Reviewerly/exHarmony huggingface dataset:
Deformable histopathology-MRI image registration using deep learning

It is important to correlate histopathology images with radiological images to establish disease signatures and radiomic features defined on radiological images. Although modern imaging modalities such as CT and MRI can provide detailed patient anatomy information, the pathological interpretation of these images may be difficult and subject to physician’s experiences. In contrast, histopathology images which provide tissue structure at cellular level is considered the gold standard for cancer diagnosis. Therefore, histopathological images fusion with radiological images helps interpretation and computer-aided disease classification and segmentation. In this study, we propose a new MR volume to histopathology slice image registration method using unsupervised deep learning (DL). The pathology images were first manually rotated to align with the MRI. Histopathology slice correspondence to the MRI slice was then established by calculating image similarity between the two. Because of the endorectal MRI coil, the MRI prostate is often pressed, causing deformation, from the posterior side. Even without endorectal coil, the bladder could cause the shape mismatch between the histology and MRI. Therefore, it is important to model the prostate deformation. In this study, a thin-plate-spline deformation model was calculated from prostate surface difference between the MRI slice and the histopathology. To match the image content, a DL-based method was proposed to deformably register the histopathology image to the MRI. The proposed method utilizes modality independent neighborhood descriptor (MIND) as the image similarity measure during network training. Tested on 10 cases, the SSIM between the two were on average 0.83 and 0.90 before and after registration. Visual inspection suggests good image registration performance of the proposed method.
Transformation of Value Orientations of Higher Education Applicants in Ukraine

The relevance of the study stems from the role of higher education institutions in shaping the national values among students of pedagogical specialities (educators, future teachers, social workers). The purpose of this study is to investigate the transformation of value orientations of Ukrainian students in the process of developing a democratic society. The study compared data from two age groups (55-60 years old; 18-28 years old). The study used a conversation that involved closed and open-ended questions. The results of the survey were analysed using the cross-sectional method, and directions for further work with the data were drawn up. The anamnesis of the responses is presented in the tables. The empirical study identified the main value orientations among students of pedagogical higher education institutions of the past and present time; investigated the hierarchy of youth values of the 80s, 90s and 2021; compared the transformation processes and formulated the main reasons for their changes in the Soviet period and in the period of independent Ukraine; determined the "brand" phenomenon of modern youth of pedagogical specialties, analysed the way self-development and self-realisation becomes a paramount value of personal orientations among young students. The materials of the study are of practical significance for the process of forming value orientations in higher education institutions. It is necessary to shape system of values among students (future teachers) as they will transmit this knowledge to the younger generation in educational institutions of different levels.
Frequency Domain Analysis of MFCC Feature Extraction in Children’s Speech Recognition System

Abstract —The research on speech recognition systems currently focuses on the analysis of robust speech recognition systems. When the speech signals are combined with noise, the recognition system becomes distracted, struggling to identify the speech sounds. Therefore, the development of a robust speech recognition system continues to be carried out. The principle of a robust speech recognition system is to eliminate noise from the speech signals and restore the original information signals. In this paper, researchers conducted a frequency domain analysis on one stage of the Mel Frequency Cepstral Coefficients (MFCC) process, the Fast Fourier Transform (FFT), in children's speech recognition system. The FTT analysis in the feature extraction process determined the effect of frequency value characteristics utilized in the FFT output on the noise disruption. The analysis method was designed into three scenarios based on the value of the employed FFT points. The differences between scenarios were based on the number of shared FFT points. All FFT points were divided into four, three, and two parts in the first, second, and third scenarios, respectively. This study utilized children's speech data from the isolated TIDIGIT English digit corpus. As comparative data, the noise was added manually to simulate real-world conditions. The results showed that using a particular frequency portion following the scenario designed on MFCC affected the recognition system performance, which was relatively significant on the noisy speech data. The designed method in the scenario 3 (C1) version generated the highest accuracy, exceeded the accuracy of the conventional MFCC method. The average accuracy in the scenario 3 (C1) method increased by 1% more than all the tested noise types. Using various noise intensity values (SNR), the testing process indicates that scenario 3 (C1) generates a higher accuracy than conventional MFCC in all tested SNR values. It proves that the selection of specific frequency utilized in MFCC feature extraction significantly affects the recognition accuracy in a noisy speech.
Correction

This article refers to:Shadows into Light: The Potential Investigative Utility of Voice Analysis with Two Types of Online Child-Sex Predators
Implementation and integration of image processing blocks in a real-time bottle classification system

A practical solution to the problems caused by the water, air, and soil pollution produced by the large volumes of waste is recycling. Plastic and glass bottle recycling is a practical solution but sometimes unfeasible in underdeveloped countries. In this paper, we propose a high-performance real-time hardware architecture for bottle classification, that process input image bottles to generate a bottle color as output. The proposed architecture was implemented on a Spartan-6 Field Programmable Gate Array, using a Hardware Description Language. The proposed system was tested for several input resolutions up to 1080 p, but it is flexible enough to support input video resolutions up to 8 K. There is no evidence of a high-performance bottle classification system in the state-of-the-art. The main contribution of this paper is the implementation and integration of a set of dedicated image processing blocks in a high-performance real-time bottle classification system. These hardware modules were integrated into a compact and tunable architecture, and was tested in a simulated environment. Concerning the image processing algorithm implemented in the FPGA, the maximum processing rate is 60 frames per second. In practice, the maximum number of bottles that can be processed would be limited by the mechanical aspects of the bottle transportation system.
Exploring learning and behavior problems of gifted children in Indonesia: A content analysis study

Although gifted children have talents and positive traits in academic area that already been supported, little empirical evidence addresses the need of support for gifted children in non-academic areas in Indonesian context. To fill this void, this study conducted a content analysis of two books about gifted children based on parents’ narratives to explore the need for support for social-emotional problems. “Menyongsong Pagi” and “Menyiangi Petang” are the two books which describe how thirteen gifted children grew up and face obstacle in their learning. Drawing on the theory of positive disintegration and Bronfenbrenner’s theory of ecological development, this study analyzed the two books to describe the uniqueness of gifted students in terms of emotion, intellectual and psychomotor still mostly seen as problems by their teachers and friends which lead to various educational issues. The implication of this study calls for the comprehensive support from teachers and schools’ leader to support gifted children both in academic and non-academic areas.
Short-Term Forecasting of Air Travellers Outflows from Bali Using Web Search Data

Air travelers have become one of the strategic indicators in the transportation sector. The official data-released by Statistics Indonesia (BPS) for thirty days-lag, makes the condition of this indicator can’t be known in real-time. By the utilization of web search data that has been briskly evolving in recent years, this study aims to explore the possibility of using web search data in performing short-term forecasting to know the general outlook of the indicator earlier. Based on this study, web search data and official statistics figures show a strong correlation and having similar movement patterns over time. The application of web search data as a predictor in time series modeling, especially on time series regression and autoregressive model (SARIMA and SARIMAX), turn out a predicted value that well-approach the actual value of the response variable. In addition, it is proven that the use of web search data can increase model accuracy. The analysis results using SARIMAX model shows that the number of air traveller’s outflows from Bali in September and October 2021 will generally be higher than the number in August 2021. The increasing number of air travelers is thought due to a decrease in Covid-19 cases which has triggered the public's confidence in travelling about to rise again.
Study of the chemical characteristics of leachate at several landfill stations in Palangka Raya

This study aims to examine the chemical characteristics of leachate at several landfill stations in Palangka Raya. The research was carried out at the lanfill in Palangka Raya City and at the Laboratory of the Banjarbaru Research and Industrial Standardization Center, in April 2021. The method used was purposive sampling method for leachate sampling. The leachate samples were grouped into four stations categories (L), there is, L1, sample sourced from active waste landfill leachate (age &lt;5 years); L2, sample sourced from passive landfill leachate (aged 5 - 10 years); L3, the sample is sourced from leachate in the inlet pond of the Leachate Water Treatment Plant (LWTP); L4, the sample is sourced from leachate from the LWTP outlet pond. The research data were statistically analyzed comparatively from each station. Parameters observed were pH, DHL, Total Dissolved Solid (TDS), Biochemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), NH3-N, Fel, Hg, Pb, and Cd. The results showed that the water leachate from lanfill in Palangka Raya City has chemical properties with content below the quality standard, namely the Pb value of 0.001-0.066 mg/L and Cd of 0.003 – 0.016 mg/, the value of Biochemical Oxygen Demand (BOD) in leachate in the inlet and outlet ponds of LWTP with a value of 105-144 mg/L. Total Dissolved Solid (TDS) in leachate in inlet ponds, LWTP outlets, and in passive landfill leachate (aged 5 - 10 years) with a value of 839-1,994 mg/L. Total Fe is 1.446 – 2.980 mg/L, Hg is 2.299-2.711 mg/L, Chemical Oxygen Demand (COD) is 347.45 – 4.290.2 mg/L and Total Ammonia (NH3-T) is 88.250-665.00 mg/L is above the quality standard. The pH value of the leachate is quite high and meets the quality standard (6-9), which ranges from 6.07 to 8.31.
Character Creation Model and Analysis of Figure Painting Fused with Texture Synthesis Style Transfer Algorithm

Texture fusion is the process of applying the style of one style image to another content image. It is an artistic creation and image editing technology. In recent years, the rapid development of deep learning has injected new power into the field of computer vision, and a large number of image style transfer algorithms based on deep learning have been proposed. At the same time, some current character conversion algorithms based on unsupervised learning also face the loss of the content and structure of the generated characters, and at the same time, they have not learned a good face deformation effect, resulting in poor image generation effects. This paper studies the relevant research background and research significance of image style transfer methods and summarizes them in the time sequence of their development; summarizes the style transfer algorithms based on deep learning, and analyzes the advantages and disadvantages of each type of algorithm. Based on the fast style transfer algorithm, this method adds a saliency detection network and designs a saliency loss function. In the training process, the difference between the saliency map of the generated image and the content image is additionally calculated, and the saliency loss is used as a part of the total loss for iterative training. Experiments show that the stylized image generated by this algorithm can better retain the salient area of the content image and has a good visual effect. Compared with the original network, the amount of parameters of the attention mechanism is very small, and there is almost no additional burden.
Assessing the Effectiveness of Competition-Based Learning Winning Video as a Pedagogical Tool in Preparing Business Plan among Accounting Undergraduate Students

The competition-based learning model is one of the pedagogical methods employed by educators in universities to overcome learners' shortcomings in their knowledge acquisition.This model can be implemented during the Covid-19 pandemic.Using the competition-based learning model, a specific learning activity was conducted via the business proposal pitching video competition for the course of Strategic Management.The winning video has been utilized as reference for future students in preparing business plan.The objective of this paper is to examine the effectiveness of using the competition-based learning winning video as a pedagogical tool for preparing the business plan in the Strategic Management course during the Covid-19 pandemic.The questionnaire was particularly developed using Google Form and distributed to all 41 students from the Bachelor of Accountancy Program, Universiti Teknologi MARA, Sarawak Branch, who underwent the course in Semester March -August 2021.Based on the descriptive results, 70% of the respondents agreed that the winning video was effective for preparing a business plan.In addition, this effective and self-reliant learning supports a self-directed and regulated learning system among accounting students.The advanced technology necessitates learning from winning video.This paper contributes to new knowledge in the area of accounting education, particularly assessing the effectiveness of the winning video as a pedagogical tool for preparing business plan in the accounting program during Covid-19 pandemic.
EFFECTIVE WAYS OF USING MEMES WITH STUDENTS

V. Novikova , E. Shmidt , M. Bolina , L. Naumenko , K. Bolshakova South Ural State Humanitarian Pedagogical University (RUSSIAN FEDERATION)
Hybrid approach to Content-Based Image Retrieval using modified multi-scale LBP and color features

The objective of the Content-Based Image Retrieval (CBIR) system is to retrieve the visually identical images from the database efficiently and effectively. It is a broad research realm with the availability of numerous applications. Performance dependency of CBIR focuses on the extraction, reduction, and selection of the features along with the practice of classification technique. In this work, we have proposed the hybrid approach of two different feature descriptors namely, Global Color Histogram and Multi-Scale Local Binary Pattern (MS-LBP). Furthermore, PCA is used for dimension reduction and LDA for the selection of features. The proposed method is evaluated concerning various benchmark datasets namely Corel-1k, Corel-5k, Corel-10k, and Ghim-10k, and results are compared based on precision and recall values at different thresholds. Euclidean distance and City Block distance are used for classification purposes. The performance study of the proposed work displays it as outperformer than the identified literature methods.
Research on Massive Image Retrieval Method of Mobile Terminal Based on Weighted Aggregation Depth Feature

Image self-coupling and feature interference lead to poor retrieval performance in massive image retrieval of mobile terminals. This paper proposes a massive image retrieval method of mobile terminals based on weighted aggregation depth features. The pixel big data detection model of massive images of mobile terminals is constructed, the collected pixel information of massive images of mobile terminals is restructured, the edge contour feature parameter set of massive images of mobile terminals is extracted, the feature fusion processing of massive images of mobile terminals is carried out in gradient pixel space by means of feature reconstruction and gray moment invariant feature analysis, the depth feature detection of massive images of mobile terminals is realized by using weighted aggregation method, the gradient value of pixels of massive images of each mobile terminal is calculated, and the optimized retrieval of massive images of mobile terminals is realized according to the fusion result of gradient weighted information. Simulation results show that this method has better feature clustering, stronger image detection and recognition, and anti-interference ability and improves the precision and recall of image retrieval.
Firefly Algorithm with Opposition-Based Learning

The firefly algorithm (FA) is a swarm intelligence optimization algorithm based on the firefly’s glow and attractive behavior. It possesses a simple design, is easy to implement, and has been applied in many engineering fields. Although FA well solves complex optimization problems, it has flaws regarding premature convergence and overall performance. Its learning style is relatively simple, as it can update individual positions merely by the fluorescence intensity between individuals. This paper proposes the firefly algorithm with opposition-based learning (FA-OL), which enriches learning to improve its capability to jump out of local optima. The opposition-based learning (OL) mechanism can promote individuals to search from the current and opposite points in the search space, and can quickly broaden the solution space. The optimal random parameter settings of FA-OL are obtained through experiments. Comprehensive experiments on nine commonly used test functions compare the proposed algorithm with the latest improved FA and seven other algorithms. The experimental results show that the introduction of OL greatly improved the search capability of FA, and FA-OL achieved better results than other algorithms.
Using Environmental Issues for Essay Writing Class: The Students’ Views and Challenges

Environmental degradation is real and presents threats to all living habitat on earth. Thus, the inclusion of environmental issues for a language class is close to a must. This paper is intended to describe 1) the students’ perspective on using environmental topics for essay writing class, and 2) their problems when writing essay. The study uses a case study design. There were 17 students participated in the research. The instruments were interview and chat documentations. The data were collected in June 2021. All data are in the form of words and sentences. The result shows that 1) most of the students said that the environmental issues are informative, important, useful, needed, effective, real, and educative. The use of environmental issues helps them to enhance their environmental awareness; 2) almost all students have psychological problems when writing an essay, such as anxiety, nervousness, the feeling of pressure in timed writing, self- demand of perfect work, and low self-confidence. Yet, the psychological problems might be triggered by the demand of more readings about environmental issues to make the essay convincing. The result implies that content-based material discussing environmental topic should be incorporated into EFL writing class.&#x0D; Keywords: environment, essay, issues, problems, views
Evaluation of Remote Data Compression Methods

The present era is one of Big Data, digitalization, Internet of Things and Internet of Everything, which imply the daily creation of an enormous amount of useful content with a very high number of producers and consumers for the online information.The ascending trend for Internet data, has made clear the necessity of defining and engineering innovative solutions for coping with redundant transfers, which led to performing smart data transfers for obtaining an increased throughput, data availability and resource utilization and implicitly to a cost reduction and to avoiding bottlenecks and denial of service issues.Internet data employed by an Internet user must be consistent, so distributed systems are gaining research interest with regard to concurrency control, atomic transfers, data replication and synchronization, compression and decompression, correction or other potential problems.Two different versions of a file have a high similarity and as synchronization is concerned, the delta between the second version and the initial version of the file applied to its initial version will provide a better transfer throughput, thus an efficient data deduplication technique is necessary and worth analyzing in order to minimize the cost of synchronization.This paper focuses on optimizing the bandwidth utilization for remote data synchronization, and proposes a prototype based on three classic open-source data compression methods.The experiments carried out show how these compression utilities along with the transfer of data perform the synchronization of large data sets between two remote sites and how the use of compression helps to reduce the data size on storage devices along with decreasing the network bandwidth significantly.The novelty of this paper lies in the fact that it combines two different compression algorithms in order to provide better compression rates.
Pengaruh Model Pembelajaran Inkuiri Terbimbing Terhadap Aktivitas dan Hasil Belajar Matematika Siswa Kelas XI Inklusi SMK

This research aims to determinethe effect of this learning model guided inquiry by the activities and learning outcomes of students in class XI inclusion in mathematical. To achieve these objectivies, aquasi experimental research designis used with post-test only non-equivalent control group design. The population in this study were all class XI students of SMK Negeri Mataram for the 2019/2020 academic year. Number of students in this population are 294 scattered in the 11 class. Samples are selected with using purposive sampling technique and selected class XI DKV A as experimental class and XI T Graphic as the control class. The instrument used to obtain student activity data wasthe student activity observation sheet while the essay question were used to obtain data on student learning outcomes. Statistical analysis of the data was performed using the t test at an error level of 5%. The data obtained shows that the experimental class learning outcomes increased by 24,5 (46 to 70,5) while thecontrol class learning outcomes increased by 16 (47,2 to 63,2). Statistical analysis of student learning outcomes shows that (2,57 &gt; 1,67). The results of the statistical analysis are supported by an increase in student activity at each meeting.