Reviewerly/exHarmony huggingface dataset:
Exploration on Content-Based Image Retrieval Methods

In the current ages, the progress in computer knowledge and multimedia solicitations has commanded to the construction of massive digital images and huge image databanks, and it is growing speedily. Here are numerous dissimilar expanses in which image retrieval shows a critical part resembling Medical systems, Forensic Labs, Tourism Elevation, etc. Thus, repossession of comparable images is an experiment. To challenge this speedy development in digital causes, it is essential to advance content-based image retrieval (CBIR) schemes, which can function on great databanks. Intelligent or deep learning way of content-based penetrating is requisite to accomplish the examining appeal with correct pictorial substances in a sensible quantity of time. There are some actually smart methods planned by investigators for effectual and vigorous content-based image retrieval. With these approaches, the quantity of approaches has been altered for the effectual image recovery of images. In this article, the review of various methods that have been utilized beginning from Image retrieval using pictorial structures like Bayesian Learning Algorithm, Self-Organizing Maps, Decision Trees Relevance Feedback, Genetic Programming, Navigation Pattern Mining, Association-Based Image Retrieval, Artificial Neural Networks (ANN) methodology is proposed and utilized for the content-based image retrieval. And the latest techniques such as the deep learning, ensemble learning methods with more number of layers and present suits the finest technique for repossession of images from the excessive databanks. In this effort, the goal is to best part the exertions of investigators who accompanied some strong exertion and to deliver an impervious perception for intellectual content-based image retrieval methods.
Deep learning supported breast cancer classification with multi-modal image fusion

Early diagnosis using deep learning of the early-stage breast cancer to enhance the diagnoses, relying on one type of image modality has the risk of missing tumors or false diagnosis. Combining two image modalities (mammography and ultrasound) for better classification and combining information from different modalities can significantly improve classification accuracy. Using dense connections has a great interest in computer vision because they enable gradient flow and attain deep supervision all through training. In particular, for DenseNet every layer is connected to other different layers in a feed-forward style, this proves spectacular performances in natural image classification tasks. The proposed DenseNet 201 with connections between the same path and different paths has overall freedom to learn more complicated combinations among the modalities. Multi-modal images are used to collect features from diverse views and produce complementary information. The experimental outcomes for different parameters (accuracy, recall, precision area under the curve, and F1 score) of the proposed method were 93.83%, 93.83%, 93.83%, 95.61%, and 93.8% respectively. The obtained results in diagnosing breast cancer using ultrasound and mammogram images show better performance compared to previous methods in assisting specialists.
Voice Query Auto Completion

Query auto completion (QAC) is the task of predicting a search engine user's final query from their intermediate, incomplete query.In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcripts as users speak.Naïvely applying existing methods fails because the intermediate transcripts often don't form prefixes or even substrings of the final transcript.To address this issue, we propose to condition QAC approaches on intermediate transcripts to complete voice queries.We evaluate our models on a speech-enabled smart television with reallife voice search traffic, finding that this ASRaware conditioning improves the completion quality.Our best method obtains an 18% relative improvement in mean reciprocal rank over previous methods.
Assessing aquatic enviromnemt quality of the urban water bodies by system analysis methods based on integrating remote sensing data

The work presents the comprehensive methodology for assessment of the state of the urban aquatic environment such as Lakes Opechen, Verbne, and Redkyne in Kyiv using the methods of system analysis. The methodology includes structural-textural analysis of the satellite images and the method based on statistical criteria. The spectral-texture analysis of the satellite images was used to get input information for remote assessment of reservoirs as index images: Normalized Difference Pond Index (NDPI), Normalized Difference Turbidity Index (NDTI), and Normalized Difference Algae Index (NDAI) computed from the Sentinel-2. The surface temperature distribution was estimated from the Landsat 8. The method based on statistical criteria is used for a detailed assessment of the aquatic environment using the obtained indexed images and the corresponding cartographic representation of the water quality. The probabilistic and statistical approaches were used to present the statistical criterion for recognizing classes of objects based on the results of measuring their informative features. These approaches are used to solve optimization problems in statistical theories of identification and recognition. This method allowed the cartographic representing of the change in the water quality and aquatic ecosystem in accordance with the reference areas of the state of the reservoir in 2017.
Full Bitcoin blockchain data made easy

Despite the fact that it is publicly available, collecting and processing the full bitcoin blockchain data is not trivial. Its mere size, history, and other features indeed raise quite specific challenges, that we address in this paper. The strengths of our approach are the following: it relies on very basic and standard tools, which makes the procedure reliable and easily reproducible; it is a purely lossless procedure ensuring that we catch and preserve all existing data; it provides additional indexing that makes it easy to further process the whole data and select appropriate subsets of it. We present our procedure in details and illustrate its added value on large-scale use cases, like address clustering. We provide an implementation online, as well as the obtained dataset.
Artificial Neural Networks and Backpropagation

Inspired by the biological neural network, here we discuss its mathematical abstraction known as the artificial neural network (ANN). Although efforts have been made to model all aspects of the biological neuron using a mathematical model, all of them may not be necessary: rather, there are some key aspects that should not be neglected when modeling a neuron. This includes the weight adaptation and the nonlinearity.
Pola Asuh Orang Tua Terhadap Motivasi Belajar Siswa SMP Unggulan ‘Aisyiyah Bantul

The first and main institution is the family, who provides early knowledge, guidance, attention and affection that they feel is very influential in the process of psychological growth and even social development in children. Parenting by parents greatly influences the success of a child. The purpose of this study is to find out the factors that influence the low motivation of student learning and to find out how the parenting of students at home, so influence children’s learning motivation. This type of research is descriptive with a qualititave approach. Research subjects are students and home room teachers. Data collected in this study used interview and observation techniques. The data collected is analyzed using interactive analysis of miles and huberman which covering 3 data reduction, data display and conclusion drawing or verification. The results showed that the learning facilities of parents have an effect but are meaningless without good assistance from parents. In the educational environment the influence of peers, teachers and class atmosphere is also a supporting factor in increasing student motivation. &#x0D; &#x0D; Keywords: Parenting; Motivation to Learn; Supporting Factor
USING OF VIRTUAL REALITY TOOLS FOR THE DEVELOPMENT OF STEAM EDUCATION IN GENERAL SECONDARY EDUCATION

The article researches the use of virtual reality to support STEAM education in general secondary school. To study the impact of STEAM education, the authors proposed a teaching project for the secondary school about convex mirrors and their importance for special visibility and human safety, where the interviewed teachers were able to learn about a real example of the implementation of STEAM education for teaching their disciplines. The purpose of the article is to analyze the meanings and approaches to the use of virtual reality by teachers for organizing the STEAM-oriented learning environment and to identify the basic requirements to VR for supporting the implementation and development of STEAM education. One of the main trends of education modernization is STEAM education, which involves the integration of the natural sciences, the technological sciences, engineering, mathematics and art in the learning process, in particular, at general secondary schools. In light of the findings, researchers indicate that electronic educational resources (including VR &amp; AR) are not only teaching tools for teachers but also a source of inspiration for students, which motivates and stimulates creative thinking. This is an important point considering that creativity is increasingly an important life skill that can help young people cope with the difficulties and uncertainties in their future careers in the fields of STEAM. Prospects for further research are seen in the creation of a model for assessing student performance in STEAM projects. The authors are planning to take into account the differences and features of the use of virtual reality for learning STEAM in schools in different countries, including Ukraine, Spain and Kazakhstan, when creating a multilevel model of STEAM-oriented learning environment using virtual reality tools. The purpose of the model is to evaluate not only the results of tests taken by students after learning this material but also their personal contribution to the final project product, their leadership skills, creative ideas and suggestions, abilities and skills in using ICT and VR in project research. In our next publications, we will focus on several Spanish and Kazakhstani secondary schools to detect the possible differences found in the use of virtual reality to support STEAM education in Ukrainian schools.
Cloud Solutions for Private Permissionless Blockchain Deployment

This paper aims to survey the security and scalability problems occurring in private permissionless blockchain systems and solutions to them. The emphasis is put on the blockchain systems hosted by cloud vendors in the form of Blockchain-as-a-Service (BaaS). The currently available solutions offered by the most appreciated cloud providers are reviewed. The most promising services are tested for the real deployment of the consent management system (CMS). Implementing the CMS atop BaaS leads to creating Consent-as-a-Service (CaaS). Through experiments, the proposed system's replication ability and its scalability are examined, along with assessing the feasibility of the consent management system development in the provided cloud environment.
Hypernet Model of Media Communication: new qualities of risks for children

The technologically determined hyperbolic growth of communicative connectivity and the imperfection of administrative mechanisms of control over this process lead to an emergence of new phenomena and new risks, including risks in relation to the most vulnerable social group – children. The article proposes a new approach to describing the nature of the risks of the media space in relation to the health and development of children. Minimization of these risks is defined by the author as the goal of ensuring the information security of children. New qualities of risks are described on the basis of a hypernet model of media communication and developed on the basis of the hypernetwork brain theory. The author specifies the properties of hypernet education and their riskiness, mediated by the network morphology of connections, which implies the impossibility or ineffectiveness of the application of customary management practices. The vulnerability of the child group to these risks stem from the psychological immaturity. The importance of the phenomenalization of “media” and “information” is noted for the purposes of information policy and security – media security. The article reveals the tendencies of the media hypernetwork that increase the risks, especially the growth in the speed and volume of information transfer provided by technologies, which excludes the action of the well-known mechanisms for regulating the circulation of information and filtering content.
Analysis of urban flood using synthetic unit hydrograph (SUH) and flood mitigation strategies along way Halim River: a case study on Seroja street, Tanjung Senang District

In the urban area, flooding becomes the most common disaster that has not been resolved until today. The utilization of river border area into housing and lack of absorption area becomes the trigger factor of urban flooding, as what is happening around Way Halim River on Seroja street. In this area, floods often happen during the rainy season, with the latest events recorded on January 21 st , 2021. Analysis of flood intensities and discharges can be parameters for the decision-making of flood mitigation strategies. This study aims to analyze the flood discharges along Way Halim River, Seroja street by comparing the flood discharges resulting from three analysis methods of Synthetic Unit Hydrograph (SUH) including Gama I SUH, Nakayasu SUH, and Snyder SUH. Finally, suitable flood mitigation strategies were also proposed in this study based on the flood discharges and rain intensities. The results showed that Nakayasu SUH had the highest peak flood discharge than Snyder SUH and Gama I SUH. Based on the results of the investigation of land suitability; and analysis of rainfall intensities and flood discharges, the proposed flood mitigation in Seroja street is by installing biopore infiltration holes along Seroja street for storing water and reducing the risk of flooding in the area.
Automatic radiofrequency ablation planning for liver tumors

Radiofrequency ablation (RFA) is widely used in the treatment of liver tumors. Computer-aided planning is needed to preoperatively provide reliable paths for puncturing electrode needles into the treatment zone with multiple clinical constraints. Under the constraints, a genetic algorithm (GA)-based method was proposed to plan the optimal needle paths without passing the import tissues, and the produced ablation zone completely and conformably cover the tumor. In the proposed method, the appropriate paths between the treatment zone and the skin were first filtered in accordance with the constraints. Then the ablation zone model was determined for each appropriate path. Each point in the treatment zone was treated as a gene, and all genes were grouped as a chromosome. The path planning optimization could be regarded as the gene expression in a chromosome. On the basis of the filtered appropriate paths and the determined ablation zone, the optimal paths were obtained through GA. In the experiment, 32 tumors from nine patients were used to evaluate the proposed method. The resultant paths ensured no import tissues were passed, and the number of used electrode needles and damaged healthy tissues by the ablation zone was minimum. Therefore, the proposed method provides reliable electrode needle paths for physicians.
Proficiency and the Use of Machine Translation: A Case Study of Four Japanese Learners

While the use of machine translation (MT) in the classroom has been explored from various perspectives, the relationship between language proficiency and MT use regarding learners' behaviors and beliefs remains unclear in the research literature.This study focused on four Japanese learners with various language proficiencies from a fourth-year Japanese language class (two advanced-level, one intermediatehigh, and one novice-high level) and investigated how they edited self-written text with MT by examining the scope and types of revisions they made as well as their perceptions about using MT for editing.The data included four types of drafts of a writing assignment: (1) D1 (self-written drafts in Japanese without the help of MT); (2) D2 (revised corresponding drafts in L1 provided by MT); (3) D3 (drafts in Japanese provided by MT based on D2); (4) D4 (revised drafts based on comparison of D1 and D3) and their reflection papers.The results show that the four participants adopted various ways of editing self-written text.While all the participants' revisions are at local levels, the two advanced level learners primarily focused on vocabulary revision while the other two learners' revisions extended to the sentence level.The findings also show that the advanced-level and intermediate-high-level learners have various degrees of positive attitudes toward using MT.In contrast, while the positive effects of MT use are acknowledged, the novice-high level learner also feels ashamed and dishonest when using MT.This article concludes with insights that can assist instructors in facilitating MT as a pedagogical tool for language learning and teaching with diverse students.
The Challenge of Achieving Zero Trust Remote Access in Multi-Cloud Environment

Zero-trust security models and architectures have recently increased in adoption due to several variables, such as the widespread use of off-premises cloud technologies, variety in IT devices, and diffusion in the Internet of Things (IoT). Users, devices, apps, and networks are all assumed to be untrustworthy in this approach, which is built on the idea of various tiers of Trust and authentication. Cybersecurity paradigms are developing, and the term "zero trust" describes the shift from static network perimeters to protecting people, things, and resources. Economic and enterprise architecture and processes can be designed using zero trust principles. In the idea of zero Trust, assets or user accounts are thought to have no implicit confidence because of their physical or network location (Internet vs local networks) or asset ownership (enterprise or personally owned). Authentication and authorization must be conducted before a connection to an organizational resource can be established. There are many different types of Cloud, including several public, private, hybrid, and on-premises. For data centers, a multi-cloud deployment strategy includes many different public cloud service providers instead of relying on a private cloud or on-premises architecture. Hybrid multi-cloud is a multi-cloud implementation that incorporates all public and private clouds and on-premises technology. This paper discusses the zero-trust security model for multi-cloud environments and applications and the obstacles to implementing it.
Graph Diffusion &amp; PCA Framework for Semi-supervised Learning

A novel framework called Graph diffusion & PCA (GDPCA) is proposed in the context of semi-supervised learning on graph structured data. It combines a modified Principal Component Analysis with the classical supervised loss and Laplacian regularization, thus handling the case where the adjacency matrix is Sparse and avoiding the Curse of dimensionality. Our framework can be applied to non-graph datasets as well, such as images by constructing similarity graph. GDPCA improves node classification by enriching the local graph structure by node covariance. We demonstrate the performance of GDPCA in experiments on citation networks and images, and we show that GDPCA compares favourably with the best state-of-the-art algorithms and has significantly lower computational complexity.
THE URGENCY OF USING AUDIOBOOK IN GENERAL COURSE LEARNING

AbstractAudiobook is one of the alternative media that can provide material in the form of sound recordings made to attract concentration, facilitate learners, and provide a creative platform in learning. The lack of use of audiobooks in learning is one of the toughest challenges for teachers to achieve the existing learning objectives, especially in university. This study descriptively explored the urgency of using audiobooks in general course learning as a form of distance learning manifestation. A total of 294 respondents filled out the questionnaire by selecting the sample using purposive sampling. The results showed that most respondents thought that the use of audiobooks in learning was a positive breakthrough that could arouse their interest and interest in being active in learning. In addition, respondents also agreed that the use of this audiobook must be strengthened by various essential elements such as the material to be delivered, the chosen voice actor, and the duration of time that will be used. Thus, the results of this study illustrate the importance of using audiobooks in learning.Keyword: Audiobook, Distance Learning, Learning Media
A Website-based Information System Design of SME Development Facilitation Registration

Small and medium enterprises (SMEs) are particularly important for the economy of West Java Province. Accordingly, The Industry and Trade Agency of West Java Province has conducted a number of facilitation programs to support the development of these SMEs. Neverheless, the implementation of facilitation programs is faced with some major problems, for example, the registration system for applying and proposing facilitation programs is still manually managed. As a result, some SMEs do not have the opportunity to propose and receive facilitation programs from the agency in addition to the several serious issues encountered by the agency to evaluate the facilitation programs. This study aims to design a website-based information system of SME development facilitation registration in the Industry and Trade Agency of West Java Province Government, Indonesia. It used the Waterfall method to develop the system. The results suggested that the system can well run as expected. In addition, the result of usability test indicates that the proposed system can be classified as acceptable because its score is 80.4.
Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: Application to evolutionary modeling of clinical pathways

The paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms. The approach is based on the identification of surrogate models for prediction of the target algorithm’s quality and performance. The proposed approach was implemented and investigated as applied to an evolutionary algorithm for discovering clusters of interpretable clinical pathways in electronic health records of patients with acute coronary syndrome. Several clustering metrics and execution time were used as the target quality and performance metrics respectively. An analytical software prototype based on the proposed approach for the prediction of algorithm characteristics and feature analysis was developed to provide a more interpretable prediction of the target algorithm’s performance and quality that can be further used for parameter tuning.