Reviewerly/exHarmony huggingface dataset:
Axial Constraints for Global Matching-Based Optical Flow Estimation

Optical flow estimation is a fundamental task that aims to find the 2-dimensional motion field by identifying correspondences between two input images. For quite a long time, the correlation volume followed by convolutional neural networks (CNN) to directly estimates the optical flow was a predominant pipeline. However, several pioneering methods proposed global matching recently, pointing out the limitation that CNN-based methods are struggling to handle large displacements due to their locality. Global matching is the step that identifies global correspondences at the pixel-level using entire correlation volumes at once with simple operations like softmax. However, when global matching with softmax is combined with commonly used regression loss in optical flow estimation, there will be a vast number of possible correlation volumes that can minimize the regression loss and correctly estimate correspondences. In other words, the training objective induces a one-to-many solution problem resulting in the presence of noisy gradients. In this paper, necessity for more constraints on the correlation volume to mitigate the aforementioned ill-posed problem is discussed. To acquire such constraints, axial cross-entropy loss (i.e. axial constraints) to restrict the correlation volume to have low variance with designed pseudo ground truth is proposed. Experimental results show that axial-constraints are applicable to off-the-shelves global matching-based optical flow estimation frameworks easily and lead to both quantitative and qualitative performance improvement without any architectural changes.
JMetaBFOP: A tool for solving global optimization problems

The Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a bio-inspired algorithm that emulates the foraging behavior of E. Coli bacteria to solve optimization problems. JMetaBFOP (Bacterial foraging-based METAheuristics For Optimization Problems) is a framework implementing the TS-MBFOA processes as a library to solve optimization problems with preloaded constraints or defined by the end user. This paper presents the framework’s design using the Unified Modeling Language (UML), the implementation of a user interface (UI) in the Java platform, and the use of a mathematical expression evaluator called mXparser. JMetaBFOP allows faster calibration of TS-MBFOA parameters with the help of the UI; it eases the experimental design setup, visualization, and evaluation of feasible and optimal results for different optimization problems with constraints, such as benchmarks and particular problems. The framework was tested in 24 test problems with results: competitive in 14 problems, feasible in 7 ones, and no feasible solutions in 3 highly constrained problems. JMetaBFOP is an open-source project available on the GitHub platform.
日本語文法誤り訂正における事前学習済みモデルを用いたデータ増強

Grammatical error correction (GEC) is commonly referred to as a machine translation task that converts an ungrammatical sentence to a grammatical sentence. This task requires a large amount of parallel data consisting of pairs of ungrammatical and grammatical sentences. However, for the Japanese GEC task, only a limited number of large-scale parallel data are available. Therefore, data augmentation (DA), which generates pseudo-parallel data, is being actively researched. Many previous studies have focused on generating ungrammatical sentences rather than grammatical sentences. To tackle this problem, this study proposes the BERT-DA algorithm, which is a DA algorithm that generates correct sentences using a pre-trained BERT model. In our experiments, we focused on two factors: the source data and the amount of data generated. Considering these elements proved to be more effective for BERT-DA. Based on the evaluation results of multiple domains, the BERT-DA model outperformed the existing system in terms of the Max Match and GLEU+.
A Fusion Deraining Network Based on Swin Transformer and Convolutional Neural Network

Single image deraining is an ill-posed problem which also has been a long-standing issue. In past few years, convolutional neural network (CNN) methods almost dominated the computer vision and achieved considerable success in image deraining. Recently the Swin Transformer-based model also showed impressive performance, even surpassed the CNN-based methods and became the state-of-the-art on high-level vision tasks. Therefore, we attempt to introduce Swin Transformer to deraining tasks. In this paper, we propose a deraining model with two sub-networks. The first sub-network includes two branches. Rain Recognition Network is a Unet with the Swin Transformer layer, which works as preliminarily restoring the background especially for the location where rain streaks appear. Detail Complement Network can extract the background detail beneath the rain streak. The second sub-network which called Refine-Unet utilizes the output of the previous one to further restore the image. Through experiments, our network achieves improvements on single image deraining compared with the previous Transformer research.
Roboethics of tourism and hospitality industry: A systematic review

This study aims to give a comprehensive analysis of customers' acceptance and use of AI gadgets and its relevant ethical issues in the tourism and hospitality business in the era of the Internet of Things. Adopting a PRISMA methodology for Systematic Reviews and Meta-Analyses, the present research reviews how tourism and hospitality scholars have conducted research on AI technology in the field of tourism and the hospitality industry. Most of the journal articles related to AI issues published in Web of Science, ScienceDirect.com and the journal websites were considered in this review. The results of this research offer a better understanding of AI implementation with roboethics to investigate AI-related issues in the tourism and hospitality industry. In addition, it provides decision-makers in the hotel industry with practical references on service innovation, participation in the design of AI devices and AI device applications, meeting customer needs, and optimising customer experience. The theoretical implications and practical interpretations are further identified.
PosKHG: A Position-Aware Knowledge Hypergraph Model for Link Prediction

Abstract Link prediction in knowledge hypergraphs is essential for various knowledge-based applications, including question answering and recommendation systems. However, many current approaches simply extend binary relation methods from knowledge graphs to n -ary relations, which does not allow for capturing entity positional and role information in n -ary tuples. To address this issue, we introduce PosKHG, a method that considers entities’ positions and roles within n -ary tuples. PosKHG uses an embedding space with basis vectors to represent entities’ positional and role information through a linear combination, which allows for similar representations of entities with related roles and positions. Additionally, PosKHG employs a relation matrix to capture the compatibility of both information with all associated entities and a scoring function to measure the plausibility of tuples made up of entities with specific roles and positions. PosKHG achieves full expressiveness and high prediction efficiency. In experimental results, PosKHG achieved an average improvement of 4.1% on MRR compared to other state-of-the-art knowledge hypergraph embedding methods. Our code is available at https://anonymous.4open.science/r/PosKHG-C5B3/ .
Student Literacy Culture of Muhammadiyah Elementary School/MI in Era of Covid-19 Pandemic

Students' literacy ability has an influence on learning activities.Furthermore, literacy has the function of forming a society that is knowledgeable and critical in life's problems.However, not all students have good literacy skills.Therefore, learning programs are needed in schools to improve the students' literacy skills.The existence of Covid-19 that has hit the world and an impact on the learning process that was originally carried out face to face at school to learn from home.It is possible to bring about a change in the culture of students in literacy.The purpose of the research described the students' literacy culture of in the era of the covid-19 pandemic.A phenomenological approach was applied to reveal the similarity of meaning which was the essence of a concept.The data was analyzed using ATLAS.tisoftware.The results of the research showed that cultural changes in the implementation of literacy.The collaboration of teachers and parents played an important role in literacy culture in the implementation of learning from home.
Online Voting System using Blockchain Technology

A widespread mistrust towards the traditional voting system has made democratic voting in any country very critical. People have seen their fundamental rights being violated. Other digital voting systems have been challenged due to a lack of transparency. Most voting systems are not transparent enough; this makes it very difficult for the government to gain voters' trust. The reason behind the failure of the traditional and current digital voting system is that it can be easily exploited. The primary objective is to resolve problems of the traditional and digital voting system, which include any kind of mishap or injustice during the process of voting. Blockchain technology can be used in the voting system to have a fair election and reduce injustice. The physical voting systems have many flaws in it as well as the digital voting systems are not perfect enough to be implemented on large scale.Furthermore,the methodology for carrying out blockchain transactions during the process of voting has been elaboratedusing Blockchain Finally, the performance evaluation of the proposed system shows that the system can beimplemented in a large-scale population
Teaching reform and practice of object-oriented programming course

This article aims at the deficiencies in the teaching of object-oriented programming courses in our school, guided by the OBE teaching concept, and aimed at cultivating applied talents. On the one hand, carry out teaching reform and practice, and carry out the design of the ideological and political education system of the course,so as to fully mobilize the initiative and enthusiasm of students,stimulate students' innovative ability, effectively cultivate students' innovative thinking ability and unity and cooperation ability, and then improve students' ability to solve problems.To provide guarantee for improving the quality of personnel training.
Exploration and practice in the course of “Website Design and Production” form the ideological and political perspective

Website Design and Production is the core course of the curriculum system for e-commerce majors. It is an integrated course of theory and practice of systematic design and development of an e-commerce website by comprehensively using HTML5+CSS3 program design, website art foundation, database and other e-commerce professional knowledge and skills. Guided by the goal of curriculum moral education, stay The knowledge points of website design and production are imperceptibly integrated into the ideological and political elements of the curriculum, and moral education runs through the whole process of teaching. At the important knowledge points, students are guided to brainstorm, imagine application scenarios, and apply what they have learned. At the same time, they pay attention to the current affairs of the country, understand the historical development and future development trend of the subject, link the content of the course with the national development, design teaching cases, and carry out Guided by the values, we can achieve the goal of making virtue, cultivating people, moistening things silently.
Errors in Students’ Writing (Reports from ProWritingAid)

This research aims to find out the types of students’ writing errors which are detected and not by ProWritingAid, a language checker software. This was a qualitative study using a document analysis method with ProWritingAid being the primary language learning instrument to analyze. To collect the data, 18 pieces of students’ writing carried out as the experimental task for this research was focused on writing personal letter. These writings were checked in the software, and manually rechecked by the researcher afterward. The results showed that ProWritingAid was able to detect 21 types of writing errors including missing pronoun, preposition, and determiner. Meanwhile, there were also undetected errors found, for example, unnecessary capitalization and missing punctuation. In conclusion, while ProWritingAid can help students to analyze their errors. However, human analysis is still needed due to the undetected errors.&lt;p&gt; &lt;/p&gt;
Teaching Strategies for Children with Expressive Language Disorder

Students who have communication disorders will need special help from teachers in the regular education environments. This research aims to investigate the English teaching strategies and activities for students with language impairment, especially expressive language disorder. This research used descriptive qualitative research design which focus on literature study from relevance references. The collected data were analysed descriptively, thus the English teaching strategies and classroom management for students with expressive language disorder could be formulated. The result of the study showed that teaching strategies that could improve expressive language are Modelling, Expansion, Choices, Waiting or Withholding, Imitation, Parallel Talk, Picture Communication, Paraphrase, and Place Things Out of the Child’s Reach. Meanwhile, the activities that is suitable to be applied for students with expressive language disorder during instruction through which those activities are also applicable at home practice; wordless books, books with simple text, bubbles, play dough, toy animals, trains sets or car, and play food. Therefore, by implementing those strategies and activities, it is expected the students with expressive language disorder could improve their language competency either in receptive or productive use.
PREDICTING MORTALITY RATE IN ICU USING MACHINE LEARNING: A STUDY

Mortality rate is the measure of number of death in a limited population or by a particular cause within a certain time period. In healthcare system Intensive Care unit (ICU) plays an important role for critical condition patients. Mortality prediction of critical condition ICU patients who needs special care is a major problem of concern. The focus of this work is to predict ICU patient’s mortality by the use of health record from ICU. Nowadays, machine learning plays an important role to resolve many health related issues which includes handling of patient’s health related data and records, development of new medical procedures and the treatment of disease like cancer, heart disease, stroke, diabetes and arthritis etc. Various machine learning models are used to analyze health records to come up with solutions for different health related issues. In this work, four popular supervised machine learning algorithms, Decision Tree(DT), Random Forest (RF), K-Nearest Neighbors (KNN) and Logistic Regression(LR) has been used to predict patients mortality in ICU. In this work, In Hospital Mortality Prediction dataset which is part of MIMIC-III database has been used. The dataset is available to download and free to use from Kaggle. In our work of mortality prediction, a maximum accuracy of 0.87 has been achieved.
DiffuD2T: Empowering Data-to-Text Generation with Diffusion

Surrounded by structured data, such as medical data, financial data, knowledge bases, etc., data-to-text generation has become an important natural language processing task that can help people better understand the meaning of those data by providing them with user-friendly text. Existing methods for data-to-text generation show promising results in tackling two major challenges: content planning and surface realization, which transform structured data into fluent text. However, they lack an iterative refinement process for generating text, which can enable the model to perfect the text step-by-step while accepting control over the process. In this paper, we explore enhancing data-to-text generation with an iterative refinement process via diffusion. We have four main contributions: (1) we use the diffusion model to improve the prefix tuning for data-to-text generation; (2) we propose a look-ahead guiding loss to supervise the iterative refinement process for better text generation; (3) we extract content plans from reference text and propose a planning-then-writing pipeline to give the model content planning ability; and (4) we conducted experiments on three data-to-text generation datasets and both automatic evaluation criteria (BLEU, NIST, METEOR, ROUGEL, CIDEr, TER, MoverScore, BLEURT, and BERTScore) and human evaluation criteria (Quality and Naturalness) show the effectiveness of our model. Our model can improve the competitive prefix tuning method by 2.19% in terms of a widely-used automatic evaluation criterion BLEU (BiLingual Evaluation Understudy) on WebNLG dataset with GPT-2 Large as the pretrained language model backbone. Human evaluation criteria also show that our model can improve the quality and naturalness of the generated text across all three datasets.
Using the Technology of Blended Learning “Flipped Classroom” in the Formation of Leadership Qualities in Future IT Specialists

The purpose of the work is to substantiate a practice-oriented interactive information and pedagogical technology of blended learning “flipped classroom” in the formation of leadership qualities in future IT specialists. The relevance of this problem is due to the process of transition of the national economy to an innovative development path and the demand in this regard for IT specialists who are ready to become leaders in their field and are able to captivate and lead their teams. Materials and methods. To achieve this goal, methods of comparative analysis of existing domestic and foreign approaches to the use of blended learning technology “flipped classroom” were used, as well as a general scientific system approach that allows to study the process of interaction between subjects and objects of educational activity at a university and the possibility of increasing the efficiency of the educational process based on the use of digital tools and distance learning system (DLS) Moodle. The developed technology was tested in the Minsk branch of the Plekhanov Russian University of Economics in work with students of the direction of training 38.03.05 “Business Informatics”, the profile of the program is “Digital Transformation of Business”. Results. In the framework of the study, a practice-oriented interactive information and pedagogical technology of blended learning “flipped classroom” was developed and successfully tested, aimed at developing leadership qualities in future IT specialists. The proposed use of blended learning technology successfully responds to the challenges that foreign and domestic researchers and practitioners face when implementing the flipped classroom technology, namely: students’ commitment to the traditional passive learning format, their lack of motivation necessary for independent work and cooperation with each other in the process of preparing for the classroom and, as a result, the unpreparedness of students for the classroom and the low level of their activity and cooperation in the classroom. Conclusion. The proposed information and pedagogical technology makes it possible to develop a corporate team culture in student groups and forms their ability to lead their teams to success in educational, research and innovation activities.
Semantic Information and the Complexity of Deduction

Abstract In the chapter “Information and Content” of their Impossible Worlds, Berto and Jago provide us with a semantic account of information in deductive reasoning such that we have an explanation for why some, but not all, logical deductions are informative. The framework Berto and Jago choose to make sense of the above-mentioned idea is a semantic interpretation of Sequent Calculus rules of inference for classical logic. I shall argue that although Berto and Jago’s idea and framework are hopeful, their definitions do not capture what is intended. This is so because the definitions are solely based on the logical complexity of an argument and they fail to capture the richness of the non-logical content of that argument. Then I will suggest some amendments to address this problem. Finally, I will extend the application of the definitions to first-order logic. It shall be observed that in some classical deductions, applying contraction may lead to hiding some epistemic impossibilities. This happens when formulae which contribute different propositions to the proof get contracted at the quantified level.
Identification of mineral associations in skarn-rare-metallic deposits in Western Uzbekistan

This article solves the applied scientific problem of identifying mineral carriers and mineral associations of platinum mineralization in the skarn-gold-rare metal Koytash and Lyangar deposits. One of the applied research tasks was to identify the forms of mineral carriers and mineral associations of platinum-metal mineralization for these deposits. We carried out geological fieldwork of the Koytash and Lyangar deposits, which included a sampling of sulphide-rare metal and silver-polymetallic ores, and conducted laboratory studies by using mass spectrometry, atomic absorption, electron microprobe, and other modern methods. This article addresses the complex formation of sulphide-rare-metal and silver-polymetallic ores of the above-mentioned deposits. The formation of mineral associations bearing rare-metal (W-Mo) mineralization that formed in the early alkaline stage, in almost all sites, is completed by sulphide-rare metal and silver-polymetallic mineralization that often carry industrial-grade concentrations of noble and other metals. We have identified a recommended location for the concomitant extraction of useful components, the mineral composition, and the impure elements in them. These data show the prospects of sulphide-rare metals and sulphidepoly-metallic ores in the Koytash deposit for precious metals, bismuth, etc.
StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information

We present a novel stereo visual odometry (VO) model that utilizes both optical flow and depth information. While some existing monocular VO methods demonstrate superior performance, they require extra frames or information to initialize the model in order to obtain absolute scale, and they do not take into account moving objects. To address these issues, we have combined optical flow and depth information to estimate ego-motion and proposed a framework for stereo VO using deep neural networks. The model simultaneously generates optical flow and depth information outputs from sequential stereo RGB image pairs, which are then fed into the pose estimation network to achieve final motion estimation. Our experiments have demonstrated that our combination of optical flow and depth information improves the accuracy of camera pose estimation. Our method outperforms existing learning-based and monocular geometry-based methods on the KITTI odometry dataset. Furthermore, we have achieved real-time performance, making our method both effective and efficient.
Research on Intermittent Hypoxia Training in Sports Based on Graph Neural Network

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