ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange

Análise da aplicabilidade das regras de ouro ao tuning de sistemas gerenciadores de bancos de dados relacionais em ambientes de computação em nuvem

Orientador : Prof. Dr. Marcos Sfair SunyeTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 07/03/2014Inclui referênciasResumo: A computação em nuvem oferece um ambiente bastante propício para o provimento de serviços de TI. A virtualização, tecnologia que compõe sua base possibilita simular sobre um computador físico, uma ou mais estações de trabalho chamadas máquinas virtuais, que permitem maior exibilidade e melhor racionalização de sua infraestrutura. A incorporação de sistemas legados aos ambientes em nuvem como forma de contenção de custo é uma demanda frequente e altamente relevante. Para isso, é comum o emprego do modelo multi-inquilino do tipo shared-hardware, no qual o sistema gerenciador de banco de dados e o sistema legado ficam hospedados em máquinas virtuais que competem, junto às demais, por recursos computacionais. Neste ambiente, é vital o emprego de estratégias de tuning que objetivam melhorias no desempenho do banco de dados. Porém, os sistemas gerenciadores de banco de dados relacionais não foram inicialmente projetados para serem executados em ambientes shared-hardware. Consequentemente, seus parâmetros de configuração, comumente alvos de regras de tuning, não consideram o fato de que os recursos disponíveis variam ao longo do tempo, devido ao provisionamento dinâmico comum em ambientes elásticos. Esta tese propõe um método de avaliação que, por meio da simulação de cargas de trabalho de acesso a disco oriundas de máquinas virtuais concorrentes, demonstra a inadequação do emprego das regras de tuning, conhecidas como regras-de-ouro, encontradas na literatura e/ou recomendadas por experts. Nossos resultados apontam para a definição de novas regras-de-ouro, específicas para ambientes virtualizados, além de viabilizar a criação de um modelo para o tuning automático de sistemas gerenciadores de banco de dados relacionais em ambientes de computação em nuvem. Palavras-Chave: sistema gerenciador de banco de dados relacional, virtualização, tuning, sistema legados, computação em nuvemAbstract: Cloud computing currently o_ers a very propitious environment for IT service provision. The virtualization, technology that compose their base enables to simulate in a physical computer one or more workstations called virtual machines that allow greater exibility and better use of its infrastructure. The incorporation of legacy systems to the cloud environments as a means of cost containment is a frequent and highly relevant demand. Therefore, it is common the use the multi-tenant model of shared-hardware type on which the database and legacy system are hosted on virtual machines that compete, with others, for computational resources. In this environment it is vital the use of tuning strategies that aim to improve the performance of the database. However, the relational database management systems were not initially designed to execute on shared-hardware environments. Consequently, its con_guration parameters, commonly targets of tuning rules, do not consider the fact that the available resources vary over time due to the common dynamic provisioning that is common in elastic environments. This thesis proposes an evaluation methodology that, simulates I/O workloads from concurrent virtual machines and demonstrates the inadequacy of the use of tuning rules, known as rules-ofthumb, found in literature and/or recommended by experts. Our results point to the new rules-of-thumb, speci_c to virtualized environments while also make feasible the creation of a model for automatic tuning of database in cloud computing environments. Keywords: relational database management system, virtualization, tuning, legacy systems, cloud computing

Provenance Management for Collaborative Data Science Workflows

Collaborative data science activities are becoming pervasive in a variety of communities, and are often conducted in teams, with people of different expertise performing back-and-forth modeling and analysis on time-evolving datasets. Current data science systems mainly focus on specific steps in the process such as training machine learning models, scaling to large data volumes, or serving the data or the models, while the issues of end-to-end data science lifecycle management are largely ignored. Such issues include, for example, tracking provenance and derivation history of models, identifying data processing pipelines and keeping track of their evolution, analyzing unexpected behaviors and monitoring the project health, and providing the ability to reason about specific analysis results. We address these challenges by ingesting, managing, and analyzing rich provenance information generated during data science projects, and using it to enable users to easily publish, share, and discover data analytics projects. We first describe the design of our unified provenance and metadata management system, called ProvDB. We adopt a schema-later approach and use a flexible graph-based provenance representation model that combines the core concepts in version control and provenance management. We describe several ingestion mechanisms for this provenance model and show how heterogeneous data analysis environments can be served with natural extensions to this framework. We also describe a set of novel features of the system including graph queries for retrospective provenance, fileviews for data transformations, introspective queries for debugging, and continuous monitoring queries for anomaly detection. We then illustrate how to support deep learning modeling lifecycle via the extensibility mechanism in ProvDB. We describe techniques to compactly store and efficiently query the rich set of data artifacts generated during deep learning modeling lifecycle. We also describe a high-level domain specific language that helps raise the abstraction level during model exploration and enumeration and accelerate the modeling process. Lastly, we propose graph query operators and develop efficient evaluation techniques to address the verbose and evolving nature of such provenance graphs. First, we introduce a graph segmentation operator, which queries the provenance of a collection of user-given vertices (e.g., versioned files, author names) via flexible boundary criteria. Second, we propose a graph summarization operator to aggregate the results of multiple segmentation operations, and allow multi-resolution interaction with the aggregation result to understand similar and abnormal behaviors in those segments

Enhancing recall and precision of web search using genetic algorithm

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to rapid growth of the number of Web pages, web users encounter two main problems, namely: many of the retrieved documents are not related to the user query which is called low precision, and many of relevant documents have not been retrieved yet which is called low recall. Information Retrieval (IR) is an essential and useful technique for Web search; thus, different approaches and techniques are developed. Because of its parallel mechanism with high-dimensional space, Genetic Algorithm (GA) has been adopted to solve many of optimization problems where IR is one of them. This thesis proposes searching model which is based on GA to retrieve HTML documents. This model is called IR Using GA or IRUGA. It is composed of two main units. The first unit is the document indexing unit to index the HTML documents. The second unit is the GA mechanism which applies selection, crossover, and mutation operators to produce the final result, while specially designed fitness function is applied to evaluate the documents. The performance of IRUGA is investigated using the speed of convergence of the retrieval process, precision at rank N, recall at rank N, and precision at recall N. In addition, the proposed fitness function is compared experimentally with Okapi-BM25 function and Bayesian inference network model function. Moreover, IRUGA is compared with traditional IR using the same fitness function to examine the performance in terms of time required by each technique to retrieve the documents. The new techniques developed for document representation, the GA operators and the fitness function managed to achieves an improvement over 90% for the recall and precision measures. And the relevance of the retrieved document is much higher than that retrieved by the other models. Moreover, a massive comparison of techniques applied to GA operators is performed by highlighting the strengths and weaknesses of each existing technique of GA operators. Overall, IRUGA is a promising technique in Web search domain that provides a high quality search results in terms of recall and precision

Smartphone User Privacy Preserving through Crowdsourcing

In current Android architecture, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to avoid unnecessary private data breach. However, the majority of regular users are not technically capable or do not care to consider privacy implications to make safe decisions. To assist the technically incapable crowd, we propose a permission control framework based on crowdsourcing. At its core, our framework runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or not the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using a transitional Bayesian inference model. The recommendation is based on aggregated expert responses and their confidence level. As a complete framework design of the system, this thesis also includes a solution for Android app risks estimation based on behaviour analysis. To eliminate the negative impact from dishonest app owners, we also proposed a bot user detection to make it harder to utilize false recommendations through bot users to impact the overall recommendations. This work also covers a multi-view permission notification design to customize the app safety notification interface based on users\u27 need and an app recommendation method to suggest safe and usable alternative apps to users

A Big Bang Big Crunch Type-2 Fuzzy Logic System for Machine Vision-Based Event Detection and Summarization in Real-world Ambient Assisted Living

The recent years have witnessed the prevalence and abundance of vision sensors in various applications such as security surveillance, healthcare and Ambient Assisted Living (AAL) among others. This is so as to realize intelligent environments which are capable of detecting users’ actions and gestures so that the needed services can be provided automatically and instantly to maximize user comfort and safety as well as to minimize energy. However, it is very challenging to automatically detect important events and human behaviour from vision sensors and summarize them in real time. This is due to the massive data sizes related to video analysis applications and the high level of uncertainties associated with the real world unstructured environments occupied by various users. Machine vision based systems can help detect and summarize important information which cannot be detected by any other sensor; for example, how much water a candidate drank and whether or not they had something to eat. However, conventional non-fuzzy based methods are not robust enough to recognize the various complex types of behaviour in AAL applications. Fuzzy logic system (FLS) is an established field of research to robustly handle uncertainties in complicated real-world problems. In this thesis, we will present a general recognition and classification framework based on fuzzy logic systems which allows for behaviour recognition and event summarisation using 2D/3D video sensors in AAL applications. I started by investigating the use of 2D CCTV camera based system where I proposed and developed novel IT2FLS-based methods for silhouette extraction and 2D behaviour recognition which outperform the traditional on the publicly available Weizmann human action dataset. I will also present a novel system based on 3D RGB-D vision sensors and Interval Type-2 Fuzzy Logic based Systems (IT2FLSs) ) generated by the Big Bang Big Crunch (BB-BC) algorithm for the real time automatic detection and summarization of important events and human behaviour. I will present several real world experiments which were conducted for AAL related behaviour with various users. It will be shown that the proposed BB-BC IT2FLSs outperforms its Type-1 FLSs (T1FLSs) counterpart as well as other conventional non-fuzzy methods, and that performance improvement rises when the number of subjects increases. It will be shown that by utilizing the recognized output activity together with relevant event descriptions (such as video data, timestamp, location and user identification) detailed events are efficiently summarized and stored in our back-end SQL event database, which provides services including event searching, activity retrieval and high-definition video playback to the front-end user interfaces

Criação de um sistema de gestão de gateways para casas inteligentes

Mestrado em Engenharia de Computadores e TelemáticaEsta dissertação enquadra-se no projeto Smart Green Home, que resulta de uma parceria entre a Bosch e a Universidade de Aveiro, e visa criar um dispositivo (Gateway) capaz de interagir com um ambiente domestico inteligente e multi-tecnologias de forma a facilitar a sua integração em instalações existentes e ser o mais possível independente de marcas. Mais ainda, pretende-se criar um sistema de gestão de Gateways em produção por forma a tornar tarefas de manutenção simples e escaláveis. Relativamente a este sistema, ele trás ainda valor acrescentado para o utilizador permitindo o acesso remoto à sua casa e monitorizar e controlar os seus dispositivos, assim como outras capacidades. Para satisfazer os requisitos deste sistema, foi desenvolvida uma solução para ambas as componentes do sistema, a Gateway e o sistema de gestão. Esta solução foi implementada com sucesso e o seu funcionamento validado de acordo com os requisitos. Por último, uma avaliação à solução final implementada, com levantamento das suas limitações, foi realizada e são expostos possíveis futuros melhoramentos para o sistema.This dissertation was done in the scope of the Smart Green Home project, that was born from a partnership between Bosch and the University of Aveiro, and strives to achieve a device (Gateway) capable of interacting with a smart home environment where multi-technologies are present allowing it to more easily integrate in existing installations along with being vendor independent. Additionally, it will be created a gateway management system to allow their maintenances to become effortless and scalable. Regarding this system, it also provides added value to the user by allowing remote access his home to monitor and control his devices, as well as other features. In order to fulfill this system requirements, a solution was developed for both of the system’s components, the Gateway and the management system. This solution was then successfully implemented and its functionality validated according to its requirements. Lastly, an evaluation to the final implemented solution was conducted, and its limitations gathered, exposing this way possible future improvements

Management for Bachelors

The textbook contains educational module, which embraces the content of main regulatory disciplines on specialists training by the direction 6.030601 “Management” in the knowledge branch 03.06 “Management and administration” of the educational and qualification level “Bachelor”. According to the content the disciplines completely conform to curricula approved by scientific and methodological commission on management and agreed with logical and structural scheme of educational process. The textbook embraces almost all aspects of bachelor training. The chapters contain questions for self-control and list of recommended literature. While creating the chapters the results of fundamental and applied scientific researches of the evaluation branch, the forecasting and management of economic potential of complicated industrial system were used

7th International Conference on Higher Education Advances (HEAd'21)

Information and communication technologies together with new teaching paradigms are reshaping the learning environment.The International Conference on Higher Education Advances (HEAd) aims to become a forum for researchers and practitioners to exchange ideas, experiences,opinions and research results relating to the preparation of students and the organization of educational systems.Doménech I De Soria, J.; Merello Giménez, P.; Poza Plaza, EDL. (2021). 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. https://doi.org/10.4995/HEAD21.2021.13621EDITORIA