Reviewerly/exHarmony huggingface dataset:
Heterogeneous Graph Attribute Completion via Efficient Meta-path Context-Aware Learning

Heterogeneous graph attribute completion (HGAC) is an emerging research direction and has drawn increasing research attention in recent years. Although making significant progress, existing HGAC methods suffer from three limitations, including (i) the ignorance of valuable meta-path information during the graph node encoding, (ii) insufficient graph context learning as only neighboring nodes are considered during the completion process, and (iii) low efficiency due to the use of heterogeneous graph neural network for downstream tasks. To address these limitations, we propose an efficient meta-path context-aware learning framework to improve the completion of missing attributes of heterogeneous graphs. In our framework, we introduce a collaborative meta-path-driven embedding scheme, which incorporates valuable meta-path prior knowledge during node sampling. Furthermore, we devise a context-aware attention mechanism to complete the missing node attributes, which captures the information of non-neighbor nodes. Finally, we adopt graph attention networks for downstream tasks, effectively improving computational efficiency. Extensive experiments on three real-world heterogeneous graph datasets demonstrate that our framework outperforms state-of-the-art HGAC methods remarkably.
EKGRL: Entity-Based Knowledge Graph Representation Learning for Fact-Based Visual Question Answering

Fact-based Visual Question Answering (FVQA) is a task aiming at answering question based on given image and external knowledge associated with it. The reasoning abilities of current FVQA models including query-based and joint learning methods are insufficient. To achieve stronger reasoning ability, we propose an entity-based knowledge graph representation learning (EKGRL) method. Our model achieves state-of-the-art performance on FVQA dataset. Furthermore, we build a psychological fact-based VQA dataset (PFVQA) containing 6129 questions from six different types, which is, as far as we know, the first VQA dataset built on psychological knowledge. We demonstrate that EKGRL continues to achieve state-of-the-art performance on PFVQA, showing the ability to maintain a good performance on reasoning and knowledge representation based on external knowledge from both commonsense and psychological domains.
Multi-view Contrastive Learning Network for Recommendation

Knowledge graphs (KGs) are being introduced into recommender systems in more and more scenarios. However, the supervised signals of the existing KG-aware recommendation models only come from the historical interactions between users and items, which will lead to the sparse supervised signal problem. Inspired by self-supervised learning, which can mine supervised signals from the data itself, we apply its contrastive learning framework to KG-aware recommendation, and propose a novel model named Multi-view Contrastive Learning Network (MCLN). Unlike previous contrastive learning methods that usually generate different views by ruining graph nodes, MCLN comprehensively considers four different views, including collaborative knowledge graph (CKG), user-item interaction graph (UIIG), and user-user graph (UUG) and item-item graph (IIG). We treat the CKG as a global-level structural view, and the other three views as local-level collaborative views. Therefore, MCLN performs contrastive learning between the four views at the local and global levels, aiming to mine the collaborative signals between users and items, between users, and between items, and the global structural information. Besides, in the construction of UUG and IIG, a receptive field is designed to capture important user-user and item-item collaborative signals. Extensive experiments on three datasets show that MCLN significantly outperforms state-of-the-art baselines.
Pseudo Labels Refinement with Stable Cluster Reconstruction for Unsupervised Re-identification

Most existing unsupervised re-identification uses a clustering-based approach to generate pseudo-labels as supervised signals, allowing deep neural networks to learn discriminative representations without annotations. However, drawbacks in clustering algorithms and the absence of discriminatory ability early in training limit better performance seriously. A severe problem arises from path dependency, wherein noisy samples rarely have a chance to escape from their assigned clusters during iterative training. To tackle this challenge, we propose a novel label refinement strategy based on the stable cluster reconstruction. Our approach contains two modules, the stable cluster reconstruction (SCR) module and the similarity recalculate (SR) module. It reconstructs more stable clusters and re-evaluates the relationship between samples and clearer cluster representatives, providing complementary information for pseudo labels at the instance level. Our proposed approach effectively improves unsupervised reID performance, achieving state-of-the-art performance on four benchmark datasets. Specifically, our method achieves 46.0% and 39.1% mAP on the challenging dataset VeRi776 and MSMT17.
Shadow Detection of Remote Sensing Image by Fusion of Involution and Shunted Transformer

In the field of remote sensing image analysis, this paper addresses the challenges of accurately detecting edge details and small shadow regions through the introduction of STAISD-Net. This end-to-end network synergistically combines the capabilities of CNN and Transformer to enhance shadow detection accuracy. Our network architecture incorporates several innovations. In the encoder, we propose an improved multiscale asymmetric involution to extract detailed features across multiple scales. Additionally, we improve Shunted Transformer to extract global features, generating four-scale global feature information. In the decoder, we employ bilinear interpolation for upsampling and skip connections for fusing the high-level and low-level image features. Finally, we generate shadow masks by integrating feature maps of different scales. Comprehensive experiments conducted on the Aerial Imagery dataset for Shadow Detection (AISD) validate the effectiveness of STAISD-Net, showing that when compared to several state-of-the-art methods, our method demonstrates superior performance, achieving higher accuracy and the shadow detection results are more consistent with actual shadows.
Innovations in the Russian School: Categorical Analysis, Social Realities and Barriers, Prospects

The purpose of the article is to identify social barriers to innovation in modern Russian schools based on the analysis of empirical sociological material, to interpret them in the context of risks and consequences for the innovative development of the general education system. Preliminary to the analysis of empirical data, the authors conduct a categorical analysis of educational innovation. The need for significant improvement and expediency are emphasised as key characteristics of innovation. The innovativeness of education is considered in conjunction with the social functions of education and the professional activities of the teacher. The fetishisation of innovation and bureaucratic practices of managing it are justifiably pointed out as a source of social barriers to the development of innovative activity in a modern school. A relatively new contradiction in the sociology of education is identified between the need for innovative development of the general education system and the presence of social barriers to the innovative activity of all participants in educational relations. Based on the analysis of the results of a sociological study conducted by online questioning, the following barriers are identified: notional, conceptual, organisational, content-managerial, blurred perception of obstacles, professional overload, competence and information. It has been revealed that all the subjects of innovation activities at school act as social sources and carriers of barriers to innovation: teachers, the administration of an educational organisation, specialists from educational authorities. It has been determined that the impact of these factors on the innovative development of the general education system is complex and systemic.&#x0D; Taking into account the high risk of their negative impact on the innovative development of the general education system, specific areas of activity for researchers and managers are formulated on a local, regional and federal scale. They concern the elaboration of the problem of innovation in the interdisciplinary synthesis of scientific disciplines studying education; development of targets, planning technology, determination of content and performance indicators of innovative development of the general education system in Russia; increasing the professional level of teachers and education managers for innovative activities; changes in the principles of innovation management in education. Systematic actions in these directions can minimise the negative manifestations of barriers to innovation and ensure a sustainable perspective for the innovative development of the education system.
Machine Learning Models for Performance of Students in Foundation and Post Foundation in a University in Oman

Due to the extensive availability of data in education, it is now common practice to analyze data in order to uncover patterns and important knowledge. They can be applied to develop strategies and policy aimed at enhancing educational outcomes. The importance of a data science and machine learning research technique is growing in the field of education, particularly when it comes to predicting results. In this study, a machine learning model called multiple linear regression is employed to forecast the academic performance of the students. Using multiple linear regression techniques, a case study of foundation and post-foundation students’performance in various math courses is provided. so that we can identify the students who are less capable and take the appropriate steps to enhance the outcomes. Also try to find out different performance criteria.
Lecture 10—VE and Video Games

In this lecture, we are going to examine the relationship between VE and video games. These two concepts have many similarities and share a common history. Notably, research in the area of VR has made possible many developments in electronic entertainment.
Knowledge Distillation of Attention and Residual U-Net: Transfer from Deep to Shallow Models for Medical Image Classification

With the widespread application of deep learning in medical image analysis, its capacity to handle high-dimensional and complex medical images has been widely recognized. However, high-accuracy deep learning models typically demand considerable computational resources and time, while shallow models are generally unable to compete with complex model. To overcome these challenges, this paper introduces a Knowledge Distillation methodology that merges features and soft labels, transfering knowledge encapsulated in the intermediate features and predictions of the teacher model to the student model. The student model imitates the teacher model’s behavior, thereby improving its prediction accuracy. Based on this, we propose the Res-Transformer teacher model bases on the U-Net architecture and the ResU-Net student model incorporates Residuals. The Res-Transformer model employs dual Attention to acquire deep feature maps of the image, and subsequently employs Hierarchical Upsampling to restore the details in these feature maps. The ResU-Net model enhances stability via Residuals and recovers the loss of image information in convolution operations through optimized skip-connection. Finally, we evaluate on multiple disease datasets. The results show that the Res-Transformer achieves accuracy up to 94.3%. By applying knowledge distillation, abundant knowledge from the Res-Transformer is transferring knowledge of the Res-Transformer to the ResU-Net model, improving its accuracy up to 7.1%.
Expressing Measure in Czech (A Corpus-Based Study)

Abstract In the contribution, we provide a theory-based and corpus-verified description of expressions for measure in Czech. We demonstrate that the measure expressions may modify quantity of entities ( approximately ten boys ), internal characteristics of events ( he works a lot ), properties ( very big ) and relations ( completely without sound ). We distinguish between the measure expressions that are an answer to the question To what extent? (Extent-modifiers) and expressions that modify an answer to the question How many? (Quantity-modifiers). The Extent-modifiers are formally, structurally and semantically more diverse than the Quantity-modifiers. For the Quantity-modifiers a list of forms and functions is provided. Theoretical knowledge stemming from the analysis will subsequently be used to improve the annotation in the Prague Dependency Treebanks. It can be also useful for other semantically-oriented descriptions of language.
Towards a Corpus-Based Dictionary of Verbal Government for the Russian Language

Abstract This paper introduces a technique for automatic verbal governance extraction in the Russian language, which encapsulates information on the grammatical features of verbnoun co-occurrences, encompassing both prepositional and non-prepositional dependencies. The construction of the dictionary, a corpus of approximately 3.5 billion words was used. The proposed method involves syntactic parsing of the texts, filtering of resultant outputs, and creating a dictionary of prepositional government. After error filtering, the dictionary contains ca. 18,000 verbs along with NP/PPs governed by these verbs.
Scientific and Pedagogical Community of Russia Universities as an Object of Resource Mobilisation

The article is devoted to a theoretical understanding of the phenomenon of mobilisational management of the resources of the scientific and pedagogical community (SPC) of Russian universities. The focus was placed on the concepts of NPS resource capacity and its mobilisation as one of the models of university management. The purpose of the article is to reveal the potential of theories of resource mobilisation, social community, resource concepts in the conceptualisation of the notion of mobilisation of NPS resource сapacity. The authors consider the main ideas and theses of theories developed in branches of sociological knowledge other than the sociology of higher education, and adapt them to the context of university management of the NPS. The authors´ interpretation of the concept of NPS as a social community and socio-professional community has been updated in the light of resource concepts. The content of the concept of NPS resource capacity is discussed as a set of opportunities into which a given university community converts the means and methods of its professional activities, as well as the associated properties inherent in scientific and pedagogical workers as a socio-professional community. One of the main results of the article is related to the substantiation of the approach to the classification of NPS resources.&#x0D; It is proposed to identify and take into account in university management practices the following five blocks of resources - socio-demographic (quantitative, age, gender resources); scientific research (research, publication, temporal resources); pedagogical (educational and pedagogical resources, advanced scientific and pedagogical qualifications, mentoring); resources for interaction between scientific and pedagogical workers (RPWs) and scientific teams (communication, resource of scientific schools and teams); moral-symbolic (symbolic and moral-ideological resources). Mobilisational university management is considered as a strategy for managing research and development staff, which involves concentrating the resources and efforts of academic staff on strategic directions and tasks of university development. As prerequisites for such a management approach, it is substantiated to take into account and update the processes of self-organisation of the NPS as a complex and highly professional community, to restore understanding, trust, and solidarity between it and the administrative and managerial corps of universities. The question is raised about the actors in the mobilisation of NPS resources, who must have a deep knowledge of the sources, features and patterns of functioning of the resources of the academic community. In conclusion, the article discusses the issue of demand in university management for a scientifically based approach to mobilisation management of NPS resources and the readiness of the administrative and managerial apparatus to use it.
The <i>Intercorp</i> Parallel Corpus with a Uniform Annotation for All Languages

Abstract Recently, the language-specific morphosyntactic annotation of InterCorp , a large multilingual parallel corpus, has been replaced by the language-uniform morphosyntactic and syntactic annotation following the guidelines of the Universal Dependencies project. Because the corpus is used predominantly by human users via a token-based concordancer, the CONLL-U format produced by the UDP ipe parser has been extended by attributes such as lemma of the token’s syntactic head or morphosyntactic categories of the content verb’s auxiliary. We conclude that despite some theoretical and practical issues, the new annotation is a promising solution to the issue of mutually incompatible tagsets within a single corpus.
Is it Possible to Re-Educate Roberta? Expert-Driven Machine Learning for Punctuation Correction

Abstract Although Czech rule-based tools for automatic punctuation insertion rely on extensive grammar and achieve respectable precision, the pre-trained Transformers outperform rule-based systems in precision and recall (Machura et al. 2022). The Czech pre-trained RoBERTa model achieves excellent results, yet a certain level of phenomena is ignored, and the model partially makes errors. This paper aims to investigate whether it is possible to retrain the RoBERTa language model to increase the number of sentence commas the model correctly detects. We have chosen a very specific and narrow type of sentence comma, namely the sentence comma delimiting vocative phrases, which is clearly defined in the grammar and is very often omitted by writers. The chosen approaches were further tested and evaluated on different types of texts.
Deepfake Detection via Fine-Grained Classification and Global-Local Information Fusion

In response to the increasing amount of deepfake content on the internet, a large number of deepfake detection methods have been recently developed. To our best knowledge, existing methods simply perform binary classification, i.e., they simply consider the deepfake images generated from different forgery methods as a single category. Unfortunately, different deepfake forgery methods usually generate deepfake images with different artifacts/appearances. Under such circumstance, a simple binary classification mechanism may limit the learning ability of the detection models, i.e., they may ignore certain forgery traces. Therefore, we propose a novel deepfake detection method via fine-grained classification and global-local information fusion. Specifically, we improve the binary classification task with a fine-grained classification mechanism, such that the deepfake detection model can learn more precise features for fake images from different forgery methods. Besides, we construct a global-local information fusion architecture to emphasize the important information in certain local regions and fuse them with global semantic information. In addition, we design a global center loss, which makes the real images features more cohesive and enlarge the distance between real and fake images features, to further enhance the generalization ability of the detection model. Extensive experiments demonstrate the effectiveness and superiority of our method.
Distractor Generation for Lexical Questions Using Learner Corpus Data

Abstract Learner corpora with error annotation can serve as a source of data for automated question generation (QG) for language testing. In case of multiple choice gapfill lexical questions, this process involves two steps. The first step is to extract sentences with lexical corrections from the learner corpus. The second step, which is the focus of this paper, is to generate distractors for the retrieved questions. The presented approach (called DisSelector) is based on supervised learning on specially annotated learner corpus data. For each sentence a list of distractor candidates was retrieved. Then, each candidate was manually labelled as a plausible or implausible distractor. The derived set of examples was additionally filtered by a set of lexical and grammatical rules and then split into training and testing subsets in 4:1 ratio. Several classification models, including classical machine learning algorithms and gradient boosting implementations, were trained on the data. Word and sentence vectors from language models together with corpus word frequencies were used as input features for the classifiers. The highest F1-score (0.72) was attained by a XGBoost model. Various configurations of DisSelector showed improvements over the unsupervised baseline in both automatic and expert evaluation. DisSelector was integrated into an opensource language testing platform LangExBank as a microservice with a REST API.
Credit Card Fraud Detection Using AI (Python)

Credit card fraud poses a formidable obstacle in the financial sector, resulting in considerable monetary damages for both individuals and institutions. The necessity for efficient fraud detection systems has become paramount due to the rising number of online transactions and the advancement of fraudulent methods. Machine learning methods have demonstrated potential in tackling this issue by utilizing past transaction data to detect fraudulent behavior. This study provides a thorough examination and evaluation of different machine learning techniques used in the detection of credit card fraud. The aim of this study is to assess and compare the efficacy of several machine learning algorithms in identifying fraudulent credit card transactions. The dataset utilized for experimentation is acquired from a prominent financial institution, including of both authentic and deceitful transactions. The dataset has been preprocessed to address missing values, outliers, and feature scaling. A variety of machine learning algorithms, such as “logistic regression, decision trees, random forests, support vector machines (SVM), and artificial neural networks (ANN),” are utilized and trained on the preprocessed information. The evaluation of each method is conducted using criteria like as “accuracy, precision, recall, and F1-score. In addition, several evaluation methods, such as k-fold cross-validation”, are used to assure the reliability of the findings. The empirical findings suggest that machine learning algorithms has the capability to accurately identify fraudulent credit card transactions. The algorithms demonstrate varying performance across different parameters, with certain algorithms displaying higher accuracy but worse precision or recall. The “Support Vector Machine (SVM)” algorithm gets the maximum accuracy rate of 98%, while the “Artificial Neural Network (ANN)” model displays the optimal balance between precision and recall..
Lemmatization of the DIA1900 Diachronic Corpus

Abstract This paper focuses on the process of lemmatization of the upcoming Czech diachronic corpus of the second half of the 19 th century, DIA1900. The article describes different approaches to the corpus lemmatization of synchronic written, spoken and diachronic corpora within the Czech National Corpus project, including single- and multilevel lemmatization and available tools used to link the variants.