Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets

Quantitative analysis of cine cardiac magnetic resonance (CMR) images for the assessment of global left ventricular morphology and function remains a routine task in clinical cardiology practice. To date, this process requires user interaction and therefore prolongs the examination (i.e. cost) and introduces observer variability. In this study, we sought to validate the feasibility, accuracy, and time efficiency of a novel framework for automatic quantification of left ventricular global function in a clinical setting.

Capacity-achieving input covariance for single-user multi-antenna channels

We characterize the capacity-achieving input covariance for multi-antenna channels known instantaneously at the receiver and in distribution at the transmitter. Our characterization, valid for arbitrary numbers of antennas, encompasses both the eigenvectors and the eigenvalues. The eigenvectors are found for zero-mean channels with arbitrary fading profiles and a wide range of correlation and keyhole structures. For the eigenvalues, in turn, we present necessary and sufficient conditions as well as an iterative algorithm that exhibits remarkable properties: universal applicability, robustness and rapid convergence. In addition, we identify channel structures for which an isotropic input achieves capacity.

Design of User Authentication System for Multi-operator wireless LANs

Langattomat lähiverkot ovat viime vuosikymmeninä saavuttaneet suuren suosion. Tässä työssä käsitellään käyttäjien todentamisjärjestelmän suunnittelua ja kehitystä langattomaan monioperaattoriverkkoon. Langattomassa monioperaattoriverkossa käyttäjillä on mahdollisuus käyttää eri operaattoreiden palveluita. Aluksi käsitellään olemassa olevia todentamismenetelmiä ja -järjestelmiä. minkä jälkeen kuvaillaan todentamisjärjestelmä langattomille monioperaattoriverkoille. Todentamisjärjestelmän ratkaisuvaihtoehtoja esitellään kaksi, niin sanotut moni- istunto - ja yksittäisistuntomalli. Moni-istuntomalli on normaali lähestymistapa käyttäjien todentamiseen tietokonejärjestelmissä. Siinä käyttäjän pitää tunnistautua ja todentaa itsensä jokaiselle verkon palvelulle erikseen. Yksittäisistuntomallissa pyritään parempaan luotettavuuteen ja käytettävyyteen. Siinä käyttäjä todentaa itsensä vain kerran ja voi sen jälkeen päästä useisiin palveluihin. Työn loppuosassa kuvaillaan suunnitellun järjestelmän toteutusta. Lisäksi ehdotetaan vaihtoehtoisia toteutustapoja, analysoidaan järjestelmän heikkouksia ja kerrotaan jatkokehitysmahdoillisuuksista.

A SOA-based Framework for Multi-Devices and Multi-Platform User Interfaces

This thesis reports investigations on applying the Service Oriented Architecture (SOA) approach in the engineering of multi-platform and multi-devices user interfaces. This study has three goals: (1) analyze the present frameworks for developing multi-platform and multi-devices applications, (2) extend the principles of SOA for implementing a multi-platform and multi-devices architectural framework (SOA-MDUI), (3) applying and validating the proposed framework in the context of a specific application. One of the problems addressed in this ongoing research is the large amount of combinations for possible implementations of applications on different types of devices. Usually it is necessary to take into account the operating system (OS), user interface (UI) including the appearance, programming language (PL) and architectural style (AS). Our proposed approach extended the principles of SOA using patterns-oriented design and model-driven engineering approaches. Synthesizing the present work done in these domains, this research built and tested an engineering framework linking Model-driven Architecture (MDA) and SOA approaches to developing of UI. This study advances general understanding of engineering, deploying and managing multi-platform and multi-devices user interfaces as a service.

Adobe Connect Quick User Guide to setting up your PC

This one-side-of-A4 guide discusses setting up your audio correctly and installing the Connect browser add-in.

Key performance indicators (KPIs) and priority setting in using the multi-attribute approach for assessing sustainable intelligent buildings

The main objectives of this paper are to: firstly, identify key issues related to sustainable intelligent buildings (environmental, social, economic and technological factors); develop a conceptual model for the selection of the appropriate KPIs; secondly, test critically stakeholder's perceptions and values of selected KPIs intelligent buildings; and thirdly develop a new model for measuring the level of sustainability for sustainable intelligent buildings. This paper uses a consensus-based model (Sustainable Built Environment Tool- SuBETool), which is analysed using the analytical hierarchical process (AHP) for multi-criteria decision-making. The use of the multi-attribute model for priority setting in the sustainability assessment of intelligent buildings is introduced. The paper commences by reviewing the literature on sustainable intelligent buildings research and presents a pilot-study investigating the problems of complexity and subjectivity. This study is based upon a survey perceptions held by selected stakeholders and the value they attribute to selected KPIs. It is argued that the benefit of the new proposed model (SuBETool) is a ‘tool’ for ‘comparative’ rather than an absolute measurement. It has the potential to provide useful lessons from current sustainability assessment methods for strategic future of sustainable intelligent buildings in order to improve a building's performance and to deliver objective outcomes. Findings of this survey enrich the field of intelligent buildings in two ways. Firstly, it gives a detailed insight into the selection of sustainable building indicators, as well as their degree of importance. Secondly, it tesst critically stakeholder's perceptions and values of selected KPIs intelligent buildings. It is concluded that the priority levels for selected criteria is largely dependent on the integrated design team, which includes the client, architects, engineers and facilities managers.

A multi-site pilot test study to measure safety climate in the university work setting

Next to leisure, sport, and household activities, the most common activity resulting in medically consulted injuries and poisonings in the United States is work, with an estimated 4 million workplace related episodes reported in 2008 (U.S. Department of Health and Human Services, 2009). To address the risks inherent to various occupations, risk management programs are typically put in place that include worker training, engineering controls, and personal protective equipment. Recent studies have shown that such interventions alone are insufficient to adequately manage workplace risks, and that the climate in which the workers and safety program exist (known as the "safety climate") is an equally important consideration. The organizational safety climate is so important that many studies have focused on developing means of measuring it in various work settings. While safety climate studies have been reported for several industrial settings, published studies on assessing safety climate in the university work setting are largely absent. Universities are particularly unique workplaces because of the potential exposure to a diversity of agents representing both acute and chronic risks. Universities are also unique because readily detectable health and safety outcomes are relatively rare. The ability to measure safety climate in a work setting with rarely observed systemic outcome measures could serve as a powerful means of measure for the evaluation of safety risk management programs. ^ The goal of this research study was the development of a survey tool to measure safety climate specifically in the university work setting. The use of a standardized tool also allows for comparisons among universities throughout the United States. A specific study objective was accomplished to quantitatively assess safety climate at five universities across the United States. At five universities, 971 participants completed an online questionnaire to measure the safety climate. The average safety climate score across the five universities was 3.92 on a scale of 1 to 5, with 5 indicating very high perceptions of safety at these universities. The two lowest overall dimensions of university safety climate were "acknowledgement of safety performance" and "department and supervisor's safety commitment". The results underscore how the perception of safety climate is significantly influenced at the local level. A second study objective regarding evaluating the reliability and validity of the safety climate questionnaire was accomplished. A third objective fulfilled was to provide executive summaries resulting from the questionnaire to the participating universities' health & safety professionals and collect feedback on usefulness, relevance and perceived accuracy. Overall, the professionals found the survey and results to be very useful, relevant and accurate. Finally, the safety climate questionnaire will be offered to other universities for benchmarking purposes at the annual meeting of a nationally recognized university health and safety organization. The ultimate goal of the project was accomplished and was the creation of a standardized tool that can be used for measuring safety climate in the university work setting and can facilitate meaningful comparisons amongst institutions.^

Setting public management research agendas : integrating the sponsor, producer and user : proceedings for the Public Management Research Conference, November 19-20, 1979, Brookings Institution, Washington, D.C.

Mode of access: Internet.

Multi-topic information filtering with a single user profile

In Information Filtering (IF) a user may be interested in several topics in parallel. But IF systems have been built on representational models derived from Information Retrieval and Text Categorization, which assume independence between terms. The linearity of these models results in user profiles that can only represent one topic of interest. We present a methodology that takes into account term dependencies to construct a single profile representation for multiple topics, in the form of a hierarchical term network. We also introduce a series of non-linear functions for evaluating documents against the profile. Initial experiments produced positive results.

Multi-Agent Security System based on Neural Network Model of User's Behavior

It is proposed an agent approach for creation of intelligent intrusion detection system. The system allows detecting known type of attacks and anomalies in user activity and computer system behavior. The system includes different types of intelligent agents. The most important one is user agent based on neural network model of user behavior. Proposed approach is verified by experiments in real Intranet of Institute of Physics and Technologies of National Technical University of Ukraine "Kiev Polytechnic Institute”.

Applying machine learning: a multi-role perspective

Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference.

Hierarchical Multi-Label Text Classification in a Low-Resource Setting

In this thesis we address a multi-label hierarchical text classification problem in a low-resource setting and explore different approaches to identify the best one for our case. The goal is to train a model that classifies English school exercises according to a hierarchical taxonomy with few labeled data. The experiments made in this work employ different machine learning models and text representation techniques: CatBoost with tf-idf features, classifiers based on pre-trained models (mBERT, LASER), and SetFit, a framework for few-shot text classification. SetFit proved to be the most promising approach, achieving better performance when during training only a few labeled examples per class are available. However, this thesis does not consider all the hierarchical taxonomy, but only the first two levels: to address classification with the classes at the third level further experiments should be carried out, exploring methods for zero-shot text classification, data augmentation, and strategies to exploit the hierarchical structure of the taxonomy during training.