Comparative Reviews vs. Regular Consumer Reviews

This study proposes and evaluates the effect of “mixed” comparative reviews on review value and compares the results with “separate” comparative and regular reviews. A total of 201 subjects have participated in the experiment conducted in this study. Results indicate that mixed comparative reviews in text format are perceived as less valuable than separate comparative reviews in text format. However, mixed comparative reviews in tabular format have more review value than those in text format and are perceived as more valuable than regular reviews of one product in either format. Unsurprisingly, the positive reviews of the target product lead to higher product attitude than negative reviews. However, this effect is weak in mixed (vs. separate) comparative reviews.

Comparative Reviews vs. Regular Consumer Reviews

This study proposes and evaluates the effect of “mixed” comparative reviews on review value and compares the results with “separate” comparative and regular reviews. A total of 201 subjects have participated in the experiment conducted in this study. Results indicate that mixed comparative reviews in text format are perceived as less valuable than separate comparative reviews in text format. However, mixed comparative reviews in tabular format have more review value than those in text format and are perceived as more valuable than regular reviews of one product in either format. Unsurprisingly, the positive reviews of the target product lead to higher product attitude than negative reviews. However, this effect is weak in mixed (vs. separate) comparative reviews.

Using an Embodiment Technique in Psychological Experiments with Virtual Reality: A Scoping Review of the Embodiment Configurations and their Scientific Purpose

Background: In recent years, psychological studies with virtual reality have increasingly involved some eEmbodiment tTechnique (ET) in which the users’ bodily movements are mapped on the movements of a digital body. However, this domain is very fragmented across disciplines and plagued by terminological ambiguity. Objective: This paper provides a scoping review of the psychological studies deploying some ET in VR. Methods: A total of 742 papers were retrieved from Scopus and the ACM Digital library using “embodiment” and “virtual reality” as keywords; after screening them, 79 were eventually retained. From each study, the following information was extracted: (a) the content of the virtual scenario, (b) the extent of the embodiment, and (c) the scientific purpose and measure of the psychological experience of embodiment. This information is summarized and discussed, as well as reported in tabular format for each study. Results: We first distinguished ET from other types of digital embodiment. Then we summarized the ET solutions in terms of the completeness of the digital body assigned to the user and of whether the digital body's appearance resembled the users' real one. Finally, we report the purpose and the means of measuring the users’sense of embodiment. Conclusion: This review maps the variety of embodiment configurations and the scientific purpose they serve. It offers a background against which other studies planning to use this technique can position their own solution and highlight some underrepresented lines of research that are worth exploring.

Using an Embodiment Technique in Psychological Experiments with Virtual Reality: A Scoping Review of the Embodiment Configurations and their Scientific Purpose

Background: In recent years, psychological studies with virtual reality have increasingly involved some eEmbodiment tTechnique (ET) in which the users’ bodily movements are mapped on the movements of a digital body. However, this domain is very fragmented across disciplines and plagued by terminological ambiguity. Objective: This paper provides a scoping review of the psychological studies deploying some ET in VR. Methods: A total of 742 papers were retrieved from Scopus and the ACM Digital library using “embodiment” and “virtual reality” as keywords; after screening them, 79 were eventually retained. From each study, the following information was extracted: (a) the content of the virtual scenario, (b) the extent of the embodiment, and (c) the scientific purpose and measure of the psychological experience of embodiment. This information is summarized and discussed, as well as reported in tabular format for each study. Results: We first distinguished ET from other types of digital embodiment. Then we summarized the ET solutions in terms of the completeness of the digital body assigned to the user and of whether the digital body's appearance resembled the users' real one. Finally, we report the purpose and the means of measuring the users’sense of embodiment. Conclusion: This review maps the variety of embodiment configurations and the scientific purpose they serve. It offers a background against which other studies planning to use this technique can position their own solution and highlight some underrepresented lines of research that are worth exploring.

RDFFrames: knowledge graph access for machine learning tools

Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.

Remote Patient Monitoring for COVID-19 Patients: Comparisons and Framework for Reporting

BackgroundRemote patient monitoring (RPM) has been implemented for COVID-19 patients by various health services at speed, without the opportunity to learn one from another. A lack of standardised reporting has hindered evaluation of RPM.AimsThe aims of this overview of RPM for COVID-19 patients are twofold: (1) to provide tabulated, descriptive information for a range of implementations to facilitate familiarization, learning and comparison; and based on this(2) to develop a framework for reporting to improve reporting consistency as a first step towards the development of reporting guidelines for RPM.MethodA rapid review of the literature for RPM for COVID-19 patients was conducted seeking studies that provided details of a specific implementation of RPM with sufficient information to compare one with another. The content of these articles was then reviewed and synthesised to a tabular format under common headings to facilitate ready comparison and to highlight omissions in reporting. Reporting consistencies and inconsistencies between the studies were then considered to develop a framework for reporting.ResultsThe studies suggested key common characteristics outlined under four headings: context, technology, process, and metrics. These were further divided into subheadings to provide a consistent tabular format to aid familiarization. Consideration of consistencies and inconsistencies in reporting suggests the following criteria be used for reporting: Dates, Rationale, Patients, Medical team, Technology provider, Communication mode, Patient equipment, Patient training, Staff training, Markers, Data Input Frequency, Thresholds for Escalation, Discharge and Metrics for: RPM Enrollment, Escalation, Patient acceptance, Staff acceptance, and Patient adherence.

A new species of Geogamasus Lee (Mesostigmata: Ologamasidae), with morphological details on species of the genus

Geogamasus lasaroi Barros, Azevedo & Castilho sp. nov. is described based on the morphology of adult females collected from soil-litter of a well preserved fragment of the natural vegetation of the Caatinga biome, in Alagoas state, northeastern Brazil. In addition, key information on the morphology of the world Geogamasus species is presented in a tabular format.

RFEX: Simple Random Forest Model and Sample Explainer for non-Machine Learning experts

Machine Learning (ML) is becoming an increasingly critical technology in many areas. However, its complexity and its frequent non-transparency create significant challenges, especially in the biomedical and health areas. One of the critical components in addressing the above challenges is the explainability or transparency of ML systems, which refers to the model (related to the whole data) and sample explainability (related to specific samples). Our research focuses on both model and sample explainability of Random Forest (RF) classifiers. Our RF explainer, RFEX, is designed from the ground up with non-ML experts in mind, and with simplicity and familiarity, e.g. providing a one-page tabular output and measures familiar to most users. In this paper we present significant improvement in RFEX Model explainer compared to the version published previously, a new RFEX Sample explainer that provides explanation of how the RF classifies a particular data sample and is designed to directly relate to RFEX Model explainer, and a RFEX Model and Sample explainer case study from our collaboration with the J. Craig Venter Institute (JCVI). We show that our approach offers a simple yet powerful means of explaining RF classification at the model and sample levels, and in some cases even points to areas of new investigation. RFEX is easy to implement using available RF tools and its tabular format offers easy-to-understand representations for non-experts, enabling them to better leverage the RF technology.

The Factorial Survey: The Impact of the Presentation Format of Vignettes on Answer Behavior and Processing Time

The factorial survey is an experimental design in which the researcher constructs varying descriptions of situations or individual persons (vignettes), which will be judged by respondents with regard to a particular aspect. Some researchers present vignettes in text format as short stories, others present the central information of vignettes in a tabular format. To date, only a few sentences have been published, by Auspurg and Hinz, on the impact of the presentation format (text vs. table) on the answer behavior of students. Empirically, no differences were found between either format. Based on an Internet experiment conducted with a quota sample, we find evidence that ordinary tabular formats outperform text vignettes in terms of total vignette nonresponse but not when it comes to processing time. The former result especially applies in the case of less well-educated people. We further find that tabular format does not perform worse than text format regarding response inconsistency.