André Lage-Freitas

Predicting case outcomes is useful for legal professionals to understand case law, file a lawsuit, raise a defense, or lodge appeals, for instance. However, it is very hard to predict legal decisions since this requires extracting valuable information from myriads of cases and other documents. Moreover, legal system complexity along with a huge volume of litigation make this problem even harder. This paper introduces an approach to predicting Brazilian court decisions, including whether they… Mostrar más

Predicting case outcomes is useful for legal professionals to understand case law, file a lawsuit, raise a defense, or lodge appeals, for instance. However, it is very hard to predict legal decisions since this requires extracting valuable information from myriads of cases and other documents. Moreover, legal system complexity along with a huge volume of litigation make this problem even harder. This paper introduces an approach to predicting Brazilian court decisions, including whether they will be unanimous. Our methodology uses various machine learning algorithms, including classifiers and state-of-the-art Deep Learning models. We developed a working prototype whose F1-score performance is ~80.2% by using 4,043 cases from a Brazilian court. To our knowledge, this is the first study to present methods for predicting Brazilian court decision outcomes. Mostrar menos

Editor of the 39th Issue of the Computação Brasil journal, invited by the Brazilian Computer Society (SBC).

Master/Worker distributed programming model enables huge remote sensing data processing by asg tasks to Workers in which data is stored. Cloud computing features include the deployment of Workers by using virtualized technologies such as virtual machines and containers. These features allow programmers to configure, create, and start virtual resources for instance. In order to develop remote sensing applications by taking advantage of high-level programming languages (e.g., R, Matlab, and… Mostrar más

Master/Worker distributed programming model enables huge remote sensing data processing by asg tasks to Workers in which data is stored. Cloud computing features include the deployment of Workers by using virtualized technologies such as virtual machines and containers. These features allow programmers to configure, create, and start virtual resources for instance. In order to develop remote sensing applications by taking advantage of high-level programming languages (e.g., R, Matlab, and Julia), s have to manually address Cloud resource deployment.
This paper presents the design, implementation, and evaluation of the Infra.jl research prototype. Infra.jl takes advantage of Julia Master/Worker programming simplicity for providing automatic deployment of Julia Workers in the Cloud. The assessment of Infra.jl automatic deployment is only ~2.8 s in two different Azure Cloud data centers. Mostrar menos

Image processing algorithms require high processing capabilities. Most of these algorithms use linear algebraic operations (BLAS), for instance, through the Combinatorial BLAS [1], and Elemental [2] programming s. Such operations may require unavailable computer processing resources.
Recent Cloud technologies include programming for image processing, nevertheless these solutions are limited. Huge image processing requires a great amount of main memory to fully store the data… Mostrar más

Image processing algorithms require high processing capabilities. Most of these algorithms use linear algebraic operations (BLAS), for instance, through the Combinatorial BLAS [1], and Elemental [2] programming s. Such operations may require unavailable computer processing resources.
Recent Cloud technologies include programming for image processing, nevertheless these solutions are limited. Huge image processing requires a great amount of main memory to fully store the data to be processed, which makes imagery processing more complex. This paper addresses the problems of enabling and easing huge image processing. We propose a solution which transparently deals with distributed underlying computing resources while providing a numerical programming interface for processing huge data sets. This solution takes advantage of Julia DistributedArrays by adding automatic for both Cloud resource management and loading of image data sets. Mostrar menos

Cloud computing has become the infrastructure of choice for building and delivering software services. A key challenge for service providers is effectively managing cloud resources in order to increase profit while maintaining service‐level agreements (SLAs) with customers. To address this challenge, this paper proposes a combination of automated mechanisms for resource and execution management. The resource management mechanisms, namely, under‐provisioning and contract rescission, reduce… Mostrar más

Cloud computing has become the infrastructure of choice for building and delivering software services. A key challenge for service providers is effectively managing cloud resources in order to increase profit while maintaining service‐level agreements (SLAs) with customers. To address this challenge, this paper proposes a combination of automated mechanisms for resource and execution management. The resource management mechanisms, namely, under‐provisioning and contract rescission, reduce resource allocation costs and minimize penalties incurred when performance objectives are violated. The execution management mechanisms, namely, crash recovery and delay recovery, minimize penalties incurred when reliability objectives are violated. The mechanisms are integrated in the Qu4DS framework and evaluated in the Grid'5000 testbed. The results show that the under‐provisioning mechanism increases profit by 20–50%, the contract rescission mechanism increases profit up to four times, and the execution management mechanisms increase profit by up to 60%. Mostrar menos

Cloud computing has evolved from the provisioning of virtual machines to the provisioning of complex services, delivered to customers under the of Service-Level Agreements (SLAs). SLAs specify the Quality of Service (QoS) that should be provided to customers as well as the billing model. A main concern for cloud service providers is to maintain the agreed SLA in order to avoid losses and penalties. Maintaining the SLA in turn requires translating the QoS to configurations of… Mostrar más

Cloud computing has evolved from the provisioning of virtual machines to the provisioning of complex services, delivered to customers under the of Service-Level Agreements (SLAs). SLAs specify the Quality of Service (QoS) that should be provided to customers as well as the billing model. A main concern for cloud service providers is to maintain the agreed SLA in order to avoid losses and penalties. Maintaining the SLA in turn requires translating the QoS to configurations of low-level mechanisms, able to enforce the agreed . Current systems provide no integrated for SLA specification, translation, and enforcement. In this paper, we propose an approach for specifying and enforcing SLAs for cloud service providers. The approach covers the creation of SLA templates under a billing model, the design of performance and fault-tolerance QoS assurance mechanisms as well as the translation of QoS to appropriate configurations of those mechanisms. We demonstrate the feasibility of our approach by using the Qu4DS framework for PaaS cloud providers. Moreover, we evaluate the impact of failures on the provider profit. The experiments were carried out on the Grid5000 testbed and demonstrate the effectiveness of ensuring fault tolerance in different scenarios. Mostrar menos