Reviewerly/exHarmony huggingface dataset:
Non-Local Mean Denoising Algorithm Based on Fractional Compact Finite Difference Scheme Effectively Reduces Speckle Noise in Optical Coherence Tomography Images

Optical coherence tomography (OCT) is used in various fields such, as medical diagnosis and material inspection, as a non-invasive and high-resolution optical imaging modality. However, an OCT image is damaged by speckle noise during its generation, thus reducing the image quality. To address this problem, a non-local means (NLM) algorithm based on the fractional compact finite difference scheme (FCFDS) is proposed to remove the speckle noise in OCT images. FCFDS uses more local pixel information when compared to integer-order difference operators. The FCFDS operator is introduced into the NLM algorithm to construct a high-precision weight calculation so that the proposed algorithm can effectively reduce the speckle noise in the OCT images. Experiments on simulations and real OCT images show that the proposed method is comparable to other state-of-the-art despeckling methods and can substantially reduce noise and preserve image details such as edges and structures. Speckle noise removal can further promote the application of the proposed algorithm in medical diagnosis and industrial detection, as it has key research value.
Improving Semi-Supervised Image Classification by Assigning Different Weights to Correctly and Incorrectly Classified Samples

Semi-supervised deep learning, a model that aims to effectively use unlabeled data to help learn sample features from labeled data, is a recent hot topic. To effectively use unlabeled data, a new semi-supervised learning model based on a consistency strategy is proposed. In the supervised part with labeled samples, the image generation model first generates some artificial images to complement the limited number of labeled samples. Secondly, the sample label mapping, as the “benchmark”, is compared to the corresponding sample features in the network as an additional loss to complement the original supervisory loss, aiming to better correct the model parameters. Finally, the original supervised loss is changed so that the network parameters are determined by the characteristics of each correctly classified sample. In the unsupervised part, the actual unsupervised loss is altered so that the model does not “treat all samples equally” and can focus more on the characteristics of misclassified samples. A total of 40 labeled samples from the CIFAR-10 and SVHN datasets were used to train the semi-supervised model achieving accuracies of 93.25% and 96.83%, respectively, demonstrating the effectiveness of the proposed semi-supervised model.
Sistem Informasi Perancangan Pendaftaran Online Berbasis Web Di Puskesmas Pataruman Kabupaten Bandung Barat

Puskesmas is a health facility that organizes public health efforts and first-level individual health efforts that always strive to achieve efficiency and effectiveness of services to the community, so it needs a facility and infrastructure that supports data processing of a website-based information system that is useful for providing information quickly and accurately. The system used at the Pataruman Health Center still uses a manual system using paper as a data processing tool so that in the end various problems arise. Such as the difficulty of controlling information on user data, patient data, patient registration, the process of searching for patient data becomes long and inaccurate due to the accumulation of paper containing data, and because of the perishable nature of paper, it is very possible to lose data. This research uses the waterfall method by using Apache 2.4.34 as a web server, PHP 5.6.38 as a programming language and MySQL 5.0.21 as a database. So the author created a website to support the performance of Pataruman Public Health Center, based on the results of research conducted at Pataruman Public Health Center. This website is designed with a simple display to make it easier for health center staff to use it. It can be concluded that this research has succeeded in creating an information system using the website method.
Video frame interpolation via residual blocks and feature pyramid networks

Various deep learning-based video frame interpolation methods have been proposed in the past few years, but how to generate high quality interpolated frames in videos with large motions, complex backgrounds and rich textures is still a challenging issue. To deal with this limitation, a frame interpolation method based on residual blocks and feature pyramids is proposed. U-Net is the main architecture of our method, which can capture multi-layer information, segment objects from the background and obtain parameters with motion information to guide frame interpolation. However, the upsampling and subsampled of U-Net will lose important information. In order to acquire more detailed contextual information, shortcut connection is used in the encoder basic module. At the same time, feature pyramid network is employed to capture features at different scales of the decoder to improve the representation of inter-frame spatial-temporal features. The experimental results show that the proposed method outperform the baseline methods in both of objective and subjective evaluations on different datasets. In particular, the method has obvious advantages on datasets which contain complex background.
Cloud-Native Applications and Services

This Special Issue presents some of the most recent innovations in cloud-native software and system engineering practices providing a broad and well-grounded picture of what the more and more frequently used term “Cloud-native” is currently used for [...]
A Review on Handwritten Character Recognition Using Advanced Techniques

Due to its widespread use, handwriting recognition has drawn a lot of interest in the domains of pattern recognition and machine learning. The application domain for optical character recognition (OCR) and handwritten character recognition (HCR) is specific. For character recognition in a system for handwriting recognition, several strategies have been proposed. Despite this, a substantial number of studies and papers outline the methods for transforming the text of a paper document into a machine-readable format. Character recognition (CR) technology may be crucial in the near future in order to process and digitize existing paper documents in order to establish a paperless environment. This essay offers a thorough analysis of the handwritten character recognition field.
Development of Evolutionary Systems Based on Quantum Petri Nets

Evolutionary systems (ES) include software applications that solve problems using heuristic methods instead of the deterministic ones. The classical computing used for ES development involves random methods to improve different kinds of genomes. The mappings of these genomes lead to individuals that correspond to the searched solutions. The individual evaluations by simulations serve for the improvement of their genotypes. Quantum computations, unlike the classical computations, can describe and simulate a large set of individuals simultaneously. This feature is used to diminish the time for finding the solutions. Quantum Petri Nets (QPNs) can model dynamical systems with probabilistic features that make them appropriate for the development of ES. Some examples of ES applications using the QPNs are given to show the benefits of the current approach. The current research solves quantum evolutionary problems using quantum genetic algorithms conceived and improved based on QPN. They were tested on a dynamic system using a Quantum Discrete Controlled Walker (QDCW).
A Novel Ensemble Strategy Based on Determinantal Point Processes for Transfer Learning

Transfer learning (TL) hopes to train a model for target domain tasks by using knowledge from different but related source domains. Most TL methods focus more on improving the predictive performance of the single model across domains. Since domain differences cannot be avoided, the knowledge from the source domain to obtain the target domain is limited. Therefore, the transfer model has to predict out-of-distribution (OOD) data in the target domain. However, the prediction of the single model is unstable when dealing with the OOD data, which can easily cause negative transfer. To solve this problem, we propose a parallel ensemble strategy based on Determinantal Point Processes (DPP) for transfer learning. In this strategy, we first proposed an improved DPP sampling to generate training subsets with higher transferability and diversity. Second, we use the subsets to train the base models. Finally, the base models are fused using the adaptability of subsets. To validate the effectiveness of the ensemble strategy, we couple the ensemble strategy into traditional TL models and deep TL models and evaluate the transfer performance models on text and image data sets. The experiment results show that our proposed ensemble strategy can significantly improve the performance of the transfer model.
Unsupervised domain adaptation with post-adaptation labeled domain performance preservation

Unsupervised domain adaptation is a machine learning-oriented application that aims to transfer knowledge learned from a seen (source) domain with labeled data to an unseen (target) domain with only unlabeled data. Recently developed techniques apply adversarial learning to learn domain-transferable features. However, current adversarial domain adaptation models suffer from the training instability of adversarial networks. Furthermore, it is unclear what the source domain pays in terms of performance during learning the domain-transferable representation. To address this issue, we propose a novel approach termed Unsupervised DomainAdaptation with Source Preservation (UDA-SP). It shares the same objective of obtaining a generalization representation between different distributions as domain adaptation techniques. Additionally, it has the new objective of preserving efficient performance in the source domain. This is accomplished by learning representations of shared and source-specific features that are separately learned from two distinct networks. Then, they are concatenated with available class information to train a new classifier that has the ability to exploit both shared and domain-specific features. We conducted a comprehensive experimental analysis on three benchmark text datasets. Experiments validate that our proposed method outperforms their competing state-of-the-art methods. Further experiments demonstrate that UDA-SP has a good ability to generalize learned knowledge to unseen domains while maintaining seen domain performance.
The Relationship between Employee Participation, Careerplanning, Training and Development, and Private University Performance: The Mediating Role of Organisational Learning

This study examines the mediating role of organisational learning in the relationship between employee participation, training & development, career planning, and private university performance.It proposes a conceptual framework to analyses these relationship.The framework is based on key theories and relevant literature in human resources management practices and organisation performance.The framework can be used to empirically test the above relationship.Data will be collected through a cross sectional survey.Methodologically, the framework will be tested using a quantitative method and SmartPLS will be used for data analysis.The proposed framework is predicted to have major managerial implications for the private higher education system seeking to improve its competitive advantage.
Development of digital competence and Internet literacy of students in foreign language lessons

The article presents an attempt to consider the leading methods and means of developing the digital competence of schoolchildren in the context of the digitalization of language education. It is noted that the most important component of teaching foreign languages ​​is the connection of the taught discipline with reality. In the era of digital technologies, ICT competence, digital competence, digital literacy, media literacy, Internet literacy, which need to be developed in order to more effectively solve the tasks set by schoolchildren, become integral competencies. The following teaching methods and tools aimed at developing the above competencies are considered: Quizziz, Kahoot, Quizlet and Learnis online platforms; interactive Internet resource Learning apps; video podcasts; PROClass knowledge quality monitoring system.
Liberate academic research from paywalls

Academic publishing is one of the most profitable industries in the world. Academic publishing is lucrative because of its unique business model. A traditional publisher covers expensive costs. It pays writers for articles. It employs editors to shape and check the articles. It pays to distribute the products to subscribers and retailers. However, academic publishing costs publishers less. Researchers give their articles to academic publishers for free. Leading scholars volunteer to judge whether the articles are worth publishing. The publishers then sell the product back to university libraries and public-funded institutions. Many researchers criticized this business model. Researchers should resist this unfair business model because it keeps raising unreasonable subscription fees, uses a sales strategy that wastes universities’ money, and exerts bad influence over researches. Universities and institutions should build open-access systems themselves instead of merely boycotting the publishers because (1) boycotting cannot effectively force publishers to change; (2) open access helps researchers find, retrieve, and use the research they need; (3) publishers charge authors expensive fees on open access; (4) scientists already have sufficient tools to build open-access systems themselves.
Pregina Art &amp; Showbiz Participation in the 14th Denpasar Festival Opening Ceremony

Purpose: The purpose of this research is to find out the role of Pregina Art &amp; Showbiz, e.g. the pre-production, production, and post-production processes, as well as the obstacles that occurred during the opening of the 14th Denpasar Festival on 10 December 2021 at Alaya Dharma Negara building, Denpasar.&#x0D; Research methods: the qualitative method used during this activity covers participant-observation, interview, and documentation.&#x0D; Results and discussion: Having participated at the activity, the writers knew what Pregina Art &amp; Showbiz Art's role was. Pregina Art &amp; Showbiz became the Event Organizer for this event and made a live stream.&#x0D; Implication: There are a special knowledge and skill on how to lead an event and how to market and sell it directly to clients via social media.
Arbitrarily Accurate Classification Applied to Specific Emitter Identification

&lt;p&gt;This article introduces a method of evaluating subsamples until any prescribed level of classification accuracy is attained, thus obtaining arbitrary accuracy. A logarithmic reduction in error rate is obtained with a linear increase in sample count. The technique is applied to specific emitter identification on a published dataset of physically recorded over-the-air signals from 16 ostensibly identical high-performance radios. The technique uses a multi-channel deep learning convolutional neural network acting on the bispectra of I/Q signal subsamples each consisting of 56 parts per million (ppm) of the original signal duration. High levels of accuracy are obtained with minimal computation time: in this application, each addition of eight samples decreases error by one order of magnitude.&lt;/p&gt;
Automatically Detecting Technical Debt Discussions

&lt;p&gt;Technical debt refers to suboptimal choices during software development that achieve short-term goals at the expense of long-term quality. Although often discussed informally, technical debt has not crystalized into a consistently applied type and remains invisible when describing issues in issue trackers that are used for coordinating work among software developers. In this study, we manually labeled references to technical debt for 1,934 tickets in the Chromium issue tracker. We used these labels to train a classifier to estimate labels for an additional 475,000 tickets. Our classifier significantly outperforms key phrase search, and we conclude that discussion of technical debt appears in about 16% of the tracked Chromium issues. This application of machine learning to locate technical debt can not only make it visible but also help in its timely resolution.&lt;/p&gt;
Auto Classification of Blooms Cognitive Domain Using Word Embedding Deep Neural Network Classifier

&lt;p&gt;In this work, CANTERBURY QUESTION SET and Yahya et al QUESTION SET were used for effective classifications.&lt;/p&gt;
DNAD: Differentiable Neural Architecture Distillation

&lt;p&gt;In order to satisfy the demand for designing efficient neural networks with an appropriate tradeoff between model performance (e.g., classifification accuracy) and computational complexity, the differentiable neural architecture distillation (DNAD) algorithm is developed based on two cores, namely, &lt;em&gt;search by deleting &lt;/em&gt;and &lt;em&gt;search by imitating&lt;/em&gt;. On the one hand, to derive neural architectures in a search space where cells of the same type no longer share the same topology, the super-network progressive shrinking (SNPS) algorithm is developed based on the framework of differentiable architecture search (DARTS), i.e., &lt;em&gt;search by deleting&lt;/em&gt;. Unlike conventional DARTS-based approaches which produce neural architectures with simple structures and derive only one architecture during the search procedure, the SNPS algorithm is able to derive a Pareto-optimal set of architectures with more complex structures (more powerful representation ability) by forcing the dynamic super-network shrink from a dense structure to a sparse one progressively. On the other hand, since knowledge distillation (KD) has shown great effectiveness to train a compact student network with the help of a powerful over-parameterized teacher network, we combine the KD with SNPS to derive the DNAD algorithm, i.e., &lt;em&gt;search by imitating&lt;/em&gt;. By minimizing the behavioral difference between the dynamic super-network and the teacher network, the over-fifitting of one-level DARTS is avoided and well-performed neural architectures are derived. Experiments on CIFAR10 and ImageNet demonstrate that both SNPS and DNAD are able to derive neural architectures in one search procedure with competitive tradeoffs between the model performance and the computational complexity.&lt;/p&gt;
Evaluating Variability of Physico-chemical Properties of Soils from Urban Park of Tasik Permaisuri Cheras for Forensic Purpose

Forensic soil analysis is commonly used to establish a link between soil collected from a suspect (known samples) and soil collected from a crime scene (questioned sample) (1). Taman Tasik Permaisuri is located about 7km from Kuala Lumpur, one of the capital cities of Malaysia. Being a densely populated residential place, the rate of crime reported is also high. Soil is one of the most common types of evidence encountered in various types of crimes (2). The understanding of the variability of soil of a place is of fundamental importance to assess reliable evidence on forensic investigations (3). Therefore, this work aimed to evaluate the physico-chemical variability of soils collected from the urban park of Taman Tasik Permaisuri. A total of 20 soil samples were collected from several proximate sites around the park. The pH of all the samples dissolved in distilled water were measured using a pH meter. Meanwhile, colour of the samples in dried and wet conditions were recorded based on the Munsell Soil Color Chart. The variability of the physico-chemical properties was first explored using levelplot and then assessed via one-way ANOVA test). Fig. 1 illustrates the variability of the 20 soil samples by pH and soil color observed under dried condition using levelplot. In overall, the soils were mainly slight acidic or neutral. The soils exhibited rather low variability in pH value despite the one-way ANOVA coupled with LSD test (Fig. 2) denoted that the samples can be divided into ten subgroups. In conclusion, the physico-chemical properties of soils collected from the urban park of Taman Tasik Permaisuri were rather homogenous with mild variability.
Quantum reduction of the Hamiltonian path problem to period-finding

This paper reduces the undirected Hamiltonian path existence problem into a period-finding problem, though in sense of quantum reduction and with help of computational basis-based quantum Fourier transform. An undirected graph is encoded into complex-valued function p(t) of natural-valued variable t by frequency assignments to vertices and walks. g(t) is then constructed from p(t) by a unitary operation such that its period depends on whether Hamiltonian paths exist or not.
A Dynamic Federated Identity Management Using OpenID Connect

Identity federation allows one to link a user’s digital identities across several identity management systems. Federated identity management (FIM) ensures that users have easy access to the available resources. However, scaling FIM to numerous partners is a challenging process due to the interoperability issue between different federation architectures. This study proposes a dynamic identity federation model to eliminate the manual configuration steps needed to establish an organizational identity federation by utilizing the OpenID Connect (OIDC) framework. The proposed model consists of three major steps to establish dynamic FIM: first, the discovery of the OpenID service provider, which indicates the location of the partner organization; second, the registration of the OpenID relying party, which allows the organization and its partner to negotiate information for establishing the federation; finally, establishing the dynamic trust federation. The proposed dynamic FIM model allows applications to provide services to end-users coming from various domains while maintaining a trust between clients and service providers. Through our proposed dynamic identity federation model, organizations can save hundreds of hours by achieving dynamic federation in runtime and serving a large number of end-users.
APPLICATION OF SMART TECHNOLOGIES IN MODERN EDUCATIONAL INSTITUTIONS: INNOVATION, FLEXIBILITY, CREATIVITY

The article considers various aspects of the application of smart technologies and smart education (smart learning) in the modern educational space. The authors emphasize that smart education is a reflection of a new type of education system aimed at the process of acquiring skills and competencies for flexible and adapted interaction of pupils and student youth with the social, economic and technological environment.&#x0D; The idea of smart education is based on the provision of various educational services, the creation of individual educational trajectories, the use of global open educational resources and creates real opportunities to use the advantages of the global information society to ensure educational needs and interests.The basic principles of smart education and the role of modern smart technologies in the domestic educational space are defined, their innovative and creative character is emphasized.The article points out the importance and necessity of taking into account three important elements of smart education in education, which determine the disclosure of its potential - innovation, flexibility and creativity.&#x0D;