Information Retrieval Methods Research Articles

Capital as an economic category. Elements of capital as accounting objects

Subject. This article examines the category of Capital in its various manifestations, the classification of its types, and the characteristics of capital components as accounting objects. Objectives. The article aims to assess the meaning and role of the category of Capital in economic, financial and accounting aspects, substantiate the importance of understanding the content of capital elements as accounting objects in order to improve the quality of accounting and information support of the organization's management system. Methods. For the study, we used the methods of information retrieval, simulation of situations, analysis and synthesis. Results. Based on the study and generalization of historical and modern approaches to substantiating the category of Capital, the article describes the multifaceted nature of this category, evaluates the content of individual elements of capital, and offers recommendations for the classification of capital in its various manifestations in order to form information in the accounting system and accounting (financial) statements. Conclusions and Relevance. The application of the research results in the educational process and practical activities will contribute to the most thorough and deep understanding of the category of Capital, its complexity and versatility. This can help reflect information on capital in accounting (financial) statements in a more correct way, assess the amount of capital and calculate indicators related to it when analyzing the economic activities of economic entities. All this together will affect the quality of management decisions of the organization's management system. The research materials can be used for the study and teaching of accounting disciplines, as well as in the practical activities of specialists in the field of accounting and finance.

Application Research of Intelligent Search System Based on Clustering Algorithm

In this paper, a multi-agent oriented personalized information retrieval method is proposed. The system is composed of 5 agents and 2 other components. The design highlights the personalization and real-time of the information obtained by users, and has the characteristics of intelligent retrieval and active retrieval. Then this paper studies it from three aspects: user model, system learning model and individual information model. Then a grouping gravity search algorithm is proposed. Compared with conventional gravity search, group gravity search is carried out for a specific decoding strategy. This algorithm is a new gravity search algorithm suitable for packet coding, which makes the packet search process approximate to the iterative optimization of classical gravity algorithm. Finally, the effectiveness of the proposed method is verified on various typical experimental cases. Experiments show that the proposed algorithm has higher classification performance than other intelligent group algorithms.

Towards a Regulatory Framework for Electronic Medical Record Data Visualization.

Physicians struggle to retrieve data from electronic medical records. We evaluated a digital tool that enhances physician efficiency in retrieving and analyzing patient information for treatment decision-making. Our use case is the care of diabetic patients. Evaluation results showed that healthcare providers who used the i4C (Insights for Care) dashboard experienced greater time efficiency than those who used traditional EMR information retrieval methods. A comprehensive evaluation of the i4C Dashboard confirms its effectiveness in facilitating diabetic care data management, as well as its potential application to a wide range of healthcare scenarios. In order to further maximize its effectiveness on clinical efficiency and patient care, future research should focus on improving its usability and scalability.

Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques

Thanks to the rapid expansion of the Internet, anyone can now access a vast array of information online. However, as the volume of web content continues to grow exponentially, search engines face challenges in delivering relevant results. Early search engines primarily relied on the words or phrases found within web pages to index and rank them. While this approach had its merits, it often resulted in irrelevant or inaccurate results. To address this issue, more advanced search engines began incorporating the hyperlink structures of web pages to help determine their relevance. While this method improved retrieval accuracy to some extent, it still had limitations, as it did not consider the actual content of web pages. The objective of the work is to enhance Web Information Retrieval methods by leveraging three key components: text content analysis, link analysis, and log file analysis. By integrating insights from these multiple data sources, the goal is to achieve a more accurate and effective ranking of relevant web pages in the retrieved document set, ultimately enhancing the user experience and delivering more precise search results the proposed system was tested with both multi-word and single-word queries, and the results were evaluated using metrics such as relative recall, precision, and F-measure. When compared to Google’s PageRank algorithm, the proposed system demonstrated superior performance, achieving an 81% mean average precision, 56% average relative recall, and a 66% F-measure.

The significance and role of contra-asset accounts in accounting theory and practice

Subject. This article deals with the issues of classification of accounts by purpose and structure, as well as the application of contra-asset accounts. Objectives. The article aims to assess the significance and role of contra-asset accounts in the accounting system in its theoretical and practical aspects, as well as clarify the name of this classification group of accounts. Methods. For the study, we used the methods of information retrieval, generalization, modeling, analysis and synthesis. Results. Based on the study and generalization of theoretical provisions that reveal the issues and problems of the use of accounting accounts, the article presents proposals for clarifying the classification of contra-asset accounts and determines their theoretical and practical significance in the formation of information in the accounting system and financial reporting. Conclusions and Relevance. The use of the results of the study can help deeper understand the significance and role of contra-asset accounts and the multifunctional nature of information provided by their use in the formation of accounting (financial) statements and the solution of various management tasks for the management of processes related to the acquisition, use, storage, assessment of the state of objects recorded in the main accounts. This will create prerequisites for the competent use of this group of accounts in the practical activities of accountants. The materials of the study can be used to study and teach the theory of accounting, financial accounting, accounting (financial) reporting, as well as in the practical activities of specialists in the field of accounting.

Information Retrieval and Machine Learning Methods for Academic Expert Finding

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.

TenseMusic: An automatic prediction model for musical tension.

The perception of tension and release dynamics constitutes one of the essential aspects of music listening. However, modeling musical tension to predict perception of listeners has been a challenge to researchers. Seminal work demonstrated that tension is reported consistently by listeners and can be accurately predicted from a discrete set of musical features, combining them into a weighted sum of slopes reflecting their combined dynamics over time. However, previous modeling approaches lack an automatic pipeline for feature extraction that would make them widely accessible to researchers in the field. Here, we present TenseMusic: an open-source automatic predictive tension model that operates with a musical audio as the only input. Using state-of-the-art music information retrieval (MIR) methods, it automatically extracts a set of six features (i.e., loudness, pitch height, tonal tension, roughness, tempo, and onset frequency) to use as predictors for musical tension. The algorithm was optimized using Lasso regression to best predict behavioral tension ratings collected on 38 Western classical musical pieces. Its performance was then tested by assessing the correlation between the predicted tension and unseen continuous behavioral tension ratings yielding large mean correlations between ratings and predictions approximating r = .60 across all pieces. We hope that providing the research community with this well-validated open-source tool for predicting musical tension will motivate further work in music cognition and contribute to elucidate the neural and cognitive correlates of tension dynamics for various musical genres and cultures.

Category Decomposition-based Within Pixel Information Retrieval Method and its Application to Partial Cloud Extraction from Satellite Imagery Pixels

Category Decomposition-based Within Pixel Information Retrieval Method and its Application to Partial Cloud Extraction from Satellite Imagery Pixels

Developing a Base Domain Ontology from Geoscience Report Collection to Aid in Information Retrieval towards Spatiotemporal and Topic Association

The efficient and precise retrieval of desired information from extensive geological databases is a prominent and pivotal focus within the realm of geological information services. Conventional information retrieval methods primarily rely on keyword matching approaches, which often overlook the contextual and semantic aspects of the keywords, consequently impeding the retrieval system’s ability to accurately comprehend user query requirements. To tackle this challenge, this study proposes an ontology-driven information-retrieval framework for geological data that integrates spatiotemporal and topic associations. The framework encompasses the development of a geological domain ontology, extraction of key information, establishment of a multi-feature association and retrieval framework, and validation through a comprehensive case study. By employing the proposed framework, users are empowered to actively and automatically retrieve pertinent information, simplifying the information access process, mitigating the burden of comprehending information organization and software application models, and ultimately enhancing retrieval efficiency.

FRAILTY ASSESSMENT FROM PAPER TO ONLINE: THE E-FI-CGA WEB APP

Abstract Background Early frailty assessment is crucial for improving care for older adults through enhancing frailty-informed care planning and execution. To increase the access and use of the established electronic Frailty Index based on the Comprehensive Geriatric Assessment tool, we developed an online software application: the web-based eFI-CGA. Methods End-user requirements primarily adapted the standalone version of the eFI-CGA and covered assessment competency, interface familiarity, accessibility and convenient use by front-line health professionals. Web app-bounded features included user-account authentication and authorization, user-space management, record-search and retrieval, and functions for data management, analysis and display. The web app development used the Microsoft Azure web server with the ASP.NET framework, combined Webforms and Model-View-Controller architecture, and C# programming language with standard client-side libraries incorporating scripting and mark-up languages. Results The web-based eFI-CGA was technically released and accessible online (efi-cga). Tests on the web pages for all the web pages (e.g., Home, Signup Login, Assessment, Search, and Analysis) using large sizes of systematically designed test data and mocked patient cohorts showed that the web software functions meet the requirements with 100% accuracy. Conclusion The web-based eFI-CGA provides a valuable remote frailty assessment and information retrieval method for healthcare professionals, promoting effective multidisciplinary integrated care of older adults. Further work is needed to allow the safe use of the web app. We are also planning a patient-oriented frailty assessment tool for community-dwelling older adults.

Investigating the current state of the Russian market for drugs treating infectious and inflammatory conditions of oral cavity and oropharynx to demonstrate the need for new drug development

Objective: to assess the relevance and commercial prospects of bringing to market a drug for the local treatment of infectious and inflammatory diseases of oral cavity and oropharynx.Material and methods. The objects of the study were the drug nomenclatures in the State Register of Medicines, the Register of Medicines of Russia, as well as data from the Federal Service for Surveillance in Healthcare. The analysis of scientific publications in the field of marketing research of drugs, found in eLibrary.ru and PubMed/MEDLINE databases was carried out. For analyzing the structure and marketing research of drugs in the study group, specific methods of information retrieval, content analysis, description, data aggregation, ranking, data grouping method, system approach, as well as such methods of marketing analysis as determining the breadth and depth of the assortment, the renewal index, the vital importance index, SWOT analysis (strengths, weaknesses, opportunities, threats) were used.Results. The assortment of drugs for the local treatment of oral cavity and oropharyngeal infectious and inflammatory diseases was structured by the nature of the active substance, type of dosage form, and country of origin. The main trends in the development of the market of drugs of this group were identified. An analysis of the range of drugs using marketing research methods made it possible to establish the breadth and completeness of the range. The calculated renewal and vitality indices confirmed the relevance of developing new drugs with the considered pharmacotherapeutic action.Conclusion. The study revealed the main trends in the development of the market of drugs of the action in question. The SWOT analysis showed a possible strategy for developing and launching a new drug on the market.

Rethinking E-Commerce Search

E-commerce search and recommendation usually operate on structured data such as product catalogs and taxonomies. However, creating better search and recommendation systems often requires a large variety of unstructured data including customer reviews and articles on the web. Traditionally, the solution has always been converting unstructured data into structured data through information extraction, and conducting search over the structured data. However, this is a costly approach that often has low quality. In this paper, we envision a solution that does entirely the opposite. Instead of converting unstructured data (web pages, customer reviews, etc) to structured data, we instead convert structured data (product inventory, catalogs, taxonomies, etc) into textual data, which can be easily integrated into the text corpus that trains LLMs. Then, search and recommendation can be performed through a Q/A mechanism through an LLM instead of using traditional information retrieval methods over structured data.

A Knowledge Graph Embedding Model Based on Cyclic Consistency—Cyclic_CKGE

Most of the existing medical knowledge maps are incomplete and need to be completed/predicted to obtain a complete knowledge map. To solve this problem, we propose a knowledge graph embedding model (Cyclic_CKGE) based on cyclic consistency. The model first uses the “graph” constructed with the head entity and relationship to predict the tail entity, and then uses the “inverse graph” constructed with the tail entity and relationship to predict the head entity. Finally, the semantic space distance between the head entity and the original head entity should be very close, which solves the reversibility problem of the network. The Cyclic_CKGE model with a parameter of 0.46 M has the best results on FB15k-237, reaching 0.425 Hits@10. Compared with the best model R-GCN, its parameter exceeds 8 M and reaches 0.417 Hits@10. Overall, Cyclic_CKGE’s parametric efficiency is more than 17 times that of R-GCNs and more than 8 times that of DistMult. In order to better show the practical application of the model, we construct a visual medical information platform based on a medical knowledge map. The platform has three kinds of disease information retrieval methods: conditional query, path query and multi-symptom disease inference. This provides a theoretical method and a practical example for realizing knowledge graph visualization.

Span prompt dense passage retrieval for Chinese open domain question answering

Dense passage retrieval is a popular method in information retrieval recently, especially in open domain question answering. It aims to retrieve related articles from massive passages to answer the question. Retriever can increase retrieval speed with less loss of accuracy compared to other methods. However, the pretrained language models used in recent research are often ineffective in semantic embedding, which will reduce accuracy. In addition, we find that contrastive learning will diverge the representation space, and Siamese models with independent parameters on both sides will decrease generalization performance. Therefore, we propose span prompt dense passage retrieval (SPDPR) based on span mask prompt tuning and parameter sharing in Chinese open-domain dense retrieval. This model can generate more efficient representation embeddings and effectively counteract the separation tendency between positive samples. We evaluate the effectiveness of SPDPR in DYKzh, as well as two Chinese datasets. SPDPR surpasses all SOTAs implemented in DYKzh and achieves a competitive result in other datasets.

Computationally Efficient Labeling of Cancer-Related Forum Posts by Non-clinical Text Information Retrieval

Modern societies produce vast amounts of digital data and merely keeping up with transmission and storage is difficult enough, but analyzing it to extract and apply useful information is harder still. Almost all research within healthcare data processing is concerned with formal clinical data. However, there is a lot of valuable but idle information in non-clinical data too; this information needs to be retrieved and activated. The present study combines state-of-the-art methods within distributed computing, text retrieval, clustering, and classification into a coherent and computationally efficient system that is able to clarify cancer patient trajectories based on non-clinical and freely available online forum posts. The motivation is: well informed patients, caretakers, and relatives often lead to better overall treatment outcomes due to enhanced possibilities of proper disease management. The resulting software prototype is fully functional and build to serve as a test bench for various text information retrieval and visualization methods. Via the prototype, we demonstrate a computationally efficient clustering of posts into cancer-types and a subsequent within-cluster classification into trajectory related classes. Also, the system provides an interactive graphical user interface allowing end-users to mine and oversee the valuable information.

ICT Skills and Competencies for LIS Professionals in Digital Environment

The rapid development of technology, digitalization of information, availability of e-resources and increasing demand of users has changed the scenario of libraries in present era. Now a day’s libraries have reached beyond the four walls of building & have converted into digital or virtual libraries. The information and communication technology has also brought the drastic change in traditional method of information storage, retrieval and dissemination, which posed challenges for LIS professionals to move from traditional role of librarian to information or technology manager. Today’s librarian needs to update and upgrade themselves with the latest technology so that they can survive in technology driven era. The present paper deliberates the changing role of librarian in the digital era. Keywords: Information professionals, Changing role, ICT, Digital libraries, etc.

Analytical and Numerical Study of Information Retrieval Method Based on Single-Layer Neural Network with Optimization of Computing Algorithm Performance

This work presents a mathematical model of a fast-acting single-layer artificial neural network applied to the task of image reconstruction after noise. For research purposes, this algorithm was implemented in the Python and C++ programming languages. The numerical simulation of the recovery efficiency of the described neural network was performed for different values of the noise factor, the number of samples required to train elements in the sample and the dimensionality of the coupling coefficients, w. The study of the mathematical model of this neural network is presented; as a result, it is possible to identify its essence, to reduce the number of operations required to recover a single element and to increase recovery accuracy by changing the order of calculation of coupling coefficients, w.

PROTOCOL: The effects of agricultural output market access interventions on agricultural, socio-economic and food and nutrition security outcomes in low- and middle-income countries: A systematic review.

Development agencies and international donors' efforts are increasingly focusing on better integrating poor and remote farmers into agricultural markets to address the chronic issues of rural poverty and hunger in low- and middle-income countries. Using systematic methods for information retrieval, critical appraisal and evidence synthesis, this research aims to examine evidence on the effects of five focal types of agricultural market access interventions: (i) farm-to-market transport infrastructure interventions; (ii) output market information interventions; (iii) initiatives creating new marketplaces and alternative marketing opportunities; (iv) contract farming initiatives; (v) interventions improving storage infrastructure. In this review, we will study evidence of the magnitude and direction of intervention effects on agricultural, socio-economic, and food and nutrition security outcomes. We will examine evidence of the distribution of reported effects across different contexts, interventions and sub-groups of the population (e.g., according to sex, socio-economic status, farm size, etc.). We will also report on included studies' risk of bias and on what evidence is available on intervention costs, or their cost-effectiveness. This protocol outlines this review's planned methods and the criteria for selecting and including studies in its analysis.

Discovery and recognition of formula concepts using machine learning

Citation-based Information Retrieval (IR) methods for scientific documents have proven effective for IR applications, such as Plagiarism Detection or Literature Recommender Systems in academic disciplines that use many references. In science, technology, engineering, and mathematics, researchers often employ mathematical concepts through formula notation to refer to prior knowledge. Our long-term goal is to generalize citation-based IR methods and apply this generalized method to both classical references and mathematical concepts. In this paper, we suggest how mathematical formulas could be cited and define a Formula Concept Retrieval task with two subtasks: Formula Concept Discovery (FCD) and Formula Concept Recognition (FCR). While FCD aims at the definition and exploration of a ‘Formula Concept’ that names bundled equivalent representations of a formula, FCR is designed to match a given formula to a prior assigned unique mathematical concept identifier. We present machine learning-based approaches to address the FCD and FCR tasks. We then evaluate these approaches on a standardized test collection (NTCIR arXiv dataset). Our FCD approach yields a precision of 68% for retrieving equivalent representations of frequent formulas and a recall of 72% for extracting the formula name from the surrounding text. FCD and FCR enable the citation of formulas within mathematical documents and facilitate semantic search and question answering, as well as document similarity assessments for plagiarism detection or recommender systems.

Neutrosophic-CNN-based image and text fusion for multimodal classification

Recent advances in technology and devices have caused a data explosion on the Internet and on our home PCs. This data is predominantly obtained in various modalities (text, image, video, etc.) and is essential for e-commerce websites. The products on these websites have both images and descriptions in text form, making them multimodal in nature. Earlier categorization and information retrieval methods focused mostly on a single modality. This study employs multimodal data for classification using neutrosophic fuzzy sets for uncertainty management for information retrieval tasks. This effort utilizes image and text data and, inspired by past techniques of embedding text over an image, attempts to classify the images using neutrosophic classification algorithms. For classification tasks, Neutrosophic Convolutional Neural Networks (NCNNs) are used to learn feature representations of the produced images. We demonstrate how a pipeline based on NCNN can be utilized to learn representations of the innovative fusion method. Traditional convolutional neural networks are vulnerable to unknown noisy conditions in the test phase, and as a result, their performance for the classification of noisy data declines. Comparing our method against individual sources on two large-scale multi-modal categorization datasets yielded good results. In addition, we have compared our method to two well-known multi-modal fusion methodologies, namely early fusion and late fusion.