Reviewerly/exHarmony huggingface dataset:
Leveraging Center-Based Sampling Theory for Enhancing Particle Swarm Classification of Textual Data

The Center-Based sampling theory is utilized to develop a variant of Particle Swarm Optimization (PSO), dubbed CBS-PSO, it is capable of dealing with the curse of the dimensionality problem, frequently encountered in data classification. The center-based sampling theory implies that the center region of a search space is promising prospect for population-based algorithms, because it contains points with higher probabilitiesy to be closer to the optimal solution; and this probability increases directly with the dimensionality of the search space. Motivated by its promising potentials for population-based algorithms, the center-based sampling theory is utilized to equip the PSO with two specialized mechanisms to attract the search toward the center region of the search space. The first mechanism employs Rocchio Algorithm (RA) to estimate the coordinates of the center point in the search space, while the second mechanism uses the RA-based estimation to generate informed particles, located at the center region, and incorporate them in the swarm to gradually attract the search for the optimal classifiers toward this promising region. The performance of the developed CBS-PSO is evaluated on the classification of three textual datasets from UC Irvine Machine Learning Repository. Moreover, to position the proposed CBS-PSO within machine learning classifiers, its performance is compared with the performance of three machine learning approaches, experimented on with the same classification tasks. The results indicate that the CBS-PSO can be regarded as a very competitive and promising classifier with much space for improvement, particularly for high dimensional data classification.
Foundations

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
Estimation of Radial Basis Function Network Centers via Information Forces

The determination of The Radial Basis Function Network centers is an open problem. This work determines the cluster centers by a proposed gradient algorithm, using the information forces acting on each data point. These centers are applied to a Radial Basis Function Network for data classification. A threshold is established based on Information Potential to classify the outliers. The proposed algorithms are analysed based on databases considering the number of clusters, overlap of clusters, noise, and unbalance of cluster sizes. Combined, the threshold, and the centers determined by information forces, show good results in comparison to a similar Network with a k-means clustering algorithm.
You Had Me on the First Click

Prepare the interface of your product – this can be a prototype, wireframe, or the finished product.
Visual similarity-based phishing detection using deep learning

Phishing is a common cybercrime that tempts a target to reveal personal and financial information by imitating a legitimate webpage. Classical techniques, such as list-based, and heuristic approaches have focused on identifying the uniform resource locator (URL). However, cybercriminals have uncovered several methods to evade URL-based techniques. Recently, researchers have started developing techniques that are based on visual similarity to detect phishing webpages. However, using visual similarity raises several computer vision-related issues such as webpage segmentation and feature extraction. Deep learning provides a suitable solution to these issues as it integrates feature extraction, feature selection, and classification in an end-to-end system. We have made the first attempt to detect phishing webpages based on visual similarity by modifying and retraining deep neural networks. A comprehensive evaluation on two publicly available databases has shown a marked improvement of around 7% classification accuracy.
An investigation of the partitioning of images with respect to compressibility and spatial complexity

Analysis of two-dimensional spatial data is important for aerial surveys of crops and soils, reconnaissance, medical diagnosis, geographical information systems, and many other domains. Partitioning of images is helpful in the processing and analysis of spatial data. Investigations have shown that splitting an image into sub-images and compressing each sub-image using fewer calculations leads to a faster and more efficient method for the compression of the main image. The relationship between partitioning, spatial information, and the ease of compressing is explored in this paper.
Hubungan Pengaruh Internet Dan Lingkungan Sekolah Terhadap Gaya Berbahasa Siswa

This study aims to determine and analyze the influence of the internet and the school environment on the language style of students in Class VII MTsN 16 Jombang. The method used in this research is a quantitative approach. The population data used in this study is student data in the 2019-2020 school year. Where there are about a total of 73 students of class VII. The total population in each class VII of MTsN 16 Tembelang Jombang has been 100% sampled, so that the total sample used by the researcher is 73 students, who were selected as a whole in each class. Based on the research that has been done, it can be concluded that the influence of the Internet and the Environment is in the high category as many as 16 respondents or equivalent to 22 percent, in the medium category as many as 31 respondents or equivalent to 42 percent and in the low category as many as 27 respondents or equivalent to 36 percent. Thus, most of the respondents, namely 31 respondents, chose Internet and Environment to be in the moderate or good enough category
An improved similarity measure for collaborative filtering-based recommendation system

Collaborative filtering (CF), a representative algorithm of recommendation systems, is a method of using information of the neighbors of active user. The main idea of CF is that users who agreed in the ratings of certain items are likely to agree again in new items. The degree to which the two users’ tendencies in the ratings of the co-rated items are consistent is measured using a similarity measure. Therefore, the similarity measure in CF plays a key role in the extraction of the representative neighbors. Studies on the improvement of similarity indicators for selecting representative neighbors are still ongoing. Recently, a new similarity measure, named OS, was proposed to enhance the recommendation performance by utilizing mathematical equations, such as the integral equation, system of linear differential equations, and non-linear systems. This study aims to understand the limitations of OS and overcome these limitations using the proposed method. In the proposed method, a sigmoid function was used to reflect preferences, such as the positive or negative sentiment of user ratings. In addition, to consider the absolute score difference, some of the formulas were modified, and finally, the performance improvement of the recommendation system was proved through experiments.
UpOnto

This work aims to model an ontology on Unit Operations and Processes as a conceptual ontology. The methodology used to model this is Methontology which is one of the best-in-class methodologies to model chemical ontologies due to the degree of vastness covered by it. The domain knowledge is gathered, and the concepts are conceptualized, axiomatized, and implemented as an XML-based ontology in the preliminary stage. The XML ontology undergoes a metamorphosis into an RDF/OWL-based robust ontology to be more user-friendly and computer-readable. The ontology is evaluated both qualitatively and quantitatively using the metrics that are encompassed in the Semiotic Approach of ontology assessment and a phenomenal reusability ratio of 73.4% is obtained which makes this ontology ready to be reused in the future with ease. Also, the ontology has been retrieved using the Machine Learning algorithms and overall accuracy of 94.95% has been achieved.
Proto-personas

Create a list of your important stakeholders, customers, and users – you can use your stakeholder map to help you get started.
An Exploratory Study of ‘Student Success’ with Local University Students: The Case Study of “A” University

The purpose of this study is to define the concept of ‘student success’ from a contextual approach by exploring the aspects and components of successful university life in the context of local university students. For this purpose, this study analyzed the reflective papers written by 51 students of “A” university in Chung-Cheong Province. The study found six themes of the concept of 'student success': 1) participating actively in college experiences, 2) development of self-concept and setting their direction of life, 3) wide and deep relationships in college, 4) strengthening subject-matter knowledge, 5) career development for the future, and 6) attitude of self-directed life. Based on this, this study contributes to expanding the concept of student success by examining the concept of ‘student success’ in the context of local university students. In addition, it suggests the need for supportive strategies for student success.
Deep Learning Algorithm Exploration for Automated Korean essay Scoring

This study was carried out for the purpose of searching for the optimal algorithm for automated scoring system of Korean essay through the comparison of deep learning-based learning models. For this purpose, in this study, deep learning algorithms such as Recurrent Neural Network (RNN), Long-Short-Term-Memory (LSTM), and Gated-Recurrent-Unit (GRU) algorithms were compared. The performance of each algorithm was evaluated based on classification accuracy, precision, recall, and F1. The empirical results showed that the LSTM and GRU algorithm-based models performed better than RNN. Although there is no significant difference in model performance between LSTM and GRU, the GRU algorithm was found to be more efficient in terms of the time required to train the model, so it could be considered to be the optimal algorithm for automated scoring if the machine leanring time is critical.
Multidimensional IRT Scale Linking Under a Mixture of Bifactor Models for Mixed-Format Tests

Like unidimensional item response theory (IRT) models, bifactor models in multidimensional IRT have a scale indeterminacy problem, and due to this problem scale linking methods are needed to place all bifactor model parameter estimates from separate calibrations on a common ability scale. Four bifactor scale linking methods including the direct least squares (DLS), mean/least squares (MLS), item category response function (ICRF), and test response function (TRF) methods have been presented for use with single-format tests. Parallel to the 2006 paper of Kim and Lee, this paper extends the four scale linking methods to a mixture of bifactor models for mixed-format tests. Each linking method extended is intended to handle mixed-format tests using any mixture of the following bifactor extensions of four unidimensional IRT models: the bifactor three-parameter logistic, bifactor graded response, bifactor generalized partial credit, and bifactor nominal response models. For generality, symmetric criterion functions are proposed for the ICRF and TRF methods. Given two sets of parameter estimates for the common items linking two test forms, each linking method estimates the dilation (slope) and translation (intercept) coefficients of a linear transformation. Simulations are conducted to investigate the performance of the four linking methods. The results indicate that overall, the ICRF method performs very well, the MLS and DLS methods perform well (the MLS method is slightly better than the DLS method), and the TRF method performs poorly in estimating the linking coefficients. The inferiority of the TRF method is mainly due to its poor estimation of the translation coefficients.
Aspects of Semantic Extension of ‘kwiyepta’

The purpose of this study is to examine pattern of changes in the meaning of the adjective ‘kwiyepta’ as short diachrony. First, the characteristics of meaning extension were examined through analysis of the target nouns and combination verbs/adjectives of ‘kwiyepta’ for the newspaper corpus. Second, the noun categories as followed, ‘kwiyepta’ were [people](57.52%) &gt; [abstract](18.76%) &gt; [inanimate object](12.33%) &gt; [animal](10.25%) &gt; [plant](1.14%). The target noun used with ‘kwiyepta’ was extended to ‘child’s appearance/behavior &gt; adult’s behavior &gt; old man’s behavior’ and it was the same as the direction of expansion of the meaning of general words as ‘human appearance/behavior &gt; animal’s figure/behavior &gt; plant’s appearance &gt; living creature &gt; nature of an abstract object’. Finally, as a result of examining the combination verbs/adjectives, ‘kwiyepta’ was evenly combined with ‘patta, hata, tokchacihata, poita, sayngkita’ throughout the period. And it was used as a pattern such as ‘kwiyewumul patta, kwiyewumul tokchacihata, kwiyepkey poita, kwiyewe poita’ etc. ‘seyksihata’ has been used since the 2000s, because ‘kwiyepta’ has been extended to mean behavioral characteristics of adults.
Retention of students ahead of the change from virtual to face to face classes: Case study from the commercial engineering department

The academic performance of our students is being presented as a permanent challenge for the academy due to its complex directly associated with the results of learning. So then, it originates the question: do academics' methodological innovation efforts impact academic performance? To answer this question, we present the results taken from these innovations applied to a Commercial Engineering course at Universidad de Antofagasta. Considering an universe of 251 students in a 4-year time horizon (2018-2021). It is proposed a teaching plann with digital resources for leraning and centered in students increasing the interaction with resources and with peers. The goal of the innovation is to improve the average grade point average by at least 5% and the failure rate (will decrease) by at least 15%. The innovation in the subject has increased the percentage of approved and the participation in lessons.
Comparison of DJS Curriculum with International Universities and Organizations

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An improved real time detection of data poisoning attacks in deep learning vision systems

Abstract The practice of using deep learning methods in safety critical vision systems such as autonomous driving has come a long way. As vision systems supported by deep learning methods become ubiquitous, the possible security threats faced by these systems have come into greater focus. As it is with any artificial intelligence system, these deep neural vision networks are first trained on a data set of interest, once they start performing well, they are deployed to a real-world environment. In the training stage, deep learning systems are susceptible to data poisoning attacks. While deep neural networks have proved to be versatile in solving a host of challenges. These systems have complex data ecosystems especially in computer vision. In practice, the security threats when training these systems are often ignored while deploying these models in the real world. However, these threats pose significant risks to the overall reliability of the system. In this paper, we present the fundamentals of data poisoning attacks when training deep learning vision systems and discuss countermeasures against these types of attacks. In addition, we simulate the risk posed by a real-world data poisoning attack on a deep learning vision system and present a novel algorithm MOVCE—Model verification with Convolutional Neural Network and Word Embeddings which provides an effective countermeasure for maintaining the reliability of the system. The countermeasure described in this paper can be used on a wide variety of use cases where the risks posed by poisoning the training data are similar.
ABOUT CREATING OPEN-SOURCE 3D MODULAR VIEWER IN PYTHON

During the development of the ShipExplorer suit of the programs, great attention was given to building the application, modularization, and code organization. During that period, successful cooperation with UNIZAG FSB was established, resulting in a new requirement that students with no real experience in SW development create new modules that will solve some specific problems. After the ShipExplorer project was put on hold, it was decided to start a new project with similar objectives, that will fulfil UNIZAG FSB requirements from the start, and help save and use the huge knowledge base gained during the development of ShipExplorer. As a result, project, “3D Modular Viewer” was created. In this paper, experience designing and implementing 3DM viewer will be presented, as well as problems that had to be solved. The choice of the programming language will be discussed, together with additional constraints resulting from that decision. Design decisions and implementation details will be reviewed, and the results will be compared with the original program, ShipExplorer.
ABOUT USING THE GAME ENGINES IN SHIPBUILDING, SOFTWARE DEVELOPER’S PERSPECTIVE

During the development of the ShipExplorer[1][2] suit of the programs, every single detail was carefully designed and implemented using low level libraries like OpenGL. The result was very sophisticated, quality program that delivered top performance with wonderful user experience. However, development of new features required theoretical and practical knowledge about many different disciplines starting from linear algebra, knowledge of OpenGL internals, and proficiency in C++ programming language. The additional problem was that every new hardware target, like mobile phones, or various HMD models had to be addressed particularly, without any code sharing. At the same time, new class of programs started appearing, so-called “Game Engine”[3] programs. This class of programs promised maximum results with a minimum effort. Screenshots with spectacular graphics and pre-generated movies were available, showing the output of these programs. Also, “out of the box” support for all major platforms and various hardware was claimed. Taken together, it was enough to rise a question if approach of using low level libraries was a correct approach in the presence of the tools like “Unreal Engine” or “Unity”. In this paper, author’s experience in working and extending Unreal Engine, will be presented. Problems using C++ programming language and internal “Blueprint” visual scripting in Unreal Engine will be analyzed. Scenario of usage as a visualization and annotation tool will be evaluated and compared to the possible alternative approaches.
Performance of COVID face mask detection based on SVM algorithm using OpenCV DNN as preprocessing model

As COVID-19 became a pandemic in the world, wearing a mask has become one of the best measures to prevent the spread of the epidemic, so face mask recognition in public places has become a very important part of controlling the epidemic. This paper mainly tests the performance of the OpenCV DNN preprocessing model (OpenCV DNN + SVM) based on the SVM algorithm model in the face mask recognition dataset. The dataset I use is from Kaggle called COVID Face Mask Detection Dataset. This dataset contains 503 face images with masks and 503 face images without masks. I test the performance of using OpenCV DNN + SVM and using only the SVM algorithm to evaluate this study by setting a control experimental group. In this study, it was found that using OpenCV DNN + SVM, the accuracy of ROI parameters and SVM parameters can reach 93.06% and F1score can also reach 93.06% without a lot of adjustment. The accuracy rate can only reach 68.31%, and the F1score reaches 68.31%. Findings suggest that the method using OpenCV DNN + SVM can achieve slightly better results in the COVID Face Mask Detection Dataset, and can perform better than only using the SVM algorithm. In addition, using OpenCV DNN preprocessing model based on the SVM algorithm plays an important role in feature extraction in face mask recognition. If the developer does enough parameters tuning, the accuracy will also increase.
Universal Photonic Logic-Matrix Unit

We propose a universal photonic logic-matrix unit using highly nonlinear fiber and microring matrix array, which realizes programmable two-input logic. And the function of comparator and half-adder has been executed at 10Gb/s.