Information Retrieval Process Research Articles

Studying technical sciences without borders: We use the navigator for open educational resources. Part 1. The Russian experience

Introduction. The introduction of information and communication technologies into learning processes expands the possibilities of using information resources and becomes one of the priorities of the transforming higher education system. These changes are reflected in the trends in the development and application of open educational resources. However, the scale and fragmentation of data on the Internet, as well as the lack of open educational resources standards, makes the information retrieval process a difficult task and requires professional skills.Purpose setting. To determine the relevance and prospects for the development of sectoral open educational resources, it is necessary to analyze their role in the modern educational system. It is important to identify the possibilities of using open access research in technical sciences as one of the tools to eliminate social inequality in higher education. To identify and present arguments for the purpose of justifying the need to develop an industry-specific «Navigator on OER of Technical Sciences» (hereinafter referred to as the «Navigator»).Methodology and methods of the study. The research methodology covers the study and analysis of scientific papers devoted to the development of tools and initiatives in the field of open educational resources in technical sciences both in Russia and abroad. This study is also based on the method of oral questioning of students, postgraduates and teachers of technical disciplines at universities in Novosibirsk. Results. An array of scientific literature and web resources has been studied, providing free access to scientific and educational information in the field of research under consideration. Taking into account empirical data, the necessity of developing a «Navigator» on open educational resources in the system of higher technical education is justified (http://lib-os.ru/issledovatelyam/resursy/obrazovatelnye- resursy/navigator-oor-techn-nauk/).Conclusion. The study of technical sciences using open educational resources contributes to the effective improvement of professional level and allows you to be aware of current solutions to innovative activities in the field of knowledge under consideration.

BRON: A blockchained framework for privacy information retrieval in human resource management

The correctness and the true validated data in Human Resource Management (HRM) are important for organizations as the data plays an impactful role in recruiting, developing, and retaining a skilled workforce. On one hand, the validated data in an organization helps in recruiting legitimate skillful employees; on the other hand, keeping the employee's data safe and maintaining privacy laws such as compliance with the General Data Protection Regulation (GDPR) is also an organization's responsibility. Besides, transparency in human resource management operations is crucial because it promotes trust and fairness within an organization. The present HRM systems are centralized in nature and their verifiable credential system is ineffective; this leads to the intentions of internal data sabotage or internal threats. Besides, the organizations' biases also become more prominent.In this paper, we address the above-mentioned problems with a blockchain framework for HRM to utilize the privacy of data access through a Privacy Information Retrieval (PIR) process. To be specific, our proposed framework called Blockchained piR of resOurces as humaN (BRON), is the first blockchain framework to show an effective mechanism to access data from organizations globally without hampering privacy. BRON uses a generalized user registration process to use the services of data access and in the background, it uses Zero-Knowledge Proofs (ZKPs) for global verification and PIR for privacy-based data retrieval. More specifically, credential verification and ZKP-based PIR are the highlights of our proposed BRON. Another interesting aspect of BRON is the use of Proof-of-Authority (PoA) to validate the anonymity and unlinkability of any HR operation. Finally, BRON has also contributed with a smart contract to incentivize the employees. BRON is very generic and easily be customizable as per the HR requirements. We run a set of experiments on BRON and observe that it is successful in providing privacy-assured data access and decentralized human resource data management. Overall, BRON provides 30% reduced latency and 35% better throughput as compared to the existing blockchain solutions in the direction of HRM.

The Nexus of AI and Vector Databases: Revolutionizing NLP with LLMs

Vector databases play a critical role in the efficiency and functionality of large language models (LLMs), providing scalable and efficient storage and retrieval of high-dimensional vectors. This paper explores the significance of vector databases in the context of LLMs, highlighting their role in information retrieval, similarity search, training, and adaptation processes. Despite the challenges posed by high-dimensional data, vector databases offer invaluable benefits in enhancing the capabilities of LLMs and driving advancements in natural language processing (NLP). Future research and development in this area promise to further optimize the integration and performance of vector databases, fueling continued innovation in LLM applications. Keywords: Vector databases, Large language models (LLMs), Natural language processing (NLP), Information retrieval, Similarity search, Training, Adaptation, Scalability, Efficiency.

The HR Insight Sidebar Extension for LinkedIn Profile

This paper explores the "HR Insights Sidebar Extension," which enhances LinkedIn profile evaluation through a chatbot, aiding informed decision-making with real-time analysis. AI-driven algorithms extract key information like skills, experience, and endorsements, generating concise summaries to streamline evaluations. Leveraging the OpenAI Assistant API's Information Retrieval and Natural Language Processing, the system extracts data from various file types. Users query the chatbot via a natural language interface, receiving direct answers and saving time. Tools like ChatGPT and Proxy curl improve profile evaluation efficiency and effectiveness for students, researchers, and professionals. Keywords- large language models, GPT-3 summarization, proxy curl, implementation, and integration.

HOW TO IMPACT WITH LIBRARY AND LIBRARIANSHIP INFORMATION RETRIEVAL & KNOWLEDGE

The impact of information retrieval and knowledge management in library and librarianship has evolved significantly with the advent of digital technologies and modern information systems. This study investigates the key role that libraries and librarians play in enhancing information retrieval processes and knowledge dissemination in academic, research, and public settings. It highlights the transformation of libraries from traditional repositories of books to dynamic centers for knowledge access and creation. The paper explores how advancements in digital libraries, metadata management, and search algorithms have enabled more efficient retrieval of information, thus empowering researchers, students, and the public. Furthermore, the research examines the skills and evolving role of librarians as key facilitators of knowledge management, curating and guiding users through vast resources. Challenges such as the digital divide, limited resources, and the need for ongoing professional development are also considered. The study concludes with recommendations for the future of librarianship, emphasizing the importance of adopting emerging technologies and continuing to support open access initiatives for better knowledge sharing.

Investing of Chatbots to Enhance the Library Services

Artificial Intelligence (AI) has become an indispensable tool in our daily lives and is a concept that refers to complex tasks that usually require significant human effort. Chatbots stand out as one of the primary applications of artificial intelligence, aiming to foster an interactive dialogue environment between humans and machines to address inquiries through textual or voice interfaces that simulate human cognition. Libraries can serve as an ideal environment for implementing this technology, where it can deliver advanced information services and streamline repetitive routine tasks typically performed by library staff. The paper discusses the applications of chatbots in automating library services, including reference services, user recommendations, information retrieval, data analysis, and other related processes, and their mechanism of action in processing natural language queries and responding to them accurately. Through a web survey, the study identified eighteen chatbot models that could be implemented in libraries. This survey provided details about these bots, including their type (free or commercial), ownership, release date, operating platforms, and key features. This allows libraries to choose from them. Integrating chatbots into libraries will open new horizons for enhancing their services and improving the user experience. Despite the benefits offered by these chatbots, they face several challenges and concerns regarding their usage, notably data privacy and security, and bias in retrieval. It is expected that in the future, these chatbots will become a fundamental component of providing library services.

Content Summarization and Question Answering System Using LLM

This paper presents a novel approach to content summarization, a pivotal task in information processing and knowledge extraction. Our methodology integrates cutting-edge technologies, including large language models (LLMs) and ad-vanced retrieval techniques, to efficiently distill extensive textual data into concise and informative summaries. By leveraging Anyscale LLMs for language comprehension and employing prompt engineering for model guidance, we propose a frame-work that prioritizes efficient document indexing and rapid retrieval using DuckDB vector stores. Additionally, we introduce the RetrievalQAWithSourcesChain framework, which combines LLMs with retrievers for proficient question answering. Em-pirical validation demonstrates the efficacy of our approach in generating high-quality content summaries with minimal manual intervention, thereby enhancing information retrieval and knowledge extraction processes. Index Terms—LLMs, Anyscale, prompt engineering, document indexing, DuckDB, vector stores, RetrievalQAWithSourcesChain framework, question answering, empirical validation

A decentralized solid compound storage facility managed by a centralized electronic platform at a growing drug discovery company

Within a growing drug discovery company, scientists acquire (either through in house synthesis or purchase) then store, retrieve, and ship solid compound samples daily between multiple locations. The efficient management and tracking of this entire process to support drug discovery is a significant challenge. This article describes a decentralized and cost-effective inventory facility that simplifies the solid compound storage and retrieval process. Standardized storage cabinets from the market are utilized, providing a cost-effective physical infrastructure. The cabinets can be distributed across storage rooms at multiple sites and arranged into spaces with a variety of dimensions, allowing the system to be retrofitted into existing facilities and scaled up easily. We can provide storage close to work areas at each location, minimizing both unnecessary movement of staff and transportation of substances. We have applied a systematic barcoding method to the compound batch identifier that correlates with its compound location. This simplifies the compound registration process as well as the process of finding and returning compounds. Additionally, a centralized electronic platform has been employed to store, update and track solid compound information, such as properties, location and quantity. Compound shipment may be initiated from different sites, and a centralized electronic platform assists the information retrieval process, ensuring each location possesses up-to-date information. The electronic platform we present streamlines the management of compound registration, location tracking, weight updates and shipment information, facilitating seamless record sharing among all stakeholders. Every step of the process can be tracked in real time by the project team. The platform can be flexibly configured to adapt to an evolving set of storage locations, with all information and processes being audited.

ADVANCMENTS IN TEXT SUMMARIZATION AND EXTRACTIVE QUESTION- ANSWERING : A MACHINE LEARNING APPROACH

In the era of social media platforms, the rapid expansion of data mining in the fields of information retrieval and natural language processing emphasizes the crucial need for automated text summarization. At the current time, pretrained word embedding techniques and sequence to sequence models can be effectively repurposed in the realm of social network summarization to efficiently condense significant information with strong encoding capabilities. However, dealing with the challenge of extended text dependency and efficiently utilizing latent topic mapping presents an increasingly significant obstacle for these computational models. In this document, we propose a topic- focused approach for both extractive and abstractive summarization, integrating Question Answering features by employing BERT and Pegasus pretrained models. A comprehensive analysis of the architectural complexities of the pre-existing models utilized in the creation of our model has been presented. The evaluation of our Text Summarization & Question Answering Model was conducted meticulously on a variety of datasets including Multi news, XSum, and CNN/Daily Mail, with experimental results indicating its achievement of state-of-the-art performance levels based on ROGUE scores. Following this, the verification of our results through human evaluation confirms that the summaries generated by our model are in alignment with the performance standards set by humans across various datasets. Furthermore, we have integrated an Extractive Question Answering task into our model, proposing an architecture leveraging BERT and conducting comparative analyses against alternative language models to gauge its efficacy. Keywords—Natural Language Processing (NLP), Text Summarization, Question & Answering (Q&A), Transformer Models, Pegasus, BERT (Bidirectional Encoder Representations from Transformers), Neural Topic Modeling, Extractive Summarization, Abstractive Summarization, Semantic Representation, Topic Embedding

O conceito de relevância na recuperação de informação

This work aims to present a bibliographical survey on the concept of relevance, its properties and characteristics, as well as systematizing its manifestations and its use in Information Science, particularly in the process of information retrieval. To achieve this, the research was based on a bibliographical survey of several authors who have relevance as an object of study. It found that the concept of relevance has several formal definitions and can be interpreted in different ways. Relevance is also strongly linked to information retrieval systems. In this context, it can be classified under two approaches: user relevance and system relevance. From the results, we conclude that relevance plays a fundamental role in Information Science, with emphasis on the information retrieval process, which often uses this concept as a metric to evaluate the effectiveness of algorithms and systems.

Leveraging Large Language Models for Tradespace Exploration

This paper proposes a method for leveraging large language models (LLMs) to improve the question-answering capabilities of artificial intelligence (AI) assistants for tradespace exploration. The method operates by querying an information space composed of fused data sources encompassing the tradespace exploration process and responding based on the gathered information. The information retrieval process is modeled as an internal dialog where an LLM-based dialog agent converses with a subquery answering agent. A case study is conducted on a next-generation soil moisture mission (SM-NG), and a generative AI assistant (named Daphne-G) is configured on it. The effect of the dialog agent and the choice of LLM are assessed by comparing the performance of three different system configurations on a validation question set. A second validation effort is conducted, comparing Daphne-G’s responses to those of a baseline template-based AI assistant, Daphne-VA. Results show that the dialog-based system is necessary for answering complex questions requiring multiple documents. Furthermore, results show that Daphne-G can correctly answer all the questions Daphne-VA can answer, while simultaneously being able to answer a greater number of questions than Daphne-VA. The results suggest that LLMs could significantly improve the outcomes of the tradespace exploration process, which may result in better and more cost-effective mission concepts being implemented.

A novel fuzzy logic-based approach for textual documents indexing

In the evolving landscape of information retrieval and natural language processing, the quest for more effective automatic keyword extraction (AKE) techniques from textual documents has become a pivotal research focus. Existing methodologies, while offering valuable insights, often grapple with the challenges posed by the imprecision and variability inherent in human language. This has led to a growing recognition of the need for innovative approaches to navigating textual content’s nuances more adeptly. In response to this imperative, this paper proposes a novel fuzzy indexing approach designed specifically for the indexing of textual documents. Fuzzy indexing, grounded in the principles of fuzzy logic, provides solutions for handling the inherent uncertainty and imprecision in natural language, especially when confronted with the intricacies of linguistic ambiguity and variability. By leveraging the power of fuzzy logic, we aim to enhance the precision of keyword extraction. This paper unfolds the intricacies of our fuzzy indexing approach, detailing the theoretical methodology through empirical evaluation and comparative analysis; we seek to demonstrate the efficacy of our approach in outperforming traditional methods in the context of fuzzy indexing for textual documents.

From traditional to tech-savvy: the evolution of Nigerian libraries in the machine learning era

Purpose This study explores the implementation of personalised information access, driven by machine learning, in Nigerian public libraries. The purpose of this paper is to address existing challenges, enhance the user experience and bridge the digital divide by leveraging advanced technologies. Design/methodology/approach This study assesses the current state of Nigerian public libraries, emphasising challenges such as underfunding and lack of technology adoption. It proposes the integration of machine learning to provide personalised recommendations, predictive analytics for collection development and improved information retrieval processes. Findings The findings underscore the transformative potential of machine learning in Nigerian public libraries, offering tailored services, optimising resource allocation and fostering inclusivity. Challenges, including financial constraints and ethical considerations, are acknowledged. Originality/value This study contributes to the literature by outlining strategies for responsible implementation and emphasising transparency, user consent and diversity. The research highlights future directions, anticipating advancements in recommendation systems and collaborative efforts for impactful solutions.

The effect of augmented reality applied to learning process with different learning styles in structural engineering education

PurposeThe purpose of this study is to scrutinize the influence of individual learning styles on the effectiveness of augmented reality (AR)-based learning in structural engineering. There has been a lack of research examining the correlation between learning efficiency and learning style, particularly in the context of quantitatively assessing the efficacy of AR in structural engineering education.Design/methodology/approachUsing Kolb’s experiential learning theory (ELT), a model that emphasizes learning through experience, students from the construction management department are assigned four learning styles (converging, assimilating, diverging and accommodating). Performance data were gathered, appraised, and compared through the three dimensions from the Knowledge, Attitude and Practices (KAP) survey model across four categories of Kolb’s learning styles in both text-graph (TG)-based and AR-based learning settings.FindingsThe findings indicate that AR-based materials positively impact structural engineering education by enhancing overall learning performance more than TG-based materials. It is also found that the learning style has a profound influence on learning effectiveness, with AR technology markedly improving the information retrieval processes, particularly for converging and assimilating learners, then diverging learners, with a less significant impact on accommodating learners.Originality/valueThese results corroborate prior research analyzing learners' outcomes with hypermedia and informational learning systems. It was found that learners with an “abstract” approach (convergers and assimilators) outperform those with a “concrete” approach (divergers and accommodators). This research emphasizes the importance of considering learning styles before integrating technologies into civil engineering education, thereby assisting software developers and educational institutions in creating more effective teaching materials tailored to specific learning styles.

Radiation injuries of organs and tissues: mechanisms of occurrence, methods of prevention and treatment: A review

In modern medicine, radiation therapy (RT) is one of the main therapies for cancer. According to the World Health Organization, about 70% of patients diagnosed with cancer require RT at some stage of treatment. Development and improvement of methods for early diagnosis of cancers, multimodal therapy for cancer patients, including surgical intervention, new drug treatment regimens, modern methods of conformal 3D-RT and proton therapy significantly improved the treatment outcomes of patients and increased their life expectancy. Modern RT is a high-tech chain of sequential use of complex technical devices and mathematical calculations to destroy the tumor without adversely affecting the surrounding normal tissues. Some patients develop radiation injuries to normal organs and tissues that reduce their quality of life. Prevention and treatment of such injuries are urgent problems of modern oncology. The article addresses the pathogenesis and mechanism of the development of systemic and local radiation responses, as well as methods of treatment and prevention of some of them. Three electronic databases, including PubMed, Cochrane and EMBASE, and Russian radiotherapists' publications, were used for systematic review in the information retrieval process.

A Novel Approach to Text Summarization Using Machine Learning

Text summarization is a key strategy in the domains of information retrieval and natural language processing (NLP). Its objective is to reduce a lengthy written document into a clearer, more succinct summary of the information it contains. When a text document is too lengthy or intricate to analyse completely, as in news stories, academic papers, or legal documents, this approach is extremely helpful. The major challenge of text summarising is to take the most important and relevant information from the original text and convey it in an understandable and concise way. In this study, extractive and abstractive summarising techniques are the two primary categories of text summary methods. The paper also presents several algorithms that have been proposed for text summarization, including TextRank, Seq2Seq, and BART. TextRank is a simple and fast algorithm that works well for short documents, Seq2Seq is a deep learning-based approach that generates high-quality summaries, and BART is a transformer-based algorithm that provides the best results on benchmark datasets. The obtained ROUGE Score after passing TextRank, BART, and Seq2SEq algorithm significant also.

Assessment of Information Literacy Competences in Peruvian University Students: An Exploratory Study

The objective was to evaluate the perception of the informational literacy competences of the university students of the Peruvian Amazon. The investigation was developed under a quantitative approach, the design was non-experimental and cross-sectional descriptive type. The sample was made up of 707 students to whom the Informational Literacy Competences Questionnaire was given, an instrument with an adequate level of content validity and reliability. According to the results, the level of development of the informational literacy competencies of 37.5% of students was low, 35% was medium, and 27.5% was high. Likewise, the dimensions of information retrieval, information assessment and information processing were also valued at a low level. However, the information communication dimension of information was valued at a medium level. On the other hand, it was found that the level of informational literacy competences was significantly associated with the year of study in which the students were (p<0.05). It was concluded that the level of development of the informational literacy competencies that characterized the students was low.

Neurophysiological processes reflecting the effects of the immediate past during the dynamic management of actions

In recent years, there has been many efforts to establish a comprehensive theoretical framework explaining the working mechanisms involved in perception-action integration. This framework stresses the importance of the immediate past on mechanisms supporting perception-action integration. The present study investigates the neurophysiological principles of dynamic perception-action bindings, particularly considering the influence of the immediate history on action control mechanisms. For this purpose, we conducted an established stimulus-response binding paradigm during EEG recording. The SR-task measures stimulus-response binding in terms of accuracy and reaction time differences depending on the degree of feature overlap between conditions. Alpha, beta and theta band activity in distinct time domains as well as associated brain regions were investigated applying time-frequency analyses, a beamforming approach as well as correlation analyses. We demonstrate, for the first time, interdependencies of neuronal processes relying on the immediate past. The reconfiguration of an action seems to overwrite immediately preceding processes. The analyses revealed modulations of theta (TBA), alpha (ABA) and beta band activity (BBA) in connection with fronto-temporal structures supporting the theoretical assumptions of the considered conceptual framework. The close interplay of attentional modulation by gating irrelevant information (ABA) and binding and retrieval processes (TBA) is reflected by the correlation of ABA in all pre-probe-intervals with post-probe TBA. Likewise, the role of BBA in maintaining the event file until retrieval is corroborated by BBA preceding the TBA-associated retrieval of perception-action codes. Following action execution, TBA shifted towards visual association cortices probably reflecting preparation for upcoming information, while ABA and BBA continue to reflect processes of attentional control and information selection for goal-directed behavior. The present work provides the first empirical support for concepts about the neurophysiological mechanisms of dynamic management of perception and action.

Improved Object Detection with Content and Position Separation in Transformer

In object detection, Transformer-based models such as DETR have exhibited state-of-the-art performance, capitalizing on the attention mechanism to handle spatial relations and feature dependencies. One inherent challenge these models face is the intertwined handling of content and positional data within their attention spans, potentially blurring the specificity of the information retrieval process. We consider object detection as a comprehensive task, and simultaneously merging content and positional information like before can exacerbate task complexity. This paper presents the Multi-Task Fusion Detector (MTFD), a novel architecture that innovatively dissects the detection process into distinct tasks, addressing content and position through separate decoders. By utilizing assumed fake queries, the MTFD framework enables each decoder to operate under a presumption of known ancillary information, ensuring more specific and enriched interactions with the feature map. Experimental results affirm that this methodical separation followed by a deliberate fusion not only simplifies the task difficulty of the detection process but also augments accuracy and clarifies the details of each component, providing a fresh perspective on object detection in Transformer-based architectures.

Toward a Model to Evaluate Machine-Processing Quality in Scientific Documentation and Its Impact on Information Retrieval

The lack of quality in scientific documents affects how documents can be retrieved depending on a user query. Existing search tools for scientific documentation usually retrieve a vast number of documents, of which only a small fraction proves relevant to the user’s query. However, these documents do not always appear at the top of the retrieval process output. This is mainly due to the substantial volume of continuously generated information, which complicates the search and access not properly considering all metadata and content. Regarding document content, the way in which the author structures it and the way the user formulates the query can lead to linguistic differences, potentially resulting in issues of ambiguity between the vocabulary employed by authors and users. In this context, our research aims to address the challenge of evaluating the machine-processing quality of scientific documentation and measure its influence on the processes of indexing and information retrieval. To achieve this objective, we propose a set of indicators and metrics for the construction of the evaluation model. This set of quality indicators have been grouped into three main areas based on the principles of Open Science: accessibility, content, and reproducibility. In this sense, quality is defined as the value that determines whether a document meets the requirements to be retrieved successfully. To prioritize the different indicators, a hierarchical analysis process (AHP) has been carried out with the participation of three referees, obtaining as a result a set of nine weighted indicators. Furthermore, a method to implement the quality model has been designed to support the automatic evaluation of quality and perform the indexing and retrieval process. The impact of quality in the retrieval process has been validated through a case study comprising 120 scientific documents from the field of the computer science discipline and 25 queries, obtaining as a result 21% high, 39% low, and 40% moderate quality.