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On the development of a Visual-Temporal-awareness Rheumatic Heart Disease classifier for Echocardiographic Videos

Rapid progress is being made in the field of artificial intelligence (AI), as milestones are being passed in a wide range of areas, including image understanding and medical diagnosis. With the processing power rapidly evolving and the increasing data availability, learning methods such as convolutional neural networks are playing a crucial role in making sense from images and videos. In this position paper, we propose to use 3D convolutions to model the temporal dependencies between video frames
from echocardiogram data. We applied the 3-dimensional Convolutional Neural Network (C3D) proposed by Tran, Du et al. for automatic identification of exams as Rheumatic Heart Disease (RHD) Positive or Negative.

Use Case on Flexible and Autonomous Manufacturing Systems for Custom-Designed Products

The use of IoT in different industries is establishing a new technological paradigm for manufacturing and sales. The possibilities of the Industry 4.0, fostered by additive manufacturing, goes from a web-based integration of production lines to the possibility of high-quality personalized products. In the Brazilian scenario, however, the use of such technologies is moving slowly. In this sense, the promotion of IoT solutions through joint university-industry activities may be the main path towards disseminating the possibilities of this technology, including flexible manufacturing through a robotic additive manufacturing approach.

Artificial Neural Networks for Resource Allocation in 5G Remote Areas

5G networks promises Enhanced Mobile Communications (EMC), Massive Machine Type Communications (MMTC) and Ultra Reliable Low Latency Communications (URLLC). Besides these three scenarios, the scenario of "access to remote areas" can be included - in which a large cell is the main requirement. The number of mobile devices is increasing at an enormous rate with the advent of IoT, machine-to-machine communication and always-connected devices [1]. Those connected devices have wildly different traffic patterns and the infrastructure will have to support all kinds of traffics with different latency, throughput and packet loss requirements. The already crowded available radio spectrum is expected to become even more crowded and must be optimized. Radio resource allocation is essential to guarantee the minimum level of service, sub partitioning the channel in both time and frequency into resource blocks, that are assigned to different users.

NECOS Project: Lightweight Slicing of CloudFederated Infrastructures

In the past decade, the Information and Communication Technology (ICT) sector has experienced rapid changes in both platform scale and application scope. The diversification of service models offered in Cloud Computing (CC), such as: (i) Software as a Service (SaaS), (ii) Platform as a Service (PaaS), and (iii) Infrastructure as a Service (IaaS). This variety of cloud services creates a new challenge for service providers that are using separately managed computing, connectivity, and storage resources to easily deploy new services, as well as enforcing reasonable Service Level Agreements (SLAs).

Control Plane Data Characterisation for an 5G NFV Environment

Cellular network operates as an important enabler for a handful of emerging business models and its operation demands an immense infrastructure and requires extensive investments on each new generation to acquire new equipment to support novel technologies. As an alternative to buying expensive network equipment on each advancement iteration, the European Telecommunications Standards Institute (ETSI) standardised the use of Network Function Virtualisation (NFV) [1], bringing network functions that were executed by specific and expensive hardware to virtualised environments. This standard allowed the inclusion of NFV on 5G network definition as a chief infrastructure component [2] alongside Software-Defined Network (SDN), another leading softwarisation technique.

Cloud Robotics: Cognitive Augmentation for Robots via the Cloud

Cloud robotics is a means of both making robot software development more efficient and augmenting robot cognition. The idea of cloud robotics goes back to web-enabled robotics which was meant as an aid to autonomous robots in case their knowledge was insufficient to deal with a specific situation which programmers did not envision beforehand. However, for the past years advances in cloud computing have made the concept more powerful and cloud robotics now paves the way for robots to have advanced artificial intelligence at their disposal.

Designing an Open IoT Ecosystem

In the current stage of IoT deployment, developers and integrators have to build and deploy the entire end-to-end components of a smart application. In the future, sensing and actuation systems will be already in place, and new developments will have to use these legacy systems, building a complex ecosystem. In this paper, we advocate that openness is a key factor for providing interoperability and facilitating the interaction of new and existing pieces of an end-to-end IoT smart application. This view is instantiated for a smart irrigation scenario with findings and insights coming from the SWAMP project that is building a smart water management platform.

Optimization Models for on-demand GPUs in the Cloud

Recently, Deep learning (DL) methods have gained popularity in many sophis- ticated medical applications like diagnostics and tumor detection. Among this class of methods, the most promising are Convolution and Recurrent Neural Networks (CNNs, RNNs) which are able to achieve almost human performance accuracy in many tasks. However, the training of such type of applications is very computing intensive tasks, so that exploiting GPUs results in 5 to 40x performance gain compared to CPUs.

Using Computational Back-ends for Artificial Intelligence in Childhood Cancer Research

Computational imaging allows the extraction of multiparametric data, leading to a new era in Radiomics (the extraction of disease features from medical images using data-characterisation and modelling algorithms), supported by the high-throughput extraction, storage, and analysis of a large amount of quantitative imaging features with clinical surrogates (Imaging Biomarkers), providing quantitative information (Virtual Biopsies) for early disease diagnosis, phenotyping, grading, targeting therapies and evaluation of disease response to treatment. Oncologic imaging represents a suitable field for the discovery and validation of new biomarkers from different imaging modalities since oncologic patients are frequently monitored for staging and follow-up of treatment response.

Compliance of the privacy regulations in an international Europe-Brazil context

The generalisation on the use of cloud services has created highly complex scenarios for customers and providers. The globalisation of the service providers, the use of provider complex Application Program Interface (API) stacks, the lack of knowledge of the backends directly affect the protection of the citizens concerning the management of their personal data. There is a concern on the international dimension of the cloud, the combination of multiple providers (services, resources, network, etc.) and the lack of control, especially in the liability, isolation and intervention.

Medical data processing on trustworthy services on an international cloud offering

The advent of computer-aided medicine has facilitated the transition from the traditional qualitative analysis of medical images to automated quantitative analysis. The qualitative analysis relies on the experience and knowledge of specialized radiologists who write their appraisal in natural language. This implies a high temporal and economic cost as well as a limitation on the secondary usage of such data for research. The generalisation of computer image analysis techniques and the increase on the power of computer systems has enabled to measure different characteristics of a medical image (such as texture, shapes, volumes, position of a component or its time evolution) to sustain by quantitative pieces of evidence the conclusions of radiology reports.