Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis.

BACKGROUND: With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research. OBJECTIVE: Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis. METHODS: A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the keywords in these articles. We created a knowledge map to show the evolution of cloud health care research. We used co-word analysis to identify the hotspots and their evolution in cloud health care research. RESULTS: The evolution and development of cloud health care services are described. In 2007-2009 (Phase I), most scholars used cloud computing in the medical field mainly to reduce costs, and grid computing and cloud computing were the primary technologies. In 2010-2012 (Phase II), the security of cloud systems became of interest to scholars. In 2013-2015 (Phase III), medical informatization enabled big data for health services. In 2016-2017 (Phase IV), machine learning and mobile technologies were introduced to the medical field. CONCLUSIONS: Cloud health care research has been rapidly developing worldwide, and technologies used in cloud health research are simultaneously diverging and becoming smarter. Cloud-based mobile health, cloud-based smart health, and the security of cloud health data and systems are three possible trends in the future development of the cloud health care field.

Abstractive summarization of long texts by representing multiple compositionalities with temporal hierarchical pointer generator network.

In order to tackle the problem of abstractive summarization of long multi-sentence texts, it is critical to construct an efficient model, which can learn and represent multiple compositionalities better. In this paper, we introduce a temporal hierarchical pointer generator network that can represent multiple compositionalities in order to handle longer sequences of texts with a deep structure. We demonstrate how a multilayer gated recurrent neural network organizes itself with the help of an adaptive timescale in order to represent the compositions. The temporal hierarchical network is implemented with a multiple timescale architecture where the timescale of each layer is also learned during the training process through error backpropagation through time. We evaluate our proposed model using an Introduction-Abstract summarization dataset from scientific articles and the CNN/Daily Mail summarization benchmark dataset. The results illustrate that, we successfully implement a summary generation system for long texts by using the multiple timescale with adaptation concept. We also show that we have improved the summary generation system with our proposed model on the benchmark dataset.

Performance validity on the Stroop Color and Word Test in a mixed forensic and patient sample.

Objective: Assessment of performance validity is an essential component of neuropsychological evaluation. Performance validity indicators contained within cognitive ability measures are particularly advantageous, as they do not require additional test administration time or material, and allow for assessment of validity across multiple cognitive domains over the course of an evaluation. The present study examined the classification accuracy of the Stroop Color and Word Test (Stroop) Word, Color, and Color-Word trials in detecting invalid performance in a mixed forensic and patient sample. Method: Archival data from a mixed-diagnosis sample were analyzed, consisting of 558 (Mage=40.8; Meducation=15.3) individuals referred for neuropsychological evaluation in clinical or forensic contexts, who completed a test battery that included the Stroop and at least two performance validity tests (PVTs). Examinees were assigned to Valid or Invalid performance groups based on PVT outcomes. Results: Word and Color scores were found to have excellent classification accuracy, whereas Color-Word yielded acceptable classification accuracy. Sensitivity and specificity values are presented for a range of cutoff scores, as are positive and negative predictive values for setting-specific base rates of invalid performance. Conclusions: Performances on the Stroop Color and Word Test, particularly the Word and Color trials, can be useful in detecting invalid performance in a mixed patient and forensic sample. Clinical implications are discussed.

Words are a context for mental inference.

Accumulating evidence indicates that context has an important impact on inferring emotion in facial configurations. In this paper, we report on three studies examining whether words referring to mental states contribute to mental inference in images from the Reading the Mind in the Eyes Test (Study 1), Baron-Cohen et al. (2001) in static emoji (Study 2), and in animated emoji (Study 3). Across all three studies, we predicted and found that perceivers were more likely to infer mental states when relevant words were embedded in the experimental context (i.e., in a forced-choice task) versus when those words were absent (i.e., in a free-labeling task). We discuss the implications of these findings for the widespread conclusion that faces or parts of faces "display" emotions or other mental states, as well as for psychology's continued reliance on forced-choice methods. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Investigating the functional neuroanatomy of concrete and abstract word processing through direct electric stimulation (DES) during awake surgery.

Neuropsychological, neuroimaging and electrophysiological studies demonstrate that abstract and concrete word processing relies not only on the activity of a common bilateral network but also on dedicated networks. The neuropsychological literature has shown that a selective sparing of abstract relative to concrete words can be documented in lesions of the left anterior temporal regions. We investigated concrete and abstract word processing in 10 patients undergoing direct electrical stimulation (DES) for brain mapping during awake surgery in the left hemisphere. A lexical decision and a concreteness judgment task were added to the neuropsychological assessment during intra-operative monitoring. On the concreteness judgment, DES delivered over the inferior frontal gyrus significantly decreased abstract word accuracy while accuracy for concrete words decreased when the anterior temporal cortex was stimulated. These results are consistent with a lexical-semantic model that distinguishes between concrete and abstract words related to different neural substrates in the left hemisphere.

Synonym-Based Word Frequency Analysis to Support the Development and Presentation of a Public Health Quality Improvement Taxonomy.

CONTEXT: Researchers and analysts have not completely examined word frequency analysis as an approach to creating a public health quality improvement taxonomy. OBJECTIVE: To develop a taxonomy of public health quality improvement concepts for an online exchange of quality improvement work. DESIGN: We analyzed documents, conducted an expert review, and employed a user-centered design along with a faceted search approach to make online entries searchable for users. To provide the most targeted facets to users, we used word frequency to analyze 334 published public health quality improvement documents to find the most common clusters of word meanings. We then reviewed the highest-weighted concepts and categorized their relationships to quality improvement details in our taxonomy. Next, we mapped meanings to items in our taxonomy and presented them in order of their weighted percentages in the data. Using these methods, we developed and sorted concepts in the faceted search presentation so that online exchange users could access relevant search criteria. RESULTS: We reviewed 50 of the top synonym clusters and identified 12 categories for our taxonomy data. The final categories were as follows: Summary; Planning and Execution Details; Health Impact; Training and Preparation; Information About the Community; Information About the Health Department; Results; Quality Improvement (QI) Staff; Information; Accreditation Details; Collaborations; and Contact Information of the Submitter. CONCLUSION: Feedback about the elements in the taxonomy and presentation of elements in our search environment from users has been positive. When relevant data are available, the word frequency analysis method may be useful in other taxonomy development efforts for public health.

An Investigation of Spatial Stimulus-Response Compatibility Effects Based on German Particles.

In the current study, we tested if stimulus-response (SR) compatibility effects of spatially ambiguous words depend on a semantic priming context. Although many words, including spatial words, can take on several meanings, this is an open question. From Experiments 1 to 3, we manipulated the likelihood that the vertical meaning of the German particles auf and ab was processed by (1) instructing the processing of vertical meaning in Experiment 1, but not in Experiments 2 and 3, and (2) by using verbs that either primed (Experiments 1 and 2) or did not prime (Experiments 1-3) the targets' vertical meanings. Spatial SR compatibility effects resulted, regardless of whether or not the processing of the vertical meaning was instructed and the vertical meaning was primed. Results suggest that the selection between vertically discriminated responses could be sufficient to elicit the participants' extraction of the vertical meaning of the ambiguous particles.

Nuevos modelos de currículum vitae en la era digital / New models of curriculum vitae on digital age

El Currículum vitae es la carta de presentación en el ámbito profesional. Permite que las instituciones valoren nuestras competencias educativas y profesionales desarrolladas. La llegada de Internet, y más recientemente del currículum digital aportan nuevos elementos fundamentales en la sociedad: visibilidad, interconexión y medición de nuestra reputación como profesionales. El objetivo de este trabajo es presentar las propuestas de currículum surgidas en los últimos años, centrándonos para ello en las creadas para investigadores. Como resultado hemos obtenido tres tipologías claramente diferenciadas: sistemas curriculares ad-hoc, propuestas institucionales de currículum normalizadoras y redes sociales académicas. El desarrollo del currículum vitae institucional y académico es un requisito que deben cumplir los profesionales que desarrollan su labor en el ámbito académico e investigador como requisito indispensable para la visibilidad de la ciencia y el desarrollo de las disciplinas

Curriculum Vitae is the presentation letter on professional field. They allow institutions to value our developed educational and professional competencies. The apparition of Internet, and more recently the digital curriculum, provide new fundamental elements in society; visibility, interconnection and measurement of our reputation as professionals. The aim of this paper is to present curriculum proposals in recent years, focusing on those created for researchers. As a result we have achieved three typologies clearly differentiated; ad-hoc curricular systems, institutional proposals for curricular standards and academic social networks. The development of institutional and academic curriculum vitae is a step that the professionals must do that develop their duty in the academicals and researching field as a compulsory duty for the insight of the science and development of the disciplines

Image classification by addition of spatial information based on histograms of orthogonal vectors.

The Bag-of-Visual-Words (BoVW) model is widely used for image classification, object recognition and image retrieval problems. In BoVW model, the local features are quantized and 2-D image space is represented in the form of order-less histogram of visual words. The image classification performance suffers due to the order-less representation of image. This paper presents a novel image representation that incorporates the spatial information to the inverted index of BoVW model. The spatial information is added by calculating the global relative spatial orientation of visual words in a rotation invariant manner. For this, we computed the geometric relationship between triplets of identical visual words by calculating an orthogonal vector relative to each point in the triplets of identical visual words. The histogram of visual words is calculated on the basis of the magnitude of these orthogonal vectors. This calculation provides the unique information regarding the relative position of visual words when they are collinear. The proposed image representation is evaluated by using four standard image benchmarks. The experimental results and quantitative comparisons demonstrate that the proposed image representation outperforms the existing state-of-the-art in terms of classification accuracy.

Setting the alarm: Word emotional attributes require consolidation to be operational.

Demonstrations of emotional Stroop in conditioned made-up words are flawed because of the lack of task ensuring similar word encoding across conditions. Here, participants were trained on associations between made-up words (e.g., 'drott') and pictures with an alarming or neutral content (e.g., 'a dead sheep' vs. 'a munching cow') in a situation that required attention to both ends of each association. To test whether word emotional attributes need to consolidate before they can hijack attention, one set of associations was learned seven days before the test, whereas the other set was learned either six hrs or immediately before the test. The novel words' ability to evoke their emotional attributes was assessed by using both Stroop and an auditory analogue called pause detection. Matching words and pictures was harder for alarming associations. However, similar learning rate and forgetting at seven days were observed for both types of associations. Pause detection revealed no emotion effect for same-day (i.e., unconsolidated) associations, but robust interference for seven-day-old (i.e., consolidated) alarming associations. Attention capture was found in the emotional Stroop as well, though only when trial n-1 referred to a same-day association. This task also showed stronger response repetition priming (independently of emotion) when trials n and n-1 both tapped into seven-day-old associations. Word emotional attributes hence take between six hrs and seven days to be operational. Moreover, age interactions between consecutive trials can be used to gauge implicitly the indirect (relational) episodic associations that develop in the meantime between the memories of individual items. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Word Processing in Scene Context: An Event-Related Potential Study in Young Children.

Semantic priming has been demonstrated in object or word contexts in toddlers. However, less is known about semantic priming in scene context. In this study, 24-month-olds with high and low vocabulary skills were presented with visual scenes (e.g., kitchen) followed by semantically consistent (e.g., spoon) or inconsistent (e.g., bed) spoken words. Inconsistent scene-word pairs evoked a larger N400 component over the frontal areas. Low-producers presented a larger N400 over the right while high-producers over the left frontal areas. Our results suggest that contextual information facilitates word processing in young children. Additionally, children with different linguistic skills activate different neural structures.

Spatial knowledge during skilled action sequencing: Hierarchical versus nonhierarchical representations.

Typists can type 4 to 5 keystrokes per second at around 95% accuracy, yet they appear to have poor declarative knowledge of key locations. Logan and Crump (2011, Psychology of Learning and Motivation, Vol. 54, pp. 1-27) accounted for this paradox by proposing that typing is hierarchically organized into two loops, with an outer loop that transforms sentences into words and passes each word, one at a time, to an inner loop that transforms each word into its constituent keystrokes; however, the nature of the inner loop's spatial knowledge is not well understood. Key locations may be learned through the experiences of locating and traversing between keys. In daily life, people tend to type structured language, and, as a consequence, certain keys and key-to-key transitions are experienced more frequently than others. Here, we asked whether or not this knowledge is structured hierarchically. For example, knowledge of key locations may be nested within representations of words, or the inner loop may rely on knowledge that is independent from higher level structures. To test this, we had people type English, English-like, and random strings during normal, partially occluded, and occluded typing. In both partially occluded and occluded typing, error rates were higher while typing random strings compared to English and English-like strings, whereas there was no difference in error rates between English and English-like strings. This suggests that typists' spatial knowledge of the keyboard is not driven by hierarchical word-level representations, but instead is likely driven by a collection of individual processes, such as knowledge of the sequential structure of language acquired by typing more frequently occurring letters.

Optimized Motor Imagery Paradigm Based on Imagining Chinese Characters Writing Movement.

BACKGROUND: motor imagery (MI) is a mental representation of motor behavior. The MI-based brain computer interfaces (BCIs) can provide communication for the physically impaired. The performance of MI-based BCI mainly depends on the subject's ability to self-modulate electroencephalogram signals. Proper training can help naive subjects learn to modulate brain activity proficiently. However, training subjects typically involve abstract motor tasks and are time-consuming. METHODS: to improve the performance of naive subjects during motor imagery, a novel paradigm was presented that would guide naive subjects to modulate brain activity effectively. In this new paradigm, pictures of the left or right hand were used as cues for subjects to finish the motor imagery task. Fourteen healthy subjects (11 male, aged 22-25 years, and mean 23.6±1.16) participated in this study. The task was to imagine writing a Chinese character. Specifically, subjects could imagine hand movements corresponding to the sequence of writing strokes in the Chinese character. This paradigm was meant to find an effective and familiar action for most Chinese people, to provide them with a specific, extensively practiced task and help them modulate brain activity. RESULTS: results showed that the writing task paradigm yielded significantly better performance than the traditional arrow paradigm (p < 0.001). Questionnaire replies indicated that most subjects thought that the new paradigm was easier. CONCLUSION: the proposed new motor imagery paradigm could guide subjects to help them modulate brain activity effectively. Results showed that there were significant improvements using new paradigm, both in classification accuracy and usability.

Recursive Bayesian Coding for BCIs.

Brain-Computer Interfaces (BCIs) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g., language model priors in spelling applications), accounts for varying brain symbol accuracy and is robust to single brain symbol query errors. This framework is paired with Maximum Mutual Information (MMI) coding which maximizes a generalization of ITR. Both are applicable to any discrete task and brain phenomena (e.g., P300, SSVEP, MI). To demonstrate the efficacy of our approach we perform SSVEP "Shuffle" Speller experiments and compare our recursive coding scheme with traditional decision tree methods including Huffman coding. MMI coding leverages the asymmetry of the classifier's mistakes across a particular user's SSVEP responses; in doing so it offers a 33% increase in letter accuracy though it is 13% slower in our experiment.

An Asynchronous P300-Based Brain-Computer Interface Web Browser for Severely Disabled People.

This paper presents an electroencephalographic (EEG) P300-based brain-computer interface (BCI) Internet browser. The system uses the "odd-ball" row-col paradigm for generating the P300 evoked potentials on the scalp of the user, which are immediately processed and translated into web browser commands. There were previous approaches for controlling a BCI web browser. However, to the best of our knowledge, none of them was focused on an assistive context, failing to test their applications with a suitable number of end users. In addition, all of them were synchronous applications, where it was necessary to introduce a "read-mode" command in order to avoid a continuous command selection. Thus, the aim of this study is twofold: 1) to test our web browser with a population of multiple sclerosis (MS) patients in order to assess the usefulness of our proposal to meet their daily communication needs; and 2) to overcome the aforementioned limitation by adding a threshold that discerns between control and non-control states, allowing the user to calmly read the web page without undesirable selections. The browser was tested with sixteen MS patients and five healthy volunteers. Both quantitative and qualitative metrics were obtained. MS participants reached an average accuracy of 84.14%, whereas 95.75% was achieved by control subjects. Results show that MS patients can successfully control the BCI web browser, improving their personal autonomy.

Adding Real-Time Bayesian Ranks to Error-Related Potential Scores Improves Error Detection and Auto-Correction in a P300 Speller.

Brain-computer interface (BCI) spellers could improve access to communication for people with profound physical disabilities; however, improved speed and accuracy of these spellers is required to make them practical for everyday use. Here we introduce the combination of P300-speller confidence with the error-related potential (ErrP) to improve online single-trial error detection and correction accuracies in a BCI speller. First, we present a mechanism for obtaining P300-confidence using a real-time Bayesian dynamic stopping framework that makes novel use of additional stimuli that occur due to epoch and filter delays. Second, we propose an ensemble of decision trees to combine ErrP and P300-confidence features. Third, we describe the unique attentional differences between error and correct feedback in our spelling interface and discuss how these differences affect ErrP physiology. We tested online error detection on 11 typically developed adults using a BCI system trained on a previous day and found an average sensitivity of 86.67% and specificity of 96.59%. Automatic correction increased selection accuracy by 13.67% and utility grew by a factor of 4.48. We found, however, that the improved performance was primarily attributable to the inclusion of P300 confidence in error detection, calling into question the significance of single-trial ErrP detection.

Training and testing ERP-BCIs under different mental workload conditions.

OBJECTIVE: As one of the most popular and extensively studied paradigms of brain-computer interfaces (BCIs), event-related potential-based BCIs (ERP-BCIs) are usually built and tested in ideal laboratory settings in most existing studies, with subjects concentrating on stimuli and intentionally avoiding possible distractors. This study is aimed at examining the effect of simultaneous mental activities on ERP-BCIs by manipulating various levels of mental workload during the training and/or testing of an ERP-BCI. APPROACH: Mental workload was manipulated during the training or testing of a row-column P300-speller to investigate how and to what extent the spelling performance and the ERPs evoked by the oddball stimuli are affected by simultaneous mental workload. MAIN RESULTS: Responses of certain ERP components, temporal-occipital N200 and the late reorienting negativity evoked by the oddball stimuli and the classifiability of ERP features between targets and non-targets decreased with the increase of mental workload encountered by the subject. However, the effect of mental workload on the performance of ERP-BCI was not always negative but depended on the conditions where the ERP-BCI was built and applied. The performance of ERP-BCI built under an ideal lab setting without any irrelevant mental activities declined with the increasing mental workload of the testing data. However, the performance was significantly improved when an ERP-BCI was built under an appropriate mental workload level, compared to that built under speller-only conditions. SIGNIFICANCE: The adverse effect of concurrent mental activities may present a challenge for ERP-BCIs trained in ideal lab settings but which are to be used in daily work, especially when users are performing demanding mental processing. On the other hand, the positive effects of the mental workload of the training data suggest that introducing appropriate mental workload during training ERP-BCIs is of potential benefit to the performance in practical applications.