Exploring Food Detection using CNNs

One of the most common critical factors directly related to the cause of a chronic disease is unhealthy diet consumption. In this sense, building an automatic system for food analysis could allow a better understanding of the nutritional information with respect to the food eaten and thus it could help in taking corrective actions in order to consume a better diet. The Computer Vision community has focused its efforts on several areas involved in the visual food analysis such as: food detection, food recognition, food localization, portion estimation, among others. For food detection, the best results evidenced in the state of the art were obtained using Convolutional Neural Network. However, the results of all these different approaches were gotten on different datasets and therefore are not directly comparable. This article proposes an overview of the last advances on food detection and an optimal model based on GoogLeNet Convolutional Neural Network method, principal component analysis, and a support vector machine that outperforms the state of the art on two public food/non-food datasets

Unsupervised feature selection for noisy data

Feature selection techniques are enormously applied in a variety of data analysis tasks in order to reduce the dimensionality. According to the type of learning, feature selection algorithms are categorized to: supervised or unsupervised. In unsupervised learning scenarios, selecting features is a much harder problem, due to the lack of class labels that would facilitate the search for relevant features. The selecting feature difficulty is amplified when the data is corrupted by different noises. Almost all traditional unsupervised feature selection methods are not robust against the noise in samples. These approaches do not have any explicit mechanism for detaching and isolating the noise thus they can not produce an optimal feature subset. In this article, we propose an unsupervised approach for feature selection on noisy data, called Robust Independent Feature Selection (RIFS). Specifically, we choose feature subset that contains most of the underlying information, using the same criteria as the Independent component analysis (ICA). Simultaneously, the noise is separated as an independent component. The isolation of representative noise samples is achieved using factor oblique rotation whereas noise identification is performed using factor pattern loadings. Extensive experimental results over divers real-life data sets have showed the efficiency and advantage of the proposed algorithm.We thankfully acknowledge the support of the Comision Interministerial de Ciencia y Tecnologa (CICYT) under contract No. TIN2015-65316-P which has partially funded this work.Peer ReviewedPostprint (author's final draft

Social Roles and Baseline Proxemic Preferences for a Domestic Service Robot

© The Author(s) 2014. This article is published with open access at Springerlink.com. This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. The work described in this paper was conducted within the EU Integrated Projects LIREC (LIving with Robots and intEractive Companions, funded by the European Commission under contract numbers FP7 215554, and partly funded by the ACCOMPANY project, a part of the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement n287624The goal of our research is to develop socially acceptable behavior for domestic robots in a setting where a user and the robot are sharing the same physical space and interact with each other in close proximity. Specifically, our research focuses on approach distances and directions in the context of a robot handing over an object to a userPeer reviewe

Utility of Parental Mediation Model on Youth’s Problematic Online Gaming

The Parental Mediation Model PMM) was initially designed to regulate children’s attitudes towards the traditional media. In the present era, because of prevalent online media there is a need for similar regulative measures. Spending long hours on social media and playing online games increase the risks of exposure to the negative outcomes of online gaming. This paper initially applied the PMM developed by European Kids Online to (i) test the reliability and validity of this model and (ii) identify the effectiveness of this model in controlling problematic online gaming (POG). The data were collected from 592 participants comprising 296 parents and 296 students of four foreign universities, aged 16 to 22 years in Kuala Lumpur (Malaysia). The study found that the modified model of the five-factor PMM (Technical mediation, Monitoring mediation, Restrictive mediation, Active Mediation of Internet Safety, and Active mediation of Internet Use) functions as a predictor for mitigating POG. The findings suggest the existence of a positive relation between ‘monitoring’ and ‘restrictive’ mediation strategies and exposure to POG while Active Mediation of Internet Safety and Active mediation of Internet use were insignificant predictors. Results showed a higher utility of ‘technical’ strategies by the parents led to less POG. The findings of this study do not support the literature suggesting active mediation is more effective for reducing youth’s risky behaviour. Instead, parents need to apply more technical mediations with their children and adolescents’ Internet use to minimize the negative effects of online gaming

Visualizing dimensionality reduction of systems biology data

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21

Validation of the Thai version of the family reported outcome measure (FROM-16)© to assess the impact of disease on the partner or family members of patients with cancer

© The Author(s). 2019Background: Cancer not only impairs a patient's physical and psychosocial functional behaviour, but also contributes to negative impact on family members' health related quality of life. Currently, there is an absence of a relevant tool in Thai with which to measure such impact. The aim of this study was to translate and validate the Family Reported Outcome Measure (FROM-16) in Thai cancer patients' family members. Methods: Thai version of FROM-16 was generated by interactive forward-backward translation process following standard guidelines. This was tested for psychometric properties including reliability and validity, namely content validity, concurrent validity, known group validity, internal consistency, exploratory and confirmatory factor analysis. Construct validity was examined by comparing the Thai FROM-16 version with the WHOQOL-BREF-THAI. Results: The internal consistency reliability was strong (Cronbach's alpha = 0.86). A Negative moderate correlation between the Thai FROM-16 and WHOQOL-BREF-THAI was observed (r = - 0.4545, p < 0.00), and known group validity was proved by a statistically significant higher score in family members with high burden of care and insufficient income. The factor analysis supported both 3-factor and 2-factor loading model with slight difference when compared with the original version. Conclusions: The Thai FROM-16 showed good reliability and validity in Thai family members of patients with cancer. A slight difference in factor analysis results compared to the original version could be due to cross-culture application.Peer reviewedFinal Published versio

Use of NON-PARAMETRIC Item Response Theory to develop a shortened version of the Positive and Negative Syndrome Scale (PANSS)

<p>Abstract</p> <p>Background</p> <p>Nonparametric item response theory (IRT) was used to examine (a) the performance of the 30 Positive and Negative Syndrome Scale (PANSS) items and their options ((levels of severity), (b) the effectiveness of various subscales to discriminate among differences in symptom severity, and (c) the development of an abbreviated PANSS (Mini-PANSS) based on IRT and a method to link scores to the original PANSS.</p> <p>Methods</p> <p>Baseline PANSS scores from 7,187 patients with Schizophrenia or Schizoaffective disorder who were enrolled between 1995 and 2005 in psychopharmacology trials were obtained. Option characteristic curves (OCCs) and Item Characteristic Curves (ICCs) were constructed to examine the probability of rating each of seven options within each of 30 PANSS items as a function of subscale severity, and summed-score linking was applied to items selected for the Mini-PANSS.</p> <p>Results</p> <p>The majority of items forming the Positive and Negative subscales (i.e. 19 items) performed very well and discriminate better along symptom severity compared to the General Psychopathology subscale. Six of the seven Positive Symptom items, six of the seven Negative Symptom items, and seven out of the 16 General Psychopathology items were retained for inclusion in the Mini-PANSS. Summed score linking and linear interpolation was able to produce a translation table for comparing total subscale scores of the Mini-PANSS to total subscale scores on the original PANSS. Results show scores on the subscales of the Mini-PANSS can be linked to scores on the original PANSS subscales, with very little bias.</p> <p>Conclusions</p> <p>The study demonstrated the utility of non-parametric IRT in examining the item properties of the PANSS and to allow selection of items for an abbreviated PANSS scale. The comparisons between the 30-item PANSS and the Mini-PANSS revealed that the shorter version is comparable to the 30-item PANSS, but when applying IRT, the Mini-PANSS is also a good indicator of illness severity.</p

Development of a scale to measure stigma related to podoconiosis in Southern Ethiopia

Background: Health-related stigma adds to the physical and economic burdens experienced by people suffering from neglected tropical diseases (NTDs). Previous research into the NTD podoconiosis showed significant stigma towards those with the disease, yet no formal instrument exists by which to assess stigma or interventions to reduce stigma. We aimed to develop, pilot and validate scales to measure the extent of stigma towards podoconiosis among patients and in podoconiosis-endemic communities. Methods: Indicators of stigma were drawn from existing qualitative podoconiosis research and a literature review on measuring leprosy stigma. These were then formulated into items for questioning and evaluated through a Delphi process in which irrelevant items were discounted. The final items formed four scales measuring two distinct forms of stigma (felt stigma and enacted stigma) for those with podoconiosis and those without the disease. The scales were formatted as two questionnaires, one for podoconiosis patients and one for unaffected community members. 150 podoconiosis patients and 500 unaffected community members from Wolaita zone, Southern Ethiopia were selected through multistage random sampling to complete the questionnaires which were interview-administered. The scales were evaluated through reliability assessment, content and construct validity analysis of the items, factor analysis and internal consistency analysis. Results: All scales had Cronbach’s alpha over 0.7, indicating good consistency. The content and construct validity of the scales were satisfactory with modest correlation between items. There was significant correlation between the felt and enacted stigma scales among patients (Spearman’s r = 0.892; p < 0.001) and within the community (Spearman’s r = 0.794; p < 0.001). Conclusion: We report the development and testing of the first standardised measures of podoconiosis stigma. Although further research is needed to validate the scales in other contexts, we anticipate they will be useful in situational analysis and in designing, monitoring and evaluating interventions. The scales will enable an evidencebased approach to mitigating stigma which will enable implementation of more effective disease control and help break the cycle of poverty and NTDs

Impaired work functioning due to common mental disorders in nurses and allied health professionals: the Nurses Work Functioning Questionnaire

Common mental disorders (CMD) negatively affect work functioning. In the health service sector not only the prevalence of CMDs is high, but work functioning problems are associated with a risk of serious consequences for patients and healthcare providers. If work functioning problems due to CMDs are detected early, timely help can be provided. Therefore, the aim of this study is to develop a detection questionnaire for impaired work functioning due to CMDs in nurses and allied health professionals working in hospitals. First, an item pool was developed by a systematic literature study and five focus group interviews with employees and experts. To evaluate the content validity, additional interviews were held. Second, a cross-sectional assessment of the item pool in 314 nurses and allied health professionals was used for item selection and for identification and corroboration of subscales by explorative and confirmatory factor analysis. The study results in the Nurses Work Functioning Questionnaire (NWFQ), a 50-item self-report questionnaire consisting of seven subscales: cognitive aspects of task execution, impaired decision making, causing incidents at work, avoidance behavior, conflicts and irritations with colleagues, impaired contact with patients and their family, and lack of energy and motivation. The questionnaire has a proven high content validity. All subscales have good or acceptable internal consistency. The Nurses Work Functioning Questionnaire gives insight into precise and concrete aspects of impaired work functioning of nurses and allied health professionals. The scores can be used as a starting point for purposeful intervention