Reviewerly/exHarmony huggingface dataset:
Machine Learning Techniques for Automatic Detection of Sickle Cell Anemia using Adaptive Thresholding and Contour-based Segmentation Method

Automatic diagnosis of diseases in the medical field using image processing techniques has evolved tremendously in recent times. Sickle cell anemia (SCA) is a kind of disease connected with red blood cells (RBCs) present in the human body in which deformation of cells take place. The purpose of this work is to propose an automatic image processing technique for the detection of this disease from microscopic blood images. This paper mainly focuses on automatic detection of SCA using a novel segmentation method encompassing local adaptive thresholding and active contour-based algorithm. For the detection of sickle cells, supervised classifiers such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used. Here, geometric features of healthy and unhealthy RBCs are calculated and applied to these classifiers. In this approach, performance is found slightly greater in SVM classifier than the ANN classifier trained with scaled conjugate gradient back-propagation (BP) algorithm and with hidden layer of ten neurons. The proposed approach achieves a maximum of 99.2% accuracy with SVM classifier. The performance is also studied for seven different training algorithms in the ANN classifier by varying the numbers of hidden layer neurons. Comparative analysis of the performances of these algorithms shows that, resilient BP algorithm and 10 numbers of hidden neurons gave moderately better performance in ANN with 99% accuracy. ANN and SVM classifier with adaptive thresholding and active contour technique is an efficient approach for the classification of patients with SCA.
Хавчуу Ам Ордын Судалгаанд Геофизикийн Аргуудыг Хэрэглэсэн Байдал

When comparing the exploration results thus far from Khavchuu with those of known mineralisation and mineral deposits in the region. The occurrence of north to north-east trending, low angle, thrust faults which also coincide with resistivity and chargeability highs, magnetic lows, As anomalies and positive Au chip samples, issimilar to that observed and documented at Boroo. The resistivity anomalies generated have been interpreted as being associated with an area of silicification or quartz veining and from the dipole-dipole results these seem to occur as shallowly dipping structures similar to those hosting the Boroo mineralisation. The chargeability highs could be interpreted as regions of sulphide mineralisation and coincide with many of the anomalous areas again as with Boroo, although in the southern area the near surface chargeability low becomes a chargeability high with depth suggesting a degree of sulphide destruction during weathering in the upper regions. Magnetic lows could be interpreted again as zones of weathering and associated destruction of magnetite in granites or sulphides in mineralised regions. Assuming that any mineralisation at Khavchuu will exhibit a similar refractory component, this broad weathering could be of significant economic importance.
Analysis of Flood Control Effectiveness in Kali Sadar Regency and City of Mojokerto

Kali Sadar is part of the main drainage system in some areas of Mojokerto Regency and City. The main channel of Kali Sadar has a length of ± 23,211 Km with a drainage area of ± 614,92 Km². And has an upstream elevation of + 900 meters and an elevation of + 20 meters downstream from sea level. Flood control planning is carried out through river normalization analysis, slope reinforcement work planning, and embankment planning. All schemes are carried out so that the planned flood discharge is able to reach the ideal condition of the flood water level in the river. The study also reviewed through the planning of drainage facilities in the form of pump houses and retention ponds. In the results of the HEC-RAS analysis the results of the planned discharge used were a 25-year return period with a maximum discharge of 1080 m2/s. After simulating there is a cross section experiencing overtopping, to overcome overtopping, parape embankments are installed and with slope reinforcement work (revetment) on the slopes of the right and left sides of the river. The purpose of slope reinforcement is to accelerate the direction of river flow towards downstream so that there is no sediment erosion, by changing the manning value. However, after the flood control circuit, the cross section of the stream is only able to accommodate a maximum discharge capacity of 151.98 m2/s. For that, it is necessary to plan a pump house. Drainage facilities, in tackling the problem of excess runoff, pump houses are used in 5 locations with a plan of 80,000 m2 and a depth of 4.5 m. The plan uses 3 pump units with a capacity of 4.32 m2/s through an operating system of 4 inflow gates and 4 outflow gates which have a door width of 4.125 m and a height of 3.5 meters. pumps turn on when the water level is 0.08 m, 2 pumps will turn on when the water level is 0.30 m, and 3 pumps will turn on when the water level is 0.39 m.
Determination of Rare Earths and Other Trace Elements in Ore Samples of Mongolia by WDXRF and INAA

Wavelength dispersive X-ray fluorescence (WDXRF) and Instrumental Neutron Activation Analyses (INAA) were used to determine the REE and other trace elements in rare earth ore samples from province Dornogovi, Lugiin gol. Seven rare-earth elements (REE) namely La, Ce, Sm, Eu, Tb, Dy, and Yb, as well as other trace elements (Zn, As, Rb, Sr, Au, Ba, Br, Ta, Hf, Th and U) have been determined by WDXRF at Central Geological Laboratory of Mongolia, Ulaanbaatar. For comparison, the same elements were determined by INAA using by epithermal neutron activation analyses at the IBR-2 reactor, FLNP JINR, Dubna. It is demonstrated that the WDXRF have satisfactorily agreed with INAA results within the limits of the uncertainty. It is shown that the values of the relative discrepancies between the WDXRF and the INAA results are in the range of 1.5-30.0 %.
X-Ray Fluorescence Analysis as a Tool of Monitoring Soil Pollution Around The Central Geological Laboratory

Heavy metal contamination of soil is a major concern from an ecological point of view. This study aims to characterize soil samples from different sites in the Central Geological Laboratory (CGL)’s area and to study heavy metal contamination in the environment. In this work Wavelength dispersive X-ray fluorescence spectrometry has been used to evaluate the heavy metals and some toxic elements pollution in the CGL area.
Unsupervised representation learning for cultural relics based on local-global bidirectional reasoning

Abstract：Existing representation learning methods of cultural relics require numerous labels.Manual la• beling is time-consuming and labor-intensive.Furthermore，supervised learning methods cannot effective• ly learn the internal structure information of point clouds.We propose an unsupervised representation learning network to extract the deep features of ceramic cultural relics.The approach is based on localglobal bidirectional reasoning.First，we propose a multi-scale shell convolution-based hierarchical encoder to extract local features at different scales.Second，the local-to-global reasoning module is used to map the extracted local features to the global features.The differences between the two types of features are measured using metric learning for iterative learning.Third，a fold-based decoder is used to obtain better 文章编号 1004-924X
An Improved Intelligence Approach to Handling Data Leakage Risks in the Corporate Information Security Process

onﬁdentialinformationisofgreatinteresttocompetingﬁrms.Thiscausesaggressionandattacks.Many people underestimate the importance of the threat, and as a result, it can lead to collapse and bankruptcy for the company. Even a single case of malpractice can result in millions in damages and the loss of customer trust. Threats are subject to data on organization structure, status, and operations. Sources of such threats are its competitors, corruptofﬁcials,andcriminals.Theyareintroducedwithcertainvalue-protectedinformationandmodiﬁedinorder to cause ﬁnancial damage. Even 20% of such a decision can result in information leakage. Sometimes the loss of companysecretscanbeduetotheinexperienceofemployeesoralackofsecuritysystems.Inthispaper,animproved intelligence approach to handling data leakage risks in the corporate information security process is proposed. Accounting automatically calculates weighted relative class systems through a complete, complex security of the most important processes and technical and organizational measures. Their combination is an antivirus system, a ﬁrewall, and protection from electromagnetic radiation. Systems protect information on electronic media sent through communication channels, access exemptions for various documents, create backup copies, and recover conﬁdential information after damage.
Unpacking the Complexities of Cryptocurrency Prices Volatility in Times of Crisis: A Time Series Data with Long-term Memory or Long-range Dependence

This article explores the complexities of cryptocurrency price volatility during times of crisis. We analyze time series data with long-term memory or long-range dependence to understand the impacts of crises on cryptocurrency prices. Specifically, we examine the effects of the Covid-19 pandemic and the Russo-Ukrainian war on cryptocurrency markets, as well as the role of investor sentiment in price fluctuations during periods of uncertainty. To do so, we use fractionally integrated models to analyze the short- and long-term effects of these external factors on cryptocurrency prices. Our study mainly focuses on Bitcoin returns volatility using specific fractionally integrated models during four sub-period of historical crises from 2014. It assesses and compares the fractionally integrated models of the GARCH, the FIGARCH-BBM, the FIGARCH-CHUNG, FIEGARCH, and the FIAPARCH-BBM during the sub-periods of the pre-Covid-19, of the Covid-19 situation, between the Covid-19 and the Russo-Ukrainian War, and of the Russo-Ukrainian War. Conditional volatility models' parameters are first estimated from the four sub-sample data series BTC/USD exchange rate returns and it is calculated. Estimated conditional volatilities are then compared to specific volatilities relying on information criteria, after which the models are ranked. Finally, we test the specifics fractionally integrated volatility models with the normality test, the Q-Statistics on Standardized Residuals Test, the ARCH Test, and the graphic analysis. The specific volatility model of the first sub-period pre-Covid-19 is FIAPARCH-BBM (2,1). BTC/USD returns evolution during the Covid-19 crisis indicates that the FIEGARCH (2,2) is the appropriate volatility model. In addition, our results find that the FIEGARCH (2,1) is the appropriate model of volatility over the third sub-period and during the Russo-Ukrainian War period. By extrapolating the results of the four events, the study showed that the series of BTC/USD returns sampled over the four sub-periods were not immune to risk leading to historical crisis situations. The fluctuations of Bitcoin data during a political or economic event influence the choice of volatility models and their coefficients. More specifically, the parameters of the determined models of conditional volatility show that a war will make cryptocurrency more important on the exchange market even than an epidemic in the example of Covid-19. Our results suggest that the pandemic and geopolitical tensions have had a significant impact on cryptocurrency prices, but investor sentiment has played a crucial role in exacerbating price volatility. Additionally, we demonstrate the effectiveness of fractionally integrated models in predicting cryptocurrency prices during times of crisis. In summary, this study provides important insights into the dynamics of cryptocurrency markets during global crises, highlighting the need for sophisticated modeling techniques to effectively capture the complexities of these markets.
Signature-based Tree for Finding Frequent Itemsets

The efficiency of a data mining process depends on the data structure used to find frequent itemsets. Two approaches are possible: use the original transaction dataset or transform it into another more compact structure. Many algorithms use trees as compact structure, like FP-Tree and the associated algorithm FP-Growth. Although this structure reduces the number of scans (only 2), its efficiency depends on two criteria: (i) the size of the support (small or large); (ii) the type of transaction dataset (sparse or dense). But these two criteria can generate very large trees. In this paper, we propose a new tree-based structure that emphasizes on transactions and not on itemsets. Hence, we avoid the problem of support values that have a negative impact on the generated tree.
STEAM Learning Assessment in the Growth and Development of Early Childhood Literacy

Assessment of children's literacy development needs to be supported by STEAM learning assessments. This study aims to analyze the application of the STEAM learning assessment in the development of early childhood literacy. This study uses an ex-post facto approach with a quantitative descriptive method of implementing STEAM learning with authentic assessment techniques. The data collection instrument was observation with a scale of 4 structured, content valid oriented; anecdote notes to record specific behaviors, and portfolios to obtain data on children's performance, knowledge, and creative skills, with three main aspects: structure, content, and holistic. Observation of learning, anecdotal notes, and analysis of work results was carried out on 20 children in group B. The data obtained was described in 5 aspects of literacy: science, technology, engineering, art, and mathematics. Literacy development is described in ordinal data and analyzed descriptively using percentage techniques. The results showed that all children developed completely regarding technological and mathematical literacy. On average, they developed very well; 90% of children developed completely in the scientific aspect, with an average development according to expectations; 60% of children developed completely in engineering literacy and 50% in artistic literacy, with an average development as expected. This research implies that the STEAM learning assessment can assist in developing early childhood literacy.
EDRM-LBP: effective directional radial median local binary pattern for face recognition

Recently a novel noise invariant descriptor directional radial median local binary pattern (DRM-LBP) is developed for two applications. In DRM-LBP, median filter is utilised in radial neighbourhood directions for thresholding rather than using raw pixels. Then mean of neighbourhoods median is used for comparison with neighbourhoods for generating DRM-LBP code. After careful evaluation three disadvantages found in DRM-LBP and these are usage of 7 × 7 patch, lack of using centre grey intensity and lack of making fruitful combination for median computation. Proposed work eliminates these demerits and presented novel descriptor effective DRM-LBP (EDRM-LBP) under noisy conditions. In EDRM-LBP, 9 × 9 patch is utilised and then radial neighbourhood pixels are incorporated with centre pixel to generate median value in all directions. In this way five values are utilised for median generation. The obtained median values are then thresholded with mean of those for making EDRM-LBP code. This concept proves very effective in noisy conditions.
Improving Student Learning Outcomes of Cooperative Social Studies Learning Model Type of Think Pair Share (TPS) Class IV SD

Abstract&#x0D; Starting from the problem that: Teachers often teach lessons using the lecture method, question and answer so that students feel bored in learning. In the learning process the teacher is active while the students are passive, because student learning is more individual in nature so that the student scores are much below average. Based on the results of the research that has been carried out, it can be seen that the results of the planning cycle I meeting I was 68, meeting II increased to 82 and in cycle II meeting I became 89. The results of teacher and student activities in cycle I meeting I were 69, meeting II increased to 81 and cycle II meeting I became 93. Social studies learning outcomes for class IV SDN 15 Patamuan District can be increased by using the Think Pair Share (TPS) Type Cooperative Model. The average results of the assessment of the two aspects of assessment, namely cognitive and affective, are as follows: Cycle I, meeting I was an average of 62.81 and meeting II, the average rose to 70.93. In cycle II, social studies learning outcomes were an average of 76.87. So by using the Cooperative Model Type Think Pair Share (TPS) social studies learning outcomes of students can be increased.&#x0D; Keywords: learning outcomes; social studies learning; cooperative model type think pair share; SD
Phenomenology of Critical Literacy for Preservice Teachers in the 21st Century Era

Critical literacy skills still need to be developed, considering that the essential power of students is not yet maximized, which is characterized by being easily provoked and having difficulty solving problems. Candidates for elementary school teachers who will later be in control at the basic education level are expected to master these abilities before they teach them to students. This study aims to analyze the extent of critical literacy mastery of elementary school teacher education students. The method used is qualitative with a phenomenological approach. The research was conducted at the Department of Elementary School Teacher Education, with 142 students. To collect data, this study used interviews, observation, and documentation. The data analysis technique uses the Bogdan and Biklen model, namely analyzing data by narrowing the findings based on phenomena. The result of this research is that the mastery of critical literacy, owned by the Elementary School Teacher Education Study Program students, is quite good even though all students do not evenly share this mastery. Mastery of critical literacy is manifested in collaborative activities, socio-cultural linkages, and the involvement of reflective thinking. This study implies that essential literacy learning should be part of the curriculum to have an honest and targeted impact.
Detecting unstable periodic points of chaotic maps by stability transformation of reservoir

Detecting unstable periodic orbits in chaotic systems based on the time series is a fundamental problem in nonlinear dynamics, but it often becomes extremely challenging one. In this study, we propose a new approach for detecting unstable periodic points using reservoir computing and stability transformation method. We connects reservoir computing, which is a well-known machine learning technique, and stability transformation method, which can detect unstable periodic points in chaotic dynamical systems, to perform unstable periodic points detection in a data-driven and model-free process. In this paper, we use an example of the Hénon map to demonstrate detecting unstable fixed point and unstable 2-periodic points.
Controlling the Computer using Hand Gestures

Human-computer interaction platforms have many ways to implement as webcams and other devices like sensors are inexpensive and can get easily be available in the market.The most powerful way of communicating between humans and machines is through gestures.For higher conveyance between the human and machine/computer to convey information, the hand gesture system is very useful.Hand gestures are a sort of nonverbal type of communication that may be employed in several fields.Research and survey papers included hand gesture applications that have acquired different alternative techniques, including those supported by sensor technology and computer vision.In this system, we aimed to build a real-time gesture recognition system using hand gestures.Particularly, we will use the convolutional neural network (CNN) throughout the process.This application presents a hand gesture-based system to control a computer that is performing different operations using a neural network.Our application is defined in five phases, Image frame acquisition, Hand tracking, Features extraction, Recognition of gestures, and Classification (performing the desired operation).An image from the webcam will be captured, and so hand detection, hand shape feature extraction, and hand gesture recognition are done.
Techniques to Improve Elementary School Students' Reading Literacy

This research is motivated by the low ability of students in literacy. The study aims to analyze the effect of scanning on the computer-based national assessment of reading literacy competence in fifth-grade students. The type of research used was a quasi-experimental design with a non-equivalent pretest-posttest control group design. This research was conducted with a total population of 253 students. The data collection method in this study is the test method, the test used is in the form of an objective test, with the types of tests used being multiple choice, complex multiple-choice, and matching questions totaling 30 validated questions. The collected data was normalized using the gain score and then analyzed using the t-test inferential statistics. Based on the results of data analysis, tcount= 9,453 more than ttable = 2,002 at a significance level of 5% (α = 0.05) with dk = (6-1) = 5, so there is a significant difference between the reading literacy competencies of students who are taught using scanning with those taught using conventional learning. Thus it can be concluded that the scanning effect on the reading literacy competence of fifth-grade students.
The facilitatory role of utterance splitting in text-based CMC

This paper focuses on the role of the structure of turns in quasi-synchronous text-based computer mediated-conversation. Prior research has found that interactants in this type of communication submit their messages in two ways: either as a long single message or as a sequence of shorter postings. We hypothesize that the latter strategy – called utterance splitting – facilitates communicating online both in terms of turn-taking (initiation of repairs and holding the floor) and sentence processing (predicting informational content and lowering entropy). To evaluate the hypothesis, an experiment in which naive participants interacted with a confederate writer was conducted. We found that although communicative success did not depend on the condition to which the participants were assigned, the conversations in which the confederate sent split utterances were, on average, shorter and re-quired less words to communicate the same intention.
Review and comparison of US, EU, and UK regulations on cyber risk/security of the current Blockchain Technologies - viewpoint from 2023

The results show that cybersecurity standards are not designed in close cooperation between the two major western blocks - US and EU. In addition, while the US is still leading in this area, the security standards for cryptocurrencies, internet-of-things, and blockchain technologies have not evolved as fast as the technologies have. The key finding from this study is that although the crypto market has grown into a multi-trillion industry, the crypto market has also lost over 70% since its peak, causing significant financial loss for individuals and cooperation’s. Despite this significant impact to individuals and society, cybersecurity standards and financial governance regulations are still in their infancy.