On Using Multiple Disabilities Profiles to Adapt Multimedia Documents

Currently, advanced technological hardware can offer mobile devices which fits in the hand with a capacity to consult documents at anytime and anywhere. Multiple user context constraints as well as mobile device capabilities may involve the adaptation of multimedia content. In this article, the authors propose a new graph-based method for adapting multimedia documents in complex situations. Each contextual situation could correspond to a physical handicap and therefore triggers an adaptation action using ontological reasoning. Consequently, when several contextual situations are identified, this leads to multiple disabilities and may give rise to inconsistency between triggered actions. Their method allows modeling relations between adaptation-actions to select the compatible triggerable ones. In order to evaluate the feasibility and the performance of their proposal, an experimental study has been made on some real scenarios. When tested and compared with some existing approaches, their proposal showed improvements according to various criteria.

Patterns of responses to COVID-19 in selected Latin American countries

The article analyses structural and context constraints and opportunities faced by Latin America in the battle against the Covid-19 pandemic. We argue that the number of deaths (and related statistics, such as number of infected or number of tests) as a proportion of the countries’ populations is an important but insufficient measure of the effectiveness of each country’s responses to the SARS-CoV-2. We test the correlation of deaths with typical structural constraints (Gross domestic product, United Nation – Human Development Index, Gini index, expenditure in health, children’s mortality rate, informality rate), and find that between countries’ differences in these measures do not help to understand the number of deaths per million inhabitants. We then move to a more in-depth analysis of 11 selected Latin American countries to show that those that chose collective responsibility (a notion developed in the article) in the management of the responses to the crisis, and that could coordinate the actions of different governing levels, fared much better than those that chose individual responsibility and low levels of coordination, irrespective of existing structural constraints.

On Using Multiple Disabilities Profiles to Adapt Multimedia Documents

Currently, advanced technological hardware can offer mobile devices which fits in the hand with a capacity to consult documents at anytime and anywhere. Multiple user context constraints as well as mobile device capabilities may involve the adaptation of multimedia content. In this article, the authors propose a new graph-based method for adapting multimedia documents in complex situations. Each contextual situation could correspond to a physical handicap and therefore triggers an adaptation action using ontological reasoning. Consequently, when several contextual situations are identified, this leads to multiple disabilities and may give rise to inconsistency between triggered actions. Their method allows modeling relations between adaptation-actions to select the compatible triggerable ones. In order to evaluate the feasibility and the performance of their proposal, an experimental study has been made on some real scenarios. When tested and compared with some existing approaches, their proposal showed improvements according to various criteria.

Brain Magnetic Resonance Imaging Segmentation Using Possibilistic Clustering Algorithm Combined with Context Constraints

Because the FCM method is simple and effective, a series of research results based on this method are widely used in medical image segmentation. Compared with the traditional FCM, the probability clustering (PCM) algorithm cancels the constraint on the normalization of each sample membership degree in the iterative process, and the clustering effect of the method is improved within a certain range. However, the above two methods only use the gray value of the image pixels in the iterative process, ignoring the context constraint relationship between the high-dimensional image pixels. The two are easily affected by image noise during the segmentation process, resulting in poor robustness, which will affect the segmentation accuracy in practical applications. In order to alleviate this problem, this paper introduces the context constraint information of image based on PCM, and proposes a PCM algorithm that combines context constraints (CCPCM) and successfully applies it to human brain MR image segmentation to further improve the noise immunity of the new algorithm. Expand the applicability of new algorithms in the medical field. Through simulation results on medical images, it is found that compared with the previous classical clustering methods, such as FCM, PCM, etc., the CCPCM has better anti-interference to different noises, and the segmentation boundary is clearer. At the same time, CCPCM algorithm introduces the spatial neighbor information adaptive weighting mechanism in the clustering process, which can adaptively adjust the constraint weight of spatial information and optimize the clustering process, thus improving the segmentation efficiency.

Processing Scalar Implicatures in Mandarin Chinese: Testing the Processing Models

This study first adopted a participant-perception test to assess the processing model of scalar implicature in Chinese. Our main aim is to distinguish among the three possible processing mechanisms: the context-driven account, the default account and the standardized account. We designed two experiments to testify these three models mentioned above: one without any context and the other one with upper and lower contexts. In our Experiment 1, we conducted test items without contexts in child and adult groups, whose aim is to test the necessity of context to scalar implicature and thus clearly discern the three models. We found though without context, both children and adults group processed scalar implicature at a medium rate, which was an evidence to deny the context driven account. However, some adults tended to be confused about the experiment purpose when facing testing items totally without context constraints, and the children participants might get help from other developed linguistic ability in their processing, like the improved numeral ability. Thus it would be clear that the context account is unreliable, but it would still be early to tell whether the results support the default account or the standardization. In Experiment 2, we added the context constraints, the upper bound context and the lower bound context. Our final results, the still processing of utterance with SI in lower bound context and a similar reaction time to the scalar implicature processing in both upper and lower bound contexts denied the default account and showed a closer relation to the standardization account.