Reviewerly/exHarmony huggingface dataset:
Formation of Mathematical Thinking in Students Using the Method of Mathematical Modeling

This article indicates and describes the formation of students' skills in mathematical thinking, shows the connection of mathematics with other disciplines by solving problems with the practical content of the school mathematics course using the method of mathematical modeling. The main goal of mathematics education is the intellectual development of students, the formation of thinking skills necessary for a person to live a full life in society. Therefore, in mathematics lessons, it is better to use pedagogical methods and technologies that develop students' creative and critical thinking. Modeling is an important tool for teaching students how to solve problems and understand the world around them. Simulation teaches the child in ways that allow him to independently work on solving problems. In a broad sense, a model is defined as a reflection of the most important properties of an object. When getting acquainted with the models of algebra and beginnings of analysis, students are shown that the connection between mathematics and its applications is carried out with the help of mathematical models. Mathematical modeling shows that all components of mathematical thinking develop, for example, functional thinking, which is characterized by the sensation of the development of general and individual relations between mathematical objects or their properties, clearly visible in connection with the idea of a function.
WaveTexNeT: Ensemble Based Wavelet-Xception Deep Neural Network Architecture for Color Texture Classification

Recognizing real visual textures in the nature have been a challenging task since they are complex and stochastic.In spite of several decades of research, classifying the real world color textures are still challenging because of the intricate nature of the textures and the lack of substantial improvement of accuracy in benchmark datasets.Deep Learning techniques have found to be effective in identifying and classifying the texture patterns to a larger extent, but it could not capture spectral information and achieve excellent results for natural images.In this paper, we propose a deep convolutional neural network architecture, WaveTexNeT that combines Wavelet convolutional neural networks (WaveletCNN) and Xception model with luminance information for classifying real-world natural textures.Spectral and spatial features are extracted from WaveletCNN and Xception model.The highlight of the work is the utilization of spectral and spatial information along with luminance for texture classification.A color space image data augmentation technique is proposed that use luminance images from YIQ model for color texture classification.This work also throws light into the significance of luminance information for texture classification.Experimental analysis of the work reports that WaveTexNeT captures better feature representations and outperforms the accuracy obtained using the state-of-the-art methods.WaveTexNeT obtained an accuracy of 90.34% and 95.01% for the describable and material perception texture datasets DTD and FMD respectively.
Collaborative Colorectal Cancer Classification on Highly Class Imbalanced Data Setting via Federated Neural Style Transfer Based Data Augmentation

The deep learning algorithms achieved promising results in the computational pathology in recent decade but the high data demand of the deep learning algorithms get stuck in the multi-institutional data collaborations. The federated learning is a novel concept, which proposes to train the models of the different sites collaboratively via an orchestrating server without leaking private data. However, the imbalanced data distributions are challenging for federated learning and result in performance decrease and destabilization. In this study, the federated version of the neural style transfer algorithm, which was offered by Gatys et al. is proposed as a data augmentation method on the highly class imbalanced configuration of Chaoyang colorectal cancer imaging dataset. The proposed method works by firstly selecting characteristic style images and then generating the gram style matrices on the local sites and then transferring them to the other imbalanced sites by not leaking any private data. The proposed method contributed the ACC, F1 Score and AUC results of pure FL by 22.07%, 42.51% and 9.65% using only 20 images for content and 5 images for style. Additionally, the experiments having different content and style numbers achieved the satisfactory and consisting results.
Implementation Reserve Geocoding on Mobile Application

Reverse geocoding is the conversion of coordinates into an address that allows you to find an address. In this study, the coordinates in question are latitude and longitude points on a mobile device that uses an Android-based operating system. The default feature of Android devices for obtaining latitude and longitude data is Assisted – GPS (Global Positioning System) while the tool for processing latitude and longitude data in order to obtain location and place names from the Assisted – GPS (Global Positioning System) data that has been obtained is the nominal API. .org. the stages of work in the research are making a flow system, designing a system with use case diagrams, designing a database, coding, implementing it on android based and web based. The results of this study are that using Cordova can be an alternative for making Android-based mobile applications without the need for the relatively heavy Android Studio program if it is run on standard or low specification hardware, 2). With this mobile absence application, it is able to make it easier for employees to take attendance when outside the office with information about working or out of town service, 3). With this application it can be a means of facilitating superiors in assessing employee performance, 4). With this application can handle the problem solved.
Integration of Information Communication Technology Instructional Resources on Students’ Motivation and Achievement in Chemistry Practical in Secondary Schools in Kenya

Purpose: The main aim of this study was to determine the types of ICT instructional resources integrated and the level of motivation and achievement in learning of chemistry practical in secondary schools.&#x0D; Methodology: The study was conducted through descriptive survey design. The target population for this study were the teachers of chemistry, form three students and the heads of the institutions who were key informants. The schools were selected using stratified random sampling to ensure that there was equal representation of the region. The teachers of chemistry and ICT assistants were selected using simple random sampling technique while purposive sampling was used to select the heads of institutions. The learners were selected using simple random sampling. In the mixed secondary schools, stratified sampling technique was used to ensure gender equality. A sample is a smaller group selected from the target population; 34 teachers of chemistry were used in this study, 14 ICT assistants were used and 282 form three students in Bungoma County. This study used questionnaires as the main tool for collecting data. Work sheet was availed to the students for the experimental research; the work sheet contained simple questions in chemistry practical. The data collected was analyzed through descriptive and inferential analysis and the results were presented in tables and figures.&#x0D; Findings: The findings showed that majority of the schools that participated in the study have integrated ICT in teaching and learning of chemistry practical. The computers were available in the sampled schools and were accessible by teachers and form three students for integration in teaching of chemistry. The smart phones were also available but only teachers accessed these smart phones. The smart phones had the appropriate content that could be integrated in teaching chemistry practical. The media platform available in the smart phones could also allow teachers to project the content by use of overhead projectors and allow learners to view simulations and virtual chemical processes. Very few schools lacked the necessary infrastructure to handle ICT integration in teaching and learning of chemistry such as electrical installation and other fittings that could enable the projection of the content. There was no school in the study area that had the smart board which could allow teachers and learners to interact and manipulate the content to suit the needs for particular specific classes.&#x0D; Unique Contribution to theory, practice and policy: It was concluded that integration of ICT instructional resources could assist learners to perform some experiments in chemistry and provide better opportunities for teachers and students to interact with the content and with each other in the course of teaching and learning chemistry.
CBPP

Privacy is one of the significant issues in today’s world. Privacy-preserving data publishing refers to providing anonymized microdata to third parties such as health centers and pharmacies. The microdata has several sensitive information about the individual and it needs to be protected before the data is disclosed. Earlier studies in privacy-preserving data publishing assumed that the individual has only one record in the microdata (1:1 dataset). However, there are multiple records for an individual with multiple values (1:M dataset). Various algorithms, techniques and models have been proposed for 1:1 microdata, although they could not prevent privacy breaches when implemented on the 1:M dataset. An efficient algorithm called class batch pid partitioning (CBPP) is proposed to provide the optimal balance between utility and privacy. In CBPP algorithm, probability of reidentifying the individuals in the anonymized data is discussed. Anatomization and generalization were performed in the proposed algorithm. The algorithm performs CBPP as an anonymization process. The CBPP algorithm overcomes three attacks: (i) background knowledge correlation; (ii) quasi-identifier correlation and (iii) non-membership correlation. The query error metric has been used to measure the utility loss. The proposed algorithm is compared with the state-of-the-art method in terms of information loss.
Mergers and Acquisitions for Business Sustainability in Emerging Markets During a Vague Era: A Literature Analysis

Mergers and acquisitions are the most popular modes of external corporate growth. Business sustainability, which seeks present returns without compromising the ability to earn returns in the future, has come under serious challenges in an environment of uncertainty. While academic literature suggests that in periods of fundamental uncertainty, there is a decline of mergers and acquisitions, this paper analyses an emerging body of research that suggests that merger and acquisition deals in a vague era actually deliver more value compared to those made in normal economic conditions, which most empirical studies do not deliver much value for the acquirer. This paper therefore suggests that firms in emerging markets can use mergers and acquisitions during a downturn to deliver superior value to shareholders. It also argues that mergers and acquisitions are a corporate governance issue, because it is at the board level that the overall strategy of the firm is implemented, including the various growth options of the firm. However, further research on merger and acquisitions during a vague era need to be conducted in various regions of emerging markets.
Digital currency banking using block chain technology

Banks are now the almost sole source of confidence for internet commerce when it comes to processing electronic payments. With a peer-to-peer electronic currency, payments can be conducted online directly between parties without going via a banking organisation. While signatures do contribute in some ways, the main benefits are lost if a trustworthy third party is still required to prevent double spending. So in this project we can implement Bit Coin based banking system can be implemented leveraging the technologies of block chains to create hash functions. Bit coin is a crypto currency, which is not supported by the government or central bank of any nation. It can be traded for goods or services with vendors who the use of bit coins payment. These bit coins are the blocks of secure data. It takes a lot of CPU resources to securely verify each individual transaction as the data is passed from one person to another while also spending money on the transaction. The P2P network monitors and verifies the moving of bit coins between users. Bit coin is more secure than other currencies in terms of cryptographic implementation, and it is difficult to carry out fraudulent transactions. In a Bit coin transaction, the block chain will connect every user on the network, and each time a transaction is entered, the network will broadcast it to all other users after it has been validated. The network will also have a copy of every transaction. The network will group transaction data into blocks and broadcast them throughout the network rather than preserving any transactions in the block chain. Every block in this chain will link to the one before it, which is known as the genesis block. Peer-to-peer networks and a consensus mechanism are used in block chain systems, eliminating the potential of data alteration.
Self-Actualization of the Main Characters in the Short Story of Filosofi Kopi by Dewi Lestari and its Implications in Literature Learning

This study aims to increase students' knowledge of various extrinsic elements of literary works. In this case, the extrinsic element is the theory of basic needs by Abraham Maslow. Extrinsic elements must be analyzed to provide more content and benefits in literary works. The study was conducted at Vocational School 73 Jakarta, especially Class XI of Culinary Art, Hotel Accommodation, and Tourism Business Management programs. Each student was required to analyze a short story by Dewi Lestari's Filosofi Kopi. Students had the option of analyzing either the intrinsic or extrinsic elements. This study took a qualitative approach and was analyzed using descriptive qualitative techniques. The data source was an anthology of a short story entitled “Filosofi Kopi” (Collection of Stories and Prose of a Decade) by Dewi Lestari, published by PT. Bentang Pustaka, eleventh edition, January 2015. Based on the analysis results in three classes, out of 104 students, 17 chose to analyze extrinsic elements; 12 understood extrinsic element analysis and the basic needs theory of Abraham Maslow; and five answered incorrectly. It means that students can understand Abraham Maslow's theory of humanistic psychology. Still, teachers need to pay more attention and provide direction on analyzing the extrinsic elements of literary works so that the students become more daring to analyze extrinsic elements of literary works.
Kajian Literatur Penilaian Guru terhadap Keterampilan Siswa dalam Membaca Puisi Tahun 2015—2021

The teacher's assessment of students' poetry reading skills has differences in research instruments, especially in the assessment aspect. The assessment instrument used in poetry reading skills is adjusted to the elements contained in the poem. This study aims to describe the literature review of the instruments used in poetry reading assessment. This study used a qualitative method with a literature review approach. The research stages were carried out starting from article collection, article reduction, article display, discussion, and conclusion. Sources of research data are in the form of national articles in the last 6 years (2015-2021). The results showed that the aspect that was often assessed was the aspect of intonation with a percentage of 21%, while the aspect that was rarely used was the aspect of articulation, interpretation and stage mastery with a percentage of 1%. There was also an addition to the assessment aspect of poetry reading from year (2015-2021).
Correlation Modeling of Moral Emotion based on Facial Image Emotion Recognition Algorithm

Correlation modeling of the moral emotion and helping behavior based on facial image emotion recognition algorithm HMM is performed in this research work. A novel deep optimization framework is proposed to quickly connect with the HMM architecture but the traditional HMM architecture directly connects all layers, and the subsequent input consists of all the feature maps in the previous sequence, and finally through deep cascade fusion. Wavelet transform can locate the time and frequency of the abnormal disturbance in the electric energy in time, so that the wavelet transform can be considered as an effective method for extracting the waveform features on abnormal disturbance. The proposed model is then applied into the emotion analysis framework. The combination of the modeling on moral emotion and helping behavior is proven to be efficient.
Frequency of Nucleated Red Blood Cells in the Peripheral Blood of ICU-Admitted Patients

Background Nucleated red blood cells (NRBCs) are not normally found in the peripheral blood of normal healthy individuals. The presence of NRBCs on an adult peripheral blood smear indicates that there is an extremely high demand for the bone marrow to manufacture RBCs and that immature red blood cells are being released into the bloodstream. Anemia, myelofibrosis, thalassemia, miliary tuberculosis, malignancies of the bone marrow (myelomas, leukemias, lymphomas), and prolonged hypoxemia are a few possible pathogenic reasons. Critically ill patients who have NRBCs have a high mortality rate and a worse prognosis.To evaluate the clinical significance of NRBCs in the peripheral blood of critically ill patients admitted to the ICU to find a cut-off to predict mortality.A cross-sectional study was carried out over a period of six months September 1, 2020, to March 31, 2021, in Lahore, Pakistan. A total of 800 critically ill patients of both sexes in the age group of 18-70 years were included. Patients younger than 18 years and patients who underwent surgery were excluded. A quantity of 3 ml of whole blood sample in an ethylenediamine tetraacetic acid (EDTA) vial from each patient was run on SYSMEX XN-9000 (Sysmex Corporation, Kobe, Hyogo, Japan) and the results were reviewed on peripheral smears.The incidence of NRBCs in ICU-admitted patients was 62.5% (500/800). The total number of NRBC-positive patients recovering after the treatment was 364 (72.8%). The overall mortality of NRBC-positive patients was 30% (150/500). It was significantly higher (p<0.001) than that of NRBC-negative patients (14%; 44/300). During treatment, the highest mortality rate was seen in patients due to malignancy (100%), followed by sepsis (58.8%). It was observed that the disease pattern and number of NRBCs were significantly different (p<0.001) among all disease groups. However, there was no statistically significant difference in NRBCs on the basis of gender (p >0.05). In our study, a cutoff of NRBCs of 2.50 showed a high risk of mortality with a sensitivity of 91%.The presence of NRBCs may predict mortality in critically ill ICU-admitted patients. Their presence in the blood may be regarded as a marker of severity suggesting a high risk of ICU death.
Remaking, hope and wellness through online connectivity

With digital connections through social media platforms becoming more prevalent in our lives and climate anxiety now commonplace there is a need to understand ways to create deeper, more meaningful connections to each other, the things around us and the planet we live on. Handmaking through digital platforms may offer these connection opportunities. Remaking can take that a step further and offer tactile solutions informed by digital connections that can help combat climate anxiety. However, to successfully utilize social media channels we need to better understand how online making promotes well-being (well-making) and how that experience differs from or intersects with making in an offline context. This article explores a making and design approach that can move beyond the social media ‘filter defence’ through collaborative remaking on Instagram, helping people individually and collectively increase our personal (and planetary) well-being.
Sound Symbolic Execution via Abstract Interpretation and Its Application to Security

Symbolic execution is a program analysis technique commonly utilized to determine whether programs violate properties and, in case violations are found, to generate inputs that can trigger them. Used in the context of security properties such as noninterference, symbolic execution is precise when looking for counter-example pairs of traces when insecure information flows are found, however it is sound only up to a bound thus it does not allow to prove the correctness of programs with executions beyond the given bound. By contrast, abstract interpretation-based static analysis guarantees soundness but generally lacks the ability to provide counter-example pairs of traces. In this paper, we propose to weave both to obtain the best of two worlds. We demonstrate this with a series of static analyses, including a static analysis called RedSoundRSE aimed at verifying noninterference. RedSoundRSE provides both semantically sound results and the ability to derive counter-example pairs of traces up to a bound. It relies on a combination of symbolic execution and abstract domains inspired by the well known notion of reduced product. We formalize RedSoundRSE and prove its soundness as well as its relative precision up to a bound. We also provide a prototype implementation of RedSoundRSE and evaluate it on a sample of challenging examples.
Pushing the Limits of Learning-Based Traversability Analysis for Autonomous Driving on CPU

Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes and evaluates a real-time machine learning-based Traversability Analysis method that combines geometric features with appearance-based features in a hybrid approach based on a SVM classifier. In particular, we show that integrating a new set of geometric and visual features and focusing on important implementation details enables a noticeable boost in performance and reliability. The proposed approach has been compared with state-of-the-art Deep Learning approaches on a public dataset of outdoor driving scenarios. It reaches an accuracy of 89.2% in scenarios of varying complexity, demonstrating its effectiveness and robustness. The method runs fully on CPU and reaches comparable results with respect to the other methods, operates faster, and requires fewer hardware resources.
Academic writing and AI: Day-3 experiment with environmental semi-conducting principle

To check the ability of AI to identify precise and detailed scientific information, I experiment with how accurate AI recognizes scientific terms, their origins, meanings, and usages, and whether the accuracy increases over time.