A Programming Language for Web Service Development

There is now widespread acceptance of Web services and service-oriented architectures. But despite the agreement on key Web services standards there remain many challenges. Programming environments based on WSDL support go some way to facilitating Web service development. However Web services fundamentally rely on XML and Schema, not on contemporary programming language type systems such as those of Java or .NET. Moreover, Web services are based on a messaging paradigm and hence bring forward the traditional problems of messaging systems including concurrency control and message correlation. It is easy to write simple synchronous Web services using traditional programming languages; however more realistic scenarios are surprisingly difficult to implement. To alleviate these issues we propose a programming language which directly supports Web service development. The language leverages XQuery for native XML processing, supports implicit message correlation and has high level join calculus-style concurrency control. We illustrate the features of the language through a motivating example

TEMPOS: A Platform for Developing Temporal Applications on Top of Object DBMS

This paper presents TEMPOS: a set of models and languages supporting the manipulation of temporal data on top of object DBMS. The proposed models exploit object-oriented technology to meet some important, yet traditionally neglected design criteria related to legacy code migration and representation independence. Two complementary ways for accessing temporal data are offered: a query language and a visual browser. The query language, namely TempOQL, is an extension of OQL supporting the manipulation of histories regardless of their representations, through fully composable functional operators. The visual browser offers operators that facilitate several time-related interactive navigation tasks, such as studying a snapshot of a collection of objects at a given instant, or detecting and examining changes within temporal attributes and relationships. TEMPOS models and languages have been formalized both at the syntactical and the semantical level and have been implemented on top of an object DBMS. The suitability of the proposals with regard to applications' requirements has been validated through concrete case studies

The 3DMA Middleware for Mobile Applications

Mobile devices have received much research interest in re- cent years. Mobility raises new issues such as more dynamic context, limited computing resources, and frequent disconnections. To handle these issues, we propose a middleware, called 3DMA, which introduces three requirements, 1) distribution, 2) decoupling and 3) decomposition. 3DMA uses a space based middleware approach combined with a set of workers which are able to act on the users behalf either to reduce load on the mobile device, or to support disconnected behavior. In order to demonstrate aspects of the middleware architecture we consider the development of a commonly used mobile application

Researcher Profile: An Interview with Jorge Ruiz-Menjivar

Jorge Ruiz-Menjivar is originally from San Salvador, El Salvador, but has had the privilege to live in several Latin American countries (e.g., Nicaragua, Costa Rica, among others), and to travel through many other regions in the world. He obtained a Bachelor’s degree in Accounting at the University of New Orleans-Louisiana State University. Then, he went on to earn a Master’s degree in Personal and Family Financial Planning at the University of Florida under the supervision of Drs. Michael S. Gutter and Martie Gillen. Recently, Jorge finished his Doctoral degree in Financial Planning, Housing and Consumer Economics from the University of Georgia under the supervision of Drs. John E. Grable and George Engelhard, Jr

Translational Functional Imaging in Surgery Enabled by Deep Learning

Many clinical applications currently rely on several imaging modalities such as Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), Computed Tomography (CT), etc. All such modalities provide valuable patient data to the clinical staff to aid clinical decision-making and patient care. Despite the undeniable success of such modalities, most of them are limited to preoperative scans and focus on morphology analysis, e.g. tumor segmentation, radiation treatment planning, anomaly detection, etc. Even though the assessment of different functional properties such as perfusion is crucial in many surgical procedures, it remains highly challenging via simple visual inspection. Functional imaging techniques such as Spectral Imaging (SI) link the unique optical properties of different tissue types with metabolism changes, blood flow, chemical composition, etc. As such, SI is capable of providing much richer information that can improve patient treatment and care. In particular, perfusion assessment with functional imaging has become more relevant due to its involvement in the treatment and development of several diseases such as cardiovascular diseases. Current clinical practice relies on Indocyanine Green (ICG) injection to assess perfusion. Unfortunately, this method can only be used once per surgery and has been shown to trigger deadly complications in some patients (e.g. anaphylactic shock). This thesis addressed common roadblocks in the path to translating optical functional imaging modalities to clinical practice. The main challenges that were tackled are related to a) the slow recording and processing speed that SI devices suffer from, b) the errors introduced in functional parameter estimations under changing illumination conditions, c) the lack of medical data, and d) the high tissue inter-patient heterogeneity that is commonly overlooked. This framework follows a natural path to translation that starts with hardware optimization. To overcome the limitation that the lack of labeled clinical data and current slow SI devices impose, a domain- and task-specific band selection component was introduced. The implementation of such component resulted in a reduction of the amount of data needed to monitor perfusion. Moreover, this method leverages large amounts of synthetic data, which paired with unlabeled in vivo data is capable of generating highly accurate simulations of a wide range of domains. This approach was validated in vivo in a head and neck rat model, and showed higher oxygenation contrast between normal and cancerous tissue, in comparison to a baseline using all available bands. The need for translation to open surgical procedures was met by the implementation of an automatic light source estimation component. This method extracts specular reflections from low exposure spectral images, and processes them to obtain an estimate of the light source spectrum that generated such reflections. The benefits of light source estimation were demonstrated in silico, in ex vivo pig liver, and in vivo human lips, where the oxygenation estimation error was reduced when utilizing the correct light source estimated with this method. These experiments also showed that the performance of the approach proposed in this thesis surpass the performance of other baseline approaches. Video-rate functional property estimation was achieved by two main components: a regression and an Out-of-Distribution (OoD) component. At the core of both components is a compact SI camera that is paired with state-of-the-art deep learning models to achieve real time functional estimations. The first of such components features a deep learning model based on a Convolutional Neural Network (CNN) architecture that was trained on highly accurate physics-based simulations of light-tissue interactions. By doing this, the challenge of lack of in vivo labeled data was overcome. This approach was validated in the task of perfusion monitoring in pig brain and in a clinical study involving human skin. It was shown that this approach is capable of monitoring subtle perfusion changes in human skin in an arm clamping experiment. Even more, this approach was capable of monitoring Spreading Depolarizations (SDs) (deoxygenation waves) in the surface of a pig brain. Even though this method is well suited for perfusion monitoring in domains that are well represented with the physics-based simulations on which it was trained, its performance cannot be guaranteed for outlier domains. To handle outlier domains, the task of ischemia monitoring was rephrased as an OoD detection task. This new functional estimation component comprises an ensemble of Invertible Neural Networks (INNs) that only requires perfused tissue data from individual patients to detect ischemic tissue as outliers. The first ever clinical study involving a video-rate capable SI camera in laparoscopic partial nephrectomy was designed to validate this approach. Such study revealed particularly high inter-patient tissue heterogeneity under the presence of pathologies (cancer). Moreover, it demonstrated that this personalized approach is now capable of monitoring ischemia at video-rate with SI during laparoscopic surgery. In conclusion, this thesis addressed challenges related to slow image recording and processing during surgery. It also proposed a method for light source estimation to facilitate translation to open surgical procedures. Moreover, the methodology proposed in this thesis was validated in a wide range of domains: in silico, rat head and neck, pig liver and brain, and human skin and kidney. In particular, the first clinical trial with spectral imaging in minimally invasive surgery demonstrated that video-rate ischemia monitoring is now possible with deep learning

A New Species of \u3ci\u3eSchizomyia\u3c/i\u3e (Diptera: Cecidomyiidae), a Pest of \u3ci\u3eFernaldia pandurata\u3c/i\u3e (Apocynaceae) in Central America

A new species of gall midge, Schizomyia loroco Gagné (Diptera:Cecidomyiidae), is described from loroco, Fernaldia pandurata (A. DC.) Woodson (Apocynaceae), from El Salvador. Females lay eggs in flower buds that then produce characteristic galls. The new species is described, illustrated, and compared to its congeners

Julian Assange: A Content Analysis of Media Framing in Newspapers around the World

Researcher conducted a content analysis in order to examine how the media framed Julian Assange after the Ecuadorian government granted him political asylum at their embassy in London on August 16, 2012. Researchers compared 380 English and Spanish language newspaper articles from North America, Europe, Australia/New Zealand, Asia, and Latin America to examine regional differences in the way Assange was framed. This study revealed that generally the tone toward Assange was mostly neutral or positive in all continents. Furthermore, European media gave more attention to Julian Assange than did media from North America or other continents. Exploratory research revealed that English language newspapers placed Julian Assange in headlines more frequently than Spanish language sources. Interestingly, even when Assange’s participation in the publication of secret documents affected many different countries, he was not given page prominence in newspapers

Swedish Foreign Direct Investments in Countries Transitioning into Stabilisation

The purpose of this thesis is to describe and analyse how and why Swedish companies choose to enter a country transitioning into stabilisation. With a qualitative, abductive approach the thesis aims at an analytical generalisation. The explorative nature of the paper is made with a multi-case study. The empirical data was generated by interviews. The theoretical framework is the foundation, which the authors aimed at extending. From the analysis two modified models of the existing theories could be formed. The first links various entry strategies flexibly to each other, instead of the existing, rigid model. It was concluded that the most appropriate entry mode is through Greenfield investments. The second modified model is the prominent OLI-framework, which was extended with an additional aspect, Network of Contacts

QoS-Aware Middleware for Web Services Composition

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online Business-to-Business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming