Automation has become increasingly prevalent in all forms of society. Activities that are too difficult for a human or to dangerous can be done by machines which do not share those downsides. In addition, tasks can be scheduled more precisely and accurately. Increases in the autonomy have allowed for a new level of tasks which are completed by teams of automated agents rather than a single one, called cooperative control. This has many benefits; but comes at the cost of increased complexity and coordination. The main thrust of research in this field is problem based, considering communication issues as a secondary feature. There is a gap considering problems in which many changes occur as rapidly as communication and the issues that arise as a result. This is the main motivation. This research presents an approach to cooperative control in highly variable systems and tackles some of the issues present in such a system. One of the most important issues is the communication network itself, which is used as an indicator for how healthy the system is an how well it may react to future changes. Therefore using the network as an input to control allows the system to navigate between conservative and aggressive techniques to improve performance while still maintaining robustness. Results are based on a test bed designed to simulate a wide variety of problem types based on: network type; numbers of actors; frequency of changes; impact of changes and method of change. The developed control method is compared to the baseline case ignoring cooperation as well as an idealized case assuming perfect system knowledge. The baseline represents sacrifices coordination to achieve a high level of robustness at reduced performance while the idealized case represents the best possible performance. The control techniques developed give a performance at least as good as the baseline case if not better for all simulations.

The purpose of this research was firstly to compare the differences in online performance between two groups trained to use the Online Public Access Catalogue at the M L Sultan Technikon using two different types of instruction, namely the teaching methods of concept-based and procedural instruction. Secondly, the objective of the research was to compare these two teaching methods in relation to first year students at the M L Sultan Technikon with differing levels of library experience, computer experience and English language experience. To meet the objectives of the research, literature was reviewed and analysed from various sources. Original research was conducted using the method of a quasi-experiment. A random sample of 120 students were split between two teaching conditions, with sixty participants in a concept-based teaching condition and sixty participants in a procedural teaching condition. Research instruments used were a background questionnaire to collect demographic information, a pre-and post test to evaluate significant differences between the teaching methods, an evaluation questionnaire to collect affective responses, direct observation, and transaction log monitoring of the searches conducted. In a one-hour lecture the concept-based group were taught general search concepts using model-based instruction techniques and the procedural lecture demonstrated methods of searching in a step-by-step fashion. Data analysis made use of Microsoft Access 97 and Excell 97 software to code and verify the data, and the Statistical Package for the Social Sciences (SPSS), v9.0 to conduct statistical analysis. The research found that first year students were generally inexperienced in the use of the online information retrieval system. The majority of the participants in the study did not have any computer experience, and made use of English as a second language. Others, although not in the majority were found to have low levels of library experience. Performance on pre-tests were generally low for these participants while those who had experience in the use of libraries, computers and who regarded English as a first language were able to make fair use of the system for simple tasks such as author and title. This suggested that prerequisite competencies needed for online searching were, library literacy, computer literacy and some proficiency in the use of English. Performance on search tasks found no significant differences on simple tasks between the teaching conditions. However, variances in performance as a result of individual differences were found. On difficult tasks participants fared better with concept-based instruction resulting in significant differences in performance. The findings of this research supported the need for online instruction to novice end-users, taking cognisance of the need for suitable venues equipped with adequate hardware, provision of staff, and allocation of sufficient time for such instruction. The research proposes that model-based teaching be encouraged, especially for difficult tasks. In the decisions made however, instruction must take note of the background of participants. Further proposals for instruction and other related aspects are discussed in the research. / Thesis (Ph.D.)-University of Natal, Pietermaritzburg, 1999.

Les nouvelles technologies de l’information et des communications occupent aujourd’hui une place importante dans les entreprises, quelle que soit la taille ou le(s) domaine(s) d’activité de ces dernières. Elles participent de manière positive au développement de la vie économique. Elles sont toutefois à l’origine d’une nouvelle forme de criminalité qui menace la sécurité et l’intégrité des systèmes informatiques dans l’entreprise. Celle-ci est d’une ampleur difficile à évaluer, mais surtout difficile à maîtriser avec les dispositions législatives déjà en place, laissant par là même apparaître qu’une adaptation au niveau juridique est inévitable. Certains pays industrialisés ont ainsi décidé de mettre en place un cadre juridique adéquat pour garantir aux entreprises la sécurité de leurs systèmes informatiques. Notre étude va justement porter sur les dispositifs mis en place par deux systèmes juridiques différents. Forcés de prendre en compte une réalité nouvelle – qui n’existait pas nécessairement il y a plusieurs années –, la France et le Canada ont décidé de modifier respectivement leurs codes pénal et criminel en leur ajoutant des dispositions qui répriment de nouvelles infractions. À travers cet exposé, nous allons analyser les infractions qui portent atteinte à la sécurité du système informatique de l’entreprise à la lumière des outils juridiques mis en place. Nous allons mesurer leur degré d’efficacité face à la réalité informatique. En d’autres termes, il s’agit pour nous de déterminer si le droit va répondre ou non aux besoins de l’informatique. / The new information and communication technologies (NICT) currently play an important role in companies, regardless of their size or field of activity; in addition they contribute positively to the economy. However, their use has led to NICT-related criminality, which threatens the security and integrity of the companies’ computer systems. NICT-related criminality has grown exponentially; its increase is hard to assess, and especially hard to control using the existing legislative provisions. Hence, legal adaptations appear unavoidable. Several First World countries have decided to set up, through different means, an adequate legal framework to guarantee the security of companies’ computer systems. Our study will focus precisely on the mechanisms that have been set by two different legal systems. France and Canada, which had to take into account a new reality–new to at least some extent–have decided to amend their respective penal and criminal codes by adding provisions that penalize further infringements. In this work, we will analyze the crimes that undermine the security of the companies’ computer systems in light of the legal tools in place. We will asess how effectively they face today’s computer world and will determine whether or not the law will meet or not the needs of this type of technology.

In graph theory graphs are mathematical objects that can be used to model networks, data structures, process scheduling, computations and a variety of other systems where the relations between the objects in the system play a dominant role.We will now consider graphs as mathematically self-contained units with rich structure and comprehensive theory; as models for many phenomena, particularly those arising in computer systems; and as structures which can be processed by a variety of sophisticated and interesting algorithms.For graph theory presentation, we need a very good graphical user interface(GUI) to approach the goal. X Window system is ideally suited for such a purpose. This package program is based on X Window system environment. With this package, we can manipulate graphs by special functions which can put nodes, put edges, delete nodes, delete edges, change the whole graph size, move graph location, and modify edge weights. / Department of Computer Science

Made available in DSpace on 2011-05-04T12:36:15Z (GMT). No. of bitstreams: 0 Previous issue date: 2009 / Esta dissertação é apresentada sob forma de artigo e tem por objetivo estudar a notificaçãode óbito de crianças menores de um ano de idade nos municípios de Fronteiras, Monsenhor Gil, Pimenteiras e Simões do estado do Piauí no biênio 2005 e 2006. Foi realizado um estudo de caso de natureza descritiva. O trabalho de campo foi feito por meio da estratégia busca ativa junto às diversas fontes de notificação de óbito oficiais e não oficiais existentes nos municípios. Foram utilizados os dados de óbito infantil do Sistema de Informação sobre Mortalidade (SIM). Buscou-se identificar o percentual de subnotificação do óbito infantil, a contribuição das diversas fontes de notificação e o perfil do óbito infantil. O estudo mostrou uma cobertura de óbito infantil para o total dos municípios de 59, 5 por cento; dentre as unidades classificadas como do sistema de saúde, o hospital foi a principal fonte de notificação para óbitos já notificados no SIM. O PSF e informante chave mostraram-se fontes relevantes para óbitos não notificados ao SIM. É possível apontar que a subnotificação do óbito infantil pode ser atribuída a questões relacionadas à garantia do acesso ao serviço de saúde como também a questões que perpassam o setor saúde, a exemplo, o nível de escolaridade materna. / This dissertation is presented in an article format and has as its objective to study the death notifications of children younger than one year of age in the cities of Fronteiras, Monsenhor Gil, Pimenteiras and Simões, in the state of Piauí from 2005 to2006. A case study of a descriptive nature was carried out. The field work was done by means of an active strategic search together with the different sources of official death notification and the non-official ones in the cities. The data of infant deaths from the Mortality Information Computer System (SIM) was used. The search to identify the percentage of under notification of infant deaths, the contribution of several sources of notification and the profile of infant deaths was done. The study showed a covering of infant death for all of the cities at 59.5%; among the units classified as the health system, the hospital was the main notification source for deaths notified at SIM. The Family Health Program and the key informant were the relevant sources for non-notified deaths at SIM. It is possible to point out that the under notification of infant deaths could be attributed to questions related to the guarantee of access to health services as well as questions which are outside the health sector, for example, the educational level of the mother.

Predizer defeitos em projetos de software é uma tarefa complexa, especialmente para aqueles projetos que estão em fases iniciais do desenvolvimento por, frequentemente, disponibilizarem de poucos dados para que modelos de predição sejam criados. A utilização da predição cruzada de defeitos entre projetos é indicada em tal situação, pois permite reaproveitar dados de projetos similares. Este trabalho propõe um estudo exploratório sobre o uso de diferentes algoritmos de classificação, seleção de métricas, e de agrupamento na construção de um modelo de predição cruzada de defeitos entre projetos. Esse modelo foi construído com o uso de uma medida de desempenho, obtida com a aplicação de algoritmos de classificação, como forma de encontrar e agrupar projetos semelhantes. Para tanto, foi estudada a aplicação conjunta de 8 algoritmos de classificação, 6 de seleção de atributos, e um de agrupamento em um conjunto de dados com 1283 projetos, resultando na construção de 61584 diferentes modelos de predição. Os algoritmos de classificação e de seleção de atributos tiveram seus desempenhos avaliados por meio de diferentes testes estatísticos que mostraram que: o Naive Bayes foi o classificador de melhor desempenho, em comparação com os outros 7 algoritmos; o par de algoritmos de seleção de atributos que apresentou melhor desempenho foi o formado pelo avaliador de atributos CFS e método de busca Genetic Search, em comparação com outros 6 pares. Considerando o algoritmo de agrupamento, a presente proposta parece ser promissora, uma vez que os resultados obtidos mostram evidências de que as predições usando agrupamento foram melhores que as predições realizadas sem qualquer agrupamento por similaridade, além de mostrar a diminuição do custo de treino e teste durante o processo de predição. / To predict defects in software projects is a complex task, especially for those projects that are in early stages of development by, often, providing few data for prediction models. The use of cross-project defect prediction is indicated in such a situation because it allows reuse data of similar projects. This work proposes an exploratory study on the use of different classification algorithms, of selection metrics, and grouping to build cross-project defect predictions models. This model was built using a performance measure, obtained by applying classification algorithms aim to find and group similar projects. Therefore, it was studied the application of 8 classification algorithms, 6 feature selection, and a cluster in a data set with 1283 projects, resulting in the construction of 61584 different prediction models. The classification algorithms and feature selection had their performance evaluated through different statistical tests showed that: the Naive Bayes was the best performance classifier, as compared with other 7 algorithms; the pair of feature selection algorithms that performed better was formed by CFS attribute evaluator and search method Genetic Search, compared with 6 other pairs. Considering the clustering algorithm, this proposal seems to be promising, since the results shows evidence that the predictions were best grouping using the predictions performed without any similarity clustering, and shows the decrease in training cost and testing during the prediction process.

Orientador: Maria Beatriz Felgar de Toledo / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-15T11:55:40Z (GMT). No. of bitstreams: 1 Rosa_Ricardoda_M.pdf: 1883897 bytes, checksum: 06a13cb1deacddc65e4123779e1ac975 (MD5) Previous issue date: 2010 / Resumo: Computação em grade e um paradigma que permite o compartilhamento de recursos heterogêneos, geograficamente distribuídos e sob administrações independentes. Esse compartilhamento deve ser realizado para otimizar a utilização de recursos e atender os requisitos de qualidade de serviço. Modelos econômicos podem ser aplicados para fornecer uma alocação justa desses recursos e incentivar a disponibilização de um maior número de recursos na grade. Nesta dissertação, será discutida uma arquitetura de grade baseada em modelos econômicos, em especial, os vários modelos de leiloes para permitir negociação entre um fornecedor e vários consumidores de recursos. Serú realizada uma anúlise sobre as diversas modalidades de leilão para verificar o comportamento de consumidores e fornecedores de recursos em um ambiente de grade / Abstract: Grid computing is a paradigm that allows the sharing of heterogeneous resources, geographically distributed and under independent administration. Sharing must be done to optimize the use of resources and meet quality of service requirements. Economic models can be applied to provide a fair allocation of these resources and to promote the entry of a greater number of resources into a grid. In this dissertation, a grid architecture based on economic models will be discussed, in particular, several auction models to allow negotiation between a provider and many consumer of resources. Different types of auction models will be analyzed to verify the behavior of consumers and providers of resources in a grid environment / Mestrado / Sistemas de Computação / Mestre em Ciência da Computação

<div> <div> <div> <p>Cloud computing offers a cost-efficient data analytics platform. This is enabled by constant innovations in tools and technologies for analyzing large volumes of data through distributed batch processing systems and real-time data through distributed stream processing systems. However, due to the sensitive nature of data, many organizations are reluctant to analyze their data in public clouds. To address this stalemate, both software-based and hardware-based solutions have been proposed yet all have substantial limitations in terms of efficiency, expressiveness, and security. In this thesis, we present solutions that enable practical and expressive confidentiality- preserving batch and stream-based analytics. We achieve this by performing computations over encrypted data using Partially Homomorphic Encryption (PHE) and Property-Preserving Encryption (PPE) in novel ways, and by utilizing remote or Trusted Execution Environment (TEE) based trusted services where needed.</p><p><br></p><p>We introduce a set of extensions and optimizations to PHE and PPE schemes and propose the novel abstraction of Secure Data Types (SDTs) which enables the application of PHE and PPE schemes in ways that improve performance and security. These abstractions are leveraged to enable a set of compilation techniques making data analytics over encrypted data more practical. When PHE alone is not expressive enough to perform analytics over encrypted data, we use a novel planner engine to decide the most efficient way of utilizing client-side completion, remote re-encryption, or trusted hardware re-encryption based on Intel Software Guard eXtensions (SGX) to overcome the limitations of PHE. We also introduce two novel symmetric PHE schemes that allow arithmetic operations over encrypted data. Being symmetric, our schemes are more efficient than the state-of-the-art asymmetric PHE schemes without compromising the level of security or the range of homomorphic operations they support. We apply the aforementioned techniques in the context of batch data analytics and demonstrate the improvements over previous systems. Finally, we present techniques designed to enable the use of PHE and PPE in resource-constrained Internet of Things (IoT) devices and demonstrate the practicality of stream processing over encrypted data.</p></div></div></div><div><div><div> </div> </div> </div>

<p>Deep Neural Networks (DNNs) have greatly advanced the state-of-the-art in a wide range of machine learning tasks involving image, video, speech and text analytics, and are deployed in numerous widely-used products and services. Improvements in the capabilities of hardware platforms such as Graphics Processing Units (GPUs) and specialized accelerators have been instrumental in enabling these advances as they have allowed more complex and accurate networks to be trained and deployed. However, the enormous computational and memory demands of DNNs continue to increase with growing data size and network complexity, posing a continuing challenge to computing system designers. For instance, state-of-the-art image recognition DNNs require hundreds of millions of parameters and hundreds of billions of multiply-accumulate operations while state-of-the-art language models require hundreds of billions of parameters and several trillion operations to process a single input instance. Another major obstacle in the adoption of DNNs, despite their impressive accuracies on a range of datasets, has been their lack of robustness. Specifically, recent efforts have demonstrated that small, carefully-introduced input perturbations can force a DNN to behave in unexpected and erroneous ways, which can have to severe consequences in several safety-critical DNN applications like healthcare and autonomous vehicles. In this dissertation, we explore approximate computing as an avenue to improve the speed and energy efficiency of DNNs, as well as their robustness to input perturbations.</p> <p> </p> <p>Approximate computing involves executing selected computations of an application in an approximate manner, while generating favorable trade-offs between computational efficiency and output quality. The intrinsic error resilience of machine learning applications makes them excellent candidates for approximate computing, allowing us to achieve execution time and energy reductions with minimal effect on the quality of outputs. This dissertation performs a comprehensive analysis of different approximate computing techniques for improving the execution efficiency of DNNs. Complementary to generic approximation techniques like quantization, it identifies approximation opportunities based on the specific characteristics of three popular classes of networks - Feed-forward Neural Networks (FFNNs), Recurrent Neural Networks (RNNs) and Spiking Neural Networks (SNNs), which vary considerably in their network structure and computational patterns.</p> <p> </p> <p>First, in the context of feed-forward neural networks, we identify sparsity, or the presence of zero values in the data structures (activations, weights, gradients and errors), to be a major source of redundancy and therefore, an easy target for approximations. We develop lightweight micro-architectural and instruction set extensions to a general-purpose processor core that enable it to dynamically detect zero values when they are loaded and skip future instructions that are rendered redundant by them. Next, we explore LSTMs (the most widely used class of RNNs), which map sequences from an input space to an output space. We propose hardware-agnostic approximations that dynamically skip redundant symbols in the input sequence and discard redundant elements in the state vector to achieve execution time benefits. Following that, we consider SNNs, which are an emerging class of neural networks that represent and process information in the form of sequences of binary spikes. Observing that spike-triggered updates along synaptic connections are the dominant operation in SNNs, we propose hardware and software techniques to identify connections that can be minimally impact the output quality and deactivate them dynamically, skipping any associated updates.</p> <p> </p> <p>The dissertation also delves into the efficacy of combining multiple approximate computing techniques to improve the execution efficiency of DNNs. In particular, we focus on the combination of quantization, which reduces the precision of DNN data-structures, and pruning, which introduces sparsity in them. We observe that the ability of pruning to reduce the memory demands of quantized DNNs decreases with precision as the overhead of storing non-zero locations alongside the values starts to dominate in different sparse encoding schemes. We analyze this overhead and the overall compression of three different sparse formats across a range of sparsity and precision values and propose a hybrid compression scheme that identifies that optimal sparse format for a pruned low-precision DNN.</p> <p> </p> <p>Along with improved execution efficiency of DNNs, the dissertation explores an additional advantage of approximate computing in the form of improved robustness. We propose ensembles of quantized DNN models with different numerical precisions as a new approach to increase robustness against adversarial attacks. It is based on the observation that quantized neural networks often demonstrate much higher robustness to adversarial attacks than full precision networks, but at the cost of a substantial loss in accuracy on the original (unperturbed) inputs. We overcome this limitation to achieve the best of both worlds, i.e., the higher unperturbed accuracies of the full precision models combined with the higher robustness of the low precision models, by composing them in an ensemble.</p> <p> </p> <p><br></p><p>In summary, this dissertation establishes approximate computing as a promising direction to improve the performance, energy efficiency and robustness of neural networks.</p>

<div>Comprehensive networking security is a goal to achieve for enterprise networks. In forensics, the traffic analysis, causality dependence in intricate program network flows is needed in flow-based attribution techniques. The provenance, the connection between stealthy advanced persistent threats (APTs) and the execution of loadable modules is stripped because loading a module does not guarantee an execution. The reports of common vulnerabilities and exposures (CVE) demonstrate that lots of vulnerabilities have been introduced in protocol engineering process, especially for the emerging Internet-of-Things (IoT) applications. A code generation framework targeting secure protocol implementations can substantially enhance security.</div><div>A novel automaton-based technique, NetCrop, to infer fine-grained program behavior by analyzing network traffic is proposed in this thesis. Based on network flow causality, it constructs automata that describe both the network behavior and the end-host behavior of a whole program to attribute individual packets to their belonging programs and fingerprint the high-level program behavior. A novel provenance-oriented library tracing system, Lprov, which enforces library tracing on top of existing syscall logging based provenance tracking approaches is investigated. With the dynamic library call stack, the provenance of implicit library function execution is revealed and correlated to system events, facilitating the locating and defense of malicious libraries. The thesis presents ProFactory, in which a protocol is modeled, checked and securely generated, averting common vulnerabilities residing in protocol implementations.</div>