Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-04-19T15:55:11Z No. of bitstreams: 1 Fábio de Oliveira Dias_.pdf: 3212992 bytes, checksum: 687279fc82a1e707a3ba09c241b5e09b (MD5) / Made available in DSpace on 2017-04-19T15:55:11Z (GMT). No. of bitstreams: 1 Fábio de Oliveira Dias_.pdf: 3212992 bytes, checksum: 687279fc82a1e707a3ba09c241b5e09b (MD5) Previous issue date: 2017-02-17 / Nenhuma / A computação móvel e ubíqua tem propiciado o advento de soluções que permitem o monitoramento em tempo real de sinais provenientes de sensores e o seu processamento por aplicações que podem executar ações de acordo com as condições encontradas. Esta característica possibilita o uso da tecnologia para o monitoramento de condições de saúde de pacientes, denominado de cuidados ubíquos. Em diversas situações, a fim de salvar vidas de pacientes, é necessária a análise de seus sinais vitais de forma a prevenir eventuais colapsos. Este trabalho se insere nestas condições, estando voltado para a antecipação de ações de times de resposta rápida baseado em análise preditiva, propondo o modelo Predictvs. Um Time de Resposta Rápida busca prevenir mortes de pacientes que tenham piora clínica fora de ambientes de Unidades de Tratamento Intensivo em hospitais. De forma diversa dos trabalhos relacionados, que se preocupam apenas com ambientes de tratamento intensivo, o modelo Predictvs busca antecipar ações dos times de resposta rápida, através da análise dos sinais vitais dos pacientes com o uso de escores de alerta precoce e regressão linear. A contribuição científica do modelo é dada em virtude da possibilidade de efetuar a predição em tempo real de possíveis situações de colapso dos pacientes através do monitoramento e análise dos sinais vitais. A avaliação do Predictvs foi efetuada com a utilização de cenários, com a implementação de um protótipo e através de diversas simulações. Análises efetuadas com cerca de 228000 medições provenientes de um dataset público apresentaram bons resultados, onde a precisão da predição para a medição seguinte se mostrou bastante alta, atingindo mais de 99% no caso da frequência cardíaca e 100% na saturação de oxigênio arterial, ultrapassando 95% nos demais sinais vitais. Além disso, o índice de falsos negativos foi consideravelmente baixo, atingindo menos de 1% na frequência cardíaca e na saturação de oxigênio arterial. O índice de falsos positivos também foi baixo, embora não tanto quanto o de falsos negativos. No entanto, predições para três ou mais medições futuras mostram queda na precisão (mesmo demonstrando valores de acerto relativamente expressivos, com diversos sinais fisiológicos acima de 98%) e aumento do número de falsos negativos e, principalmente, de falsos positivos. / The mobile and ubiquitous computing has allowed the emergence of solutions that enable real-time monitoring of signals coming from sensors and processing for applications that can perform actions according to the conditions found. This feature enables the use of this technology for monitoring health conditions of patients, called ubiquitous healthcare. In several situations, in order to save his lives, it is necessary to analyze the vital signs of patients to prevent any collapses. This work is part of these conditions and is aimed at anticipating the actions of rapid response teams based on predictive analysis, proposing the Predictvs model. A Rapid Response Team intends to prevent deaths in patients who have clinical deterioration outside of intensive care units in hospitals environments. Differently of related works, which are concerned only with intensive care environments, the Predictvs model seeks to anticipate the actions of teams of rapid response through the analysis of vital signs of patients with the use of early warning scores and linear regression. The scientific contribution of the presented model is that we could better predict possible collapse situations of patients, through the monitoring and analysis of vital signs. The Predictvs evaluation was performed with the use of scenarios, implementation of a prototype and several simulations. Analyzes performed with about 228,000 measurements from a public dataset showed good results, where the accuracy of the prediction for the next measurement was very high, reaching more than 99% in the case of heart rate and 100% in arterial oxygen saturation, surpassing 95% in other vital signs. In addition, the false negative index was considerably lower, reaching less than 1% in heart rate and arterial oxygen saturation. The rate of false positives was also low, although not so much as that of false negatives. However, predictions for three or more future measurements show a drop in accuracy (even showing relatively expressive set values with several physiological signals above 98%) and an increase in the number of false negatives and, mainly, false positives.

The SAP HANA database is positioned as the core of the SAP HANA Appliance to support complex business analytical processes in combination with transactionally consistent operational workloads. Within this paper, we outline the basic characteristics of the SAP HANA database, emphasizing the distinctive features that differentiate the SAP HANA database from other classical relational database management systems. On the technical side, the SAP HANA database consists of multiple data processing engines with a distributed query processing environment to provide the full spectrum of data processing -- from classical relational data supporting both row- and column-oriented physical representations in a hybrid engine, to graph and text processing for semi- and unstructured data management within the same system. From a more application-oriented perspective, we outline the specific support provided by the SAP HANA database of multiple domain-specific languages with a built-in set of natively implemented business functions. SQL -- as the lingua franca for relational database systems -- can no longer be considered to meet all requirements of modern applications, which demand the tight interaction with the data management layer. Therefore, the SAP HANA database permits the exchange of application semantics with the underlying data management platform that can be exploited to increase query expressiveness and to reduce the number of individual application-to-database round trips.

Cognitive autonomy of an autonomous system depends on its cyber module's ability to comprehend the actions and intent of the applications and services running on that system. The autonomous system should be able to accomplish this without or with limited human intervention. These mission-critical autonomous systems are often deployed in unpredictable and dynamic environments and are vulnerable to evasive cyberattacks. In particular, some of these cyberattacks are Advanced Persistent Threats where an attacker conducts reconnaissance for a long period time to ascertain system features, learn system defenses, and adapt to successfully execute the attack while evading detection. Thus an autonomous system's cognitive autonomy and cybersecurity intelligence depend on its capability to learn, classify applications (good and bad), predict the attacker's next steps, and remain operational to carryout the mission-critical tasks even under cyberattacks. In this dissertation, we propose novel learning and prediction models for enhancing cognitive autonomy and cybersecurity in autonomous systems. We develop (1) a model using deep learning along with a model selection framework that can classify benign and malicious operating contexts of a system based on performance counters, (2) a deep learning based natural language processing model that uses instruction sequences extracted from the memory to learn and profile the behavior of evasive malware, (3) a scalable deep learning based object detection model with data pre-processing assisted by fuzzy-based clustering, (4) fundamental guiding principles for cognitive autonomy using Artificial Intelligence (AI), (5) a model for privacy-preserving autonomous data analytics, and finally (6) a model for backup and replication based on combinatorial balanced incomplete block design in order to provide continuous availability in mission-critical systems. This research provides effective and computationally efficient deep learning based solutions for detecting evasive cyberattacks and increasing autonomy of a system from application-level to hardware-level. <br>

<p><strong>Graph Neural Networks</strong> (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - <strong>clustering and classification</strong>. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.</p>