294 research outputs found

    Inter-module code analysis techniques for software maintenance

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    The research described in this thesis addresses itself to the problem of maintaining large, undocumented systems written in languages that contain a module construct. Emphasis is placed on developing techniques for analysing the code of these systems, thereby helping a maintenance programmer to understand a system. Techniques for improving the structure of a system are presented. These techniques help make the code of a system easier to understand. All the code analysis techniques described in this thesis involve reasoning with, and manipulating, graphical representations of a system. To help with these graph manipulations, a set of graph operations are developed that allow a maintenance programmer to combine graphs to create a bigger graph, and to extract subgraphs from a given graph that satisfy specified constraints. A relational database schema is developed to represent the information needed for inter-module code analysis. Pointers are given as to how this database can be used for inter-module code analysis

    Survey of data mining approaches to user modeling for adaptive hypermedia

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    The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio

    Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks

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    Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks. To this end, our models exploit computation using Quaternion algebra and hypercomplex spaces, enabling not only expressive inter-component interactions but also significantly (75%75\%) reduced parameter size due to lesser degrees of freedom in the Hamilton product. We propose Quaternion variants of models, giving rise to new architectures such as the Quaternion attention Model and Quaternion Transformer. Extensive experiments on a battery of NLP tasks demonstrates the utility of proposed Quaternion-inspired models, enabling up to 75%75\% reduction in parameter size without significant loss in performance.Comment: ACL 201

    The Association of Modified Early Warning Score on Patient Outcomes in Medical-Surgical Units in an Academic Medical Center

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    Background: A nationwide problem that has been regularly overlooked is poor recognition of deteriorating patients which can lead to increased severity of illness. One way to avoid adverse events is early detection of clinical deterioration using vital signs via Modified Early Warning Score (MEWS). However, the majority of the hospitals across the United States are not utilizing this. MEWS implementation can minimize adverse events by early recognition, which will lead to early intervention and improved patients’ outcomes. Objective: The aim of this quasi-experimental study was to determine if MEWS implementation will reduce the number of adverse outcomes. Methodology: The setting is an academic, acute care, level one trauma center in mid-atlantic U.S. with 385 beds. This study used convenient sampling of adult medical surgical patients who had Rapid Response Team (RRT) or Code Blue activation. A total N=281 sample size was obtained, n=102 for the pre-MEWS and n=179 in post-MEWS implementation. A retrospective chart review was conducted to determine if there was a reduction of adverse outcomes such as cardiopulmonary arrest, unplanned ICU admission, unexpected death, or unplanned surgery after implementation. Results: Even though it was not statistically significant, MEWS implementation demonstrated 10% reduction of unplanned ICU admission and 0.42% reduction of unplanned surgery. However, the proportion of patients requiring code blue activation significantly increased in the post-MEWS implementation (0.98% vs 8.9%) with a p value of 0.0074. Conclusion: MEWS implementation is a valid tool to alert nurses in identifying a deteriorating patient condition for timely escalation of care

    State v. Alley Clerk\u27s Record Dckt. 40428

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    https://digitalcommons.law.uidaho.edu/idaho_supreme_court_record_briefs/1849/thumbnail.jp

    Plasmonic near-field localization of silver core-shell nanoparticle assemblies via wet chemistry nanogap engineering

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    Silver nanoparticles are widely used in the field of plasmonics because of their unique optical properties. The wavelength-dependent surface plasmon resonance gives rise to a strongly enhanced electromagnetic field, especially at so-called hot spots located in the nanogap in-between metal nanoparticle assemblies. Therefore, the interparticle distance is a decisive factor in plasmonic applications, such as surface-enhanced Raman spectroscopy (SERS). In this study, the aim is to engineer this interparticle distance for silver nanospheres using a convenient wet-chemical approach and to predict and quantify the corresponding enhancement factor using both theoretical and experimental tools. This was done by building a tunable ultrathin polymer shell around the nanoparticles using the layer-by-layer method, in which the polymer shell acts as the separating interparticle spacer layer. Comparison of different theoretical approaches and corroborating the results with SERS analytical experiments using silver and silver polymer core shell nanoparticle clusters as SERS substrates was also done. Herewith, an approach is provided to estimate the extent of plasmonic near-field enhancement both theoretically as well as experimentally

    Simulation Model Of An Air Freshener Flows In A Room By An Automatic Spray

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    The worldwide air freshener market was worth 10,124.4millionin2017andisanticipatedtobeworth10,124.4 million in 2017 and is anticipated to be worth 13,279.1 million by 2025, growing at a CAGR (compound annual growth rate) of 3.5 percent between 2018 and 2025. The air freshener market has been divided into four segments: type, end-use, distribution channel, and region. The market has been divided into several types, including electric, spray, gel, and others. An air freshener is a product that releases a scent to remove unwanted odours from a space. For customer satisfaction, maximizing the scent of an air freseher in a space is of paramount importance. In this project, the circulation of an air-freshener flows in a particular space were studied to enhance the scent produced by an automatic spray. A simulation model of an automatic air freshener spray with a different number of it in the living room were presented to investigate the effect of the number of automatic spray in a room on scent circulation. The analysis results shows that the living room with the highest number of automatic air freshener has the highest percentage of the volume occupied with value of 0.95%. This project findings may contribute to guide customer purchases to enhance their satisfaction of the freshener product. Then will benefit the manufacturer
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