Reviewerly/exHarmony huggingface dataset:
Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics

Measuring the similarity of images is a fundamental problem to computer vision for which no universal solution exists. While simple metrics such as the pixel-wise L2-norm have been shown to have significant flaws, they remain popular. One group of recent state-of-the-art metrics that mitigates some of those flaws are Deep Perceptual Similarity (DPS) metrics, where the similarity is evaluated as the distance in the deep features of neural networks.However, DPS metrics themselves have been less thoroughly examined for their benefits and, especially, their flaws. This work investigates the most common DPS metric, where deep features are compared by spatial position, along with metrics comparing the averaged and sorted deep features. The metrics are analyzed in-depth to understand the strengths and weaknesses of the metrics by using images designed specifically to challenge them. This work contributes with new insights into the flaws of DPS, and further suggests improvements to the metrics. An implementation of this work is available online: https://github.com/guspih/deep_perceptual_similarity_analysis/
PELATIHAN MENDELEY TERHADAP PENINGKATAN KEMAMPUAN SITASI PADA MAHASISWA PROGRAM STUDI PENDIDIKAN GURU SEKOLAH DASAR

: Citations are a mandatory part of writing academic books and other scientific articles. In fact, many STKIP Melawi students are confused or do not understand the art of scientific writing, citation procedures, and the use of the Mendeley application in their writings. As a result, there are only a few scientific papers or papers that do not fully follow the rules of citation and bibliography. The types of activities carried out in this PkM are training, in which resource persons provide material directly to participants, support in the practical application of the material provided, and interaction between speakers and participants. Consists of questions and answers and discussion. The target number is 50 students. The resource person for this training activity is the executor of the training activity itself, namely the PGSD STKIP Melawi lecturer who mastered the first material, citation techniques, and achieved an average score of 78. This shows that participants have a better understanding of the material than before participating in this training. , which is 48 . Participants' understanding of the second material, Techniques for Using Mendeley Applications was also higher than before attending this training, from an average score of 40 to an average score of 77. It can be downloaded to your computer or laptop, making it easy to automatically create a bibliography that spelled correctly. Mastering citation techniques in the Mendeley application will make it easier to create bibliography and improve your writing. The importance of this training is to add references according to the rules and get the main capital with good and correct writing
Company Profile Website Development Using Prototyping Method (Study Case: Pt. Cipta Indah Bangun Pertiwi)

Fabrication is a field that is much needed in the industry now due to the increasing industrial sector, one of the companies working in this sector is PT Cipta Indah Bangun Pertiwi. Even though it has been operating for more than 1 decade, it still does not have a website that helps them to reach more customers. For this reason, the author wants to help in achieving sales targets for partners by forming a website that is created as a medium to show the company profile of Cipta Indah Bangun Pertiwi that consists of, detailed information related to company history, contacts, addresses and services provided. This website is designed using couple of programming languages, such as Javascript, and HTML5. This research is expected to help PT CIBP with this website, making it easier for readers to know the background profile and can increase potential customers.
Nested Conformal Prediction Sets for Classification with Applications to Probation Data

Risk assessments to help inform criminal justice decisions have been used in the United States since the 1920s. Over the past several years, statistical learning risk algorithms have been introduced amid much controversy about fairness, transparency and accuracy. In this paper, we focus on accuracy for a large department of probation and parole that is considering a major revision of its current, statistical learning risk methods. Because the content of each offender's supervision is substantially shaped by a forecast of subsequent conduct, forecasts have real consequences. Here we consider the probability that risk forecasts are correct. We augment standard statistical learning estimates of forecasting uncertainty (i.e., confusion tables) with uncertainty estimates from nested conformal prediction sets. In a demonstration of concept using data from the department of probation and parole, we show that the standard uncertainty measures and uncertainty measures from nested conformal prediction sets can differ dramatically in concept and output. We also provide a modification of nested conformal called the localized conformal method to match confusion tables more closely when possible. A strong case can be made favoring the nested and localized conformal approach. As best we can tell, our formulation of such comparisons and consequent recommendations is novel.
An Integrated Online Mechanism for Feature Selection and Classification in Streaming Data

&lt;p&gt;This is an original reasearch work.&lt;/p&gt;
Compressing the Activation Maps in Deep Convolutional Neural Networks and the Regularization Effect of Compression

&lt;p&gt;Deep learning has dramatically improved performance in various image analysis applications in the last few years. However, recent deep learning architectures can be very large, with up to hundreds of layers and millions or even billions of model parameters that are impossible to fit into commodity graphics processing units. We propose a novel approach for compressing high-dimensional activation maps, the most memory-consuming part when training modern deep learning architectures. To this end, we also evaluated three different methods to compress the activation maps: Wavelet Transform, Discrete Cosine Transform, and Simple Thresholding. We performed experiments in two classification tasks for natural images and two semantic segmentation tasks for medical images. Using the proposed method, we could reduce the memory usage for activation maps by up to 95%. Additionally, we show that the proposed method induces a regularization effect that acts on the layer weight gradients. &lt;/p&gt;
Requirements Management Processes; an insight into the RMP activities in software industry and literature (A Case Study)

no software development exists in industries without the proper relevant software requirements. All the cycles of design and development are based on it. We can say that its play as the backbone for the whole development process. In this paper, process analysis of requirement management in the software industry in the light of real industrial survey through a proper questionnaire. All the results analysis based on a given filled survey by a specific company employee. Their results are based on the requirement life cycle (Trace, Maintain, Prioritize, Access, Approve) that is explained in the result section.
Adoption of digital vaccination services: It is the click flow, not the value. An empirical analysis of the vaccination management of the COVID-19 pandemic in Germany.

This research paper examines the adoption of digital services for the vaccination in the COVID-19 pandemic in Germany. Based on a survey in Germany’s federal state with the highest vaccination rate, which used digital vaccination services, its platform configuration and adoption barriers are analyzed to understand existing and future levers for optimizing vaccination success. Though technological adoption and resistance models have been originally developed for consumer goods markets, this study gives empirical evidence for the applicability of an adjusted model explaining platform adoption for vaccination services in special and for digital health services in general. In this model, the configuration areas personalization, communication, and data management have a remarkable effect to lower adoption barriers, but only functional and psychological factors affect the adoption intention. Above all, the usability barrier stands out with the strongest effect while the often-cited value barrier is not significant at all. Personalization is found to be the most important factor for managing the usability barrier and thus for addressing the needs, preferences, situation, and ultimately the adoption of the citizens as users. Implications are given for policy makers and managers in such pandemic crisis to focus on the click flow and server-to-human interaction rather than emphasizing value messages or touching traditional factors.
ON THE PHYSICAL NATURE OF SUBJECTIVE PSYCHOLOGICAL TIME

A simple temperature dependence of rate changing a psychological time tps(tС) in human was suggested: (∂tps/∂Sps)=Dps·δТ, where Dps is some cоnstant, Sps is the psychological entropy, characterizing current human physical/mental state; δТ is the normalized body temperature δТ=(tС – tСn), tС – the now temperature and tСn – the normal temperature. This dependence as an empirical result of personal experience in “perception” of time intervals (~10 min) at high body temperature (tС ≥38.20С) was ascertained. It has been shown the determining factor to successfully finding the task of quantitatively estimating a duration and changing rate of the psychological time there is cooperation of two thinking components – consciousness and subconsciousness. The optimal physical conditions for involving the subconscious results in significant strengthening of the brain capacity (synergy effect) were found out. The possible application of the formulas obtained for analyzing the changes of psychological time with temperature and entropy was suggested. Using on the calculations performed the graphical temperature dependences δТ, δtps (Dps ·∆Sps) were plotted, that confirmed the expected growth of these parameters.
Cognitional and Intentionality Analysis as the Key to Epistemic Foundation

Since Descartes, the quest for the foundation in epistemology has suffered a series of setbacks.The consequence of the opposition against an epistemic foundation is epistemic skepticism.The irony of the skeptic position is that scepticism in all its hues is self-refuting.Although the establishment of a foundation is essential for coherent epistemology, the quest for epistemic foundation has suffered some oppositions because most attempts at establishing foundational epistemology have focused on intentional signs or productsbeliefs, concepts, propositions, etc.In this essay, I argue that in order to establish foundational epistemology, cognitional and intentionality analysis should take priority over conceptual analysis.Hence, instead of focusing on intentional signs or products, attention should be paid to intentional actsexperiencing, understanding and judgingof the knowing subject.In other words, I argue that paying adequate attention to human cognitional structure is vital in the defense of epistemic foundation and that the foundation is found in the structure of human knowing rather than in the products of human knowing.Focusing on cognitional analysis will help to account for both epistemic foundation and epistemic pluralism.The shift from conceptual to cognitional and intentionality analysis has implications for the articulation of the African perspective on knowledge since the human cognitional structure is the same, but contextual differences arise because of one's epistemic environment.Following the example of Bernard Lonergan, I argue that self-knowledge or self-affirmation of the knower, as he terms it, is the paradigmatic case for the establishment of epistemic foundation.
Confirmative Component Analysis of Management Optimization of Educational Organizations

This research aimed to analyze the corroborative components and examine the correspondence between the corroborative structure of the management of educational organizations and the empirical data. The sample consisted of 400 educational administrators in Thailand. The instrument used in this research was a questionnaire with a confidence value of 0.95 in the whole questionnaire. For Confirmative Component Analysis and second corroborative component analysis, the results showed that Educational Organization Management Optimization Components. The components whose weights were arranged in descending order include Organizational structure, Human Resources, Organization Management and Leadership. The results showed that first confirmed components that all index values ​​were found to pass the criteria. They had an element weight of 0.01-0.15 with a statistical significance of .01 and a second-order confirmation component analysis result. All index values ​​were found to pass the criteria. The constituent weights of 0.26-1.26 were statistically significant at .01. The harmonization index between the model and the empirical data was p = 0.09, CMIN/DF = 1.59, GFI = 0.99 and RMSEA = 0.02. It made you aware of the components of organizational management optimization in education to be used to further develop educational organization management efficiency.
A robust model for spot virtual machine bidding in the cloud market using information gap decision theory (IGDT)

The spot market is one of the most common cloud markets where cloud providers, such as Amazon EC2, rent their surplus computing resources at lower prices in the form of spot virtual machines (SVMs).In this market, which is often managed through an auction mechanism, users seek optimal bidding strategies for renting SVMs to minimize cost and risk.Uncertainty in the price of SVMs and their low availability/reliability is a challenging issue to bid on the user side.In this paper, we present a robust model for minimizing the cost of executing tasks by considering the uncertainty of the price of SVMs based on the Information Gap Decision Theory (IGDT).It evaluates the risk-aversion and the risk-seeker nature of the user's bidding strategy and measures the cost of risk/immunity.The main advantage of this method is that it formulates user decisions without the need for any presumption about the price distribution of SVMs.With this decision-support system, users can rely on the predicted confidence intervals to make the optimal bid for future time slots according to the selected risk level.The results are compared with Monte Carlo simulations and a scenario-based approach to evaluate the effectiveness of the proposed IGDT-based model.Evaluation results based on historical Amazon EC2 prices confirm the efficiency of the proposed method to handle the uncertain nature of the price of SVMs in terms of significant criteria such as robustness cost, opportunity cost, uncertainty budget, and execution time.
A Behavior Sequence Based Psychological Feature Extraction Model for Students in English Teaching

With the rapid development of science and technology, intelligent teaching has been gradually popularized in the campus, and the change of relevant teaching modes has also been carried out. In the new English teaching, it is necessary to analyze the students’ psychological behavior. In order to get accurate results, the research captures the students’ behavior as a behavior sequence, and uses the improved combined kernel support vector machine to extract features on the basis of approximate entropy and local mean decomposition. At the same time, it is compared with the traditional support vector machine, the traditional least square vector machine and the back-propagation neural network algorithm. The experimental results show that the improved algorithm has higher accuracy, recall and accuracy than the other three algorithms. The overall accuracy of the improved algorithm is 82.53%, the highest accuracy is 90.12%, the overall recall is 58.64% and the overall precision is 86.92%. The experimental results show that the improved algorithm has a significant improvement in performance, and can be applied to the extraction of students’ psychological characteristics. Therefore, the algorithm can be used in conventional blended English teaching, which is convenient for teachers to carry out English teaching more efficiently.
Combination of machine learning algorithms and Resnet50 for Arabic Handwritten Classification

The recognition or classification of Arabic handwritten characters is extremely crucial in many applications and, at the same time, one of the biggest challenges that machine learning faces. The emergence of deep learning, particularly Convolutional Neural Networks (CNN), is considered a suitable technique to face these challenges. In this research paper, an investigation model is proposed to make recognition for Arabic handwriting utilizing one of CNN architectures: ResNet50 architecture, after replacing the last layer with one of two types of machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to reduce training time and increase overall accuracy. Our experimental work was performed on three data sets: Arabic Handwritten Character Dataset (AHCD), Alexa Isolated Alphabet Dataset (AIA9K), and Hijja Dataset. Experimental results show that combining ResNet50 with random forest produces more accurate and consistent results than the ResNet50 model produces by itself. Finally, the comparison with the other methods across all data sets demonstrates the robustness and effectiveness of the combination of random forest with the ResNet50 approach. Where the modified ResNet50 architecture has achieved a rate of 92.37%, 98.39%, and 91.64%, while the combination architecture has achieved 95%, 99%, and 92.4% for AIA9K, AHCD, and Hijja datasets, respectively.
Deploying to Traditional Server Based Environments

The most common way to deploy production-grade web apps today is in a traditional server-based environment. This chapter covers how to package your web app so you can deploy it in several types of environments, be it a single-node dedicated server or a distributed multi-server Kubernetes cluster.
TensorShield: Tensor-based Defense Against Adversarial Attacks on Images

Recent studies have demonstrated that machine learning approaches like deep neural networks (DNNs) are easily fooled by adversarial attacks. Subtle and imperceptible perturbations of the data are able to change the result of deep neural networks. Leveraging vulnerable machine learning methods raises many concerns especially in domains where security is an important factor. Therefore, it is crucial to design defense mechanisms against adversarial attacks. For the task of image classification, unnoticeable perturbations mostly occur in the high-frequency spectrum of the image. In this paper, we utilize tensor decomposition techniques as a preprocessing step to find a low-rank approximation of images which can significantly discard high-frequency perturbations. Recently a defense framework called Shield could "vaccinate" Convolutional Neural Networks (CNN) against adversarial examples by performing random-quality JPEG compressions on local patches of images on the ImageNet dataset. Our tensor-based defense mechanism outperforms the SLQ method from Shield by 14% against FastGradient Descent (FGSM) adversarial attacks, while maintaining comparable speed.
Lexical Theoretical Development in Applied Tamil Grammar Texts

After 19th century Tamil literature has gone through many dimensions. Advent of print media, focus on language education and the establishment of new educational institutions are the reason for the development of many language based grammatical texts. The newly added grammatical elements are recorded in the grammar texts. Applied Tamil grammar texts explains the syllable, series and words based on grammar books. However, the new modern language theory records the changes that have appeared in the language system and highlights the language and grammar in the theory of linguistics. Tamil grammar books explains and defines grammar based on a Tamil text Nannul. Types of words are explained in the linguistic point of view by adapting the etymology mentioned in the Nannul. It also explains the new adjectives and adverbs and records the newly developed grammatical elements which are used in modern language. Word classification is divided into three levels. They are, classification based on alphabetic, classification based on case of words and classification based on grammatical usage. Contemporary Tamil tradition examines some of the techniques adopted by Tamil grammarians to define the word. Present Tamil grammarians distinguishes nouns and verbs on the basis of verb or on the basis of object. People also began approaching Tamil Grammar based on English grammar because of the abundance usage of English language. Tamil grammar text ‘Nalla Nool Eluthavaenduma’ explains grammar in a very simple way and it is considered to be the best grammar manual. Tamil grammar text explores the structure of Tamil language. Lexical grammar is explained in terms of linguistics. The four types of words such as noun, verb, interjection and adjective examine the changes that occurred in the language. Thus, the article gives a clear idea about lexical theoretical development in applied Tamil grammar texts.
PENGEMBANGAN MEDIA E-BOOK PIGUDA BANTEN UNTUK MENINGKATKAN PEMAHAMAN LAGU DAN BAHASA DAERAH PADA PESERTA DIDIK

Abstract. This study aims to develop e-book media piguda Banten and test its feasibility. The background is that the media is still very minimal regarding the introduction of songs and regional languages. The researcher uses the research and development method of the ADDIE model from Dick and Carry which consists of analysis, design, development, implementation, and evaluation. The sample of this study was the fifth grade elementary school students, totaling 12 students. Data collection techniques in this study used expert validation sheets and classroom teachers. Validation results were obtained from media experts with a percentage of 88% and validation results were obtained from material experts with a percentage of 100% and validation results by class teachers with a percentage of 82.5%. The results of the implementation of the use of e-book media were tested with pretest and posttest and got an increase. In the pretest student learning outcomes are 52.5 with a percentage of 32% and as many as 4 students have reached the KKM, while in the posttest the average score obtained is 80 with a percentage result of 75% and as many as 9 students have reached the KKM. The results of data analysis using the N-gain formula got an average value of 0.6 with each category of student answers increasing. The results of the media response of piguda e-books by students obtained a percentage of 91% which stated "very feasible". The conclusion of this study is that the Piguda Banten e-book media is suitable for use in learning.&#x0D; Keywords: Piguda E-book, Song, Banten regional language.