Reviewerly/exHarmony huggingface dataset:
Ethereum Cryptocurrency Entry Point and Trend Prediction using Bitcoin Correlation and Multiple Data Combination

Deep learning methods have achieved significant success in various applications, including trend signal prediction in financial markets.However, most existing approaches only utilize price action data.In this paper, we propose a novel system that incorporates multiple data sources and market correlations to predict the trend signal of Ethereum cryptocurrency.We conduct experiments to investigate the relationship between price action, candlestick patterns, and Ethereum-Bitcoin correlation, aiming to achieve highly accurate trend signal predictions.We evaluate and compare two different training strategies for Convolutional Neural Networks (CNNs), one based on transfer learning and the other on training from scratch.Our proposed 1-Dimensional CNN (1DCNN) model can also identify inflection points in price trends during specific periods through the analysis of statistical indicators.We demonstrate that our model produces more reliable predictions when utilizing multiple data representations.Our experiments show that by combining different types of data, it is possible to accurately identify both inflection points and trend signals with an accuracy of 98%.
Handwritten Character Recognition to Obtain Editable Text

Optical character recognition involves distinguishing, grouping, and, in specific cases, remedying optical images/designs in a computerized picture. Online printed text, disconnected text, and transcribed reports may be in every way focused on for acknowledgment. Various applications, for example, postal addresses, be handled rapidly. Character acknowledgment depends intensely on division, include extraction, and grouping draws near. To successfully deal with message, an OCR goes through many stages, including optical checking, area division, pre-handling, division, portrayal, highlight extraction, preparing and acknowledgment, and post-handling. Random Forest, Decision Tree, MLP, and KNN might be utilized in the preparation stage to make the framework more effective at handling a lot of information. Transcribed text acknowledgment is a functioning subject of study. A few OCR strategies and their impediments are, covered as well as an outline of the forecast Season of Random Forest, Decision Tree, MLP, and KNN based frameworks. We upgrade this thought by adding picture and sound sources of info.
Why urban communities from low-income and middle-income countries participate in public and global health research: protocol for a scoping review

Introduction As the number of people living in cities increases worldwide, particularly in low-income and middle-income countries (LMICs), urban health is a growing priority of public and global health. Rapid unplanned urbanisation in LMICs has exacerbated inequalities, putting the urban poor at increased risk of ill health due to difficult living conditions in cities. Collaboration with communities in research is a key strategy for addressing the challenges they face. The objective of this scoping review is, therefore, to identify factors that influence the participation of urban communities from LMICs in public and global health research. Methods and analysis We will develop a search strategy with a health librarian to explore the following databases: MEDLINE, Embase, Web of Science, Cochrane, Global Health and CINAHL. We will use MeSH terms and keywords exploring the concepts of ‘low-income and middle-income countries’, ‘community participation in research’ and ‘urban settings’ to look at empirical research conducted in English or French. There will be no restriction in terms of dates of publication. Two independent reviewers will screen and select studies, first based on titles and abstracts, and then on full text. Two reviewers will extract data. We will summarise the results using tables and fuzzy cognitive mapping. Ethics and dissemination This scoping review is part of a larger project to be approved by the University of Montréal’s Research Ethics Committee for Science and Health in Montréal (Canada), and the Institutional Review Board of the James P Grant School of Public Health at BRAC University in Dhaka (Bangladesh). Results from the review will contribute to a participatory process seeking to combine scientific evidence with experiential knowledge of stakeholders in Dhaka to understand how to better collaborate with communities for research. The review could contribute to a shift toward research that is more inclusive and beneficial for communities.
Enhancing Recommender Systems by Fusing Diverse Information Sources through Data Transformation and Feature Selection

Recommender systems aim to recommend items to users by taking into account their probable interests.This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach.The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items.This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model.To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques.Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories.We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model.We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets.According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems.Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets.Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%.Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.
Better Defunctionalization through Lambda Set Specialization

Higher-order functions pose a challenge for both static program analyses and optimizing compilers. To simplify the analysis and compilation of languages with higher-order functions, a rich body of prior work has proposed a variety of defunctionalization techniques, which can eliminate higher-order functions from a program by transforming the program to a semantically-equivalent first-order representation. Several modern languages take this a step further, specializing higher-order functions with respect to the functions on which they operate, and in turn allowing compilers to generate more efficient code. However, existing specializing defunctionalization techniques restrict how function values may be used, forcing implementations to fall back on costly dynamic alternatives. We propose lambda set specialization (LSS), the first specializing defunctionalization technique which imposes no restrictions on how function values may be used. We formulate LSS in terms of a polymorphic type system which tracks the flow of function values through the program, and use this type system to recast specialization of higher-order functions with respect to their arguments as a form of type monomorphization. We show that our type system admits a simple and tractable type inference algorithm, and give a formalization and fully-mechanized proof in the Isabelle/HOL proof assistant showing soundness and completeness of the type inference algorithm with respect to the type system. To show the benefits of LSS, we evaluate its impact on the run time performance of code generated by the MLton compiler for Standard ML, the OCaml compiler, and the new Morphic functional programming language. We find that pre-processing with LSS achieves run time speedups of up to 6.85x under MLton, 3.45x for OCaml, and 78.93x for Morphic.
Mostly Automated Proof Repair for Verified Libraries

The cost of maintaining formally specified and verified software is widely considered prohibitively high due to the need to constantly keep code and the proofs of its correctness in sync—the problem known as proof repair . One of the main challenges in automated proof repair for evolving code is to infer invariants for a new version of a once verified program that are strong enough to establish its full functional correctness. In this work, we present the first proof repair methodology for higher-order imperative functions, whose initial versions were verified in the Coq proof assistant and whose specifications remained unchanged. Our proof repair procedure is based on the combination of dynamic program alignment, enumerative invariant synthesis, and a novel technique for efficiently pruning the space of invariant candidates, dubbed proof-driven testing , enabled by the constructive nature of Coq’s proof certificates. We have implemented our approach in a mostly-automated proof repair tool called Sisyphus. Given an OCaml function verified in Coq and its unverified new version, Sisyphus produces a Coq proof for the new version, discharging most of the new proof goals automatically and suggesting high-confidence obligations for the programmer to prove for the cases when automation fails. We have evaluated Sisyphus on 10 OCaml programs taken from popular libraries, that manipulate arrays and mutable data structures, considering their verified original and unverified evolved versions. Sisyphus has managed to repair proofs for all those functions, suggesting correct invariants and generating a small number of easy-to-prove residual obligations.
Lucy Richardson and Mean Modified Wiener Filter for Construction of Super-Resolution Image

The ultimate goal of the Super-Resolution (SR) technique is to generate the High-Resolution (HR) image by combining the corresponding images with Low-Resolution (LR), which is utilized for different applications such as surveillance, remote sensing, medical diagnosis, etc. The original HR image may be corrupted due to various causes such as warping, blurring, and noise addition. SR image reconstruction methods are frequently plagued by obtrusive restorative artifacts such as noise, stair casing effect, and blurring. Thus, striking a balance between smoothness and edge retention is never easy. By enhancing the visual information and autonomous machine perception, this work presented research to improve the effectiveness of SR image reconstruction The reference image is obtained from DIV2K and BSD 100 dataset, these reference LR image is converted as composed LR image using the proposed Lucy Richardson and Modified Mean Wiener (LR-MMWF) Filters. The possessed LR image is provided as input for the stage of bicubic interpolation. Afterward, the initial HR image is obtained as output from the interpolation stage which is given as input for the SR model consisting of fidelity term to decrease residual between the projected HR image and detected LR image. At last, a model based on Bilateral Total Variation (BTV) prior is utilized to improve the stability of the HR image by refining the quality of the image. The results obtained from the performance analysis show that the proposed LR-MMW filter attained better PSNR and Structural Similarity (SSIM) than the existing filters. The results obtained from the experiments show that the proposed LR-MMW filter achieved better performance and provides a higher PSNR value of 31.65dB whereas the Filter-Net and 1D,2D CNN filter achieved PSNR values of 28.95dB and 31.63dB respectively.
Investigation on Motion Teaching Video Compression Algorithm Based on Artificial Intelligence

Sports are a common activity of human beings, and their development levels vary in different fields.With the continuous progress of science and technology and the increasing level of computer technology, many new algorithms have emerged.This article mainly studied the method and implementation principle of Motion Teaching Video Compression (MTVC) based on artificial intelligence, and verified through MATLAB language simulation experiments the problems and improvement measures in the storage, processing, and output result analysis of commonly used data under the applicable parameters of this control strategy.Finally, the functionality of the algorithm model was tested.The test results showed that the compression ratio of the system was above 85%.The video clarity was very high, and the video bitrate was above 720kbps.The video decoding time was within 3 to 5 seconds.This could improve the compression effect of traditional sports teaching videos and enhance students' interest in learning.
Analysis of the budget realization system in higher education: A case study

The budget realization system in an institution is crucial in the success of the institution. State Polytechnic of Malang (Polinema) is one of the vocational higher education Institutions in Indonesia that implements the system as a form of compliance with existing laws and regulations and still has several problems with its implementation. This study aims to analyze the problem of the budget realization system at the State Polytechnic of Malang. This study uses a qualitative research method and uses a fishbone diagram for the process of classifying the data. This paper explains that 5 points cause problems in the budget realization system, namely machines, methods, people, management, and time. The study makes recommendations for a successful implementation of the budget realization system and also makes recommendations for future researchers. Keywords: Budget, budget realization system, higher education, problem analysis;
Automatic Detection of Sign Language Fingerspelling on Combined Features and Feature Selection using Improvised Battle Royale Optimisation Algorithm

Sign language is a need for deaf pupils to communicate with one another.People who are not deaf often do not learn sign language to interact with deaf people.It is also necessary to have an interpreter to explain the sign's meaning to others who are unfamiliar with it.Several unresolved issues, such as uncontrolled signing situations, various types of light, and varying degrees of partial occlusion, have adversely impacted hand gesture recognition efficacy.The suggested technique is unusual because it employs integrated features created by combining features obtained using conventional handcrafted feature extraction methods with deep learning models.Understandably the combined characteristics will include some repetitive and unnecessary characteristics, increasing computation time and wasting resources.We prevent this by using feature selection (FS) before providing the classifier with the merged features.We present the improved version of the newly developed Battle Royale Optimisation, IBROA, for feature selection.The characteristics are fed into a classifier for classification.Experiments were carried out, and the findings show that the proposed IBROA, which utilises integrated features and feature selection, outperforms classifiers and shows novel and efficient techniques for feature selection in Sign language classification.
Deep Dive Speech Technology

Abstract This chapter provides an in-depth account of current research activities and applications in the field of Speech Technology (ST). It discusses technical, scientific, commercial and societal aspects in various ST sub-fields and relates ST to the wider areas of Natural Language Processing and Artificial Intelligence. Furthermore, it outlines breakthroughs needed, main technology visions and provides an outlook towards 2030 as well as a broad view of how ST may fit into and contribute to a wider vision of Deep Natural Language Understanding and Digital Language Equality in Europe. The chapter integrates the views of several companies and institutions involved in research and commercial application of ST.
Exploration of Multimodal Integration Education in Ethnic Vocal Music Teaching

Purpose: From a practical perspective, the scientific research level of ethnic vocal music teachers is not ideal.At present, in vocal music teaching, there is a widespread personalized teaching method based on experience, which leads to teachers placing too much emphasis on language expression and neglecting the expression of words.At the same time, there is a lack of theoretical understanding of the relationship between teaching process and teaching effectiveness.Method: In the new era, exploring the teaching mode of ethnic vocal music is an exploration of the teaching mode of vocal music.It is required to reform the teaching of ethnic vocal music from different perspectives, from two aspects: "seeking roots" and "opening up".In the perspective of multimodal integration of educational culture, it is necessary to inherit and promote ethnic music culture, and fundamentally change the ideological concepts of ethnic vocal music teachers, in order to comprehensively improve their teaching ability and professional quality.Result: 53% of the respondents were satisfied with the multimodal integrated education in this article, while 8% considered it average.Conclusion: This article provided a good guarantee for cultivating a large number of excellent ethnic vocal talents.
Spanish Stylometric Features to Determine Gender and Profession of Ecuadorian Twitter Users

Few studies on group authorship attribution have been conducted in Ecuador. The aim of this article is to analyze the importance of stylometric characteristics to determine the gender and profession of Twitter users in Ecuador using Machine Learning (ML) techniques for authorship attribution. The project corresponds to a quantitative-bibliographic type of research, with experimental design conducted to evaluate various stylometric features and classification algorithms. Its development consists of extracting tweets from users in Ecuador that will be divided for training and testing. For the preprocessing of the information, phraseological and word frequency features are implemented. Subsequently, the classifier methods Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Multi-Layer Perceptron (MLP) and Gradient Boosting (GB) are trained, and their performance is evaluated using the Cross Validation technique and metrics to choose the best classifier for gender and profession prediction. Finally, the results are presented in observable and measurable behaviors. Random Forest with an f1 of 0.7419 was determined as the best classifier for predicting gender and the MLP method with f1 of 0.8969 for profession, outperforming the other classifiers. It was possible to determine the gender and profession of Ecuadorian Twitter users from short texts published in Spanish using stylometric characteristics and frequently used words together with machine learning methods. This research is of great interest because it applies current technological methods and provides optimal solutions in authorship attribution of short texts.
Digital Video Technology and Image Detection Algorithms in Film and Television Post-Production

With the advent of the information age, a large amount of digital information is flooding human's daily life and people can easily access it. Due to the massive use of smartphones, tablets and other mobile devices, digital video has become a mainstream multimedia application and is widely used in various fields such as video teaching, video conferencing, video-on-demand, entertainment and social networking, news and judicial criminal investigation. Digital video has become an important medium for the daily transmission of information. The main objective of this paper is to investigate the application of digital video technology and image detection algorithms in film and television post-production. This paper first introduces scene boundary detection, analyses the reasons why existing sampling strategies still suffer from the loss of optimal foreground and background pixel pairs; investigates fuzzy multi-criteria foreground and background pixel pair evaluation methods, proposes an infrared pedestrian classification algorithm based on fully automatic keying enhancement, and finally conducts experiments on video scene boundary detection and analyses the results. The experimental results show that the method proposed in this paper performs relatively better in terms of detection rate and accuracy compared with the method using only lens visual features, although its recall and accuracy rate are still somewhat lower compared to a movie with a smoother video pace.
Optimization of apparatus translation algorithm for English complex long sentences based on multi-layer filtering

English apparatus translation is a handle mechanism for spontaneous tongue, but most English apparatus translations rely too much on the manual summary of tongue experts when acquiring grammar rules, and there are large errors in the process of English apparatus understanding under some weakened rules, and there are semantics. The disadvantage of large error in feature extraction. Apparatus translation is an vital research field of spontaneous tongue handle, and it has a wide range of application prospects in today's message society and military departments. Apparatus translation is an vital research direction in the field of spontaneous tongue handle. In the existing English-Chinese apparatus translation system, long sentence analysis is a research difficulty. With the burgeon of message technology, patent or technical long sentences with huge amount of words and complex structure have widely appeared in people's daily work. How to translate these long sentences reasonably and effectively has become an urgent problem to be solved by the existing apparatus translation technology. The means used in this paper is multi-layer filtering, which can effectively filter redundant phrase pairs in the phrase table and reduce the excessive consumption of time and space in decoding.
Formation of professional and digital competence in Canadian pedagogical universities

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
Training of future teachers of the Ukrainian language and literature in the conditions of the educational and communicative environment of higher education institutions as a system

International scientific conference «Innovative aspects of the development of philological sciences» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 216 pages.