Concept Of Retrieval Research Articles

Fourier ptychographic microscopy with multi-height illumination based on energy threshold pre-search

Fourier ptychographic microscopy (FPM) technology combines the concepts of synthetic aperture imaging, ptychography, and phase retrieval to address the contradiction between the large field of view and high resolution in traditional microscopy and can achieve high-resolution amplitude and phase images with a large field of view. However, for most samples, the primary information is concentrated in the low-frequency region, and traditional single-height FPM may suffer from insufficient sampling, leading to low reconstruction accuracy. In addition, the reconstruction process typically requires a large number of low-resolution images, which also significantly reduces the reconstruction efficiency. To overcome these issues, this paper proposes a form of FPM with multi-height illumination based on an energy threshold pre-search. This method simply involves moving the LED array to three planes for multi-height sample illumination on the traditional FPM hardware, thus improving the sampling conditions and enhancing the reconstruction accuracy. The low-resolution images acquired in this way are then screened using an energy threshold method to select images with higher energy, and a phase retrieval method is employed to reconstruct high-resolution complex amplitude images. The results of simulations and experiments demonstrate that compared to traditional methods, our approach not only improves the reconstruction accuracy but also reduces the number of low-resolution images by at least approximately 60%, thereby significantly enhancing the reconstruction efficiency.

A Novel Method of Secured Data Distribution Using Sharding Zkp and Zero Trust Architecture in Blockchain Multi Cloud Environment

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates the sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further, we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files (greater than 50 MB) or Big data files (greater than 1 TB) require greater computational complexity, we leverage the sharding concept to enhance both space and time complexity in cloud storage. Hence, the large files are divided into shards and stored in different database servers. We also employ a blockchain smart contract to enhance secure retrieval of the file and also a secure access method, which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data’s authenticity is shared with the verifier at the destination cloud, rather than the actual data themselves. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies required to store data. Since the environment maintains the privacy of the user data and the raw data of the user is not stored anywhere, the entire environment is set up as a Zero trust model.

Retrieval-augmented generation using domain-specific text: a pilot study

The natural language processing (NLP) field has witnessed remarkable advancements with the advent of large language models (LLMs) like GPT, Gemini, Claude, etc. These models are trained on vast amounts of text data, allowing them to generate human-like responses for various tasks. However, despite their impressive capabilities, LLMs have limitations in their ability to incorporate and reason over external knowledge that is not in their training data. This limitation of LLMs is particularly evident in the case of specific domain knowledge. This situation has given rise to the concept of retrieval augmented generation (RAG), an approach that combines the generative power of LLMs with the ability to retrieve and integrate relevant information from external knowledge sources. This research attempts to use RAG as a module in an application designed to answer questions concerning a specific domain, namely social philosophy/philosophy of management, using a published book from the respective domain as an external source. The paper analyzes the mentioned application output, draws conclusions, and traces future directions to improve the accuracy of the output.

Fourier Quantum Process Tomography

The characterization of a quantum device is a crucial step in the development of quantum experiments. This is accomplished via Quantum Process Tomography, which combines the outcomes of different projective measurements to deliver a possible reconstruction of the underlying process. The tomography is typically performed by processing an overcomplete set of measurements and extracting the process matrix from maximum-likelihood estimation. Here, we introduce Fourier Quantum Process Tomography, a technique which requires a reduced number of measurements, and benchmark its performance against the standard maximum-likelihood approach. Fourier Quantum Process Tomography is based on measuring probability distributions in two conjugate spaces for different state preparations and projections. Exploiting the concept of phase retrieval, our scheme achieves a complete and robust characterization of the setup by processing a near-minimal set of measurements. We experimentally test the technique on different space-dependent polarization transformations, reporting average fidelities higher than 90% and significant computational advantage.

How Mandarin-English bilinguals interpret qián/forward: impact of language proficiencies on retrieval of temporal concepts.

Mandarin qián and English forward are semantically equivalent in the domain of Space, but could be semantically opposite in the domain of Time. In other words, equivalent spatial lexical items could convey opposite temporal concepts. What temporal concepts conveyed by qián and forward would be retrieved by Mandarin-English (M-E) bilinguals with different language proficiencies? Drawing a sample from college students in Mainland China, this study examines how L1 and L2 proficiencies would affect M-E bilinguals' retrieval of temporal concepts by examining their interpretation of the Mandarin temporal metaphor of qián and the English temporal metaphors of forward. The results show that L1 temporal concepts would be retrieved more frequently than L2 temporal concepts regardless of the testing languages, that L1 and L2 proficiencies were not predictors for the way of interpretation, and that the higher L2 proficiency group could retrieve temporal concepts in line with the testing languages with higher accuracy than the lower L2 proficiency group. The findings suggest that bilinguals with higher L2 proficiency may be able to represent temporal concepts with language tags or may have an attentional and/or inhibitory control advantage.

A Knowledge-Based Deep Learning Approach for Automatic Fake News Detection using BERT on Twitter

Fake news generation and propagation is a major challenge of the digital age, resulting in various social impacts namely bandwagon, validity, echo chamber effects, deceiving the public with spams, misinformation, malicious content and many more. The widespread proliferation of fake news not only fosters misinformation but also undermines the credibility of news sources. The veracity of the information is a major concern at all the stages of generation, publication, and propagation. To comprehend the critical need for addressing this pervasive problem, this research paper presents a framework for automatic detection of fake news using a knowledge-based approach. An automatic fact checking mechanism is applied using concepts of Information Retrieval (IR), Natural Language Processing (NLP) and Graph theory. The knowledge base is generated using Twitter dataset, which basically contains four attributes: Subject-Predicate-Object (SPO) triplet, SPO sentiment polarity, SPO occurrence, and topic modeling. These attributes serve as pivotal indicators for the development of a knowledge base, subsequently employed to detect prevalent patterns and traits linked to deceptive or false information. We have employed Named Entity Recognition (NER) model to extract SPO triples and Latent Dirichlet Allocation (LDA) for topic modeling, thereby contributing to knowledge base generation. To evaluate the efficacy and efficiency of our proposed model, we utilize deep learning algorithms like RNN, GRU, LSTM, GPT-3 and BERT Transformer providing an acceptable level of accuracy. This research paper delivers valuable insights into addressing the proliferation of fake news on Twitter, employing data-driven approaches and advanced deep learning algorithms.

CONTENT-BASED IMAGE RETRIEVAL UNTUK MENGIDENTIFIKASI JENIS KAYU BERDASARKAN CITRA DIGITAL MENGGUNAKAN ALGORITMA EIGENIMAGE

Indonesia is a tropical country that has no less than 4,000 types of trees. Potential tree species estimated 400 botanical species (species), included in 198 genera (genus) of the 68 tribes (families). Anatomical features include composition, shape, and size of cell or tissue intruders, which can only be observed clearly by using the aid of a magnifying glass like a magnifying glass or microscope. Along with the development of computerized technology, pattern recognition has much to do with a variety of applications and algorithms. One technique to identify an image is to distinguish the texture are the basic components forming the image. This study developed a computer-based technologies to perform pattern recognition (image). The system uses image recognition wood type pore structure of the wood. The introduction of wood types to apply the concept of content-based image retrieval (CBIR) using the algorithm Eigenimage. Identification is done by pattern matching. The test results by using 129 sample, showed the percentage of the system's ability to identify the image timber correctly by 97% (sensitivity), the percentage of the system's ability to recognize the type of wood does not match the sample, 58% (specificity), positive predictive value of 90% (PPV), a negative predictive value of 83% (NPV), and accuracy rate of 89% with an error rate of 11%.

Evaluation of retrieval concepts for high-level radioactive waste from a deep geological disposal in operation

Abstract. This research project evaluates retrieval concepts. For this purpose, an evaluation method for assessing retrieval concepts is developed. Safety-relevant criteria for the evaluation of individual retrieval measures and the technically necessary components have to be identified. The criteria are developed on the basis of generic, host-rock-specific concepts. Different national and international retrieval concepts are considered. Different repository concepts (host rocks and technical concepts) require different retrieval concepts. The influences and effects of implementing the requirement for retrievability must already be identified and evaluated in the scope of the representative preliminary safety assessment (rvSU) included in the site selection procedure in Germany. This requires a suitable evaluation method that facilitates an assessment and classification of the different concepts, based on their measures for retrievability and their safety-related impacts. Both host-rock-specific requirements and different emplacement concepts (borehole and drift emplacement) must be considered. The BASE (German Federal Office for the Safety of Nuclear Waste Management) research project “Evaluation of Retrieval Concepts” (BeRK) addresses the following research questions: how could different retrievability measures and specific retrieval concepts be comparatively evaluated? How can measures for implementing and optimising retrievability be assessed in terms of their safety significance? How can trade-offs be reconstructed? The criteria to be developed and their derivation are to be presented in a comprehensible manner and are compiled in a criteria catalogue. The research project contributes to a better understanding of the repository designs with regard to a possible retrievability and allows for better assessments regarding the preliminary safety assessments.

JAVANESE MEDICINAL MEASURE LEXICONS (NUMERAL CLASSIFIERS) IN SERAT PRIMBON RERACIKAN JAMPI JAWI

Traditional medicine in a global context has been observed widely in several countries. Research on traditional medicine has not been massively conducted in Indonesia, especially in Java. The present research, in response to the area of this subject, is an initial study to observe and examine the traditional medicine in Java, especially the medicine measurement system recorded in Serat Primbon Racikan Jampi Jawi. The measure system in traditional medicine has various lexicons as has been revealed in the lexicalization of numeral classifiers and the cultural context of the measurement system in traditional medicine recorded in Serat Primbon Racikan Jampi Jawi. The theoretical perspectives are composed of culture, semantics, and lexicography; using note-taking techniques as the data collection, dictionary method as the data analysis, definition and meaning description as the data presentation as well as its meaning relations. Based on the observation, there were 77 (seventy-seven) Javanese measure lexicons consisting of 60 (sixty) base words, 5 (five) affixed words, and 13 (thirteen) word combinations in Serat Primbon Racikan Jampi Jawi. Meaning relations found from the lexicons were abstract meaning relations by using 7 (seven) concepts of retrieval and concrete features with 6 (six) compositional constructions. The measure was obtained from 9 (nine) other fields of lexicon and was used to measure 25 (twenty-five) types of ingredients with one-to-one relations, one-to-two or more relations, and two or more-to-one relations. This research indicated that (1) The form of language adapts to the creativity and references in the measure; (2) The meaning of the measure characterizes the experience and knowledge of the Javanese people on the traditional medicine system in their everyday life; and (3) The measure system is the answer to the needs, specifications, and inheritance of the local wisdom of the Javanese people, particularly in the field of traditional medicine.

Information Retrieval Performance in Text Generation using Knowledge from Generative Pre-trained Transformer (GPT-3)

The rise of advanced language models like GPT-3 and text generation has witnessed remarkable progress. However, leveraging the vast amount of knowledge within these models to enhance information retrieval performance remains an area that needs to be explored. This research used Artificial Intelligence, specifically the OpenAI GPT-3 language model, to create an application to help make written content. This research investigates the impact of incorporating GPT-3's knowledge into text generation processes and evaluates its influence on information retrieval tasks. Several features in text generation generate text that requires exact information, such as specifications for a product and accurate descriptions of a job or product, which are included in the concept of information retrieval in text creation by language models. The research used the few-shot learning method in the GPT-3 language model. The generated responses are then evaluated using established information retrieval metrics such as precision, recall, and F1-score. The findings of this research reveal the effectiveness of utilizing GPT-3's knowledge in enhancing information retrieval performance. The generated responses demonstrate improved relevance to user queries, resulting in the same performance precision and recall scores compared to other paid text generator websites. Application results are testing in capabilities of retrieving some information. Application capabilities tested on other commercial text generator engines. The test results obtained BERTscore 86\% (precision), 88\% (recall), and 87\% (F1-Score).

A Shared Hippocampal Network in Retrieving Science-related Semantic Memories.

In responding to the calls for revisiting the role that hippocampus (HIP) plays in semantic memory retrieval, this study used functional neuroimaging-based connectivity technique to elucidate the functional brain network involved in retrieving the correct and incorrect science-related semantic memories. Unlike episodic memory retrieval, the 40 scientific concepts learned during middle and high school were selected to assess 46 science majors' semantic memory retrieval and correctness monitoring, which requires neither the support of spatial information nor events to retrieve the memory. Our results demonstrated that HIP was significantly and robustly engaged in the semantic memory retrieval of correct scientific concepts than incorrect ones. Importantly, the Granger causality analysis indicated that effective connectivity of [Formula: see text] and [Formula: see text] was shared by the semantic memory retrieval of both correct and incorrect scientific concepts. On the other hand, the strengths of connectivity in the [Formula: see text] and [Formula: see text] brain networks appeared more pronounced during the processing of correct scientific concepts than of incorrect ones. The shared hippocampal networks highlight the role of the HIP as a hub to coordinate the INS, ACC, and MTG, in turn, support the semantic memory retrieval of scientific concepts.

Comparative analysis of image classification with retrieval system

Currently, the term 'content-based image retrieval' seems to be a highly attentive system for handling the broad image datasets since the data storage mechanisms and image acquisition are becoming the most empowered logic in image processing. The previous CBIR system has been proposed under nonlinear similarity matching measure in a logarithmic scale and informative pattern descriptor has quantified the range of similarity content. This article implements a novel CBIR system that emphasises the classification concept using a deep belief network (DBN) classifier. In this concept, apart from the image retrieval, the used classifier classifies the respective classes of retrieved images. Finally, the proposed local vector pattern (PLVP) with DBN classifier (PLVP-DBN) compares its performance over other conventional retrieval concepts: PLVP-with log similarity, PLVP-without log similarity, and also with neural network (NN) classifier.

Ontology-based hierarchical retrieval model for digital English teaching information

In order to overcome the low recall and precision of traditional English teaching information retrieval model, this paper designs a hierarchical retrieval model of digital English teaching information based on ontology. TF-IDF and simhash algorithm are used to judge the similarity of digital English teaching database documents and calculate the weight of English teaching information retrieval keywords. Using the relationship between different retrieval concepts to build a semantic network diagram, a hierarchical retrieval model of digital English teaching information was built, the retrieval results according to the user's interest were adjusted, and more accurate retrieval results are obtained. The experimental results show that the recall rate of the model is more than 94%, the precision rate is more than 96%, and the average retrieval time is only 0.44s, which shows that the recall rate and precision rate of the design model are higher, and the retrieval time is shorter.

Декомпозиційне видобування та пошук концептуальних знань

An ability to extract hidden and implicit knowledge, their integration into a knowledge base, and then retrieval of required knowledge items are important features of knowledge processing for many modern knowledge-based systems. However, the complexity of these tasks depends on the size of knowledge sources, which were used for extraction, the size of a knowledge base, which is used for the integration of extracted knowledge, as well as the size of a search space, which is used for the retrieval of required knowledge items. Therefore, in this paper, we analyzed the internal semantic dependencies of homogeneous classes of objects and how they affect the decomposition of such classes. Since all subclasses of a homogeneous class of objects form a complete lattice, we applied the methods of formal concept analysis for the knowledge extraction and retrieval within the corresponding concept lattice. We found that such an approach does not consider internal semantic dependencies within a homogeneous class of objects, consequently, it can cause inference and retrieval of formal concepts, which are semantically inconsistent within a modeled domain. We adapted the algorithm for the decomposition of homogeneous classes of objects, within such knowledge representation model as object-oriented dynamic networks, to perform dynamic knowledge extraction and retrieval, adding additional filtration parameters. As the result, the algorithm extracts knowledge via constructing only semantically consistent subclasses of homogeneous classes of objects and then filters them according to the attribute and dependency queries, retrieving knowledge. In addition, we introduced the decomposition consistency coefficient, which allows estimation of how much the algorithm can reduce the search space for knowledge extraction and improves the performance. To demonstrate some possible application scenarios for the improved algorithm, we provided an appropriate example of knowledge extraction and retrieval via decomposition of a particular homogeneous class of objects.

Semantic Search System for Common Cultural Traditions of the Greater Mekong Subregion

This article presents the development of a semantic search system for the common cultural traditions of the Greater Mekong Subregion, which has been studied using common cultural tradition data from Thailand, Laos, and Cambodia. Our system presents the implementation of semantic search to solve the problem of semantic gaps in common cultural characteristic concepts for information access and retrieval. The field of cultural heritage management represents a growing area of research that contains valuable information for users, researchers, and domain experts. Finding information on traditions or cultures of interest among unstructured materials requires a laborious search process. In this paper, we demonstrate how to extract semantic entities and relationships represented in the information using the theory of information science, specifically the Knowledge Organization (KO) approach, which involves determining the concepts and semantic relations of a particular issue and establishing their relations with relevant concepts. It serves as a foundation for knowledge structure development and ontology development, which contribute to effective information searching and knowledge access. The system was developed based on systematic ontology using the Hozo Ontology Editor and Database and ontology mapping using the Ontology Application Management (OAM) framework, which helps distinguish between relevant and irrelevant data, thereby leading to search effectiveness. To cut down on the time wasted in browsing and searching, reduce associated user frustration, and support the research of domain experts, more selective user access is needed.

Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion

Time series mapping of snow volume in the mountains at global scales and at resolutions needed for water resource management is an unsolved challenge to date. Snow depth mapping by differencing surface elevations from airborne lidar is a mature measurement approach filling the observation gap operationally in a few regions, primarily in mountain headwaters in the Western United States. The same concept for snow depth retrieval from stereo- or multi-view photogrammetry has been demonstrated, but these previous studies had limited ability to determine the uncertainties of photogrammetric snow depth at the basin scale. For example, assessments used non-coincident or discrete points for reference, masked out vegetation, or compared a subset of the fully snow-covered study domain. Here, using a unique data set with simultaneously collected airborne data, we compare snow depth mapped from multi-view Structure from Motion photogrammetry to that mapped by lidar at multiple resolutions over an entire mountain basin (300 km2). After excluding reconstruction errors (negative depths), SfM had lower snow-covered area (∼27%) and snow volume (∼16%) compared to lidar. The reconstruction errors were primarily in areas with vegetation, shallow snow (< 1 m), and steep slopes (> 60°C). Across the overlapping snow extent, snow depths compared well to lidar with similar mean values (< 0.03 m difference) and snow volume (± 5%) for output resolutions of 3 m and 50 m, and with a normalized median absolute deviation of 0.19 m. Our results indicate that photogrammetry from aerial images can be applied in the mountains but would perform best for deeper snowpacks above tree line.

A Comparative Optimization Model of Japanese Literature Characteristics for Cognitive Retrieval of Cross-Language Information.

Cross-language information cognitive retrieval has grown in importance as a study area due to the multilingual character of Internet resources and the diversifying languages spoken by users. This paper analyses and illustrates the key characteristics of Japanese literature from three aspects: ideological structure, structural form, and emotional expression, and makes a straightforward comparison with Chinese literature using a number of well-known Japanese literary masterpieces as examples. An important aspect of this literature is that it is full of lingering feelings, leaving room for the readers to imagine. The lingering charm is endless, obscure, and meaningful, which is the traditional style of Japanese literature. It also focuses on the depoliticization of Japanese literature, that is, the relative separation between literature and politics. The subjectivity of Japanese writing, which goes beyond just conveying feelings but involves less objective description, is related to this. The process of cross-language information retrieval in foreign nations is primarily separated into three stages, according to the changes in research objects. Currently, adding a language conversion mechanism to a monolingual information retrieval system serves as the primary language information retrieval solution. Currently, nearly 40% of the global popularity of literature comes from Japan. In this article, the background and concept of cross-language information retrieval are introduced, and its types, system models, and several key cross-language information retrieval methods are explained, and some solutions to the factors influencing the cross-language information retrieval effect are suggested.

Distributed representation learning and intelligent retrieval of knowledge concepts for conceptual design

Data-driven conceptual design is rapidly emerging as a powerful approach to generate novel and meaningful ideas by leveraging external knowledge especially in the early design phase. Currently, most existing studies focus on the identification and exploration of design knowledge by either using common-sense or building specific-domain ontology databases and semantic networks. However, the overwhelming majority of engineering knowledge is published as highly unstructured and heterogeneous texts, which presents two main challenges for modern conceptual design: (a) how to capture the highly contextual and complex knowledge relationships, (b) how to efficiently retrieve of meaningful and valuable implicit knowledge associations. To this end, in this work, we propose a new data-driven conceptual design approach to represent and retrieve cross-domain knowledge concepts for enhancing design ideation. Specifically, this methodology is divided into three parts. Firstly, engineering design knowledge from the massive body of scientific literature is efficiently learned as information-dense word embeddings, which can encode complex and diverse engineering knowledge concepts into a common distributed vector space. Secondly, we develop a novel semantic association metric to effectively quantify the strength of both explicit and implicit knowledge associations, which further guides the construction of a novel large-scale design knowledge semantic network (DKSN). The resulting DKSN can structure cross-domain engineering knowledge concepts into a weighted directed graph with interconnected nodes. Thirdly, to automatically explore both explicit and implicit knowledge associations of design queries, we further establish an intelligent retrieval framework by applying pathfinding algorithms on the DKSN. Next, the validation results on three benchmarks MTURK-771, TTR and MDEH demonstrate that our constructed DKSN can represent and associate engineering knowledge concepts better than existing state-of-the-art semantic networks. Eventually, two case studies show the effectiveness and practicality of our proposed approach in the real-world engineering conceptual design.

Different Types of CBIR Applications : A Survey

Visual information in large digital databases has been grown abundantly in the recent years and content based image retrieval (CBIR) attracts the interest of image processing oriented research people. The CBIR systems are vital performance due to the usage of features such as color, texture and shape of the query image. Searching a collection of images that have similarities with input images and without knowing the name of the image, makes a search system that applies the concept of content-based image retrieval (CBIR), is very necessary. The successful of the CBIR system creates the intension to apply it on many real world fields such as search engine, web and human disease analysis etc., This paper surveys a lot of applications based on the CBIR systems and this paper concentrates on the four types of applications which are belongs with medical field, remote sense imaging, forensic, security applications. The recent papers which are written showed that the applications which are reviewed with the explanation about technologies used to enhance the study of CBIR based applications and the methods which are adopted for CBIR in recent period. This paper is more useful for the people who want to continue their research in content based image retrieval.

Robust Fourier ptychographic microscopy via a physics-based defocusing strategy for calibrating angle-varied LED illumination.

Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technique for wide-field, high-resolution microscopy with a high space-bandwidth product. It integrates the concepts of synthetic aperture and phase retrieval to surpass the resolution limit imposed by the employed objective lens. In the FPM framework, the position of each sub-spectrum needs to be accurately known to ensure the success of the phase retrieval process. Different from the conventional methods with mechanical adjustment or data-driven optimization strategies, here we report a physics-based defocusing strategy for correcting large-scale positional deviation of the LED illumination in FPM. Based on a subpixel image registration process with a defocused object, we can directly infer the illumination parameters including the lateral offsets of the light source, the in-plane rotation angle of the LED array, and the distance between the sample and the LED board. The feasibility and effectiveness of our method are validated with both simulations and experiments. We show that the reported strategy can obtain high-quality reconstructions of both the complex object and pupil function even the LED array is randomly placed under the sample with both unknown lateral offsets and rotations. As such, it enables the development of robust FPM systems by reducing the requirements on fine mechanical adjustment and data-driven correction in the construction process.