An integrated architecture for shallow and deep processing

We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis

Content-based video indexing for the support of digital library search

Presents a digital library search engine that combines efforts of the AMIS and DMW research projects, each covering significant parts of the problem of finding the required information in an enormous mass of data. The most important contributions of our work are the following: (1) We demonstrate a flexible solution for the extraction and querying of meta-data from multimedia documents in general. (2) Scalability and efficiency support are illustrated for full-text indexing and retrieval. (3) We show how, for a more limited domain, like an intranet, conceptual modelling can offer additional and more powerful query facilities. (4) In the limited domain case, we demonstrate how domain knowledge can be used to interpret low-level features into semantic content. In this short description, we focus on the first and fourth item

Information needs on research data creation

Researchers’ data related information needs are growing. This paper reports the findings of a study with archaeologists and cultural heritage professionals focussing on data reuse related meta-information needs. Interviews with (N=)10 archaeologists and cultural heritage professionals. Qualitative coding and content analysis. Four types of paradata needs (data on processes, e.g. data creation) are identified, including 1) scope, 2) provenance, 3) methods and 4) knowledge organisation and representation paradata. Knowledge organisation and representation paradata has been least explored both in research and practises so far. The findings point to a need to develop the understanding of the needs and means of documentation of knowledge organisation and representation. The findings contribute to the data literacy of researchers producing and using data descriptions, and to the study of how paradata can be created and used. Further, the findings indicate that distance-to-data is a significant parameter in determining whether information needs are continuous or discrete. Further, the most likely type of reuse should guide the level and type of paradata. Finally, the findings underline that in spite of the comprehensiveness of available meta-information, it will be incomplete. Complementary means — including collaboration with data creators and meta-information extraction approaches — are needed to increase information reusability.Peer Reviewe

A user-oriented network forensic analyser: the design of a high-level protocol analyser

Network forensics is becoming an increasingly important tool in the investigation of cyber and computer-assisted crimes. Unfortunately, whilst much effort has been undertaken in developing computer forensic file system analysers (e.g. Encase and FTK), such focus has not been given to Network Forensic Analysis Tools (NFATs). The single biggest barrier to effective NFATs is the handling of large volumes of low-level traffic and being able to exact and interpret forensic artefacts and their context – for example, being able extract and render application-level objects (such as emails, web pages and documents) from the low-level TCP/IP traffic but also understand how these applications/artefacts are being used. Whilst some studies and tools are beginning to achieve object extraction, results to date are limited to basic objects. No research has focused upon analysing network traffic to understand the nature of its use – not simply looking at the fact a person requested a webpage, but how long they spend on the application and what interactions did they have with whilst using the service (e.g. posting an image, or engaging in an instant message chat). This additional layer of information can provide an investigator with a far more rich and complete understanding of a suspect’s activities. To this end, this paper presents an investigation into the ability to derive high-level application usage characteristics from low-level network traffic meta-data. The paper presents a three application scenarios – web surfing, communications and social networking and demonstrates it is possible to derive the user interactions (e.g. page loading, chatting and file sharing ) within these systems. The paper continues to present a framework that builds upon this capability to provide a robust, flexible and user-friendly NFAT that provides access to a greater range of forensic information in a far easier format

Health literacy environment of breast and cervical cancer among black African women globally: a systematic review protocol of mixed methods

Adequate health literacy is a necessity to enable effective decision making to seek, access and utilise appropriate health care service. Evidence exists indicating a low level of general health literacy among Black African women, especially those with a refugee background. Breast and cervical are the most common cancers, with Black African women or women with African ethnicity being disproportionately overrepresented. The level of health literacy specific to breast and cervical cancer among Black African women, especially those with a refugee background, has not been reviewed systematically. The present study describes a protocol for a systematic review of the available evidence on the level of health literacy specific to breast and cervical cancer among Black African women globally. We will perform a systematic review of the available quantitative and qualitative studies. The search will include studies that describe the level of health literacy specific to breast and cervical cancer among Black African women. We will conduct a preliminary search on Google scholar to build the concepts for search terms, and a full search strategy using the identified concepts and keywords across four databases namely PubMed, SCOPUS, CINAHL and Web of Sciences. We will use Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to schematically present the search strategy. We will use the standardized Joanna Briggs Institute quality appraisal and selection tool to recruit studies, and the data extraction tool to synthesise the information extracted from the recruited studies. We will be guided by socioecological theory and Indigenous epistemology to synthesise the non-quantifiable information thematically, and pool the quantitative information using meta-analysis, based on the availability of information

Bayesian Semi-supervised Keyphrase Extraction and Jackknife Empirical Likelihood for Assessing Heterogeneity in Meta-analysis

This dissertation investigates: (1) A Bayesian Semi-supervised Approach to Keyphrase Extraction with Only Positive and Unlabeled Data, (2) Jackknife Empirical Likelihood Confidence Intervals for Assessing Heterogeneity in Meta-analysis of Rare Binary Events. In the big data era, people are blessed with a huge amount of information. However, the availability of information may also pose great challenges. One big challenge is how to extract useful yet succinct information in an automated fashion. As one of the first few efforts, keyphrase extraction methods summarize an article by identifying a list of keyphrases. Many existing keyphrase extraction methods focus on the unsupervised setting, with all keyphrases assumed unknown. In reality, a (small) subset of the keyphrases may be available for an article. To utilize such information, we propose a probability model based on a semi-supervised setup. Our method incorporates the graph-based information of an article into a Bayesian framework so that our model facilitates statistical inference, which is often absent in the existing methods. To overcome the difficulty arising from high-dimensional posterior sampling, we develop two Markov chain Monte Carlo algorithms based on Gibbs samplers, and compare their performance using benchmark data. We further propose a false discovery rate (FDR) based approach for selecting the number of keyphrases, while the existing methods use ad-hoc threshold values. Our numerical results show that the proposed method compared favorably with state-of-the-art methods for keyphrase extraction. In meta-analysis, the extent to which effect sizes vary across component studies is called heterogeneity. Typically, it is reflected by a variance parameter in a widely used random-effects (Re) model. In the literature, methods for constructing confidence intervals (CIs) for the parameter often assume that study-level effect sizes be normally distributed. However, this assumption may be violated in practice, especially in meta-analysis of rare binary events. We propose to use jackknife empirical likelihood (JEL), a nonparametric approach that uses jackknife pseudo-values, to construct CIs for the heterogeneity parameter, which lifts the requirement of normality in the Re model. To compute jackknife pseudo-values, we employ a moment-based estimator and consider two commonly used weighing schemes (i.e., equal and inverse variance weights). We prove that with each scheme, the resulting log empirical likelihood ratio follows a chi-square distribution asymptotically. We further examine the performance of the proposed JEL methods and compare them with existing CIs through simulation studies and data examples that focus on data of rare binary events. Our numerical results suggest that the JEL method with equal weights compares favorably with other alternatives, especially when (observed) effect sizes are non-normal and the number of component studies is large. Thus, it is worth serious consideration in statistical inference

Efficacy of Chlorhexidine after Oral Surgery Procedures on Wound Healing: Systematic Review and Meta-Analysis

Supplementary Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/antibiotics12101552/s1.Our objective was to evaluate qualitatively and quantitatively, through a systematic review and meta-analysis, available evidence on the efficacy of chlorhexidine (CHX) when applied after oral surgery on wound healing and related clinical parameters. MEDLINE/PubMed, Embase, CENTRAL, Web of Science, and Scopus were searched for studies published before January 2023. The quality of the methodology used in primary-level studies was assessed using the RoB2 tool; meta-analyses were performed jointly with heterogeneity and small-study effect analyses. Thirty-three studies and 4766 cases were included. The results point out that the application of CHX was significantly more effective, compared to controls where CHX was not employed, providing better wound healing after oral surgery (RR = 0.66, 95% CI = 0.55–0.80, p < 0.001). Stratified meta-analyses confirmed the higher efficacy of 0.20% CHX gel vs. other vehicles and concentrations (p < 0.001, respectively). Likewise, the addition of chitosan to CHX significantly increased the efficacy of surgical wound healing (p < 0.001). The use of CHX has also been significantly beneficial in the prevention of alveolar osteitis after any type of dental extraction (RR = 0.46, 95% CI = 0.39–0.53, p < 0.001) and has also been effective when applied as a gel for a reduction in pain after the surgical extraction of third molars (MD = −0.97, 95% CI = −1.26 to −0.68, p < 0.001). In conclusion, this systematic review and meta-analysis demonstrate on the basis of evidence that the application of CHX exerts a beneficial effect on wound healing after oral surgical procedures, significantly decreasing the patient’s risk of developing surgical complications and/or poor wound healing. This benefit was greater when CHX was used at 0.20% in gel form with the addition of chitosan

Sharing Video Emotional Information in the Web

Video growth over the Internet changed the way users search, browse and view video content. Watching movies over the Internet is increasing and becoming a pastime. The possibility of streaming Internet content to TV, advances in video compression techniques and video streaming have turned this recent modality of watching movies easy and doable. Web portals as a worldwide mean of multimedia data access need to have their contents properly classified in order to meet users’ needs and expectations. The authors propose a set of semantic descriptors based on both user physiological signals, captured while watching videos, and on video low-level features extraction. These XML based descriptors contribute to the creation of automatic affective meta-information that will not only enhance a web-based video recommendation system based in emotional information, but also enhance search and retrieval of videos affective content from both users’ personal classifications and content classifications in the context of a web portal.info:eu-repo/semantics/publishedVersio