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The Observatory of Public Spending (or ODP, in Portuguese) is a special unit of Brazil's Ministry of Transparency, Monitoring and Office of the Comptroller-General (or CGU, in Portuguese) responsible for monitoring public spending and gathering managerial and audit information to support the work of CGU internal auditors. One of the most important themes monitored by this unit is Public Procurements and Government Suppliers which have won these procurement processes. Image analysis of many of these suppliers headquarters revealed suspicious landscapes, such as rural areas, isolated places or slums. These landscapes could be an indication of fake suppliers with poor capacity of delivering public goods and services. However, checking thousands of landscapes in order to find these fake suppliers would be a very expensive task. Our objective then is to discover what are the possible groups of scenes involving government suppliers, given that these images were not previously labeled, as automatically as possible. For that reason, we used Places CNN, a pretrained convolutional neural network for scene recognition presented by Zhou et al., which was trained on 205 scene categories with 2.5 million images, for scene recognition on Brazilian Government Suppliers.
Manufacturing industries are changing rapidly towards more flexibility and autonomy. The RoboCup Logistics League (RCLL) and RoboCup@Work tackle research questions in this domain focusing on automated reasoning and planning, and mobile manipulation respectively. However, future scenarios will require both aspects (and more) and will most likely operate with more heterogeneous systems. In this paper, we propose a cross-over challenge to foster closer cooperation among the two leagues to address these challenges. We outline four integration milestones and propose a specific scenario and task for the first milestone. The effort is driven by stakeholders of both leagues.
Sebastian Zug, Tim Niemueller, Nico Hochgeschwender, Kai Seidensticker, Martin Seidel, Tim Friedrich, Tobias Neumann, Ulrich Karras, Gerhard Kraetzschmar, and Alexander Ferrein
Vibration control is crucially important in ensuring a smooth ride for vehicle passengers. This study sought to design a suspension system for a car such that its mode of vibration would be predominantly bouncing at lower speeds, and primarily pitching at higher speeds. Our study used analytical and numerical methods to choose appropriate springs and dampers for the front and rear suspension. After an initial miscalculation, we succeeded in arriving at appropriate shocks for the vehicle with the desired modes of vibration at the specified frequencies. We then assessed the maximum bouncing and pitching that the vehicle would experience under a specific set of conditions: travel at 40 km/hr over broken, rough terrain. Our testing showed moderate success in our suspension design. We successfully damped the force being transmitted to both the front and rear quarter car somewhat, while ensuring that the modes of vibration fell into the desired shapes at the desired frequency ranges.
We explore ways of allowing for the offloading of computationally rigorous tasks from devices with slow logical processors onto a network of anonymous peer-processors. Recent advances in secret sharing schemes, decentralized consensus mechanisms, and multiparty computation (MPC) protocols are combined to create a P2P MPC market. Unlike other computational "clouds", ours is able to generically compute any arithmetic circuit, providing a viable platform for processing on the semantic web. Finally, we show that such a system works in a hostile environment, that it scales well, and that it adapts very easily to any future advances in the complexity theoretic cryptography used. Specifically, we show that the feasibility of our system can only improve, and is historically guaranteed to do so.
Las bases de datos relacionales han sido las herramientas por excelencia para el almacenamiento de la información en los sistemas informáticos. No obstante, las bases de datos NoSQL, como tendencia, han venido ganando espacio especialmente por la escalabilidad y velocidad en sus tiempos de respuestas. PostgreSQL ha incorporado algunas características de tipo NoSQL, como el almacenamiento efímero y el manejo de datos JSON; características que pueden aprovecharse para realizar acciones desde el gestor dándole mayor potencia. El objetivo de este artículo es evaluar, mediante toda la documentación encontrada, el comportamiento de las características NoSQL de PostgreSQL frente a un gestor NoSQL, comparandola con MongoDB, respecto a los tiempos de respuestas y dar a conocer las ventajas de uno con respecto al otro.
Palabras Claves: Características NoSQL en PostgreSQL, MongoDB, PostgreSQL