The Research on Improving Algorithms for Hilltop to Improve Search Quality

2016 ◽  
pp. 149-158
Author(s):  
Peng Lu ◽  
Xiao Cong
Keyword(s):  
Search Quality

scholarly journals Upskilling health and care workers with augmented and virtual reality: protocol for a realist review to develop an evidence-informed programme theory

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e050033
Author(s):  
Norina Gasteiger ◽  
Sabine N van der Veer ◽  
Paul Wilson ◽  
Dawn Dowding
Keyword(s):  
Virtual Reality ◽  
Grey Literature ◽  
Healthcare Providers ◽  
Realist Review ◽  
Relevant Information ◽  
Care Providers ◽  
Programme Theory ◽  
Clinical Educators ◽  
Continued Learning ◽  
Search Quality

IntroductionAugmented reality (AR) and virtual reality (VR) are increasingly used to upskill health and care providers, including in surgical, nursing and acute care settings. Many studies have used AR/VR to deliver training, providing mixed evidence on their effectiveness and limited evidence regarding contextual factors that influence effectiveness and implementation. This review will develop, test and refine an evidence-informed programme theory on what facilitates or constrains the implementation of AR or VR programmes in health and care settings and understand how, for whom and to what extent they ‘work’.Methods and analysisThis realist review adheres to the Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) standards and will be conducted in three steps: theory elicitation, theory testing and theory refinement. First, a search will identify practitioner, academic and learning and technology adoption theories from databases (MEDLINE, Scopus, CINAHL, Embase, Education Resources Information Center, PsycINFO and Web of Science), practitioner journals, snowballing and grey literature. Information regarding contexts, mechanisms and outcomes will be extracted. A narrative synthesis will determine overlapping configurations and form an initial theory. Second, the theory will be tested using empirical evidence located from the above databases and identified from the first search. Quality will be assessed using the Mixed Methods Appraisal Tool (MMAT), and relevant information will be extracted into a coding sheet. Third, the extracted information will be compared with the initial programme theory, with differences helping to make refinements. Findings will be presented as a narrative summary, and the MMAT will determine our confidence in each configuration.Ethics and disseminationEthics approval is not required. This review will develop an evidence-informed programme theory. The results will inform and support AR/VR interventions from clinical educators, healthcare providers and software developers. Upskilling through AR/VR learning interventions may improve quality of care and promote evidence-based practice and continued learning. Findings will be disseminated through conference presentations and peer-reviewed journal articles.

Experiments with Language-based Aids in Information Retrieval Systems

1988 ◽  
Vol 11 (1-2) ◽  
pp. 33-46 ◽  
Author(s):  
Tove Fjeldvig ◽  
Anne Golden
Keyword(s):  
Information Retrieval ◽  
Text Retrieval ◽  
Considerable Improvement ◽  
Controlled Experiments ◽  
Compound Words ◽  
Search Results ◽  
Retrieval Systems ◽  
Complete Search ◽  
Information Retrieval Systems ◽  
Search Quality

The fact that a lexeme can appear in various forms causes problems in information retrieval. As a solution to this problem, we have developed methods for automatic root lemmatization, automatic truncation and automatic splitting of compound words. All the methods have as their basis a set of rules which contain information regarding inflected and derived forms of words – and not a dictionary. The methods have been tested on several collections of texts, and have produced very good results. By controlled experiments in text retrieval, we have studied the effects on search results. These results show that both the method of automatic root lemmatization and the method of automatic truncation make a considerable improvement on search quality. The experiments with splitting of compound words did not give quite the same improvement, however, but all the same this experiment showed that such a method could contribute to a richer and more complete search request.

MULTI-VERSION MUSIC SEARCH USING ACOUSTIC FEATURE UNION AND EXACT SOFT MAPPING

2009 ◽  
Vol 03 (02) ◽  
pp. 209-234 ◽  
Author(s):  
YI YU ◽  
KAZUKI JOE ◽  
VINCENT ORIA ◽  
FABIAN MOERCHEN ◽  
J. STEPHEN DOWNIE ◽  
...  
Keyword(s):  
Collision Probability ◽  
Acoustic Feature ◽  
Music Retrieval ◽  
Feature Sets ◽  
Soft Mapping ◽  
Audio Features ◽  
Music Audio ◽  
Locality Sensitive Hash ◽  
Principle Of Similarity ◽  
Search Quality

Research on audio-based music retrieval has primarily concentrated on refining audio features to improve search quality. However, much less work has been done on improving the time efficiency of music audio searches. Representing music audio documents in an indexable format provides a mechanism for achieving efficiency. To address this issue, in this work Exact Locality Sensitive Mapping (ELSM) is suggested to join the concatenated feature sets and soft hash values. On this basis we propose audio-based music indexing techniques, ELSM and Soft Locality Sensitive Hash (SoftLSH) using an optimized Feature Union (FU) set of extracted audio features. Two contributions are made here. First, the principle of similarity-invariance is applied in summarizing audio feature sequences and utilized in training semantic audio representations based on regression. Second, soft hash values are pre-calculated to help locate the searching range more accurately and improve collision probability among features similar to each other. Our algorithms are implemented in a demonstration system to show how to retrieve and evaluate multi-version audio documents. Experimental evaluation over a real "multi-version" audio dataset confirms the practicality of ELSM and SoftLSH with FU and proves that our algorithms are effective for both multi-version detection (online query, one-query vs. multi-object) and same content detection (batch queries, multi-queries vs. one-object).

scholarly journals Reporting of the Role of the Expert Searcher in Cochrane Reviews

2006 ◽  
Vol 1 (4) ◽  
pp. 3 ◽  
Author(s):  
Li Zhang ◽  
Margaret Sampson ◽  
Jessie McGowan
Keyword(s):  
Systematic Reviews ◽  
Search Strategy ◽  
Data Extraction ◽  
Cochrane Library ◽  
Eligibility Criteria ◽  
Statistical Decision ◽  
Cochrane Reviews ◽  
Statistical Decisions ◽  
Search Quality

Introduction - This study applied the principles of evidence based information practice to clarify the role of information specialists and librarians in the preparation of Cochrane systematic reviews and to determine whether information specialists impact the quality of searching in Cochrane systematic reviews. Objectives - This research project sought to determine how the contribution of the person responsible for searching in the preparation of Cochrane systematic reviews was reported; whether the contribution was recognized through authorship or acknowledgement; the qualifications of the searcher; and the association between the type of contributorship and characteristics of the search strategy, assessability, and the presence of certain types of errors. Methods - Data sources: The Cochrane Database of Systematic Reviews, The Cochrane Library 3 (2002). Inclusion criteria: The study included systematic reviews that met the following criteria: one or more sections of the Cochrane Highly Sensitive Search Strategy were utilised, primary studies were either randomised controlled trials (RCTs) or quasi-RCTs, and included and excluded studies were clearly identified. Data extraction: Two librarians assessed the searches for errors, establishing consensus on discordant ratings. Results - Of the 169 reviews screened for this project, 105 met all eligibility criteria. Authors fulfilled the searching role in 41.9% of reviews studied, acknowledged persons or groups in 13.3%, a combination in 9.5%, and the role was not reported in 35.2% of reviews. For the 78 reviews in which meta-analyses were performed, the positions of those responsible for statistical decisions were examined for comparative purposes. The statistical role was performed by an author in 47.4% of cases and unreported in the same number of cases. Insufficient analyzable data was obtained regarding professional qualifications (3/105 for searching, 2/78 for statistical decisions). Search quality was assessed for 66 searches across 74 reviews. In general, it was more possible to assess the search quality when the searcher role was reported. An association was found between the reporting of searcher role and the presence of a consequential error. There was no association between the number of consequential errors and how the contribution of the searcher was reported. Conclusions - Qualifications of the persons responsible for searching and statistical decision-making were poorly reported in Cochrane reviews, but more complete role reporting is associated with greater assessability of searches and fewer substantive errors in search strategies.

scholarly journals Scaling High-Quality Pairwise Link-Based Similarity Retrieval on Billion-Edge Graphs

2022 ◽  
Vol 40 (4) ◽  
pp. 1-45
Author(s):  
Weiren Yu ◽  
Julie McCann ◽  
Chengyuan Zhang ◽  
Hakan Ferhatosmanoglu
Keyword(s):  
Web Search ◽  
Similarity Score ◽  
High Quality ◽  
Deterministic Method ◽  
Large Graphs ◽  
Guaranteed Accuracy ◽  
Semantic Difference ◽  
Speed Up ◽  
Novel Method ◽  
Search Quality

SimRank is an attractive link-based similarity measure used in fertile fields of Web search and sociometry. However, the existing deterministic method by Kusumoto et al. [ 24 ] for retrieving SimRank does not always produce high-quality similarity results, as it fails to accurately obtain diagonal correction matrix  D . Moreover, SimRank has a “connectivity trait” problem: increasing the number of paths between a pair of nodes would decrease its similarity score. The best-known remedy, SimRank++ [ 1 ], cannot completely fix this problem, since its score would still be zero if there are no common in-neighbors between two nodes. In this article, we study fast high-quality link-based similarity search on billion-scale graphs. (1) We first devise a “varied- D ” method to accurately compute SimRank in linear memory. We also aggregate duplicate computations, which reduces the time of [ 24 ] from quadratic to linear in the number of iterations. (2) We propose a novel “cosine-based” SimRank model to circumvent the “connectivity trait” problem. (3) To substantially speed up the partial-pairs “cosine-based” SimRank search on large graphs, we devise an efficient dimensionality reduction algorithm, PSR # , with guaranteed accuracy. (4) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument in [ 24 ] that “if D is replaced by a scaled identity matrix (1-Ɣ)I, their top-K rankings will not be affected much”. (5) We propose a novel method that can accurately convert from Li et al.  SimRank ~{S} to Jeh and Widom’s SimRank S . (6) We propose GSR # , a generalisation of our “cosine-based” SimRank model, to quantify pairwise similarities across two distinct graphs, unlike SimRank that would assess nodes across two graphs as completely dissimilar. Extensive experiments on various datasets demonstrate the superiority of our proposed approaches in terms of high search quality, computational efficiency, accuracy, and scalability on billion-edge graphs.

Named Entity Based Ranking with Term Proximity for XML Retrieval

2018 ◽  
Vol 8 (2) ◽  
pp. 57-77 ◽  
Author(s):  
Abubakar Roko ◽  
Shyamala Doraisamy ◽  
Azreen Azman ◽  
Azrul Hazri Jantan
Keyword(s):  
Keyword Search ◽  
Query Term ◽  
Search System ◽  
Keyword Query ◽  
Xml Retrieval ◽  
Named Entity ◽  
Data Content ◽  
Search Quality ◽  
The Impact ◽  
Confidence Value

In this article, an indexing scheme that includes the named entity category for each indexed term is proposed. Based on this, two methods are proposed, one to infer the semantics of an XML element based on its data content, called the confidence value of the element, and the second method computes the proximity scores of the query terms. The confidence value of an element is obtained based on the probability of a named entity category in the data content of the underlying XML element. The proximity score of the query terms measures the proximity and ordering of the query term within an XML element. The article then shows how a ranking function uses the confidence value of an XML element and proximity score to mitigate the impact of higher frequency terms and compute the relevance between a keyword query and an XML fragment. Finally, a keyword search system is introduced and experiments show that the proposed system outperforms existing approaches in terms of search quality and achieve a higher efficiency.

scholarly journals Development and Evaluation of Clinical Practice Guideline for Delirium in Long-Term Care

2020 ◽  
Vol 17 (21) ◽  
pp. 8255
Author(s):  
Eunhye Jeong ◽  
Jinkyung Park ◽  
Sung Ok Chang
Keyword(s):  
Clinical Practice ◽  
Development Process ◽  
Healthcare Professionals ◽  
Long Term Care ◽  
Structured Interviews ◽  
Term Care ◽  
Barriers To Implementation ◽  
Selection For ◽  
Search Quality

Delirium is highly prevalent and leads to several bad outcomes for older long-term care (LTC) residents. For a more successful translation of delirium knowledge, Clinical Practice Guidelines (CPGs) tailored to LTC should be developed and applied based on the understanding of the barriers to implementation. This study was conducted to develop a CPG for delirium in LTC and to determine the barriers perceived by healthcare professionals related to the implementation of the CPG. We followed a structured, evidence- and theory-based procedure during the development process. After a systematic search, quality appraisal, and selection for eligible up-to-date CPGs for delirium, the recommendations applicable to the LTC were drafted, evaluated, and confirmed by an external group of experts. To evaluate the barriers to guideline uptake from the users’ perspectives, semi-structured interviews were conducted which resulted in four major themes: (1) a lack of resources, (2) a tendency to follow mindlines rather than guidelines, (3) passive attitudes, and (4) misunderstanding delirium care in LTC. To minimize adverse prognoses through prompt delirium care, the implementation of a CPG with an approach that comprehensively considers various barriers at the system, practice, healthcare professional, and patients/family levels is necessary.

Job-Search Behavior as a Multidimensional Construct: A Review of Different Job-Search Behaviors and Sources

2014 ◽  
Author(s):  
Greet van Hoye
Keyword(s):  
Job Search ◽  
Search Behavior ◽  
Theoretical Models ◽  
Future Research ◽  
Job Seekers ◽  
Job Search Behavior ◽  
Research Findings ◽  
Unidimensional Construct ◽  
Search Behaviors ◽  
Search Quality

Both theoretical models of job search and empirical research findings suggest that job-search behavior is not a unidimensional construct. This chapter addresses the multidimensionality of job-search behavior and provides a systematic review of the different job-search behaviors and sources studied in the job-search literature and their relationships with antecedent variables and employment outcomes. Organized within three major dimensions (effort/intensity, content/direction, and temporal/persistence), job-search effort and intensity, job-search strategies, preparatory and active job-search behaviors, formal and informal job sources, specific job-search behaviors, job-search quality, job-search dynamics, and job-search persistence are discussed. This review strongly suggests that it is essential to consider all the dimensions of job-search behavior for understanding job-search success in both practice and research. This study points to a number of key implications for job seekers and employment counselors as well as crucial directions for future research.

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