1,721,026 research outputs found
Constraint-Based Verification of a Mobile App Game Designed for Nudging People to Attend Cancer Screening
Full text linkIn Norway, cervical cancer prevention involves the participation of as many eligible women aged 25-69 years as possible. However, reaching and inviting every eligible women to attend cervical cancer screening and HPV vaccination is difficult. Using social nudging and gamification in modern means of communication can encourage the participation of unscreened people. Simula Research Laboratory together with the Cancer Registry of Norway have developed FightHPV, a mobile app game intended to inform adolescent and eligible women about cervical cancer screening and HPV vaccination while they play and, to facilitate their further participation to prevention campaigns. However, game design and health information transfer can be hard to reconcile, as the design of each game episode is more guided by the release of information than gameplay and playing difficulty. In this paper, we propose a constraint-based model of FightHPV to evaluate the difficulty of each episode and to help the game designer in improving the player experience. This approach is relevant to facilitate social nudging of eligible women to participate to cervical cancer screening and HPV vaccination, as shown by the initial deployment of FightHPV and tests performed in focus groups. The design of this mobile app can thus be regarded as a new application case of Artificial Intelligence techniques such as gamification and constraint programming
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Validating Uncertainty-Aware Virtual Sensors For Industry 4.0
Full text linkIn industry 4.0 manufacturing, sensors provide information about the state, behavior, and performance of processes. Therefore, one of the main goals of Industry 4.0 is to collect high-quality data to realize its business goal, namely zero-defect manufacturing, and high-quality products. However, hardware sensors cannot always gather quality data due to several factors. First, industrial 4.0 deploys sensors in harsh environments. Consequently, measurements are likely to be corrupted by errors such as outliers, noise, or missing values. Sensors can, over time, be subject to faults such as bias, drifting, complete failure, and precision degradation. Moreover, direct sensing of a process variable can be unavailable due to environmental constraints such as surface temperature being beyond the range of the physical sensor.
A virtual sensor is a tools to solve these problems by allowing for online estimation of process variables when the physical sensor is unreliable or unavailable. Deep learning method is effective in developing virtual sensors; however, it assumes that the data used for training and deployment are independent and identical (i. i. d). Therefore, deep learning in high-risk environments, such as industry 4.0, is challenging because if i.i.d assumptions fail to hold, the model may make errors that lead to disastrous consequences, such as financial losses, reputational damage, or even death. We can prevent model mistakes only if the model estimates the uncertainty of its predictions. Unfortunately, current deep learning-based virtual sensors are created using frequentist models, making them unable to capture uncertainty accurately. In this thesis, we explore the possibility of Bayesian convolutional neural networks (BCNN) to generate uncertainty-aware virtual sensors for Industry 4.0.
We use two publicly available realistic industrial datasets to generate virtual sensors and conduct experiments. CNC Mill Tool Wear data (CNC) from CNC milling machine provided by the University of Michigan, and Tennessee Eastman Process data (TEP) provided by Eastman Chemical Company for process monitoring and control studies. The root-mean-square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2) is used to evaluate the predictive capability of the generated virtual sensor. The performance is compared to that of the standard neural network-based virtual sensor, namely convolutional neural network (CNN) and long short-term memory (LSTM). We demonstrated Bayesian neural networks' ability to quantify uncertainty by computing the coverage probability of the uncertainty. Additionally, we tested whether the estimated uncertainty could detect changes in input data distribution using the fault injection method.
Our BCNN virtual sensor had the best R-squared scores, with R2 = 0.99 on CNC and R2 = 0.98 on TEP data. The result of the coverage probability score indicates a reasonably good uncertainty estimate. However, despite predictive uncertainty detecting faults in input datasets, its accuracy declined as fault length increased
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
