Web-Based Research in Psychology

The present article reviews web-based research in psychology. It captures principles, learnings, and trends in several types of web-based research that show similar developments related to web technology and its major shifts (e.g., appearance of search engines, browser wars, deep web, commercialization, web services, HTML5…) as well as distinct challenges. The types of web-based research discussed are web surveys and questionnaire research, web-based tests, web experiments, Mobile Experience Sampling, and non-reactive web research, including big data. A number of web-based methods are presented and discussed that turned out to become important in research methodology. These are one-item-one-screen design, seriousness check, instruction manipulation and other attention checks, multiple site entry technique, subsampling technique, warm-up technique, and web-based measurement. Pitfalls and best practices are described then, especially regarding dropout and other non-response, recruitment of participants, and interaction between technology and psychological factors. The review concludes with a discussion of important concepts that have developed over 25 years and an outlook on future developments in web-based research.

A TOPOGRAPHY OF VIRTUAL INFLUENCERS

Informed by my first six months of doctoral research, this paper offers a topography of virtual influencers that at once acknowledges their continuation of and breaking with the precedents of a lineage of “virtual beings” who have achieved celebrity status. Responding to the ahistoricism of much recent commentary, it draws on archival press and web research to situate virtual influencers at the intersection of technological advancements, discourses, and anxieties similarly characterising Hollywood’s “synthespians” at the turn of the twenty-first century; the legacy of “virtual idols” in East Asia (also known as “Vocaloids” in Japan); and the latter’s recent democratisation by a new generation of “vTubers” across video-sharing sites. Recognising this cross-medium migration of virtual celebrity—from anime, video games and blockbuster cinema to the participatory web—this paper adopts a platform-specific lens to highlight the affordances, cultures and vernaculars of specific social media as essential to virtual influencers’ aspiration to, and attainment and maintenance of, attention and fame.

Facade-X: An Opinionated Approach to SPARQL Anything

The Semantic Web research community understood since its beginning how crucial it is to equip practitioners with methods to transform non-RDF resources into RDF. Proposals focus on either engineering content transformations or accessing non-RDF resources with SPARQL. Existing solutions require users to learn specific mapping languages (e.g. RML), to know how to query and manipulate a variety of source formats (e.g. XPATH, JSON-Path), or to combine multiple languages (e.g. SPARQL Generate). In this paper, we explore an alternative solution and contribute a general-purpose meta-model for converting non-RDF resources into RDF: Facade-X. Our approach can be implemented by overriding the SERVICE operator and does not require to extend the SPARQL syntax. We compare our approach with the state of art methods RML and SPARQL Generate and show how our solution has lower learning demands and cognitive complexity, and it is cheaper to implement and maintain, while having comparable extensibility and efficiency.

Ultrasound Measurement of Endometrial Thickness for Detecting Endometrial Malignancy with Uterine Bleeding in Postmenopausal Women- A Systematic Review

Background: Post-menopausal Bleeding is extremely doubtful of existence a mark intended for incidence of EC as well as around 5% to 12% of post-menopausal bleeding consequences commencing endometrial cancer. EC is greatest communal Gynecologic Malignancy into established countries. Thin endometrium (≤4 mm) devours precise high NPV (99%). Thickened Endometrium has been considered by way of sign intended for an offensive assessment. Aim: To determine the ET measurement for detecting endometrial malignancy with uterine bleeding in post-menopausal females. Methods: The search was led according to Systematic Reviews Ethics. Searched databases were: Google scholar and Web Research from January 2014 and November 2020. Around nine studies were designated for this Systemic Review. We mined subsequent facts: Design of study, mean age and range, menopause duration, range and mean, BMI range and mean, as well as ET range and mean. Conclusion: From beyond examination this review concluded that, the Endometrial Thickness among the suggested alteration in cut off 3-5 mm within repetitive ultrasound practice must be ended in elevation threat females identify for malignancy before in Postmenopausal females as well as ultrasound is attested chosen a convenient implement to diagnose. Keywords: Post-menopausal Bleeding, Endometrial Thickness, Endometrial Cancer.

Linked Data Triples Enhance Document Relevance Classification

Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news-feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% and show significant improvement over state-of-the art models, like BERT. The findings are tested and empirically validated on an experimental dataset and on two standardized pre-classified news sources, namely the Reuters and 20 News groups datasets.

Ultrasound Measurement of Endometrial Thickness for Detecting Endometrial Malignancy

Background: Post-menopausal Bleeding is extremely doubtful of existence a mark intended for incidence of EC as well as around 5% to 12% of post-menopausal bleeding consequences commencing endometrial cancer. EC is greatest communal Gynecologic Malignancy into established countries. Thin endometrium (≤4 mm) devours precise high NPV (99%). Thickened Endometrium has been considered by way of sign intended for an offensive assessment. Aim: To determine the ET measurement for detecting endometrial malignancy with uterine bleeding in post-menopausal females. Methods: The search was led according to Systematic Reviews Ethics. Searched databases were: Google scholar and Web Research from January 2014 and November 2020. Around nine studies were designated for this Systemic Review. We mined subsequent facts: Design of study, mean age and range, menopause duration, range and mean, BMI range and mean, as well as ET range and mean. Conclusion: From beyond examination this review concluded that, the Endometrial Thickness among the suggested alteration in cut off 3-5 mm within repetitive ultrasound practice must be ended in elevation threat females identify for malignancy before in Postmenopausal females as well as ultrasound is attested chosen a convenient implement to diagnose. Keywords: Post-menopausal Bleeding, Endometrial Thickness, Endometrial Cancer.

A review of the semantic web field

Tracing the triumphs and challenges of two decades of Semantic Web research and applications.

A decade of Semantic Web research through the lenses of a mixed methods approach

The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily performed through qualitative, top-down style approaches, that rely on expert knowledge. More recently, data-driven, bottom-up approaches have been proposed that offer a quantitative analysis of the evolution of a research domain. In this paper, we aim to provide a broader and more complete picture of Semantic Web topics and trends by adopting a mixed methods methodology, which allows for the combined use of both qualitative and quantitative approaches. Concretely, we build on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches (Rexplore, Saffron, PoolParty), on a corpus of Semantic Web papers published between 2006 and 2015. In this process, we both use the latter for “fact-checking” on the former and also to derive key findings in relation to the strengths and weaknesses of top-down and bottom-up approaches to research topic identification. Although we provide a detailed study on the past decade of Semantic Web research, the findings and the methodology are relevant not only for our community but beyond the area of the Semantic Web to other research fields as well.