Reviewerly/exHarmony huggingface dataset:
Graph Convolution Collaborative Filtering with Dense Embeddings

Recommender systems have been a vital part of many e-commerce and online services. These systems support profound user personalization by mining the semantic interactions between users and items in the database, thereby increasing customer satisfaction and revenue. Furthermore, collaborative filtering research has significantly advanced in recent years with the compelling expressiveness of graph-based neural networks. In this paper, we introduce a novel collaborative filtering approach that represents users and items as graph embeddings and effectively exploits the knowledge from these embeddings. First, the feature vectors for users and items are refined with multiple embedding propagation layers. We then use many dense layers to get extra information as much as possible, main purpose is to support embedding vectors in the propagation process and allow our model to better learning of the user-item interaction, making a noticeable difference in performance from prior works. Finally, we combine them before mapping to a predicted score. The proposed method has been empirically proven superior to the baselines and competitive with modern approaches on public benchmarks.
Analysing Android Apps Classification and Categories Validation by Using Latent Dirichlet Allocation

A key step in publishing on Google Play Store (GPS) is the manual selection of the app category. The category is highly relevant for users when searching for a suitable app. To prevent misclassification, existing work focused on automating the apps’ categories identification through different learning methods. However, most existing approaches do not consider a validation of the categories. This research proposes Latent Dirichlet Allocation (LDA) for categories’ validation and for identification of similar apps. LDA can provide human-understandable topics (mixture of words) and it can help in discovering Android apps’ categories based on their descriptions. For diversity, the most popular 5,940 apps in US and Romania were considered. LDA performance is evaluated under different scenarios defined by data set processing methods. The evaluation relies on the user’s defined categories from GPS. LDA topics are labeled with categories’ names based on these and by applying cosine similarity and human interpretation. Results show a model with a corpus containing various parts of speech (i.e., nouns, adjectives, verbs) and improved with phrases can achieve a precision of 0.69. Moreover, the analysis hints there might exist discrepancies between the GPS guideline regarding the categories’ content and their actual content, but further studies are required.
Actual educational technologies in a digital educational environment

At present the importance of digital educational resources in the context of the digital economy is great. Digital educational technologies play the role of a serious driver leading to constructive changes in the field of the institution of education in the direction of its qualitative modernization. The digital restructuring of the education sector determined the emergence and promotion of relevant educational technologies as a means of adapting educational institutions to changing environmental conditions. The problem of the research is relevant and consists in the disclosure of relevant educational technologies in a digital educational environment. The purpose of the research is to analyze scientific data, as well as our own experience, to reveal relevant educational technologies in a digital educational environment. The research methods used are analysis of scientific and methodological literature, pedagogical observation, generalization. The results are the following: the problem of application of digital technologies in Physical culture and sports has been considered; the main trends in the field of educational technologies in the field of physical culture and sports indicated; functions of EdTech as relevant digital tools in educational activities described. The conclusion has been made that the development of Physical culture and sports is inextricably linked with the development of digital industry; digital educational technologies partly contribute to solving a number of problems: they motivate people to go in for Physical culture and sports and competitive sports; raise awareness among athletes with a wide range of digital decision support systems; allow you to organize the day regimen and individual trainings; monitor the training process, etc.
A robust and interpretable deep learning framework for multi-modal registration via keypoints

We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression. We use this observation to drive the end-to-end learning of keypoints tailored for the registration task, and without knowledge of ground-truth keypoints. This framework not only leads to substantially more robust registration but also yields better interpretability, since the keypoints reveal which parts of the image are driving the final alignment. Moreover, KeyMorph can be designed to be equivariant under image translations and/or symmetric with respect to the input image ordering. Finally, we show how multiple deformation fields can be computed efficiently and in closed-form at test time corresponding to different transformation variants. We demonstrate the proposed framework in solving 3D affine and spline-based registration of multi-modal brain MRI scans. In particular, we show registration accuracy that surpasses current state-of-the-art methods, especially in the context of large displacements. Our code is available at https://github.com/alanqrwang/keymorph.
Semi-TMS: an efficient regularization-oriented triple-teacher semi-supervised medical image segmentation model

Objective. Although convolutional neural networks (CNN) and Transformers have performed well in many medical image segmentation tasks, they rely on large amounts of labeled data for training. The annotation of medical image data is expensive and time-consuming, so it is common to use semi-supervised learning methods that use a small amount of labeled data and a large amount of unlabeled data to improve the performance of medical imaging segmentation.Approach. This work aims to enhance the segmentation performance of medical images using a triple-teacher cross-learning semi-supervised medical image segmentation with shape perception and multi-scale consistency regularization. To effectively leverage the information from unlabeled data, we design a multi-scale semi-supervised method for three-teacher cross-learning based on shape perception, called Semi-TMS. The three teacher models engage in cross-learning with each other, where Teacher A and Teacher C utilize a CNN architecture, while Teacher B employs a transformer model. The cross-learning module consisting of Teacher A and Teacher C captures local and global information, generates pseudo-labels, and performs cross-learning using prediction results. Multi-scale consistency regularization is applied separately to the CNN and Transformer to improve accuracy. Furthermore, the low uncertainty output probabilities from Teacher A or Teacher C are utilized as input to Teacher B, enhancing the utilization of prior knowledge and overall segmentation robustness.Main results. Experimental evaluations on two public datasets demonstrate that the proposed method outperforms some existing semi-segmentation models, implicitly capturing shape information and effectively improving the utilization and accuracy of unlabeled data through multi-scale consistency.Significance. With the widespread utilization of medical imaging in clinical diagnosis, our method is expected to be a potential auxiliary tool, assisting clinicians and medical researchers in their diagnoses.
ProVe: A pipeline for automated provenance verification of knowledge graphs against textual sources

Knowledge Graphs are repositories of information that gather data from a multitude of domains and sources in the form of semantic triples, serving as a source of structured data for various crucial applications in the modern web landscape, from Wikipedia infoboxes to search engines. Such graphs mainly serve as secondary sources of information and depend on well-documented and verifiable provenance to ensure their trustworthiness and usability. However, their ability to systematically assess and assure the quality of this provenance, most crucially whether it properly supports the graph’s information, relies mainly on manual processes that do not scale with size. ProVe aims at remedying this, consisting of a pipelined approach that automatically verifies whether a Knowledge Graph triple is supported by text extracted from its documented provenance. ProVe is intended to assist information curators and consists of four main steps involving rule-based methods and machine learning models: text extraction, triple verbalisation, sentence selection, and claim verification. ProVe is evaluated on a Wikidata dataset, achieving promising results overall and excellent performance on the binary classification task of detecting support from provenance, with 87.5 % accuracy and 82.9 % F1-macro on text-rich sources. The evaluation data and scripts used in this paper are available in GitHub and Figshare.
Comparison of Artificial Decision Techniques for Detection of Sarcastic News Headlines

Newspapers are a rich informational source. A headline of an article sparks an interest in the reader. So, news providing agencies tend to create catchy headlines to attract the reader's attention onto them, and this is how sarcasm manages to find its way into news headlines. Sarcasm employs the use of words that carry opposite meaning with respect to what needs to be conveyed. This leads to the need of developing methods by which we can correctly predict whether a piece of text, or news for that matter, truthfully means what it says or is simply being sarcastic about it. Here, the authors have used a dataset containing 55,329 tuples consisting of news headlines from The Onion and the Huffington Post, which was taken from Kaggle, on which they applied feature extraction techniques such as Count Vectorizer, TF-IDF, Hashing Vectorizer, and Global Vectorizer (GloVe). Then they applied seven classifiers on the obtained dataset. The experimental results showed that the highest accuracies among the ML models were 81.39% for LR model with Count Vectorizer, 79.2% for LR model with TF-IDF Vectorizer, and 78% for SVM model with Count Vectorizer. They also obtained the best accuracy of 90.7% using the Bi-LSTM Deep Learning Model. They have trained the seven models and compared them based on their respective accuracies and F1-Scores.
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension

Incorporating prior knowledge has been identified as a promising approach to enhance existing pre-training models in cloze-style machine reading, as observed in recent studies. Despite the use of external knowledge graphs (KG) and transformer-based models like BERT in most existing models, the identification of the most pertinent ambiguous entities in KG, as well as the extraction of the optimal subgraphs, remains problematic. To address these challenges, we introduce the LUKE-Graph model, which constructs a heterogeneous graph based on the intuitive relationships between entities in the documents without relying on external KGs. We then employ a Relational Graph Attention (RGAT) network to combine the reasoning information of the graph with the contextual representation generated by the pre-trained LUKE model. In this way, we can take advantage of LUKE, to derive an entity-aware representation; and a graph model - to exploit relation-aware representation. Furthermore, we present Gated-RGAT, an enhancement to RGAT that incorporates a gating mechanism to control the question information during the graph convolution operation. This mechanism emulates the human reasoning process in selecting the most suitable entity candidate based on question information. Our experimental results demonstrate that the proposed LUKE-Graph model surpasses the LUKE state-of-the-art model on the ReCoRD dataset, which focuses on commonsense reasoning, and the WikiHop dataset, which centers on multi-hop reasoning problems.
“Is Proton Good Enough?” - A Performance Comparison Between Gaming on Windows and Linux

In this paper a performance comparison of Proton - a Linux-based compatibility layer - and native Windows is carried out for the purpose of video gaming. Proton, being a fork project of Wine, is a compatibility layer and just as Wine allows to run Windows software in general on Linux - Proton is specialized to run Windows native games on Linux. Recently Proton gained traction because of being the platform used in Valve gaming console Steam Deck. So the ability to play Windows games is crucial for new owners of this Linux-based console, when their whole game library so far have been played only on Windows. With this study, through a more detailed analysis, we try to answer a general question, for this purpose, “Is Proton good enough?”.
(S)Talking women: portrayal of females in online discourse of social media in Albania

The aim of this paper is to explore Albanian social media discussion threads, prioritizing the way women are portrayed. The development of social media discourse has been empowered through the increasing influence of digital media, permeating all areas of life recently. ‘Privacy is no longer a social norm’, and intentional abuse of media-derived power has led to malicious intentions infiltrating online social media discourse, threatening loyal and straightforward communication, and giving rise to denigration, negativity, and dismay. Based on the current view of scholars in the field of language and digital communication that text-based studies need to move forward by incorporating other modes of communication and the fact that all human communication is, after all, multimodal, we intend to bring examples to illustrate the choice of language and images by Albanians in online social media in reaction to current popular events. The common thread that unites all examples is related to the depiction of females, unfortunately, in derogatory terms, and gendered culpability. It is hoped that the paper will raise awareness about such dominating problematic approaches toward females in Albania, as manifested in the language used.
Detecting Duplicate Multiple Choice Questions in the Large Question Bank

A question bank is a database of questions in a variety of formats used as a central repository for building tests. Question banks are the core tool for innovative testing and assessment of learners’ learning outcomes. It can be built and added over time from many sources and by many people. So creating new questions may result in duplicate questions, and deciding whether to include that question in the real database will take time to manually search across data files. This study proposes an end-to-end machine learning architecture to combine the information from the text data and the data from the image in question through optical character recognition into an encoded vector. From there, the system can query to find similar questions based on similarity ranking. The machine learning model was evaluated on a part of the question bank at FPT University, a university of information technology in Vietnam. The obtained F1 score of 0.95 proves that the model can be applied to intelligently manage the question bank of the FPT education system as well as that of other educational institutions.
Integrating Blockchain with AI and 5G to Mitigate Covid-19: A Comprehensive Literature Review

&nbsp; The annual ConV2X is a leading international health tech symposium driving real world evidence, strategy, research, operations and trends to create a blueprint for a new digital health era. The 2021 symposium&nbsp;featured a scientific program of academic/research presentations in addition to business and industry talks. The research track&nbsp;focused on exploring and sharing developments in blockchain and emerging technologies in health and clinical medicine. Submissions were based on original research, conceptual frameworks, proposed applications, position papers, case studies, and real-world implementation. Selection was based on a peer-review process. Faculty, students, and industry researchers were encouraged to submit abstracts to present&nbsp;ideas before an informed and knowledgeable audience of industry leaders, policy makers, funders, and researchers. This presentation was selected by the scientific review committee. Submission Review Committee Dave Kochalko, CEO of ARTiFACTS Anjum Khurshid, UT Austin Carlos Caldas, UT Engineering Gil Alterovitz, Harvard Medical School Kayo Fujimoto, UT Health Houston Lei Zhang, University of Glasglow Sean Manion, CSciO of ConsenSys Health Vijayakuman Varadarajan, University of South Wales Vikram Dhillon, Wayne State University Yuichi Ikeda, Kyoto University &nbsp
An efficient plug-and-play regularization method for full waveform inversion

Abstract Nonlinear inverse problems arise in various fields ranging from scientific computation to engineering technology. Inverse problems are intrinsically ill-posed, and effective regularization techniques are necessary. The core of a suitable regularization method is to introduce the prior information of the model via an explicit or implicit regularization function. Plug-and-play regularization is a flexible framework that integrates the most effective denoising priors into an iterative algorithm, and it has recently shown great potential in the solution of linear ill-posed problems. Unlike traditional regularization methods, plug-and-play regularization does not require an explicit regularization function to represent the prior information of the model. In this work, by using total variation, block-matching and three-dimensional filtering, and fast and flexible denoising convolutional neural network denoisers, we propose a novel iterative regularization algorithm based on the alternating direction method of multipliers method. The combination of total variation and block-matching three-dimensional filtering regularizers can take advantage of the sparsity and nonlocal similarity in the solution of inverse problems. When combined with traditional and novel regularizers, deep neural networks have been shown to be an effective regularization approach, which can achieve state-of-the-art performance. Finally, we apply the proposed algorithm to the full waveform inversion problem to show the effectiveness of our method. Numerical results demonstrate that the proposed algorithm outperforms existing inversion methods in terms of quantitative measures and visual perceptual quality.
Prospects for distance learning in Kazakhstani universities in the context of active digitalization due to the COVID–19 pandemic

During the COVID–19 pandemic, the Kazakhstani education system completely switched to online education. The so-called digitalization has embraced all spheres of social and economic life. Online learning has come to be seen as an alternative to traditional one. The restructuring of the education system took place with the help of digital technologies, which contributed to an increase in its competitiveness. Technological advances in Artificial Intelligence have become part of everyday life. The use of online learning technologies has helped to preserve the educational infrastructure. At the same time, learning from home and, as a result, unequal conditions in access to the Internet affected the quality of education, academic integrity, and motivation. The research aims to analyze the effects of distance learning on students and understand whether blended learning will become an effective education paradigm in the post-academic era. An extensive literature review of articles based on Elsevier, Science Direct and Google Scholar databases using keywords such as “digital technologies in education”, “blended learning”, “learning in the context of the COVID–19 pandemic” was carried out. A comparative analysis of the results of quantitative research among students of the Kurmangazy Kazakh National Conservatory, Turan University and Al-Farabi KazNU is conducted. The research results demonstrate the attitude of students towards e-learning. There is a request for the introduction of blended learning, regardless of the educational programs.
ETHNO-CULTURAL EDUCATION OF PRIMARY SCHOOL TEACHERS IN THE SYSTEM OF TRAINING AND ADVANCED TRAINING

The article deals with the problem of ethno-cultural education of primary school teachers, substantiates the relevance of professional training and advanced training in this area. The author examines the concepts of "ethno–cultural education", "ethno-cultural competence of a teacher", "advanced training", "regional ethno-cultural component", "folk culture", draws attention to the professional qualities of the ethno-cultural orientation of a modern teacher, the formation of a scientific system of knowledge, skills and abilities of teachers in the implementation of the regional ethno-cultural component of the content of the educational program of primary general education The article presents the results of a study of the topics of advanced training courses in ethno-cultural education for primary school teachers from the websites of pedagogical universities of different Russian regions (Vologda, Kurgan, Moscow region, Republic: Bashkortostan, Buryatia, Dagestan, North Ossetia - Alania, Tatarstan, Chechen, Chuvash, etc.). The author attempts to build the structure of the content of the program of advanced training courses in ethno-cultural education of primary school teachers through the study of regional folk art culture, decorative and applied arts, regional arts and crafts. It is represented by three modules: a conceptual and pedagogical module; Modernization of the content of education and the use of new pedagogical technologies for the ethno-cultural education of younger schoolchildren. The article is of a scientific and methodological nature, contains theoretical generalizations and methodological recommendations and can be useful to researchers and practitioners of the primary general education system. The study was conducted within the framework of the state assignment of the Ministry of Education of the Russian Federation for the implementation of applied research work "Studying the experience and role of ethnopedagogy in the conditions of the Federal State Educational Standard (implementation in a network format is proposed)" № 073-00071-21-01.