Abstract

Abstract
We propose an adaptive secrecy scheme using polar codes with random frozen bits for a general wiretap channel, in which to protect the data from a potential eavesdropper, part or all of the frozen bits are randomly generated per message. To assess the secrecy level of the proposed scheme, three types of decoding strategies are evaluated: a matching decoder which knows the positions of all inserted bits inside the blocklength and tries to estimate them using the same decoding techniques, a blind decoder which treats all the frozen bits as the same value, and a random decoder which considers those dynamic bits as random at the receiver. Results are presented in terms of the system security gap, assuming an adaptive decoding strategy. It is shown that the system achieves combined secrecy and reliability. The proposed scheme does not assume knowledge of the eavesdropper's channel when defining the indices of information and frozen bits. © 2019 IEEE.

Abstract
Skin temperature regulation is dependent on the autonomic nervous system function, which may be impaired in patients with neuropathy. Literature reporting thermographic assessment of patients with established diagnosis of Diabetic Foot (DF) is scarce, but this information is completely absent in patients suffering from Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). The aim of this study is to compare skin temperature distribution in patients with DF and TTR-FAP. Thermograms of the dorsal and plantar surfaces of twelve neuropathic patients, six with DF and six with TTR-FAP, were assessed and compared. Skin temperature was significantly higher in the diabetic foot group, in both regions of interest. Thermal symmetry values were high, but similar in both groups. The bias between the right and left foot was smaller, with smaller limits of agreement in TTR-FAP patients, suggesting a lower agreement between the temperature of the right and left feet in DF patients.

Abstract
Although the growth of video content in online platforms has been happening for some time, searching and browsing these assets is still very inefficient as rich contextual data that describes the content is still not available. Furthermore, any available descriptions are, usually, not linked to timed moments of content. In this paper, we present an approach for making social web videos available on YouTube more accessible, searchable and navigable. By using the concept of crowdsourcing to collect the metadata, our proposal can contribute to easily enhance content uploaded in the YouTube platform. Metadata, collected as a collaborative annotation game, is added to the content as time-based information in the form of descriptions and captions using the YouTube API. This contributes for enriching video content and enabling navigation through temporal links.

Abstract
Infantile Hemangiomas (IH) make up the most common type of benign vascular tumors affecting children. They can grow for several months until beginning to involute. In present-day clinical practice there's no objective monitoring protocol. For more objective measures, an automatic evaluation system (CAD system) is needed to aid clinicians in assessing the effectiveness of a given patient's response to a treatment. One of the stages of these systems is the lesion segmentation. This work addresses the automatic segmentation of lesions in IH. Acknowledging that the methods in the literature for IH lesion segmentation lag behind the state-of-the-art in the image segmentation community, we conduct a comparison of various methodologies for the segmentation of the IH, including both shallow and deep methodologies. Acknowledging the lack of data in the field for a robust learning of deep models, we also evaluate transfer learning techniques to benefit from knowledge extracted in other skin lesions. The best results were obtained with the shortest path method and a multiscale convolutional neural network that merges two pipelines working at different scales. Although promising, the results put in evidence the need for better databases, collected under suitable acquisition protocols. © 2018 IEEE.

Abstract
The smart grid environment requires the enhancement of various computational tools, especially for routine tasks of data acquisition and system monitoring. This paper presents the building blocks of a conceptual framework to be used as the basis for the construction of novel distributed remote metering systems with utilization of the cutting edge Blockchain technology. The proposed methodology is suitable for processing a large volume of data aimed at monitoring modern electric power distribution grids. As a proof of concept, a collaborative metering system is conceived based on the Blockchain technology, being primarily capable of: dealing with the entirety of the collected data (conveniently stored and filtered); assuring data integrity by means of cryptography; optimizing implementation/operation costs of the telecommunication services involved. Simulation results concerning the reliability and performance of the designed distributed remote metering system are presented.