Abstract
The use of sensors in the real world is on the rise, providing information on medical diagnostics for healthcare and improving quality of life. Optical fiber sensors, as a result of their unique properties (small dimensions, capability of multiplexing, chemical inertness, and immunity to electromagnetic fields) have found wide applications, ranging from structural health monitoring to biomedical and point-of-care instrumentation. Furthermore, these sensors usually have good linearity, rapid response for real-time monitoring, and high sensitivity to external perturbations. Optical fiber sensors, thus, present several features that make them extremely attractive for a wide variety of applications, especially biomedical applications. This paper reviews achievements in the area of temperature optical fiber sensors, different configurations of the sensors reported over the last five years, and application of this technology in biomedical applications.

Abstract
The optic disc (OD) and the fovea are relevant landmarks in fundus images. Their localization and segmentation can facilitate the detection of some retinal lesions and the assessment of their importance to the severity and progression of several eye disorders. Distinct methodologies have been developed for detecting these structures, mainly based on color and vascular information. The methodology herein described combines the entropy of the vessel directions with the image intensities for finding the OD center and uses a sliding band filter for segmenting the OD. The fovea center corresponds to the darkest point inside a region defined from the OD position and radius. Both the Messidor and the IDRiD datasets are used for evaluating the performance of the developed methods. In the first one, a success rate of 99.56% and 100.00% are achieved for OD and fovea localization. Regarding the OD segmentation, the mean Jaccard index and Dice’s coefficient obtained are 0.87 and 0.94, respectively. The proposed methods are also amongst the top-3 performing solutions submitted to the IDRiD online challenge. © Springer Nature Switzerland AG 2020.

Abstract
We develop a new approach for building cryptographic implementations. Our approach goes the last mile and delivers assembly code that is provably functionally correct, protected against side-channels, and as efficient as handwritten assembly. We illustrate our approach using ChaCha20Poly1305, one of the two ciphersuites recommended in TLS 1.3, and deliver formally verified vectorized implementations which outperform the fastest non-verified code. We realize our approach by combining the Jasmin framework, which offers in a single language features of high-level and low-level programming, and the EasyCrypt proof assistant, which offers a versatile verification infrastructure that supports proofs of functional correctness and equivalence checking. Neither of these tools had been used for functional correctness before. Taken together, these infrastructures empower programmers to develop efficient and verified implementations by "game hopping", starting from reference implementations that are proved functionally correct against a specification, and gradually introducing program optimizations that are proved correct by equivalence checking. We also make several contributions of independent interest, including a new and extensible verified compiler for Jasmin, with a richer memory model and support for vectorized instructions, and a new embedding of Jasmin in EasyCrypt.

Abstract
Data science has started to become one of the most important skills one can have in the modern world, due to data taking an increasingly meaningful role in our lives. The accessibility of data science is however limited, requiring complicated software or programming knowledge. Both can be challenging and hard to master, even for the simple tasks. With this in mind, we have approached this issue by providing a new data science platform, termed DS4All.Curation, that attempts to reduce the necessary knowledge to perform data science tasks, in particular for data cleaning and curation. By combining HCI concepts, this platform is: simple to use through direct manipulation and showing transformation previews; allows users to save time by eliminate repetitive tasks and automatically calculating many of the common analyses data scientists must perform; and suggests data transformations based on the contents of the data, allowing for a smarter environment. © 2020, Springer Nature Switzerland AG.

Abstract
In the era of industrial revolution 4.0, an automotive industry flourished in a way that was never before. As different features added in the driver assistance systems time-to-time, hence, nowadays the driving process is not so much tedious as it seems before. This ensures the operation of hassle-free driving, which leads towards autonomous vehicles. Overall, it opens up many opportunities for researchers and business communities, but at the same time, raises concerns and issues for discussion that need to be analyzed before put the final product (here, in the sense of fully automated vehicle) on the road. We conducted an online-survey (N = 3139) with participants from 146 nations was participated and assembled their valuable feedback on the automated driving and human presence in the autonomous vehicles considering all the levels of driving automation. This paper explores the results in terms of useful implications, which highlights and implied us to re-examine the present regulations and policies in automated driving and autonomous vehicles. © 2020, Springer Nature Switzerland AG.

Abstract