media, so that they can make users want to recommend this media to others.
INCREASING USE OF LAND CALCULATOR APPLICATIONS FOR HATIVE VILLAGE COMMUNITIES IN AMBON CITY

The millennial era has an impact that is digitally laden with the development of technology and information, so it needs to be utilized in improving self-ability and community empowerment. One of the efforts is to use an android application, Land Calculator to support community needs related to determining the boundaries of community-owned areas. Most people do not understand the use of this application. The purpose of the service in Hative village is to improve the ability and skills of the community in utilizing the Land Calculator application to obtain certainty about the boundaries of community-owned areas. The target subjects are heads of families who are domiciled in Hative village. The method used is direct learning through lectures and the practice of using the Land Calculator application in community-owned areas. The lectures were carried out with the intention that the public had initial knowledge about the Land Calculator application. After that, it was continued with mentoring during practice at community locations. The process of operating the Land Calculator application goes well. The results achieved show that there is a change in the level of understanding and enthusiasm among PKM participants. This is realized through the ability to independently use the Land Calculator application without having to be accompanied by a resource person.
Personalized Sliding Window Recommendation Algorithm Based on Sequence Alignment

With the explosive growth of the amount of information in social networks, the recommendation system, as an application of social networks, has attracted widespread attention in recent years on how to obtain user-interested content in massive data. At present, in the process of algorithm design of the recommending system, most methods ignore structural relationships between users. Therefore, in this paper, we designed a personalized sliding window for different users by combining timing information and network topology information, then extracted the information sequence of each user in the sliding window and obtained the similarity between users through sequence alignment. The algorithm only needs to extract part of the data in the original dataset, and the time series comparison shows that our method is superior to the traditional algorithm in recommendation Accuracy, Popularity, and Diversity.
Development and deployment of a web application to control and query school assessments

This document describes the development and deployment of a web system based on open-source technologies and dedicated to the control and query of school grades, which is a flexible and easily administrable method for people who do not have notions of databases or systems.
Adaptive Driving Style Classification through Transfer Learning with Synthetic Oversampling

Driving style classification does not only depend on objective measures such as vehicle speed or acceleration, but is also highly subjective as drivers come with their own definition. From our perspective, the successful implementation of driving style classification in real-world applications requires an adaptive approach that is tuned to each driver individually. Within this work, we propose a transfer learning framework for driving style classification in which we use a previously developed rule-based algorithm for the initialization of the neural network weights and train on limited data. Therefore, we applied various state-of-the-art machine learning methods to ensure robust training. First, we performed heuristic-based feature engineering to enhance generalized feature building in the first layer. We then calibrated our network to be able to use its output as a probabilistic metric and to only give predictions above a predefined neural network confidence. To increase the robustness of the transfer learning in early increments, we used a synthetic oversampling technique. We then performed a holistic hyperparameter optimization in the form of a random grid search, which incorporated the entire learning framework from pretraining to incremental adaption. The final algorithm was then evaluated based on the data of predefined synthetic drivers. Our results showed that, by integrating these various methods, high system-level performance and robustness were met with as little as three new training and validation data samples in each increment.
Pengembangan Media Gambar Seri untuk Meningkatkan Kemampuan Bercerita

The purpose of this study was to analyze the use of picture series learning media that was developed in improving storytelling skills in grade I students. This research is a Research and Development (R &amp; D). Data collection techniques with tests, non-tests, and interviews. The effectiveness analysis was carried out by comparing the results of the pretest and posttest tested with the mean difference test (Independent Sample t Test). The results showed that after being validated by media experts and linguists, learning media were suitable to be used to improve storytelling skills. The effectiveness of serial image media as measured using a before-after experimental design in field trials is known to have a pretest value of 69, and an average posttest value of 81.89. Based on the normality test with the One Sample Kolmogorov Smirnov Test, the probability value of t-statistics&gt; Level of Significant = 0.05, namely the pretest value of 0.852 and the posttest value of 0.778 (&gt; 0.05), thus the research data is normally distributed. The results of the t-test obtained a significance value (2-tailed) 0.000 &lt;0.05. Thus, it can be concluded that there is a significant (significant) difference between the pretest and posttest scores in Indonesian language learning, it can be interpreted that serial picture media can improve storytelling skills in grade I KD students. 4.9. Using the right vocabulary and expressions to introduce yourself, your family, and the people in your place of residence in a simple spoken and written form.
Malicious URL Detection Using Decision Tree-based Lexical Features Selection and Multilayer Perceptron Model

Network information security risks multiply and become more dangerous. Hackers today generally target end-to-end technology and take advantage of human weaknesses. Furthermore, hackers take advantage of technology weaknesses by applying various methods to attack. Nowadays, one of the greatest dangers to the modern digital world is malicious URLs, and stopping them is one of the biggest challenges in the field of cyber security. Detecting harmful URLs using machine learning and deep learning algorithms have been the subject of various academic papers. However, time and accuracy are the two biggest challenges of these tools. This paper proposes a multilayer perceptron (MLP) model that utilizes two significant aspects to make it more practical, lightweight, and fast: Using only lexical features and a decision tree (DT) algorithm to select the best relevant subset of features. The effectiveness of the experimental outcomes is evaluated in terms of time, accuracy, and error reduction. The results show that a MLP model using 35 features could achieve an accuracy of 94.51% utilizing only URL lexical features. Furthermore, the model is improved in time after applying the DT as feature selection with a slight improvement in accuracy and loss.
Neural Networks with the Correlative Layer for Multi-label Classiﬁcation

Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
Pengembangan Media Kubus Bergambar untuk Meningkatkan Kemampuan Membaca Siswa Kelas II Sekolah Dasar

The purpose of this research is to describe the development of pictorial cube media and to describe the effectiveness of pictorial cube media. This study uses the Research and Development (R&amp;D) model. Data collection techniques with tests, non-tests, and interviews. The results showed that the validity of the illustrated cube learning media by media expert validators was 88.75 (very feasible) and 90.67 (very feasible) for language eligibility. The effectiveness of the illustrated cube media as measured by the before-after experimental design is known that the average value before using the media is smaller than the average value after using the media, thus the media developed is effective to be used as a medium for learning Indonesian. The value of F standard gain = 3.101 with a significance level of 0.088, this proves that the value variance between the experimental class group 1 and the experimental class group 2 is homogeneous. The standard gain t value for the same variance is (-5.497) which is greater than the ttable value of 0.05, the t value for df = 30 is 1.697. The results show that the value of tcount &gt; ttable is tcount = (-5.497) &gt; ttable 0.05 = 1.697. Thus, it can be concluded that there is a significant (significant) difference between the pretest and posttest scores in Indonesian language learning, this means that pictorial cube media can improve the second grade students' understanding of the basic Indonesian language lessons. 3.3. Determine vocabulary and concepts about the geographical environment, economic, social, and cultural life in the surrounding environment in Indonesian or regional languages ​​through written, oral, visual and/or environmental exploration texts.