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Accompanying with the Internet growth explosion, more and more information disseminates on the Web. The large amount of information, however, causes the information overload problem that disturbs users who desire to search and find useful information online. Information retrieval and information filtering arise to compensate for the searching and comprehending ability of the users. Recommender systems as one of the information filtering techniques emerge when users cannot describe their requirements precisely as keywords. Collaborative filtering (CF) compares novel information with common interests shared by a group of people to make the recommendations. One of its methods, the Model-based CF, generates predicted recommendation based on the model learned from the past opinions of the users. However, two issues on model-based CF should be addressed. First, data quality of the rating matrix input can affect the prediction performance. Second, most current models treat the data class as the nominal scale instead of ordinal nature in ratings. The objective of this research is thus to propose a model-based CF algorithm that considers data reliability and data scale in the model. Three experiments are conducted accordingly, and the results show our proposed method outperforms other counterparts especially under data of mild sparsity degree and of large scale. These results justify the feasibility of our proposed method in real applications.

The applications of wireless sensor networks, WSN, are getting popular in the different areas reaching from daily usage to industrial usage. The performance evaluation of WSN deployed in industrial and high-voltage areas is receiving a great attention and becoming an interesting area of research. This thesis addresses the performance issues of WSN in high-voltage and harsh industrial environments. This study has been carried out at the facilities of High Voltage Test Lab of ABB. Typically, wireless sensor network contains wireless field devices (nodes) connected to a base station via a central gateway. The gate way centralizes information gathered and processed by the nodes. The nodes can communicate with each other and with the gateway via radio wave. The quality and usability of the data sent by WSN can be degraded due to the packet loss and delay. In the presence of high-voltage, the electromagnetic interference, EMI, can affect the performance of WSN. In this study the performance of WSN is evaluated in terms of packet loss and delay. We also focus on the effect of EMI on hardware devices as well as on signal transmission. EMI was expected at wide frequency band due to harsh industrial and high voltage environments. It was expected that EMIs could increase a bit error rate and/or packet loss. The EMI can also change the sensitivity of the nodes. For the performance evaluation of WSN network throughput, latency, path stability, data reliability and average value of the received signal strength indicator, RSSI, are used and measured. The results show that the electromagnetic frequencies of harsh industrial and high voltage environments affect the wireless sensor network performance. / 0046700375216

"The many advantages of cloud computing are increasingly attracting individuals and organizations to outsource their data from local to remote cloud servers. In addition to cloud infrastructure and platform providers, such as Amazon, Google, and Microsoft, more and more cloud application providers are emerging which are dedicated to offering more accessible and user friendly data storage services to cloud customers. It is a clear trend that cloud data outsourcing is becoming a pervasive service. Along with the widespread enthusiasm on cloud computing, however, concerns on data security with cloud data storage are arising in terms of reliability and privacy which raise as the primary obstacles to the adoption of the cloud. To address these challenging issues, this dissertation explores the problem of secure and reliable data outsourcing in cloud computing. We focus on deploying the most fundamental data services, e.g., data management and data utilization, while considering reliability and privacy assurance. The first part of this dissertation discusses secure and reliable cloud data management to guarantee the data correctness and availability, given the difficulty that data are no longer locally possessed by data owners. We design a secure cloud storage service which addresses the reliability issue with near-optimal overall performance. By allowing a third party to perform the public integrity verification, data owners are significantly released from the onerous work of periodically checking data integrity. To completely free the data owner from the burden of being online after data outsourcing, we propose an exact repair solution so that no metadata needs to be generated on the fly for the repaired data. The second part presents our privacy-preserving data utilization solutions supporting two categories of semantics - keyword search and graph query. For protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. We define and solve the challenging problem of privacy-preserving multi- keyword ranked search over encrypted data in cloud computing. We establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. We first propose a basic idea for keyword search based on secure inner product computation, and then give two improved schemes to achieve various stringent privacy requirements in two different threat models. We also investigate some further enhancements of our ranked search mechanism, including supporting more search semantics, i.e., TF × IDF, and dynamic data operations. As a general data structure to describe the relation between entities, the graph has been increasingly used to model complicated structures and schemaless data, such as the personal social network, the relational database, XML documents and chemical compounds. In the case that these data contains sensitive information and need to be encrypted before outsourcing to the cloud, it is a very challenging task to effectively utilize such graph-structured data after encryption. We define and solve the problem of privacy-preserving query over encrypted graph-structured data in cloud computing. By utilizing the principle of filtering-and-verification, we pre-build a feature-based index to provide feature-related information about each encrypted data graph, and then choose the efficient inner product as the pruning tool to carry out the filtering procedure."

In the development stages of a Digital twin of production assets, especially machine tools, real time process monitoring and data gathering proves to be vital. Having a monitoring system that monitors and updates the operators or managers in real time, helps improve productivity in terms of reducing downtime through predictive/preventive analytics and by incorporating in process quality assessment capabilities. When it comes to Real time monitoring of machine tools andprocesses, sensor technologies have proven to be the most effective and widely researched. Years of research and development have paved the way for many smart sensor technologies that come both in built with the machine tools as well as external applications. However, these technologies prove to be expensive and complicated to implement especially for Small and Medium Enterprises. This thesis focuses on evaluating and testing a simple, cost-efficient monitoring system using inexpensive sensor technologies that would help optimize an existing Digital twin setup for machine tools for Small and Medium Enterprises. Experiments with a 5 axis CNC machine tool using different tools and varying operating parameters, materials were performed,and the relevant sensor data were collected, mapped, analysed for accuracy and benchmarking. The thesis also evaluates the integration of this data with the information already collected from other sources, improve existing data reliability, and provides guidelines on this could be transformed usefully to create more value to SME’s.

Orientador: Kátia Cristina Portero McLellan / Resumo: BARIM, E.M. Escore Finlandês de Risco de Diabetes - FINDRISC: tradução e adaptação transcultural, estudo de prevalência e aplicações. 2019. 132f. Tese (Doutorado) – Faculdade de Medicina de Botucatu, Universidade Estadual Paulista, Botucatu, 2019. O diabetes mellitus tipo 2 (DM2) é um problema de saúde pública a nível mundial. Existem evidências que mudanças no estilo de vida podem prevenir ou retardar o desenvolvimento de DM2 em indivíduos com risco elevado de desenvolver a doença. O Escore Finlandês de Risco de Diabetes - FINDRISC é um instrumento de rastreamento não invasivo desenvolvido com o objetivo de predizer o desenvolvimento de DM2 no futuro e vem sendo usado em diferentes países. Com o decorrer do tempo o FINDRISC também passou a ser utilizado para predizer e/ou rastrear diferentes afecções. Os objetivos desta tese foram (i) traduzir e adaptar transculturalmente a versão original do FINDRISC para o português brasileiro e avaliar a sua confiabilidade; (ii) estratificar os usuários de um centro de atenção primária quanto ao risco de desenvolver DM2 utilizando o FINDRISC e investigar os fatores associados com risco alto e muito alto; e (iii) mapear as aplicações do FINDRISC através da realização de uma revisão integrativa da literatura. Três manuscritos compuseram essa tese. O primeiro manuscrito descreveu o processo de tradução e adaptação transcultural da versão original do FINDRISC para o português do Brasil de acordo com as recomendações desenvolvidas pela força t... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: BARIM, E.M. Finnish Diabetes Risk Score - FINDRISC: translation and cross-cultural adaptation, cross-sectional study and clinical uses. 2019. 132 pages (PhD Thesis) - São Paulo State University - UNESP, Medical School, Botucatu, 2019. Type 2 diabetes mellitus (T2DM) is a public health problem worldwide. There is evidence that lifestyle changes can prevent or delay the onset of T2DM for individuals at high risk for T2DM. The Finnish Diabetes Risk Score - FINDRISC is a non-invasive screening tool initially developed to predict the risk of developing T2DM in adults and it has been used in different countries. Over time, FINDRISC has also been used to predict and detect different conditions. The aims of this thesis were (i) to describe the process of translation and cross-cultural adaptation of the original version of FINDRISC into Brazilian Portuguese and to assess its reliability; (ii) to stratify users of a primary care center according to their risk of developing T2DM using the FINDRISC questionnaire and investigate factors associated with high and very high-risk score (FINDRISC≥15 points); and (iii) to map the applications of the FINDRISC questionnaire by performing an integrative literature review. The first manuscript described the process of translation and cross-cultural adaptation of the original version of the FINDRISC questionnaire into Brazilian Portuguese in accordance with the recommendations developed by the International Society for Pharmacoeconomics and Outcomes... (Complete abstract click electronic access below) / Doutor

Elektroniniu formatu saugomos informacijos apimtys ir svarba vis didėja. Pamažu į elektroninę erdvę perkeliama vis daugiau dokumentų ir viešųjų paslaugų. Anksčiau dokumentų ir duomenų originalumą bei teisingumą patvirtindavo dokumento originalas saugomas archyve, ant kurio visuomet turėjo būti atsakingų asmenų parašai. Siekiant užtikrinti elektroninių dokumentų autentiškumą sukurta elektroninio parašo technologija. Elektroninis parašas – efektyvus būdas užtikrinti dokumento autentiškumą, t.y. patikima ir pasitikėjimą kelianti technologija. Ką daryti tuo atveju, kai reikia didinti pasitikėjimą duomenimis, kurie gaunami iš dokumento ir išsaugomi kaip tam tikros sistemos duomenys? Daliai tokių duomenų būdinga savybė keistis, pvz. moters pavardė po santuokos, kiti duomenys atvirkščiai – turi likti nepasikeitę visą savo gyvavimo periodą, pvz. eksperimentų rezultatai, finansinės apskaitos duomenys. Pirmuoju atveju dažnai tikslinga žinoti kas, kada ir kaip keitė duomenys. Antruoju – tikslinga įsitikinti, kad duomenys tikrai nebuvo pakeisti. Šio darbo metu nagrinėjama būtent pastaroji problema, t.y., kaip padidinti pasitikėjimą duomenimis, kurie turi išlikti autentiški. Darbo metu analizuojama sukurta sistema, kuria siekiama užtikrinti duomenų, o ne dokumentų autentiškumą, panaudojant elektroninį parašą. Taip pat atliekamas eksperimentas palyginantis jau egzistuojančio sprendimo ir pasiūlytos sistemos charakteristikas. / The importance and volume of information stored in electronic form grows in days rather than years. More and more documents and public services are gradually being moved to electronic space. Some time before, document and data originality were endorsed by the original documents which were stored in archives. To ensure electronic documents originality the electronic signature technology was created. Electronic signature is an effective way to ensure the authenticity of the document, i.e. this is a reliable and raising confidence technology. What should we do in case of a need to increase confidence in the data, which are derived from the document and saved as a third party system data? One kind of the data have tendency to change, for example woman's surname after marriage. The other kind has opposite feature – it remains unchanged throughout the period of its existence, for example results of experiments, the accounting data. In the first case is often useful to know who, when and how changed the data. The second case is a bit different – it is crucial to make sure that the data did not change at all. This work is a research on the latter issue, i.e. how to increase confidence in the data, which must remain authentic. A system was developed during the research. It uses electronic signature to prove data authenticity rather than document. We also conducted an experiment comparing the existing and the proposed system performance.

No description available.

Les réseaux de capteurs sans fil émergent comme une technologie innovatrice qui peut révolutionner et améliorer notre façon de vivre, de travailler et d'interagir avec l'environnement physique qui nous entoure. Néanmoins, l'utilisation d'une telle technologie soulève de nouveaux défis concernant le développement de systèmes fiables et sécurisés. Ces réseaux de capteurs sans fil sont souvent caractérisés par un déploiement dense et à grande échelle dans des environnements limités en terme de ressources. Les contraintes imposées sont la limitation des capacités de traitement, de stockage et surtout d'énergie car ils sont généralement alimentés par des piles.Nous visons comme objectif principal à travers cette thèse à proposer des solutions permettant de garantir un certain niveau de fiabilité dans un RCSF dédié aux applications sensibles. Nous avons ainsi abordé trois axes, qui sont :- Le développement de méthodes permettant de détecter les noeuds capteurs défaillants dans un RCSF,- Le développement de méthodes permettant de détecter les anomalies dans les mesures collectées par les nœuds capteurs, et par la suite, les capteurs usés (fournissant de fausses mesures).- Le développement de méthodes permettant d'assurer l'intégrité et l'authenticité des données transmise dans un RCSF. / Wireless sensor networks emerge as an innovative technology that can revolutionize and improve our way to live, work and interact with the physical environment around us. Nevertheless, the use of such technology raises new challenges in the development of reliable and secure systems. These wireless sensor networks are often characterized by dense deployment on a large scale in resource-onstrained environments. The constraints imposed are the limitation of the processing, storage and especially energy capacities since they are generally powered by batteries.Our main objective is to propose solutions that guarantee a certain level of reliability in a WSN dedicated to sensitive applications. We have thus proposed three axes, which are:- The development of methods for detecting failed sensor nodes in a WSN.- The development of methods for detecting anomalies in measurements collected by sensor nodes, and subsequently fault sensors (providing false measurements).- The development of methods ensuring the integrity and authenticity of transmitted data over a WSN.

Battery powered wireless sensors are widely used in industrial and regulatory monitoring applications. This is primarily due to the ease of installation and the ability to monitor areas that are difficult to access. Additionally, they can be left unattended for long periods of time. However, there are many challenges to successful deployments of wireless sensor networks (WSNs). In this thesis we draw attention to two major challenges. Firstly, with a view to extending network range, modern WSNs use mesh network topologies, where data is sent either directly or by relaying data from node-to-node en route to the central base station. The additional load of relaying other nodes’ data is expensive in terms of energy consumption, and depending on the routes taken some nodes may be heavily loaded. Hence, it is crucial to locate routes that achieve energy efficiency in the network and extend the time before the first node exhausts its battery, thus improving the network lifetime. Secondly, WSNs operate in a dynamic radio environment. With changing conditions, such as modified buildings or the passage of people, links may fail and data will be lost as a consequence. Therefore in addition to finding energy efficient routes, it is important to locate combinations of routes that are robust to the failure of radio links. Dealing with these challenges presents a routing optimisation problem with multiple objectives: find good routes to ensure energy efficiency, extend network lifetime and improve robustness. This is however an NP-hard problem, and thus polynomial time algorithms to solve this problem are unavailable. Therefore we propose hybrid evolutionary approaches to approximate the optimal trade-offs between these objectives. In our approach, we use novel search space pruning methods for network graphs, based on k-shortest paths, partially and edge disjoint paths, and graph reduction to combat the combinatorial explosion in search space size and consequently conduct rapid optimisation. The proposed methods can successfully approximate optimal Pareto fronts. The estimated fronts contain a wide range of robust and energy efficient routes. The fronts typically also include solutions with a network lifetime close to the optimal lifetime if the number of routes per nodes were unconstrained. These methods are demonstrated in a real network deployed at the Victoria & Albert Museum, London, UK.