Abstract
Huge efforts and resources are spent every year on prevention and recovery of cyberattacks targeting users, services and network infrastructures. Software-Defined Networking (SDN) is a technology providing advances to the field of security with the ability of programming the network, promoting high-performance solutions and efficient resource utilization at low costs, as the use of specialized hardware is avoided. The present paper aims at exploring the SDN paradigm to develop an SDN-based framework for prevention and mitigation of malicious attacks throuhgt the network. The framework design and proposal has concerns regarding the efficient use of network and computational resources, distributing the inspection of suspicious flows by distinct Intrusion Detection Systems. For this purpose, a load-balancing strategy for traffic inspection is devised, allowing to balance both the usage of resources and the analysis of traffic flows. In this way, this paper also sheds light on the usage of OpenFlow messages to build distributed SDN-based applications with the mentioned properties.

Abstract
This paper proposes a scheme to determine multiple parameters of a medium using multiple localized surface plasmon resonances (SPR) in a D-shaped photonic crystal fiber (PCF) whose flat surface is covered by two adjacent gold layers of different thicknesses. We show how to customize plasmon resonances at different wavelengths with very low cross-talk between them, thus allow obtaining the optical dispersion, the average refractive index and the temperature of the sample. Since the surface plasmon resonances are excited at distinct spectral channels, the sensing structure can be used to determine simultaneously these parameters.

Abstract

Abstract
This paper presents a high-resolution imaging system developed for plankton imaging in the context of the MarinEye integrated biological sensor [1]. This sensor aims to produce an autonomous system for marine integrated physical, chemical and biological monitoring combining imaging, acoustic, sonar, and fraction filtration systems (coupled to DNA/RNA preservation) as well as sensors for targeting physical-chemical variables in a modular and compact system that can be deployed on fixed and mobile platforms, such as the TURTLE robotic deep sea lander [2]. The results obtained with the system both in laboratory conditions and in the field are presented and discussed, allowing the characterization and validation of the performance of the Autonomous High-Resolution Image Acquisition System for Plankton.

Abstract
Economic efficiency assessments based on Data Envelopment Analysis are scarce compared to technical efficiency studies, even in for-profit firms. Some aspects justify this scarcity, such as the difficulty to estimate accurate prices, given their variability over time. In many situations, external benchmarking is hindered due to organizations' unique nature and the barriers to sharing information considered critical to competitiveness. The use of internal benchmarking can overcome some of these difficulties. This study conducted an internal benchmarking analysis of a broiler production system, focusing on cost efficiency. We conducted longitudinal case-based research over six years (2014-2019). The concepts of throughput accounting of the Theory of Constraints were applied to structure the DEA model (inputs, prices, and output). The Critical Incident Technique was used to explore the effects of interventions on the production system's cost efficiency. The results show that the broiler production system could reduce 32% of the total cost per unit of production if the balance of inputs suggested by the DEA evaluation was used. This work contributes to the literature by showing the potential of internal benchmarking to explore the evolution of cost efficiency over time. From a practical perspective, this study is important for managers by showing how to measure the impact of management actions on performance, providing valuable information to guide continuous improvement.

Abstract
The agricultural sector plays a fundamental role in our society, where it is increasingly important to automate processes, which can generate beneficial impacts in the productivity and quality of products. Perception and computer vision approaches can be fundamental in the implementation of robotics in agriculture. In particular, deep learning can be used for image classification or object detection, endowing machines with the capability to perform operations in the agriculture context. In this work, deep learning was used for the detection of grape bunches in vineyards considering different growth stages: the early stage just after the bloom and the medium stage where the grape bunches present an intermediate development. Two state-of-the-art single-shot multibox models were trained, quantized, and deployed in a low-cost and low-power hardware device, a Tensor Processing Unit. The training input was a novel and publicly available dataset proposed in this work. This dataset contains 1929 images and respective annotations of grape bunches at two different growth stages, captured by different cameras in several illumination conditions. The models were benchmarked and characterized considering the variation of two different parameters: the confidence score and the intersection over union threshold. The results showed that the deployed models could detect grape bunches in images with a medium average precision up to 66.96%. Since this approach uses low resources, a low-cost and low-power hardware device that requires simplified models with 8 bit quantization, the obtained performance was satisfactory. Experiments also demonstrated that the models performed better in identifying grape bunches at the medium growth stage, in comparison with grape bunches present in the vineyard after the bloom, since the second class represents smaller grape bunches, with a color and texture more similar to the surrounding foliage, which complicates their detection.