109 research outputs found

    Time exceptions in sequence diagrams

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    UML sequence diagrams partially describe a system. We show how the description may be augmented with exceptions triggered by the violation of timing constraints and compare our approach to those of the UML 2.1 simple time model, the UML Testing Profile and the UML profile for Schedulability, Performance and Time. We give a formal definition of time exceptions in sequence diagrams and show that the concepts are compositional. An ATM example is used to explain and motivate the concepts

    Phadiatop Infant in the Diagnosis of Atopy in Children with Allergy-Like Symptoms

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    Background and Objective. Allergy-like symptoms such as wheezing and eczema are common in young children and an early diagnosis is important to initiate correct management. The objective of this study was to evaluate the diagnostic performance of Phadiatop Infant, an in vitro test for determination of early sensitisation to food and inhalant allergens. Patients and Methods. The study was conducted, retrospectively, using frozen sera from 122 children (median age 2.7 years) admitted to the hospital with suspected allergic symptoms. The doctor's diagnosis atopic/nonatopic was based on routinely used procedures such as clinical evaluation, SPT, total and allergen-specific IgE antibodies. The performance of Phadiatop Infant was evaluated in a blinded manner against this diagnosis. Results. Eighty-four of the 86 children classified as atopic showed a positive Phadiatop Infant test. Thirty-six were classified as nonatopic, 32 of who had a negative test. With a prevalence of atopy of 70% in this population, this gives a sensitivity of 98%, a specificity of 89%, and a positive and negative predictive value of 95% and 94%, respectively. Conclusion. The results from the present study suggest that Phadiatop Infant could be recommended as a complement to the clinical information in the differential diagnosis on IgE-mediated disease in young children with allergy-like symptoms

    Cortisol levels and cognitive profile in major depression: A comparison of currently and previously depressed patients

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    Source at https://doi.org/10.1016/j.psyneuen.2018.08.024.The association between depressive symptoms and elevated cortisol levels, and depression and cognitive functioning, has been less robust in outpatients with symptoms in the mild to moderate range. Furthermore, the association between elevated cortisol levels and cognitive functioning is unclear. In the present study, currently depressed (n = 37), previously depressed (n = 81) and never depressed controls (n = 50) were assessed on a range of neuropsychological measures. Salivary cortisol was measured in the morning and evening. Participants with current depression were non-hospitalized and had symptoms predominately in the mild to moderate range. Elevated salivary evening cortisol, but not morning cortisol, was significantly related to depressive symptoms. The difference in cortisol levels between the previously depressed group and the never depressed controls was not significant. The groups had significantly different cognitive profiles, with the currently depressed performing poorer on tasks related to working memory compared to the never depressed controls. Both the currently and previously depressed performed worse on attentional tasks. The findings indicate that outpatients with mild to moderate depression have elevated cortisol levels and limited mild cognitive impairments. Furthermore, mild impairments in attention may persist after remission, indicating that this could be a trait-marker in depression. The present study did not find support for a significant relationship between cortisol and cognitive functioning

    Vurdering av Initial Report D-Tagatose

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    Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations

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    Video streaming is predicted to become the dominating traffic in mobile broadband networks. At the same time, adaptive HTTP streaming is developing into the preferred way of streaming media over the Internet. In this paper, we evaluate how different components of a streaming system can be optimized when serving content to mobile devices in particular. We first analyze the media traffic from a Norwegian network and media provider. Based on our findings, we outline benefits and challenges for HTTP streaming, on the sender and the receiver side, and we investigate how HTTP-based streaming affects server performance. Furthermore, we discuss various aspects of efficient coding of the video segments from both performance and user perception point of view. The final part of the paper studies efficient adaptation and delivery to mobile devices over wireless networks. We experimentally evaluate and improve adaptation strategies, multilink solutions, and bandwidth prediction techniques. Based on the results from our evaluations, we make recommendations for how an adaptive streaming system should handle mobile devices. Small changes, or simple awareness of how users perceive quality, can often have large effects

    Enhancing questioning skills through child avatar chatbot training with feedback

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    Training child investigative interviewing skills is a specialized task. Those being trained need opportunities to practice their skills in realistic settings and receive immediate feedback. A key step in ensuring the availability of such opportunities is to develop a dynamic, conversational avatar, using artificial intelligence (AI) technology that can provide implicit and explicit feedback to trainees. In the iterative process, use of a chatbot avatar to test the language and conversation model is crucial. The model is fine-tuned with interview data and realistic scenarios. This study used a pre-post training design to assess the learning effects on questioning skills across four child interview sessions that involved training with a child avatar chatbot fine-tuned with interview data and realistic scenarios. Thirty university students from the areas of child welfare, social work, and psychology were divided into two groups; one group received direct feedback (n = 12), whereas the other received no feedback (n = 18). An automatic coding function in the language model identified the question types. Information on question types was provided as feedback in the direct feedback group only. The scenario included a 6-year-old girl being interviewed about alleged physical abuse. After the first interview session (baseline), all participants watched a video lecture on memory, witness psychology, and questioning before they conducted two additional interview sessions and completed a post-experience survey. One week later, they conducted a fourth interview and completed another postexperience survey. All chatbot transcripts were coded for interview quality. The language model’s automatic feedback function was found to be highly reliable in classifying question types, reflecting the substantial agreement among the raters [Cohen’s kappa (κ) = 0.80] in coding open-ended, cued recall, and closed questions. Participants who received direct feedback showed a significantly higher improvement in open-ended questioning than those in the non-feedback group, with a significant increase in the number of open-ended questions used between the baseline and each of the other three chat sessions. This study demonstrates that child avatar chatbot training improves interview quality with regard to recommended questioning, especially when combined with direct feedback on questioning
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