Effective Information Retrieval Research Articles

Knowledge Ply Chat

Despite their ability to store information and excel at many NLP tasks with fine-tuning, large language models tend to have issues about accurately accessing and altering knowledge, which leads to performance gaps in knowledge-intensive tasks compared to domain-specific architectures. Additionally, these models face problems when it comes to having transparent decision-making processes or updating their world knowledge. To mitigate these limitations, we propose a Retrieval Augmented Generation (RAG) system by improving the Mistral7B model specifically for RAG tasks. The novel training technique includes Parameter-Efficient Fine-Tuning (PEFT) which enables efficient adaptation of large pre- trained models on-the-fly according to task-specific requirements while reducing computational costs. In addition, this system combines pre-trained embedding models that use pre-trained cross-encoders for effective retrieval and reranking of information. This RAG system will thus leverage these state-of-the-art methodologies towards achieving top performances in a range of NLP tasks such as question answering and summarization.

DTF-AT: Decoupled Time-Frequency Audio Transformer for Event Classification

Convolutional neural networks (CNNs) and Transformer-based networks have recently enjoyed significant attention for various audio classification and tagging tasks following their wide adoption in the computer vision domain. Despite the difference in information distribution between audio spectrograms and natural images, there has been limited exploration of effective information retrieval from spectrograms using domain-specific layers tailored for the audio domain. In this paper, we leverage the power of the Multi-Axis Vision Transformer (MaxViT) to create DTF-AT (Decoupled Time-Frequency Audio Transformer) that facilitates interactions across time, frequency, spatial, and channel dimensions. The proposed DTF-AT architecture is rigorously evaluated across diverse audio and speech classification tasks, consistently establishing new benchmarks for state-of-the-art (SOTA) performance. Notably, on the challenging AudioSet 2M classification task, our approach demonstrates a substantial improvement of 4.4% when the model is trained from scratch and 3.2% when the model is initialised from ImageNet-1K pretrained weights. In addition, we present comprehensive ablation studies to investigate the impact and efficacy of our proposed approach. The codebase and pretrained weights are available on https://github.com/ta012/DTFAT.git

Natural language processing for drug information extraction : Advancing knowledge discovery in biomedical literature

Natural language processing (NLP) has emerged as an important tool in the biomedical industry for bettering medication information extraction and other knowledge gathering. In this study, we explore the use of natural language processing (NLP) techniques to glean useful insights from massive volumes of biological literature, with the goal of better comprehending data pertaining to drugs. We want to automate the extraction of crucial aspects such drug interactions, side effects, and efficacy by utilizing sophisticated language models and semantic analysis. Effective and comprehensive drug information retrieval will be encouraged. Our innovation improves the drug development process by facilitating the rapid exploration of big databases for previously unknown connections and patterns. By facilitating the synthesis of data from several sources, NLP in biomedical research speeds up information extraction, which in turn accelerates drug development and improves patient care by allowing for evidence-based decision making. This research demonstrates the promising potential of natural language processing (NLP) for unraveling the complexities of drug-related data, ushering in a new era of learning in the biomedical field.

Bayesian Learning of Graph Substructures

Graphical models provide a powerful methodology for learning the conditional independence structure in multivariate data. Inference is often focused on estimating individual edges in the latent graph. Nonetheless, there is increasing interest in inferring more complex structures, such as communities, for multiple reasons, including more effective information retrieval and better interpretability. Stochastic blockmodels offer a powerful tool to detect such structure in a network. We thus propose to exploit advances in random graph theory and embed them within the graphical models framework. A consequence of this approach is the propagation of the uncertainty in graph estimation to large-scale structure learning. We consider Bayesian nonparametric stochastic blockmodels as priors on the graph. We extend such models to consider clique-based blocks and to multiple graph settings introducing a novel prior process based on a Dependent Dirichlet process. Moreover, we devise a tailored computation strategy of Bayes factors for block structure based on the Savage-Dickey ratio to test for presence of larger structure in a graph. We demonstrate our approach in simulations as well as on real data applications in finance and transcriptomics.

Research on privacy information retrieval model based on hybrid homomorphic encryption

The computational complexity of privacy information retrieval protocols is often linearly related to database size. When the database size is large, the efficiency of privacy information retrieval protocols is relatively low. This paper designs an effective privacy information retrieval model based on hybrid fully homomorphic encryption. The assignment method is cleverly used to replace a large number of homomorphic encryption operations. At the same time, the multiplicative homomorphic encryption scheme is first used to deal with the large-scale serialization in the search, and then the fully homomorphic encryption scheme is used to deal with the remaining simple operations. The depth of operations supported by the fully homomorphic scheme no longer depends on the size of the database, but only needs to support the single homomorphic encryption scheme to decrypt the circuit depth. Based on this hybrid homomorphic encryption retrieval model, the efficiency of homomorphic privacy information retrieval model can be greatly improved.

Efficient information retrieval model: overcoming challenges in search engines-an overview

<span>Search engines play a vital role in information retrieval (IR) indexing and processing vast and diverse data, which now encompasses the ever-expanding wealth of multimedia content. However, search engine performance relies on the efficiency and effectiveness of their information retrieval systems (IRS). To enhance search engine performance, there is a need to develop more efficient and accurate IRS that retrieves relevant information quickly and accurately. To address this challenge, various approaches, including inverted indexing, query expansion, and relevance feedback, have been proposed for IR. Although these approaches have shown promising results, but their effectiveness and limitations require a comprehensive examination This research aims to investigate the challenges and opportunities in designing an efficient IRS for search engines and identify key areas for improvement and future research. The study involves a comprehensive literature review on information retrieval impacting academia, industry, healthcare, e-commerce, and other domains. Researchers rely on search engines to access relevant scientific papers, professionals use them to gather market intelligence, and consumers utilize them for product research and decision-making. The findings of this study will contribute to the development of more efficient and effective information retrieval systems, leading to improved search engine performance and user satisfaction.</span>

Developing a Semantically Based Query Recommendation for an Electronic Medical Record Search Engine: Query Log Analysis and Design Implications.

An effective and scalable information retrieval (IR) system plays a crucial role in enabling clinicians and researchers to harness the valuable information present in electronic health records. In a previous study, we developed a prototype medical IR system, which incorporated a semantically based query recommendation (SBQR) feature. The system was evaluated empirically and demonstrated high perceived performance by end users. To delve deeper into the factors contributing to this perceived performance, we conducted a follow-up study using query log analysis. One of the primary challenges faced in IR is that users often have limited knowledge regarding their specific information needs. Consequently, an IR system, particularly its user interface, needs to be thoughtfully designed to assist users through the iterative process of refining their queries as they encounter relevant documents during their search. To address these challenges, we incorporated "query recommendation" into our Electronic Medical Record Search Engine (EMERSE), drawing inspiration from the success of similar features in modern IR systems for general purposes. The query log data analyzed in this study were collected during our previous experimental study, where we developed EMERSE with the SBQR feature. We implemented a logging mechanism to capture user query behaviors and the output of the IR system (retrieved documents). In this analysis, we compared the initial query entered by users with the query formulated with the assistance of the SBQR. By examining the results of this comparison, we could examine whether the use of SBQR helped in constructing improved queries that differed from the original ones. Our findings revealed that the first query entered without SBQR and the final query with SBQR assistance were highly similar (Jaccard similarity coefficient=0.77). This suggests that the perceived positive performance of the system was primarily attributed to the automatic query expansion facilitated by the SBQR rather than users manually manipulating their queries. In addition, through entropy analysis, we observed that search results converged in scenarios of moderate difficulty, and the degree of convergence correlated strongly with the perceived system performance. The study demonstrated the potential contribution of the SBQR in shaping participants' positive perceptions of system performance, contingent upon the difficulty of the search scenario. Medical IR systems should therefore consider incorporating an SBQR as a user-controlled option or a semiautomated feature. Future work entails redesigning the experiment in a more controlled manner and conducting multisite studies to demonstrate the effectiveness of EMERSE with SBQR for patient cohort identification. By further exploring and validating these findings, we can enhance the usability and functionality of medical IR systems in real-world settings.

Exploration of Chinese cultural communication mode based on the Internet of Things and mobile multimedia technology.

Image retrieval technology has emerged as a popular research area of China's development of cultural digital image dissemination and creative creation with the growth of the Internet and the digital information age. This study uses the shadow image in Shaanxi culture as the research object, suggests a shadow image retrieval model based on CBAM-ResNet50, and implements it in the IoT system to achieve more effective deep-level cultural information retrieval. First, ResNet50 is paired with an attention mechanism to enhance the network's capacity to extract sophisticated semantic characteristics. The second step is configuring the IoT system's picture acquisition, processing, and output modules. The image processing module incorporates the CBAM-ResNet50 network to provide intelligent and effective shadow play picture retrieval. The experiment results show that shadow plays on GPU can retrieve images at a millisecond level. Both the first image and the first six photographs may be accurately retrieved, with a retrieval accuracy of 92.5 percent for the first image. This effectively communicates Chinese culture and makes it possible to retrieve detailed shadow-play images.

Cyber Information Retrieval Through Pragmatics Understanding and Visualization

The amount of cybersecurity-related information is extraordinarily increasing, given the fast-growing number of cybersecurity attacks and the significant influence brought by them. How to efficiently obtain and precisely understand the relevant knowledge in the sea of information on cybersecurity becomes a challenge. In this article, we propose an innovative cybersecurity retrieval scheme that supports automatic indexing and searching of cybersecurity information based on semantic contents and hidden metadata. The proposed scheme leverages a customized neural model that incorporates new linguistic features and word embedding by identifying the entities related to cybersecurity incidents from the text. We implement a novel cybersecurity search engine to demonstrate effective, understandable and pragmatic cybersecurity information retrieval based on the proposed schema. Comprehensive performance evaluation over real-world datasets has been conducted to validate the new algorithms and techniques developed for cybersecurity information retrieval. The new engine makes it possible to conduct augmented search, cybersecurity analytics, and visualization, with the ultimate goal of providing direct and efficient results to help people obtain and truly understand cybersecurity information.

Automated patent classification for crop protection via domain adaptation

AbstractPatents show how technology evolves in most scientific fields over time. The best way to use this valuable knowledge base is to use efficient and effective information retrieval and searches for related prior art. Patent classification, that is, assigning a patent to one or more predefined categories, is a fundamental step towards synthesizing the information content of an invention. To this end, architectures based on Transformers, especially those derived from the BERT family have already been proposed in the literature and they have shown remarkable results by setting a new state‐of‐the‐art performance for the classification task. Here, we study how domain adaptation can push the performance boundaries in patent classification by rigorously evaluating and implementing a collection of recent transfer learning techniques, for example, domain‐adaptive pretraining and adapters. Our analysis shows how leveraging these advancements enables the development of state‐of‐the‐art models with increased precision, recall, and F1‐score. We base our evaluation on both standard patent classification datasets derived from patent offices‐defined code hierarchies and more practical real‐world use‐case scenarios containing labels from the agrochemical industrial domain. The application of these domain adapted techniques to patent classification in a multilingual setting is also examined and evaluated.

Developing online search strategy skills for effective information retrieval: An activity-based approach

Developing online search strategy skills for effective information retrieval: An activity-based approach

Synonym‐based multi‐keyword ranked search with secure k‐NN in 6G network

AbstractSixth Generation (6G) integrates the next generation communication systems such as maritime, terrestrial, and aerial to offer robust network and massive device connectivity with ultra‐low latency requirement. The cutting edge technologies such as artificial intelligence, quantum machine learning, and millimetre enable hyper‐connectivity to extend the development of mobile communication. The potential technologies utilise different computing infrastructures, on‐demand software, platforms, and machine intelligence to access a massive amount of data in real‐time. The delivery of real‐time data demands third‐party remote access to exploit the resources such as network service and storage to meet user requirements. Moreover, certain computing infrastructures such as cloud, edge, and fog recommend privacy protection to guarantee effective data storage and information retrieval by a data centre. As a result, multi‐keyword searching over encrypted cloud data is commonly exercised to retrieve the cloud data with service integrity. With the use of mobile cloud computing, the data generating IoT device can easily outsource any kind of complex data management system from the local network system. It may commercialise the system site to achieve better resource‐saving. However, data privacy and protection are expected to ensure using encryption techniques before any sensitive data is outsourced via data generating IoT devices. Thus, this paper presents a synonym‐based multi‐keyword ranked search over encrypted cloud data in order to solve privacy‐preserving and computation cost. The proposed mechanism constructs an m‐Way search tree to allot an index vector for each document which applies a depth‐first search to compute top score ranking to realise better search efficiency. The examination results show that the assumed index vectors can be subdivided into the sub‐vectors before storing them into the index tree to achieve better security and computation efficiency.

INFORMATION SUPPORT OF OCCUPATIONAL SAFETY CONSULTING ON THE BASIS OF THE MONITORING SYSTEM

The article identifies the need to improve the information support of occupational safety consulting by creating a monitoring system. This is due to changes in the regulatory framework of labor protection, as well as changes in the structure of State Labour Service of Ukraine. The development of a monitoring system is due to the need for comprehensive modernization of information and analytical support in the field of labor protection. The lack of a subsystem of analytical data processing complicates the implementation of queries and prevents effective multicriteria information retrieval in databases, creating integrated reports, complicates or prevents the use of mathematical modeling techniques to predict occupational safety and occupational risk assessment. The basic principles on which the system of state monitoring of labor protection should be based are determined (the principle of purposefulness, the principle of planning, the scientific principle, the principle of objectivity, etc.). The main functions of the monitoring system for information support of occupational safety consulting are: accounting and analysis of indicators of labor protection; labor protection planning; organization and control of work on the implementation of labor protection measures; evaluation of the results of the planned activities. The implementation of the proposed principles and approaches to the development of a monitoring system becomes possible in the active development of information technology, as a result of which computer and communication technology and software have become widespread in all industrial and scientific fields. For the functioning of the monitoring system it is necessary to develop a set of methods, techniques and algorithms that establish the procedure for collecting, processing, analyzing and using data in the process of management activities in the field of labor protection. The use of the results of the monitoring system will increase the level of awareness of managers and specialists who carry out management in the field of labor protection, as well as ensure the scientific validity of management decisions.

Reducing the dependency of having prior domain knowledge for effective online information retrieval

AbstractSometimes Internet users struggle to find what they are looking for on the Internet due to information overload. Search engines intend to identify documents related to a given keyphrase on the Internet and provide suggestions. Having some background knowledge about a topic or a domain will help in building effective search keyphrases that will lead to accurate results in information retrieval. This is further pronounced among students that rely on the internet to learn about a new topic. Students might not have the required background knowledge to build effective keyphrases and find what they are looking for. In this research, we are addressing this problem, and aim to help students find relevant information online. This research furthers existing literature by enhancing information retrieval frameworks through keyphrase assignment, aiming to expose students to new terminologies, therefore reducing the dependency of having background knowledge about the domain under study. We evaluated this framework and identified how it can be enhanced to suggest more effective search keyphrases. Our proposed suggestion is to introduce a keyphrase Ranking Mechanism that will improve the keyphrase assignment part of the framework by taking into consideration the part‐of‐speech of the generated keyphrases. To evaluate the proposed approach, various data sets were downloaded and processed. The results obtained showed that our proposed approach produces more effective keyphrases than the existing framework.

Development of an Internet-Based Information/DataResource Platform during the COVID-19 Pandemic Using Hierarchical Clustering

There is heavy usage of Internet-based information resource platformsdue to the COVID-19 pandemic. If it can present the search results as a list of groupsof related terms and context, that would be ideal. An additional enhancement would be to display information in the search results in a classified manner. Therefore, the research is proposing an effective and efficient information retrieval system. The methodology adopted follows the conventional Information Retrieval (IR) system design except that Cosine similarity and hierarchical clustering were introduced for search result ranking. The Average Precision of the proposed system was compared with that of flat clustering such as K-means. The result of the evaluation shows that hierarchical clustering improved in precision, being10% better than flat clustering result retrieval result. This led to greater usability, userexperience,and efficiency of the system

Development of an Internet-Based Information/DataResource Platform during the COVID-19 Pandemic Using Hierarchical Clustering

there is heavy usage of Internet-based information resource platformsdue to the COVID-19 pandemic. If it can present the search results as a list of groupsof related terms and context, that would be ideal. An additional enhancement would be to display information in the search results in a classified manner. Therefore, the research is proposing an effective and efficient information retrieval system. The methodology adopted follows the conventional Information Retrieval (IR) system design except that Cosine similarity and hierarchical clustering were introduced for search result ranking. The Average Precision of the proposed system was compared with that of flat clustering such as K-means. The result of the evaluation shows that hierarchical clustering improved in precision, being10% better than flat clustering result retrieval result. This led to greater usability, userexperience,and efficiency of the system.

Strategic and Sustainable Implementation of 5S in a Beef Abattoir

In recent time workplace organization and easy information retrieval help in achieving optimum productivity through maximum utilization of the resources available, significantly reducing industrial lead-time and waste thus resulting in low production cost and increase return-on-investment (ROI). This paper is a study of the effective and efficient implementation of 5S processes in a beef abattoir. Thus, the paper employs both qualitative (case study) and quantitative (statistical analysis like 5S scorecard and 5S audit performance) methods. This research identifies and outlines 5S “best practice” issues overlooked such as unneeded items lying around, torn sign displays, labels and shelves not partitioned, bins not clearly stored in demarcated areas and storage tools not clearly shown with sign panels and labels. Furthermore, budget constraints and the abattoir unreadiness to adopt the 5S system inhibits the smooth implementation of the 5S phases required. Therefore, this research managed to map out a 5S lean-system implementation framework for the case company X beef abattoirs. Finally, the research recommended effective process on how 5S can efficiently save the industry on planning to reduce waste in processes such as lead-time in effective information retrieval system, safety issues to mitigate non-value adding activities and space utilization, for improved productivity.

The Information Retrieval Competencies of Students Faced with Remote and Blended Teaching

The article describes the problems and features of information retrieval activity of students amid the extreme transition of universities to remote education. The results of a student survey are described, allowing to gain some initial insights into the information retrieval competencies of students in a new reality, when the only opportunity for them is remote access to electronic information systems and electronic educational resources. The competencies necessary for effective information retrieval in a remote and blended teaching have been listed and characterized.

Secondary school students’ development of arguments for complex geographical conflicts using the internet

ABSTRACT In secondary school geography lessons, students are encouraged to form argumentatively founded opinions on complex geographical conflicts. For these conflicts, there is no one right solution and the content quality of the argumentation lies especially in the multi-perspective approach to the conflict and the integration of spatial information. The Internet offers a wealth of multi-perspective and spatial information on a great number of geographical conflicts worldwide. However, the digital information is neither checked nor filtered nor didactically prepared. This study examined the ability of 20 German secondary school students in developing arguments on a complex geographical conflict after searching the Internet for information. The students’ information search and their concurrent verbalisations were taped using screen and audio capture technology. The developed arguments have been assessed using defined criteria for argumentations on geographical conflicts. The analysis of the arguments showed that the students included a range of perspectives , which suggests that they were able to use the Internet as a source for obtaining multi-perspective information on the conflict. However, whilst effective digital information retrieval was the pre-condition in understanding the geographical conflict, it has not shown to guarantee the development of a high-quality argumentation.

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatio-temporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”.