Reviewerly/exHarmony huggingface dataset:
Learning Causal Structure on Mixed Data with Tree-Structured Functional Models

Discovering causal relations from observational data is at the heart of scientific research. Most causal discovery methods assume that the data have only one variable type. In real-world problems, however, data can consist of a mixture of continuous, discrete, and categorical variables. In this paper, we examine the causal discovery problem on mixed data. We introduce a general tree-structured functional causal model, which is well suited for characterizing the generating mechanisms of mixed data by allowing non- differentiability and nonlinearity. We present corresponding identifiability results, showing that under mild conditions, the causal directions can be uniquely determined from observational distributions. Further, we prove that the causal direction between continuous and discrete variables is generally identifiable under a much larger function class. Based on the theoretical findings, we propose an effective causal discovery method leveraging a consistent score function and powerful tree-learning techniques. Experiments on both synthetic and real data verify the effectiveness of our approach.
UKRAINIAN VOCAL STAGE: PERFORMING ASPECT

The article is devoted to the development and role of the stage in forming the cultural background of the modern era and establishing a dialogue between art, society, and the state. The main tendencies of development and functional preferences of the stage in the contemporary information and communication space are revealed, its structural and genre modifications are traced. With the receipt of the status of an "independent" country, the Ukrainian stage begins to institutionalize. If, until that time, it played the role of compensation for the oppressed complex of ethnic underestimation, then from the time of gaining independence, it has become an independent cultural phenomenon. Differentiation occurs at several levels at once. The most important thing is genre and style. Ukrainian stage is divided into popular, folk, and rock levels. An essential factor in the functioning of Ukrainian pop music is the emergence of such a phenomenon as the pop art industry. The saturation of contemporary music trends correlates with the amount of investment. Accordingly, the art of music becomes dependent on general trends in the country's economy.
A survey on fast and incremental technologies for big data

Big data is characterized by large amount of quantity, complex structure and real time performance. The main difficulty in processing big data is to improve the processing speed and accuracy. In this paper, some fast and incremental technologies are introduced, which usually used to increase the processing speed of the big data. And this paper also illustrates the application fields of these technologies. In order to speed up the query and classification of big data, index technique is proposed. The application of indexing technique can optimize candidate data sets, which could be used in data clustering and reachable query problems. In view of the increasing dynamic data, an incremental technique is used to process the big data. This method only considers the data that has changed, rather than re-modeling all the data each time as traditional methods did. Compared with traditional algorithms, incremental technique could get faster speed and higher accuracy in data prediction and graph query. In order to process the mixed big data quickly, some mixed frame for big data is given in this paper. The main mixed frames include flow-batch mixing frame and graph-flow fusion frame, which based on mixed data processing technique. At last, some experiments are given to prove the feasibility of these techniques.
Analysis of Cursive Text Recognition Systems: A Systematic Literature Review

Regional and cultural diversities around the world have given birth to a large number of writing systems and scripts, which consist of varying character sets. Developing an optimal character recognition for such a varying and large character set is a challenging task. Unlimited variations in handwritten text due to mood swings, varying writing styles, changes in medium of writing, and many more puzzle the research community. To overcome this problem, researchers have proposed various techniques for the automatic recognition of cursive languages like Urdu, Pashto, and Arabic. With the passage of time, the field of text recognition matured, and the number of publications exponentially increased in the targeted field. It is very difficult to find all the techniques developed, calculate the time and resource consumptions, and understand the cost–benefit tradeoffs among these techniques. These tradeoffs resist making this technology able for practical use. To address these tradeoffs, this article systematic analysis to identify gaps in the literature and suggest new enhanced solution accordingly. A total of 153 of the most relevant articles from 2008 to 2022 are analyzed in this systematic literature review (SLR) work. This systematic review process shows (1) the list of techniques suggested for cursive text recognition purposes and its capabilities, (2) set of feature extraction techniques proposed, and (3) implementation tools used to design and simulate the empirical studies in this specialized field. We have also discussed the emerging trends and described their implications for the research community in this specialized domain. This systematic assessment will ultimately help researchers to perform an overview of the existing character/text recognition approaches, recognition capabilities, and time consumption and subsequently identify the areas that requires a significant attention in the near future.
Mobile Technology-Mediated Language Learning: A Quantitative Study to Unravel Language Learners’ Achievement and Autonomy

This study investigates the impact of mobile technology-mediated language learning (MTMLL) on the learners’ Balinese language learning achievement and autonomy. A quantitative approach research with quasi-experimental and survey designs was conducted. The data were collected through the tests and MTMLL questionnaire administration to fifty-eight primary school students in Bali, Indonesia. The data were analyzed statistically using SPSS 23 version. The results indicate that MTMLL has a positive impact on learners' achievement and autonomy. Participants reported that learners using MTMLL showed higher levels of achievement compared to those who did not use this technology. Additionally, MTMLL provided them with more control over their learning and allowed them to study at their own pace and convenience. The findings of this study contribute to the literature on MTMLL and local language learning by highlighting the importance of technology in promoting learners’ achievement and autonomy. The study suggests that MTMLL can be an effective tool for language learners to enhance their learning experience and achieve better outcomes.
INTERACTIVE LEARNING PLATFORM WITH GAMIFICATION ELEMENTS

In the modern world, the main source of learning and other information is the Internet, as a result of whichthe demand for online education is constantly growing in the digital space. The development of modern informationtechnologies in Ukraine creates conditions for the development of online courses and powerful learning platforms. Themain element of online learning is a high-quality and effective learning platform. The transfer of learning courses fromthe real world to the virtual one requires specialized platforms and services that allow the structuring of learning contentand changing the format of the presentation of learning material. Based on the analysis of existing learning platforms, itwas found that among students there is a demand for the use of game technologies during lessons, which, according tothe results of scientific research, enrich the learning process. Therefore, the development of a learning platform withelements of gamification is relevant. An interactive learning platform with elements of gamification and artificialintelligence was demonstrated. This platform will contribute to the development of e-learning, conducting online classesby teachers for students at a new level.
Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising

In this paper, a convolved feature vector based adaptive fuzzy filter is proposed for impulse noise removal. The proposed filter follows traditional approach, i.e., detection of noisy pixels based on certain criteria followed by filtering process. In the first step, proposed noise detection mechanism initially selects a small layer of input image pixels, convolves it with a set of weighted kernels to form a convolved feature vector layer. This layer of features is then passed to fuzzy inference system, where fuzzy membership degrees and reduced set of fuzzy rules play an important part to classify the pixel as noise-free, edge or noisy. Noise-free pixels in the filtering phase remain unaffected causing maximum detail preservation whereas noisy pixels are restored using fuzzy filter. This process is carried out traditionally starting from top left corner of the noisy image to the bottom right corner with a stride rate of one for small input layer and a stride rate of two during convolution. Convolved feature vector is very helpful in finding the edge information and hidden patterns in the input image that are affected by noise. The performance of the proposed study is tested on large data set using standard performance measures and the proposed technique outperforms many existing state of the art techniques with excellent detail preservation and effective noise removal capabilities.
Quantum Multi-Resolution Measurement with application to Quantum Linear Solver

Quantum computation consists of a quantum state corresponding to a solution, and measurements with some observables. To obtain a solution with an accuracy $\epsilon$, measurements $O(n/\epsilon^2)$ are required, where $n$ is the size of a problem. The cost of these measurements requires a large computing time for an accurate solution. In this paper, we propose a quantum multi-resolution measurement (QMRM), which is a hybrid quantum-classical algorithm that gives a solution with an accuracy $\epsilon$ in $O(n\log(1/\epsilon))$ measurements using a pair of functions. The QMRM computational cost with an accuracy $\epsilon$ is smaller than $O(n/\epsilon^2)$. We also propose an algorithm entitled QMRM-QLS (quantum linear solver) for solving a linear system of equations using the Harrow-Hassidim-Lloyd (HHL) algorithm as one of the examples. We perform some numerical experiments that QMRM gives solutions to with an accuracy $\epsilon$ in $O(n\log(1/\epsilon))$ measurements.
Adjusting phase-covariant qubit channel performance with non-unitality

Abstract We analyze quantum communication properties of phase-covariant channels depending on their degree of non-unitality. In particular, we derive analytical formulas for the minimal and maximal channel fidelity on pure states and maximal output purity. Next, we introduce a measure of non-unitality and show how to manipulate between unital and maximally non-unital maps by considering classical mixtures of quantum channels. Finally, we prove that maximal fidelity and maximal output purity increase with non-unitality and present several examples. Interestingly, non-unitality can also prolong quantum entanglement and lead to its rebirth.
Measuring Teachers' Visual Expertise Using the Gaze Relational Index Based on Real-world Eye-tracking Data and Varying Velocity Thresholds

This article adds to the understanding of teachers' visual expertise by measuring visual information processing in real-world classrooms (mobile eye-tracking) with the newly introduced Gaze Relational Index (GRI) metric, which is defined as the ratio of mean fixation duration to mean fixation number. In addition, the aim was to provide a methodological contribution to future research by showing to what extent the selected configurations (i.e. varying velocity thresholds and fixation merging) of the eye movement event detection algorithm for detecting fixations and saccades influence the results of eye-tracking studies. Our study leads to two important take-home messages: First, by following a novice-expert paradigm (2 novice teachers & 2 experienced teachers), we found that the GRI can serve as a sensitive measure of visual expertise. As hypothesized, experienced teachers' GRI was lower, suggesting that their more fine-graded organization of domain-specific knowledge allows them to fixate more rapidly and frequently in the classroom. Second, we found that the selected velocity threshold parameter alter and, in the worst case, bias the results of an eye-tracking study. Therefore, in the interest of further generalizability of the results within visual expertise research, we emphasize that it is highly important to report configurations that are relevant for the identification of eye movements.
New Technological Advancements and Its Impact on Healthcare System

The worth of wellbeing in human existence is incomprehensible. In the circle of wellbeing, as in any remaining areas, the advances delivered and concentrates on led in this field have brought about contrasts. The motivation behind this exploration is to take a gander at the requests in the area of wellbeing and to give models to recognizing the best innovation. Three new innovation improvements in writing are researched in this review. Inside the extent of the review, the ideas of edge processing, mist com putting, and distributed computing were investigated. These three thoughts have been concentrated in the writing, and regions where they can be leaned toward in the field of medical services have been given. Besides, the significance of futurist medical care frameworks, which will cover all wellbeing administrations, is featured at the finish of the review.
Kid Mode on or not? A Critical Analysis from Perspective of Child-Computer Interaction

Children now use leisure software more and more frequently through electronic devices such as mobile phones, computers, and tablets. Therefore, it is particularly important to solve the problems that arise when children interact with computers. Most of the leisure software are also commonly used by adults. Although most of these software have child protection mechanisms (such as the kid mode), due to some shortcomings in their functions and interactions, the needs of parents and children cannot be fully met. How to solve these defects is an important issue. In this article, we examined how to further develop these deficiencies dependent on the standards of the principles of participatory design and user-centered design. We first divide these deficiencies into two types, namely, the inability to meet the needs of children and the inability to meet the needs of parents. Next, we put forward our suggestions for these defects from the three perspectives of functionality, interactivity and visual communication. Finally, we found that our research has certain limitations and raised expectations for future researchers.
Multi-Scale Feature Fusion of Covariance Pooling Networks for Fine-Grained Visual Recognition

Multi-scale feature fusion techniques and covariance pooling have been shown to have positive implications for completing computer vision tasks, including fine-grained image classification. However, existing algorithms that use multi-scale feature fusion techniques for fine-grained classification tend to consider only the first-order information of the features, failing to capture more discriminative features. Likewise, existing fine-grained classification algorithms using covariance pooling tend to focus only on the correlation between feature channels without considering how to better capture the global and local features of the image. Therefore, this paper proposes a multi-scale covariance pooling network (MSCPN) that can capture and better fuse features at different scales to generate more representative features. Experimental results on the CUB200 and MIT indoor67 datasets achieve state-of-the-art performance (CUB200: 94.31% and MIT indoor67: 92.11%).
Preparation of histology slides and photomicrographs: Indispensable techniques in anatomic education.

The relevance of histology slides in microscopy cannot be overemphasized. However, a major challenge to many scientists in the field stems from paucity of published data to nearly lack of access to a comprehensive technical guide to prepare tissue samples as needed for practical demonstrations, reviews or diagnostic purposes. The current research project was carried out to produce histology glass slides and photomicrographs, as well as to document the step-by-step procedures for model by concerned practitioners and scientists. The standard procedures for paraffin method of histological preparation were employed. This involves sample fixation, dehydration, clearing, impregnation, embedding, section-cutting, staining and mounting. Harris hematoxylin and eosin were used for section staining. Tissue specimens were sourced from laboratory rats (Rattus norvegicus). Other materials used included reagents- formalin, alcohol, xylene, distyrene plasticizer xylene (DPX) as well as instruments/equipment- embedding mould, water bath, oven and rotary microtome. Photomicrographs were produced using digital camera attached to light microscope that is connected to a computer interface. 500 pieces set of slides comprising 20 different organ specimens was produced as samples, with each specimen mounted on 25 glass slides of either longitudinal or transverse sections. 40 digital images taken at low and high magnifications were also generated from the various specimens and subsequently stored on a disk. This project occupies a special place in practice due to the clarity about the techniques and the good quality of the slide series obtained which both contribute to knowledge.
Canonicity of Proofs in Constructive Modal Logic

In this paper we investigate the Curry-Howard correspondence for constructive modal logic in light of the gap between the proof equivalences enforced by the lambda calculi from the literature and by the recently defined winning strategies for this logic. We define a new lambda-calculus for a minimal constructive modal logic by enriching the calculus from the literature with additional reduction rules and we prove normalization and confluence for our calculus. We then provide a typing system in the style of focused proof systems allowing us to provide a unique proof for each term in normal form, and we use this result to show a one-to-one correspondence between terms in normal form and winning innocent strategies.
A Deep Learning based Human Detection and Tracking for Security Surveillance Systems

All around the world, the crime rate has been increasing day by day, causing a rise in security issues. Closed-Circuit Television (CCTV) cameras have been installed throughout the world with the aim of decreasing crime and increasing public safety. The usage of CCTV cameras helps to increase crime detection accuracy significantly. Daily, a considerable amount of data has been recorded through CCTV cameras. Detection and recognition of culprits in the recorded data is a challenging task as it takes a lot of time, and human interaction is also involved. So, there is a need to develop a system that performs real-time detection and tracking of humans. This paper proposes a human detection and tracking system based on deep learning that assigns a unique ID to humans who enter the video scene. Multi-Task Cascaded Convolutional Neural Networks (MTCNN) and FaceNet models are used to achieve the desired target. The MTCNN model is trained on the WIDER SPACE dataset to perform human detection. FaceNet is used for human identification that is trained on the LFW dataset. The proposed system has been evaluated on 50 video sequences captured in different environments and achieved 97% average accuracy.
A Methodological Point Of View In A Systematic Review Of The Documentation Of The Effect Of Volatility On Cryptocurrency Investment

In this study, we examine the literature that has been published between the years of 2009 and 2022, and we synthesize its contributions to the analysis of volatility during the past ten years, particularly with regard to investments in cryptocurrencies. Papers from journals with a Scopus index are included in our selections. We undertake a literature review and a bibliometric analysis using the VOS-viewer program. Our results are presented in terms of the approaches employed to simulate cryptocurrency volatility as well as in accordance with their key conclusions on the aforementioned assets' volatility and their use in managing portfolios. Our analysis indicates that the Markowitz models appear to be more popular among authors and that continue to be dominated by the GARCH expansion method and hybrid models are thought to be the best machine learning techniques. We also discover that investors can take into account cryptocurrencies because of their capacity to enhance the performance of diversified portfolios.&#x0D; &#x0D;
Challenges of contact tracing during COVID-19 pandemic response in a tertiary hospital in Northwestern Nigeria

Abstract Background: Contact tracing is a traditional pillar of infectious disease control, especially for illnesses involving direct transmission from person to person, such as COVID-19. Several challenges have arisen from COVID-19 contact tracing activities, particularly in low-resource settings. These include refusal of positive clients to disclose their close contacts, difficulties in conducting risk assessment for contacts traced, among others. Objectives: To explore the activities and identify challenges of contact tracing during COVID -19 pandemic response in a tertiary hospital in Northwestern Nigeria from May, 2020 to March, 2021. Methodology: A mixed method approach was done with quantitative secondary data analysis of COVID-19 contacts traced, and qualitative assessment through Key Informant Interviews (KII) of Ahmadu Bello University Teaching Hospital staff involved in COVID-19 outbreak response during the period. Results: A total of 2,249 clients were tested for COVID-19, of which 925 (41.1%) were healthcare workers. The identified challenges included problems with contact identification, delay in notification of results, refusal to disclose contacts by cases, contacts refusing to allow risk assessment, and health workers being overwhelmed by the task of contact tracing. Conclusion: Challenges identified include refusal of cases to disclose their contacts, overwhelming number of contacts, and delay in notification of results. There is need to institute contact tracing protocols to mandate cases to disclose their contacts, train more manpower to reduce the burden of contact tracing, and improve the notification of results.
A Blockchain Based Energy Management System for Microgrids

Abstract: The use of renewable energy sources is increasing, but their volatility makes it difficult to maintain a balance between energy generation and consumption. To address this issue, we propose a peer-to-peer microgrid energy market where consumers and prosumers can trade self-produced energy and keep the profits. This encourages investment in renewable energy plants and local balancing of supply and demand. To make this possible, we propose a blockchain-based microgrid energy market without central intermediaries. The permissioned blockchain framework ensures that only eligible participants can join, and each entity maintains its own transaction information while being able to retrieve information from other entities with permission. This model is distributed and promotes plug-and-play. We have developed a WebApp SET, which uses a blockchain wallet to sign in users and allow them to share their energy needs anonymously. The simulation results show that this method is feasible and suitable for privacy protection and P2P energy management in a decentralized energy system.