Reviewerly/exHarmony huggingface dataset:
Extracting named entities from Russian-language documents with different expressiveness of structure

This work is devoted to solving the problem of recognizing named entities for Russian-language texts based on the CRF model. Two sets of data were considered: documents on refinancing with a good document structure, semi-structured texts of court records. The model was tested under various sets of text features and CRF parameters (optimization algorithms). In average for all entities, the best F-measure value for structured documents was 0.99, and for semi-structured ones 0.86.
Explicit Knowledge Graph Reasoning for Conversational Recommendation

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Recently, there has been a rise of using knowledge graphs (KGs) for CRSs, where the core motivation is to incorporate the abundant side information carried by a KG into both the recommendation and conversation processes. However, existing KG-based CRSs are subject to two defects: (1) there is a semantic gap between the learned representations of utterances and KG entities, hindering the retrieval of relevant KG information; (2) the reasoning over KG is mostly performed with the implicitly learned user interests, overlooking the explicit signals from the entities actually mentioned in the conversation. To address these drawbacks, we propose a new CRS framework, namely the K nowledge E nhanced C onversational R easoning (KECR) model. As a user can reflect her/his preferences via both attribute- and item-level expressions, KECR jointly embeds the structured knowledge from two levels in the KG. A mutual information maximization constraint is further proposed for semantic alignment between the embedding spaces of utterances and KG entities. Meanwhile, KECR utilizes the connectivity within the KG to conduct explicit reasoning of the user demand, making the model less dependent on the user’s feedback to clarifying questions. As such, the semantic alignment and explicit KG reasoning can jointly facilitate accurate recommendation and quality dialogue generation. By comparing with strong baselines on two real-world datasets, we demonstrate that KECR obtains state-of-the-art recommendation effectiveness, as well as competitive dialogue generation performance.
Performance Comparison of Directed Acyclic Graph-Based Distributed Ledgers and Blockchain Platforms

Since the introduction of the first cryptocurrency, Bitcoin, in 2008, the gain in popularity of distributed ledger technologies (DLTs) has led to an increasing demand and, consequently, a larger number of network participants in general. Scaling blockchain-based solutions to cope with several thousand transactions per second or with a growing number of nodes has always been a desirable goal for most developers. Enabling these performance metrics can lead to further acceptance of DLTs and even faster systems in general. With the introduction of directed acyclic graphs (DAGs) as the underlying data structure to store the transactions within the distributed ledger, major performance gains have been achieved. In this article, we review the most prominent directed acyclic graph platforms and evaluate their key performance indicators in terms of transaction throughput and network latency. The evaluation aims to show whether the theoretically improved scalability of DAGs also applies in practice. For this, we set up multiple test networks for each DAG and blockchain framework and conducted broad performance measurements to have a mutual basis for comparison between the different solutions. Using the transactions per second numbers of each technology, we created a side-by-side evaluation that allows for a direct scalability estimation of the systems. Our findings support the fact that, due to their internal, more parallelly oriented data structure, DAG-based solutions offer significantly higher transaction throughput in comparison to blockchain-based platforms. Although, due to their relatively early maturity state, fully DAG-based platforms need to further evolve in their feature set to reach the same level of programmability and spread as modern blockchain platforms. With our findings at hand, developers of modern digital storage systems are able to reasonably determine whether to use a DAG-based distributed ledger technology solution in their production environment, i.e., replacing a database system with a DAG platform. Furthermore, we provide two real-world application scenarios, one being smart grid communication and the other originating from trusted supply chain management, that benefit from the introduction of DAG-based technologies.
Impact of E-Banking SQ on CS: A PLS-SEM Approach

This study is aimed to evaluate the relation between SQ dimension of e-banking and CS to know the dimensions having the strongest impact on customer's satisfaction.This study employed a self-administered questionnaire with nine SERVQUAL scale categories and questions about CS to obtain data from 558 respondents.Additionally, PLS-SEM was employed to test the suggested structural model.Findings reported that out of nine dimensions, five dimensions (Reliability, Responsiveness, Tangibility, Convenience and Website Design) have positive and significant influence on CS. Whereas credibility, security &amp; empathy have positive but non-significant effect on CS and cost have negative but insignificant influence on CS. Website Design has highest significant effect on CS followed by reliability, convenience, responsiveness and tangibility. Findings suggest thatall SQ dimensions are important but a particular focus should be given to website design for enhancing CS.
Uncovering Systematic Risk in Crypto currency Markets: An Empirical Investigation Using DCC-GARCH Model.

This study presents an analysis of the occurrence of structural flaws and spillovers of volatility among eight popular digital currencies, such as Bit coin (BTC), Litecoin (LTC), Ripple (XRP), BNBPrice, DOGECOINPrice,ETHEREUMPrice, TETHERPrice, and USDCOINPrice. The analysis covers the period from December 25, 2019, to August 25, 2022, utilizing various statistical tests such as the Chow Breakpoint Test, Cumulative Sum test, The Granger Causality Test, the LM test for ARCH, and Dynamic Conditional Correlation (DCC) GARCH model. The results of this research reveal being present structural breaks in all the evaluated cryptocurrencies, highlighting the unpredictable nature of the cryptocurrency market. Additionally, these cryptocurrencies exhibit notable volatility spillovers and substantial positive correlations, which point to limited benefits of diversification within the cryptocurrency market. (Chowdhury, 2020; Treiblmaier, 2018; Quispe, 2023). These results have implications for investors, policymakers, and other stakeholders in the cryptocurrency market. The study recommends including cryptocurrencies as an important component in investment portfolios to stimulate returns and reduce overall portfolio risks, it is noted that direct investment incryptocurrencies can generate higher abnormal returns, but this comes with increased risk due to their inherent volatility. Investor preference for firms involved in cryptocurrency is influenced by factors such as legal protection and familiarity. Thus, policymakers should prioritize financial stability and implement careful regulation of cryptocurrency-related announcements to prevent artificial premiums and fraudulent activities (Chowdhury, 2020; Treiblmaier, 2018; Quispe, 2023). Furthermore, the analysis highlights the presence of high volatility spillover effects among certain cryptocurrencies, particularly Bitcoin, Ethereum, and Litecoin.. While volatility offersdiversification advantages, concerns arise due to the lack of intrinsic value and dividends in cryptocurrencies (Özdemir, 2022). The presence of systematic structural breaks suggests the possibility of manipulative behaviors and potential trading strategies that warrant further investigation. The DCC GARCH analysis reveals a high correlation and significant volatility spillover effects among most cryptocurrencies. These findings emphasize the need for a more diversified cryptocurrency market to mitigate risk and promote stability within this emerging financial sector.
Development of a Camera Motion Estimation Method Utilizing Motion Blur in Images

Motion blur presents a significant challenge in visual SLAM. Since a satisfying performance highly relies on clear and feature-rich images, the system will easily fail to extract features and lose track due to motion blur. To address this issue, this study introduces a novel solution that estimates the camera motion using blur features extracted from images. The proposed approach models the relationship between camera motion and motion blur, creating a comprehensive motion blur dataset labeled with camera motion. By decoupling the motion estimation process into predicting magnitude and direction separately, a neural network is trained using blur and depth images as inputs to output the camera motion. Experimental results demonstrate that the proposed method successfully estimates motion from blur, even in long-term blur scenarios. This method can potentially serve as an auxiliary motion estimation module to enhance the robustness and accuracy of the visual odometer when motion blur is encountered.
Deviance matrix factorization

We investigate a general matrix factorization for deviance-based data losses, extending the ubiquitous singular value decomposition beyond squared error loss. While similar approaches have been explored before, our method leverages classical statistical methodology from generalized linear models (GLMs) and provides an efficient algorithm that is flexible enough to allow for structural zeros via entry weights. Moreover, by adapting results from GLM theory, we provide support for these decompositions by (i) showing strong consistency under the GLM setup, (ii) checking the adequacy of a chosen exponential family via a generalized Hosmer-Lemeshow test, and (iii) determining the rank of the decomposition via a maximum eigenvalue gap method. To further support our findings, we conduct simulation studies to assess robustness to decomposition assumptions and extensive case studies using benchmark datasets from image face recognition, natural language processing, network analysis, and biomedical studies. Our theoretical and empirical results indicate that the proposed decomposition is more flexible, general, and robust, and can thus provide improved performance when compared to similar methods. To facilitate applications, an R package dmf with efficient model fitting and family and rank determination is also provided.
Hierarchical a Fortiori Reasoning with Dimensions

In recent years, a model of a fortiori argumentation, developed to describe legal reasoning based on precedent, has been successfully applied in the field of artificial intelligence to improve interpretability of data-driven decision systems. In order to make this model more broadly applicable for this purpose, work has been done to expand the knowledge representation on the basis of which it functions, as the original model accommodates only binary propositional information. In particular, two separate expansions of the original model emerged; one which accounts for non-binary input information, and a second which accommodates hierarchically structured reasoning. In the present work we unify these expansions to a single model, incorporating both dimensional and hierarchical information.
Practical training model for students in tourism and hospitality：Description of the elements of the practical training system at the university

&lt;p align="justify"&gt;The article discusses the actual problems of practical training in the tourism and hospitality industry in Russia and identifies the main problems of training specialists at Russian specialized universities. The main focus is on building partnerships between universities and employer organizations in order to train highly qualified specialists. &lt;strong&gt;Purpose:&lt;/strong&gt; The research is aimed at creating an effective model of practical training based on the interaction of the university with employer organizations within the framework of the training of specialists in the tourism and hospitality industry. &lt;strong&gt;Design/Methodology/Approach:&lt;/strong&gt; The work is based on scientific publications devoted to evaluating the effectiveness of the existing system of personnel training for the tourism and hospitality industry, studying its features, building models of vocational education, and using practice-oriented programs in the training of specialists. To study the problems of practical training of personnel for tourism and hospitality, systematic and structural approaches were used as a methodological basis, as well as methods of analysis and synthesis, study of models of cooperation between universities and employers, and methods of monitoring and evaluating the quality of training specialists. To obtain empirical data, the analysis of the needs of the labor market for specialists in the hospitality industry was carried out, as well as the study of models of cooperation between universities and employers. &lt;strong&gt;Results:&lt;/strong&gt; In the course of the work, the author has formed a model of practical training of specialists in the tourism and hospitality industry, including the purpose and objectives, process requirements, organization conditions and requirements for the results of the process. The innovative nature of the proposals lies in the development of new models of practical training based on gamification technology. The direction of further research may include the development of a methodology for the organization of the university’s interaction with employer organizations in the framework of practical training. &lt;strong&gt;Conclusion:&lt;/strong&gt; The results of the study can be used by professional educational organizations to organize the process of practical training of students, which will effectively solve the problem of training personnel for tourism and hospitality. The social consequences of organizing the process of practical training for students will include increasing the competitiveness of graduates in the labor market, improving the quality of tourist and hotel services, introducing innovations into the tourism and hospitality industry, and developing startups.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Object detection in optical imaging of the Internet of Things based on deep learning

This article endeavors to enhance image recognition technology within the context of the Internet of Things (IoT). A dynamic image target detection training model is established through the convolutional neural network (CNN) algorithm within the framework of deep learning (DL). Three distinct model configurations are proposed: a nine-layer convolution model, a seven-layer convolution model, and a residual module convolution model. Subsequently, the simulation model of CNN image target detection based on optical imaging is constructed, and the simulation experiments are conducted in scenarios of simple and salient environments, complex and salient environments, and intricate micro-environment. By determining the optimal training iterations, comparisons are drawn in terms of precision, accuracy, Intersection Over Union (IoU), and frames per second (FPS) among different model configurations. Finally, an attention mechanism is incorporated within the DL framework, leading to the construction of an attention mechanism CNN target detection model that operates at three difficulty levels: simple, intermediate, and challenging. Through comparative analysis against prevalent target detection algorithms, this article delves into the accuracy and detection efficiency of various models for IoT target detection. Key findings include: (1) The seven-layer CNN model exhibits commendable accuracy and confidence in simple and salient environments, although it encounters certain instances of undetected images, indicating scope for improvement. (2) The residual network model, when employing a loss function comprising both mean square error (MSE) and cross entropy, demonstrates superior performance in complex and salient environments, manifesting high precision, IoU, and accuracy metrics, thereby establishing itself as a robust detection model. (3) Within intricate micro-environments, the residual CNN model, utilizing loss functions of MSE and cross entropy, yields substantial results, with precision, IoU, and FPS values amounting to 0.99, 0.83, and 29.9, respectively. (4) The CNN model enriched with an attention mechanism outperforms other models in IoT target image detection, achieving the highest accuracy rates of 24.86%, 17.8%, and 14.77% in the simple, intermediate, and challenging levels, respectively. Although this model entails slightly longer detection times, its overall detection performance is excellent, augmenting the effectiveness of object detection within IoT. This article strives to enhance image target detection accuracy and speed, bolster the recognition capability of IoT systems, and refine dynamic image target detection within IoT settings. The implications encompass reduced manual recognition costs and the provision of a theoretical foundation for optimizing imaging and image target detection technologies in the IoT context.
Transfer learning using mobileNet for rice seed image classification

Rice is the world's primary source of carbohydrates, especially in Asia. Quality rice requires good seed breeding. In this research, we classified rice seeds. The experiment using public data consists of five classes. Each class contains 2,000 images. The total amount of image data is 10,000. Classification uses mobileNet, which consists of 13 depthwise separable convolutions consisting of depthwise (DW) and pointwise (PW) convolutional layers. Each DW and PW is followed by batch normalization and Rectified Linear Unit activation. At the end, there is Global Average pooling and two dense layers. The trial uses transfer learning with initial weights from imageNet. The first to twelfth convolutional layers freeze. That is, they do not train the weights in them. On the 13th or last convolutional layer, fine-tuning is carried out. Experimental data is divided into training, validation, and testing. The testing results show that accuracy is 99.55%, precision 99.55%, recall 99.08%, and f1-score 99.31%.
TAMNR: a network embedding learning algorithm using text attention mechanism

Because many existing algorithms are mainly trained based on the structural features of the networks, the results are more inclined to the structural commonality of the networks. These algorithms ignore the rich external information and node attributes (such as node text content, community and labels, etc. ) that have important implications for network data analysis tasks. Existing network embedding algorithms considering text features usually regard the co-occurrence words in the node’s text, or use an induced matrix completion algorithm to factorize the text feature matrix or the network structure feature matrix. Although this kind of algorithm can greatly improve the network embedding performance, they ignore the contribution rate of different co-occurrence words in the node’s text. This article proposes a network embedding learning algorithm combining network structure and co-occurrence word features, also incorporating an attention mechanism to model the weight information of the co-occurrence words in the model. This mechanism filters out unimportant words and focuses on important words for learning and training tasks, fully considering the impact of the different co-occurrence words to the model. The proposed network representation algorithm is tested on three open datasets, and the experimental results demonstrate its strong advantages in node classification, visualization analysis, and case analysis tasks.
Problem Section

School Science and MathematicsEarly View PROBLEMS Problem Section Albert Natian, Corresponding Author Albert Natian [email protected] Correspondence Albert Natian Email: [email protected]Search for more papers by this author Albert Natian, Corresponding Author Albert Natian [email protected] Correspondence Albert Natian Email: [email protected]Search for more papers by this author First published: 11 December 2023 https://doi.org/10.1111/ssm.12634Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
ContrastVAE++: Enhancing Contrastive Variational AutoEncoder Performance through Posterior Collapse Alleviation and Language Model-based Data Augmentation

Sequential recommendation systems utilize the user"s temporal behavioral information to identify preferences and provide relevant items. ContrastVAE, a VAE-based SOTA recommendation model, has proposed an effective method for sequential recommendation by combining contrastive learning and data augmentation. However, ContrastVAE has a posterior collapse problem in which the decoder does not consider the encoder condition when generating sequential information, and it uses a data augmentation technique inefficient to learn the latent space. To overcome these limitations, this study proposes ContrastVAE++, which alleviates the posterior collapse problem and applies an advanced augmentation technique. ContrastVAE++ employs context-based data augmentation to consider the correlation between items, Dirichlet-distributed noise to apply a distribution dependent on the encoder output, and decoder cross-attention for effective latent vector representation. Our experimental results show that ContrastVAE++ outperforms ContrastVAE on all experimental data.