Abstract
Looking for health information is one of the most popular activities online. However, the specificity of language on this domain is frequently an obstacle to comprehension, especially for the ones with lower levels of health literacy. For this reason, search engines should consider the readability of health content and, if possible, adapt it to the user behind the search. In this work, we explore methods to assess the readability of health content automatically. We propose features capable of measuring the specificity of a medical text and estimate the knowledge necessary to comprehend it. The features are based on information retrieval metrics and the log-likelihood of a text with lay and medico-scientific language models. To evaluate our methods, we built and used a dataset composed of health articles of Simple English Wikipedia and the respective documents in ordinary Wikipedia. We achieved a maximum accuracy of 88% in binary classifications (easy versus hard-to-read). We found out that the machine learning algorithm does not significantly interfere with performance. We also experimented and compared different features combinations. The features using the values of the log-likelihood of a text with lay and medico-scientific language models perform better than all the others.

Abstract
Accurate 3D reconstruction and realistic visualization of cultural heritage allow experts to fine-tune their theories on the lost links in the history of civilization. Although the 3D reconstruction is a significant challenge, precisely because of the state of degradation over the years, it constitutes a crucial task for experts to study and interact with long disappeared settlements and structures. Furthermore, the public, in general, will be provided with the conditions to explore them in virtual environments, thus fostering cultural, social, and scientific participation. Highly accurate reconstruction is, nevertheless, a very complex task, where all stages of image synthesis must be carefully executed from highly detailed 3D models to obtain a faithful depiction of the object of interest. Meanwhile, the textual descriptions and geospatial data collected by archaeologists on-site may be used to overcome the absence of visual information. Still, this data will not suffice, in which case procedural modeling turns out to be essential to avoid a great deal of time and labor-consuming modeling processes. Procedural modeling tools automatically generate three-dimensional models through computational processes that extend the base information according to a specific algorithm. In order to avoid reprograming the procedural modeling systems, we use mathematical methods that operate on parametrical symbolic descriptions that, flexibly, can model different types of objects. The most used mathematical methods are fractal geometry and formal grammars, particularly L-systems and shape grammars. In this chapter, we will approach the current advances in the area of procedural modeling and how these tools can be used to generate 3D models of cultural heritage. We also explore the relevant dimension of time, extending the modeling tasks to 4D. These applications do not focus on very specific landmarks, like cathedrals or palaces, which require manual effort or image-based techniques to capture the model with a high level of visual fidelity. Instead, we focus on modeling cities and their evolutions or the surroundings of these landmarks, that allow for an increased automation of the modeling process. © 2020, Springer Nature Switzerland AG.

Abstract
Epilepsy affects approximately 1% of the world's population. Semiology of epileptic seizures contain major clinical signs to classify epilepsy syndromes currently evaluated by epileptologists by simple visual inspection of video. There is a necessity to create automatic and semiautomatic methods for seizure detection and classification to better support patient monitoring management and diagnostic decisions. One of the current promising approaches are the marker-less computer-vision techniques. In this paper an end-to-end deep learning approach is proposed for binary classification of Frontal vs. Temporal Lobe Epilepsies based solely on seizure videos. The system utilizes infrared (IR) videos of the seizures as it is used 24/7 in hospitals' epilepsy monitoring units. The architecture employs transfer learning from large object detection "static" and human action recognition "dynamic" datasets such as ImageNet and Kinectics-400, to extract and classify the clinically known spatiotemporal features of seizures. The developed classification architecture achieves a 5-fold cross-validation f1-score of 0.844 +/- 0.042. This architecture has the potential to support physicians with diagnostic decisions and might be applied for online applications in epilepsy monitoring units. Furthermore, it may be jointly used in the near future with synchronized scene depth 3D information and EEG from the seizures.

Abstract
In Wireless Sensor Networks (WSN), typically composed of nodes with resource constraints, leveraging efficient processes is crucial to enhance the network lifetime and, consequently, the sustainability in ultra-dense and heterogeneous environments, such as smart cities. Particularly, balancing the energy required to transport data efficiently across such dynamic environments poses significant challenges to routing protocol design and operation, being the trade-off of reducing data redundancy while achieving an acceptable delivery rate a fundamental research topic. In this way, this work proposes a new energy-aware epidemic protocol that uses the current state of the network energy to create a dynamic distribution topology by self-adjusting each node forwarding behavior as eager or lazy according to the local residual battery. Simulated evaluations demonstrate its efficiency in energy consumption, delivery rate, and reduced computational burden when compared with classical gossip protocols as well as with a directional protocol.

Abstract
A national census is an official count of a country's population that aims to motivate and measure sustainable development. Traditionally, a census is a cumbersome manual operation that involves distributing surveys to all households in the country through field agents or by mail. Recently, some countries have utilized voluntary electronic submissions in addition to the manual work to reduce costs and increase efficiency. However, an increasing number of countries are resorting to a register-based census that uses pre-existing official registers to derive its data. This paper describes Oman's upcoming register-based census, e-Census 2020, and analyses it against the European Commission's necessary conditions that facilitate a successful transition from a traditional to a register-based census [1]. © 2020 ACM.

Abstract
This paper compares the export promotion system of advanced and emerging economies in fifty countries. Results show that advanced economies offer, on average, more complete export promotion system, i.e. a greater variety of Export Promotion Programs (EPPs) than emerging economies. Advanced countries offer more financial support, informational services, facilitating activities and education and training services. The specific services that contribute most to these differences are also identified, which is important for national export promotion agencies and policy makers to upgrade their offer to firms in order for them to be better prepared for international trade interactions, especially emerging economies.