SCIENTIFIC PAPERS

- Vinicius Dias, Carlos H. C. Teixeira, Dorgival Guedes & Wagner Meira Jr. - Federal University of Minas Gerais (Belo Horizonte, Brazil) - Srinivasan Parthasarathy - The Ohio State University (Columbus, USA)

 

In this paper we propose Fractal, a high performance and high productivity system for supporting distributed graph pattern mining (GPM) applications. Fractal employs a dynamic (auto-tuned) load-balancing based on a hierarchical and locality-aware work stealing mechanism, allowing the system to adapt to diferent workload characteristics. Additionally, Fractal enumerates subgraphs by combining a depth-irst strategy with a from scratch processing paradigm to avoid storing large amounts of intermediate state and, thus, improves memory efficiency. Regarding programmer productivity,...

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Authors

  •  Leandro Marinho and Caio Nóbrega (Federal University of Campina Grande) - Brazil

Abstract: The increase in sophistication and complexity of recommendation algorithms has turned them into black boxes where the algorithmic reasoning behind the predictions is hard to understand by users. A popular approach for increasing model interpretability in the machine learning community is the Locally Interpretable Model-agnostic Explanations (LIME), which proposes to learn local interpretable models for explaining single predictions...

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  • Daniel Fireman - Federal Institute of Alagoas (Brazil)
  • João Brunet, Raquel Lopes, David Quaresma, David Fireman and Tiago Emmanuel Pereira - Federal University of Campina Grande (Brazil)

Most of the modern cloud web services execute on top of runtime environments like .NET’s Common Language Runtime or Java Runtime Environment. On the one hand, runtime environments provide several off-the-shelf benefits like code security and cross-platform execution. On the other hand, runtime’s features such as just-in-time compilation and...

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Authors:

  • Nádia Medeiros, Naghmeh Ivaki, Marco Vieira (University of Coimbra) and Pedro Costa (University of Coimbra & Polytechnic Institute of Coimbra) - Portugal

Trustworthiness is a paramount concern for users and customers in the selection of a software solution, specially in the context of complex and dynamic environments, such as Cloud and IoT. However, assessing and benchmarking trustworthiness (worthiness of software for being trusted) is a challenging task, mainly due to the variety of application scenarios (e.g., business critical, safety-critical),...

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Authors:

  • João R. Campos, Marco Vieira, Ernesto Costa, DEI/CISUC, University of Coimbra, Portugal

The growing complexity of software makes it difficult or even impossible to detect all faults before deployment, and such residual faults eventually lead to failures at runtime. Online Failure Prediction (OFP) is a technique that attempts to avoid or mitigate such failures by predicting their occurrence based on the analysis of past data and the current state of a system. Given recent technological developments, Machine Learning (ML) algorithms have shown their ability...

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  • Alexandre Braga and Ricardo Dahab - University of Campinas (Brazil)

The widespread misuse of cryptography in software systems is the most frequent source of cryptography-related security problems. Several misuses of cryptography have been found to be recurrent in software in general, resulting in vulnerabilities exploitable in real attacks. There is a huge gap between what cryptologists see as misuses of cryptography and what developers see as unsafe use of cryptographic technology. This chapter contributes to fill this gap by addressing the...

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  • Konstantinos Moutselos, Ilias Maglogiannis, Dimosthenis Kyriazis, Vasiliki Diamantopoulou - University of Piraeus (Greece)

Big Data Analytics are indispensable components of architectures dealing with processing and visualizing results of diverse healthcare-related information sources. In this work, we propose a versatile cloud design where the Health Analytic Tools (HATs) are decoupled from the Datastore and the User-Interface parts, still preserving the element of system trust. This design offers advantages over the process of modifying and...

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  • Leandro Marinho, Jefferson Caldeira, Ricardo S. Oliveira - Federal University of Campina Grande (Brazil)
  • Christoph Trattner - University of Bergen (Norway)

We are often unable to plan menus ahead, thus making poor and unhealthy choices of meals. Besides healthy, one may want menus in which ingredients harmonize and cover well the available ingredients in the pantry. In this paper, we propose a novel multi-objective-based recommender of menus that features an optimal balance between nutritional aspects, harmony and coverage of...

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  • Altigran da Silva, Edleno de Moura and Rosiane Rodrigues - Federal University of Amazonas (Brazil)
  • Péricles de Oliveira - NOKIA Solutions and Networks
  • Li Zhang - IBM T. J. Watson Research Center, NY, USA

Several systems for processing keyword queries over relational databases rely on the generation and evaluation of Candidate Networks (CNs), i.e., networks of joined relations that when processed as SQL queries, provide a relevant answer to the input keyword query. Although the evaluation of CNs has...

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Authors:

  • Eugenio Gianniti e Danilo Ardagna - Politecnico di Milano, Milan, Italy
  • Li Zhang - IBM T. J. Watson Research Center, NY, USA

Recent years saw an increasing success in the application of deep learning methods across various domains and for tackling different problems, ranging from image recognition and classification to text processing and speech recognition. In this paper we propose and validate an approach to model the execution time for training convolutional neural...

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