Precise Queries Research Articles

Does conversation lead to better searches? Investigating single-shot and multi-turn spoken searches with children

Interactive speech technologies such as Siri and Amazon Echo allow people to search for information via speech. These systems generally take a single spoken statement or query as input, which may not be enough to fully capture the user’s information need. Therefore, multi-turn conversations have been proposed to establish more detail. A conversational approach can particularly benefit children who experience more difficulty formulating precise queries. We carried out a study with 32 children comparing multi-turn conversations with single-shot (or Query-Response) interactions. We compared the descriptions of the information needs, as well as children’s search experience. Findings indicate that more elaborate descriptions of children’s information needs (more keywords) were gathered through conversational search, leading to more focused search results compared with the Query-Response search. Further analysis of children’s responses yielded insights into their preferences for conversing with speech-based interfaces. This paper also offers design and methodological recommendations for conversational agents that support children’s information search.

Modified LDA vector and feedback analysis for short query Information Retrieval systems

Abstract Information Retrieval systems benefit from the use of long queries containing a large volume of search-relevant information. This situation is not common, as users of such systems tend to use very short and precise queries with few keywords. In this work we propose a modification of the Latent Dirichlet Allocation (LDA) technique using data from the document collection and its vocabulary for a better representation of short queries. Additionally, a study is carried out on how the modification of the proposed LDA weighted vectors increase the performance using relevant documents as feedback. The work shown in this paper is tested using three biomedical corpora (TREC Genomics 2004, TREC Genomics 2005 and OHSUMED) and one legal corpus (FIRE 2017). Results prove that the application of the proposed representation technique, as well as the feedback adjustment, clearly outperforms the baseline methods (BM25 and non-modified LDA).

ChatGPT revisited: Using ChatGPT-4 for finding references and editing language in medical scientific articles

The attainment of academic superiority relies heavily upon the accessibility of scholarly resources and the expression of research findings through faultless language usage. Although modern tools, such as the Publish or Perish software program, are proficient in sourcing academic papers based on specific keywords, they often fall short of extracting comprehensive content, including crucial references. The challenge of linguistic precision remains a prominent issue, particularly for research papers composed by non-native English speakers who may encounter word usage errors. This manuscript serves a twofold purpose: firstly, it reassesses the effectiveness of ChatGPT-4 in the context of retrieving pertinent references tailored to specific research topics. Secondly, it introduces a suite of language editing services that are skilled in rectifying word usage errors, ensuring the refined presentation of research outcomes. The article also provides practical guidelines for formulating precise queries to mitigate the risks of erroneous language usage and the inclusion of spurious references. In the ever-evolving realm of academic discourse, leveraging the potential of advanced AI, such as ChatGPT-4, can significantly enhance the quality and impact of scientific publications.

Secure semantic search using deep learning in a blockchain-assisted multi-user setting

Deep learning-based semantic search (DLSS) aims to bridge the gap between experts and non-experts in search. Experts can create precise queries due to their prior knowledge, while non-experts struggle with specific terms and concepts, making their queries less precise. Cloud infrastructure offers a practical and scalable platform for data owners to upload their data, making it accessible to intended data users. However, the contemporary single-owner/single-user (S/S) approach to DLSS schemes falls short of effectively leveraging the inherent multi-user capabilities of cloud infrastructure. Furthermore, most of these schemes delegate the dissemination of secret keys to a single trust point within the mutual distrust scenario in cloud infrastructure. This paper proposes a Secure Semantic Search using Deep Learning in a Blockchain-Assisted Multi-User Setting (S3DBMS)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(S^3DBMS)$$\\end{document}. Specifically, the seamless integration of attribute-based encryption with transfer learning allows the construction of DLSS in multi-owner/multi-user (M/M) settings. Further, blockchain’s smart contract mechanism allows a multi-attribute authority consensus-based generation of user private keys and system-wide global parameters in a mutual distrust M/M scenario. Finally, our scheme achieves privacy requirements and offers improved security and accuracy.

Development of Web-Oriented Systems: Ontological Approach

The purpose of the article is to study, analyze the possibilities and substantiate the creation of a web-oriented system of a catering establishment (pizzeria) based on the ontological approach, as well as to develop the corresponding software. Achieving the aim of the study involves the following tasks: forming a multi-level ontological model of the subject area (catering establishment – pizzeria) and developing an appropriate web-oriented system on its basis. The research methods are the main methodological approaches and technological tools for web-based systems development based on the use of an appropriate multi-level ontological model. Such methods include, in particular, systemic and comparative analyses (to identify the features of web-oriented systems of catering establishments) and the expert evaluation method (in determining the objects of the subject area and the relations between them). The scientific novelty is the study and analysis of modern technologies for the development of web-oriented systems and multi-level ontological modelling of various subject areas, including catering establishments (e.g., pizzerias), the results of which can be used in the development of their software product – a web-oriented system of a catering establishment. Conclusions. The paper investigates the known views on the modern approach to the development of web-oriented systems based on the implementation of the ontological approach. The proposed ontological model of a catering establishment (pizzeria) is described as a formal specification of knowledge about the selected subject area, its objects and the relationship between them (for a clear and complete representation of the subject area), which plays a significant role at different stages of creating and modifying a web-oriented system. The proposed ontological approach, which involves the development of an appropriate multi-level ontological model, has been used in the design of a web-based system for a restaurant (pizzeria). The use of an ontological model in the development of a web-based system of a catering establishment (pizzeria) helps to reduce the risk of errors in the structure of the system, the component and elemental composition of the system and its information base; more accurate perception of the subject area (catering establishments specializing in different cuisines of the peoples of the world (for example, Ukrainian, Japanese, American, but preferring Italian); use in the model representation of the subject area not only formalized means (formal grammar, descriptive logic, etc.) but also natural languages; making more precise queries to the system’s information base (database and the corresponding knowledge base).

Heuristic Approach in Contemporary Chinese Digital Art: Following the Traditions of Buddhism

ABSTRACT This research aims to investigate how Artificial Intelligence-driven digital art, specifically Chatbot GPT-4, influences an individual’s comprehension of traditional Chinese Buddhism through the heuristic approach. The method of semi-structured interviews was established as a question framework that could be modified through synchronous analysis of the Chatbot GPT-4 responses. All the acquired information was processed using the record-keeping method and subsequently analysed employing the focus coding method. As a result of the analysis of the responses generated by the chatbot to structured interview questions, it was possible to determine that the optimal response for the system pertained to concise and precise queries that involved numerical information analysis (dates, formulas, chronology of events, etc.). In such instances, response time did not exceed 1.5 seconds. Notably, 90% of the questions received structured responses. In the case of essay writing, a series of three attempts were undertaken. In each case, the total content volume did not exceed 750 words: the first essay consisted of 450 words (45%), the second essay extended to 725 words (72.5%), and the third essay was composed of 710 words (71%).

BDMasker: Dynamic Data Protection System for Open Big Data Environment

PDF HTML XML Export Cite reminder BDMasker: Dynamic Data Protection System for Open Big Data Environment DOI: 10.21655/ijsi.1673-7288.00297 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Big data has become a national basic strategic resource, and the opening and sharing of data is the core of China's big data strategy. Cloud native technology and lake-house architecture are reconstructing the big data infrastructure and promoting data sharing and value dissemination. The development of the big data industry and technology requires stronger data security and data sharing capabilities. However, data security in an open environment has become a bottleneck, which restricts the development and utilization of big data technology. The issues of data security and privacy protection have become increasingly prominent both in the open source big data ecosystem and the commercial big data system. Dynamic data protection system under the open big data environment is now facing challenges in regards such as data availability, processing efficiency, and system scalability. This paper proposes the dynamic data protection system BDMasker for the open big data environment. Through a precise query analysis and query rewriting technology based on the query dependency model, it can accurately perceive but does not change the original business request, which indicates that the whole process of dynamic masking has zero impact on the business. Furthermore, its multi-engine-oriented unified security strategy framework realizes the vertical expansion of dynamic data protection capabilities and the horizontal expansion among multiple computing engines. The distributed computing capability of the big data execution engine can be used to improve the data protection processing performance of the system. The experimental results show that the precise SQL analysis and rewriting technology proposed by BDMasker is effective. The system has good scalability and performance, and the overall performance fluctuates within 3% in the TPC-DS and YCSB benchmark tests. Reference Related Cited by

Optimisation Techniques for Flexible SPARQL Queries

Resource Description Framework datasets can be queried using the SPARQL language but are often irregularly structured and incomplete, which may make precise query formulation hard for users. The SPARQLARlanguage extends SPARQL 1.1 with two operators—APPROX and RELAX—to allow flexible querying over property paths. These operators encapsulate different dimensions of query flexibility, namely, approximation and generalisation, and they allow users to query complex, heterogeneous knowledge graphs without needing to know precisely how the data is structured. Earlier work has described the syntax, semantics, and complexity of SPARQLAR, has demonstrated its practical feasibility, but has also highlighted the need for improving the speed of query evaluation. In the present article, we focus on the design of two optimisation techniques targeted at speeding up the execution of SPARQLARqueries and on their empirical evaluation on three knowledge graphs: LUBM, DBpedia, and YAGO. We show that applying these optimisations can result in substantial improvements in the execution times of longer-running queries (sometimes by one or more orders of magnitude) without incurring significant performance penalties for fast queries.

Folklore and Reproach: The Experience of Sentiment Analysis Based on Constructions

This article presents a pilot experiment to identify reproach in folklore material. The theoretical core of the work is the idea that reproach, like all linguistic phenomena, should have formal expression in language. From which it follows that reproach can be identified in the text on the basis of its formal characteristics, relying on the tools of construction grammar, and not only with the help of semantics and context. Concrete examples demonstrate the effectiveness of the method, its reliability and evidence-based nature in comparison with approaches based on context and pragmalinguistic content. Thus, the study faced two tasks. Firstly, it presents the experiment to conduct sentiment analysis in the absence of any academic corpus of folklore texts at the time of writing the article. Although the volume of fundamental editions of folklore texts is more than significant, the natural absence of specialized lexical and grammatical tagging in such texts, which allows creating corpus queries that are heterogeneous in structure, returns a researcher to manual processing of folklore material without hope of automating the procedure. At the same time, the existence of recognized electronic texts allows you to make accurate queries using the interface of programs for editing and reading documents in PDF and DJVU formats. Secondly, the method of identifying reproach on the basis of constructions and the results of using this method in the form of concrete examples of the reproach in folklore texts are demonstrated. The advantages and disadvantages of working with precise queries in interfaces of PDF-Xchange and DJVU-reader programs are analyzed. The analysis of the obtained results is proposed, as well as the “bottlenecks&8j1; and prospects of such research are discussed.

Semantic Sequence Labeling Model of Power Dispatching Based on Deep Long Short Term Memory Network

As the decision-making brain for power system operation, grid regulation and operation is a comprehensive decision-making control that combines a large amount of data, mechanism analysis, operating procedures and professional experience, and a new generation of artificial intelligence development ideas and evolution characterized by data-driven and knowledge-guided. The directions are very close. However, the current scheduling control is still based on experience and manual analysis. The massive and diverse data of the control center and the lack of logical models between the plans require a large amount of experience and knowledge associations by the control personnel. There are more repetitive human brain labor and relatively low intelligence. Therefore, deep learning is applied to the learning of power control knowledge, and a semantic understanding network based on deep Long Short Term Memory is proposed. It uses sequence labeling to extract in-depth semantic related information of different keywords and query questions, and finds key information about language problems in order to achieve fine-grained and precise query. Experiments show that the proposed network model is superior to the previous methods, and it achieves better performance in the joint extraction of fine-grained evaluation words and evaluation objects, extracts the key information and deep semantic information of query problems and corresponding cases, and realizes power scheduling based on voice interaction The model can be effectively applied in the field of power dispatching and solve a large number of problems in power dispatching and control.

The effect of the coronavirus disease-2019 challenge on oral and dental health education: A systematic review

BACKGROUND AND AIM: The effects of the coronavirus disease-2019 (COVID-19) pandemic on theoretical education and practical-clinical trainings of dentistry have been highly destructive, and electronic learning (e-learning) with various advantages and disadvantages is the only way in this period for education in order to prevent the spread of the virus. Therefore, the present study aimed to investigate the effects of the COVID-19 challenge on dental health trainings worldwide through a systematic review study.METHODS: The keywords containing “education”, “dentistry”, and “Corona virus” were extracted from the Medical Subject Headings (MeSH) and the precise query of the research was designed. Then, the searches were performed in five main electronic databases, i.e., PubMed, Web of Science (ISI), Scopus, Embase, and Google Scholar, with no time and place limitations. In order to complete the study, manual search was also performed. Then, the studies were screened according to the systematic instructions of the review and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) chart was designed. Finally, the selected studies were reviewed to extract the required information.RESULTS: Eventually, the studies included in the final review were 12 (out of 1851). The present study showed that the study situation during the COVID-19 outbreak was mainly based on online instructions, teleconference, and video conferencing around the world, aiming at distance learning. In some countries, hands-on learning and home-based simulation learning (HBSL) have been the widely used methods to train dental practical courses during the pandemic. Regarding the item of “the effect of COVID-19 on theoretical training and knowledge”, the students' general satisfaction and positive attitudes showed that e-learning has been successful in the coverage of theoretical courses. In the item of “the status of clinic training during the pandemic”, it was revealed that changes in training practical courses led to dissatisfaction with performance, reduced self-esteem, and insufficient training to dental students; thus, they asked for supplementary and review courses in the training program.CONCLUSION: There have been gaps in preparation, facilities, policies, and feedback of dental colleges around the world in training dental courses in the conditions of crisis. Therefore, it is suggested to perform further studies to investigate the efficiency and effectiveness of technology-based learning (TB learning).

A Personalized Search Query Generating Method for Safety-Enhanced Vehicle-to-People Networks

Distracted driving due to smartphone use is one of the key reasons for road accidents. However, the 6G super-heterogeneous network systems and highly differentiated application scenarios require highly elastic and endogenous information services involving the use of smart apps, and related information retrieval by drivers in modern Vehicle-to-People (V2P) Networks. The tension raised due to the conflicting attention requirements of driving and information retrieval can be resolved by designing information retrieval solutions that demand minimal user interaction. In this paper, we construct a Personalized Search Query Generator (PSQG) to reduce driver-mobile interaction during information retrieval in the 6 G era. This system has a query generator and a query recommendation component that update two sets of relationships dynamically: one is the query and the title, another is search and recommendation. The proposed system learns a user's intent based on historical query records and recommends personalized queries, thus reducing the driver-mobile interaction time. We deploy the system into a real search engine and conduct several online experiments. These experiments are conducted using a custom constructed dataset comprising ten million samples. We use the BLEU-score metric and perform A/B testing. The results demonstrate that our system can assist users in making precise queries efficiently. The proposed system can improve drivers' safety if used in smartphones and other information retrieval systems in vehicles.

Query Optimization on Large Scale Nested Data with Service Tree and Frequent Trajectory

Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.

Social media surveillance for perceived therapeutic effects of cannabidiol (CBD) products

BackgroundCBD products have risen in popularity given CBD's therapeutic potential and lack of legal oversight, despite lacking conclusive scientific evidence for widespread over-the-counter usage for many of its perceived benefits. While medical evidence is being generated, social media surveillance offers a fast and inexpensive alternative to traditional surveys in ascertaining perceived therapeutic purposes and modes of consumption for CBD products. MethodsWe collected all comments from the CBD subreddit posted between January 1 and April 30, 2019 as well as comments submitted to the FDA regarding regulation of cannabis-derived products and analyzed them using a rule-based language processing method. A relative ranking of popular therapeutic uses and product groups for CBD is obtained based on frequency of pattern matches including precise queries that entail identifying mentions of the condition, a CBD product, and some “trigger” phrase indicating therapeutic use. We validated the social media-based findings using a similar analysis on comments to the U.S. Food and Drug Administration's (FDA) 2019 request-for-comments on cannabis-derived products. ResultsCBD is mostly discussed as a remedy for anxiety disorders and pain and this is consistent across both comment sources. Of comments posted to the CBD subreddit during the monitored time span, 6.19% mentioned anxiety at least once with at least 6.02% of these comments specifically mentioning CBD as a treatment for anxiety (i.e., 0.37% of total comments). The most popular CBD product group is oil and tinctures. ConclusionSocial media surveillance of CBD usage has the potential to surface new therapeutic use-cases as they are posted. Contemporary social media data indicate, for example, that stress and nausea are frequently mentioned as therapeutic use cases for CBD without corresponding evidence, that affirms or denies, in the research literature. However, the abundance of anecdotal claims warrants serious scientific exploration moving forward. Meanwhile, as FDA ponders regulation, our effort demonstrates that social data offers a convenient affordance to surveil for CBD usage patterns in a way that is fast and inexpensive and can inform conventional electronic surveys.

RESULTING APPROXIMATE QUERIES USING XML DATA

Daunting for users to formulate precise queries and search correct answers. Therefore, approximate matching is introduced to trot out the problem in respondent users’ queries, and this matching might be addressed by 1st reposeful the structure and content of a given question, so longing for answers that match the relaxed queries. Ranking and returning the foremost relevant results of a question became the foremost widespread paradigm in XML query process. However, the present proposals don't adequately take structures into consideration and those them so lack the strength to elegantly mix structures with contents to answer the relaxed queries. To handle this drawback, we have a tendency to 1st propose a classy framework of question relaxations for supporting approximate queries over XML information. The answers underlying this framework aren't compelled to strictly satisfy the given question formulation, instead they will be based on properties inferable from the initial question. we have a tendency to then develop a unique top-k retrieval approach.

Program synthesis by type-guided abstraction refinement

We consider the problem of type-directed component-based synthesis where, given a set of (typed) components and a query type , the goal is to synthesize a term that inhabits the query. Classical approaches based on proof search in intuitionistic logics do not scale up to the standard libraries of modern languages, which span hundreds or thousands of components. Recent graph reachability based methods proposed for Java do scale, but only apply to monomorphic data and components: polymorphic data and components infinitely explode the size of the graph that must be searched, rendering synthesis intractable. We introduce type-guided abstraction refinement (TYGAR), a new approach for scalable type-directed synthesis over polymorphic datatypes and components. Our key insight is that we can overcome the explosion by building a graph over abstract types which represent a potentially unbounded set of concrete types. We show how to use graph reachability to search for candidate terms over abstract types, and introduce a new algorithm that uses proofs of untypeability of ill-typed candidates to iteratively refine the abstraction until a well-typed result is found. We have implemented TYGAR in H+, a tool that takes as input a set of Haskell libraries and a query type, and returns a Haskell term that uses functions from the provided libraries to implement the query type. Our support for polymorphism allows H+ to work with higher-order functions and type classes, and enables more precise queries due to parametricity. We have evaluated H+ on 44 queries using a set of popular Haskell libraries with a total of 291 components. H+ returns an interesting solution within the first five results for 32 out of 44 queries. Our results show that TYGAR allows H+ to rapidly return well-typed terms, with the median time to first solution of just 1.4 seconds. Moreover, we observe that gains from iterative refinement over exhaustive enumeration are more pronounced on harder queries.

HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATED STRONG DATAGUIDE

With the emergence of XML as de facto format for storing and exchanging information over the Internet, the search for ever more innovative and effective techniques for their querying is a major and current concern of the XML database community. Several studies carried out to help solve this problem are mostly oriented towards the evaluation of so-called exact queries which, unfortunately, are likely (especially in the case of semi-structured documents) to yield abundant results (in the case of vague queries) or empty results (in the case of very precise queries). From the observation that users who make requests are not necessarily interested in all possible solutions, but rather in those that are closest to their needs, an important field of research has been opened on the evaluation of preferences queries. In this paper, we propose an approach for the evaluation of such queries, in case the preferences concern the structure of the document. The solution investigated revolves around the proposal of an evaluation plan in three phases: rewriting-evaluation-merge. The rewriting phase makes it possible to obtain, from a partitioning -transformation operation of the initial query, a hierarchical set of preferences path queries which are holistically evaluated in the second phase by an instrumented version of the algorithm TwigStack. The merge phase is the synthesis of the best results.

Using Communities of Words Derived from Multilingual Word Vectors for Cross-Language Information Retrieval in Indian Languages

We investigate the use of word embeddings for query translation to improve precision in cross-language information retrieval (CLIR). Word vectors represent words in a distributional space such that syntactically or semantically similar words are close to each other in this space. Multilingual word embeddings are constructed in such a way that similar words across languages have similar vector representations. We explore the effective use of bilingual and multilingual word embeddings learned from comparable corpora of Indic languages to the task of CLIR. We propose a clustering method based on the multilingual word vectors to group similar words across languages. For this we construct a graph with words from multiple languages as nodes and with edges connecting words with similar vectors. We use the Louvain method for community detection to find communities in this graph. We show that choosing target language words as query translations from the clusters or communities containing the query terms helps in improving CLIR. We also find that better-quality query translations are obtained when words from more languages are used to do the clustering even when the additional languages are neither the source nor the target languages. This is probably because having more similar words across multiple languages helps define well-defined dense subclusters that help us obtain precise query translations. In this article, we demonstrate the use of multilingual word embeddings and word clusters for CLIR involving Indic languages. We also make available a tool for obtaining related words and the visualizations of the multilingual word vectors for English, Hindi, Bengali, Marathi, Gujarati, and Tamil.

A new approach to classify and rank events based videos based on Event of Detection.

In the ongoing days, the development of sight and sound substance and information stockpiling produces colossally. Clients can extricate any kind of data they require from recordings. This outcomes in quick development of video information, client's discover complexity while procurement their important data. To address this, EBR (Event Based Ranking) propose another way to deal with group and rank mixed media occasions based recordings as per client's advantage. Clients are generally keen on the best positioned and occasion pertinent recordings of returned query output. An occasion based positioning methodology which empowers clients to iteratively peruse the video as per their inclination. The proposed conspire has new way to deal with order and rank occasions based recordings. It improves the exactness of video recovery which incorporates certain functionalities for customized look. The data of clients' input is used in re-positioning technique to additionally enhance the recovering exactness. It gives the customized lastly re-positioned pertinent outcomes to shape a brought together precise query output. EBR is more precise in foreseeing and positioning client particular inclinations and diminishes the time many-sided quality. This Paper proposed a calculation comprises of: video rank calculation, occasion term suggestion, and client criticism and client session.

A SPEED-UP GEOMETRIC CHANGE DETECTION ALGORITHM FOR VECTOR SURFACE FEATURE SET

Abstract. At present, most of the researches on geometric change detection of vector data, they store the change detection results in the database, so they pay more attention to the accuracy of results, but not to the speed of processing. Nowadays, many applications require real-time change detection on vector data and rapid presentation of the result. Although the existing algorithms use spatial index technology to improve the processing speed, the processing time is still beyond the range that people can bear. In order to reduce processing time, this paper takes the vector surface feature set as the research object, trying to reduce the redundancy of the candidate set that seriously affects the efficiency of change detection. Based on the regular use of spatial index created with geometric Minimum Bounding Rectangle, this paper uses geometric shrinkage technique and precise query technique to reduce the size of the candidate set for detection, so as to achieve the goal of speeding up. Finally, using five years of farmland data and resident data from Ezhou City, Hubei Province, China, a change detection experiment was conducted. The experiment proved that the geometric shrinkage and precise query techniques can effectively improve the processing speed.