The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Reprogrammable Controller Design From High-Level Specification

Existing techniques in high-level synthesis mostly assume a simple controller architecture model in the form of a single FSM. However, in reality more complex controller architectures are often used. On the other hand, in the case of programmable processors, the controller architecture is largely defined by the available control-flow instructions in the instruction set. With the wider acceptance of behavioral synthesis, the application of these methods for the design of programmable controllers is of fundamental importance in embedded system technology. This paper describes an important extension of an existing architectural synthesis system targeting the generation of ASIP reprogrammable architectures. The designer can then generate both style of architecture, hardwired and programmable, using the same synthesis system and can quickly evaluate the trade-offs of hardware decisions

BTV2P: Blockchain-based Trust Model for Secure Vehicles and Pedestrians Networks

With the arrival of connected and autonomous vehicles, Vehicle-to-Pedestrian (V2P) communications are promising to facilitate efficient future of mobility on the road by ensuring maximum protection and safety for both drivers and pedestrians. However, this new technology poses new security and privacy challenges that should be taken into account. For instance, a probable malicious node claiming to be a legitimate pedestrian or vehicle within the network can impact the traffic flow, or even cause serious congestion and traffic accidents by broadcasting fake observations or phenomena on the roads. Therefore, it is crucial to identify legitimate vehicles and road users against adversaries pretending to be one. The aim of this paper is to address these issues, by proposing a distributed trust management scheme that relies on blockchain technology and a trust computation approach for efficient and secure management of trust relationships between pedestrians and vehicles in Vehicle-to-Pedestrian (V2P) networks

Automatic adaptation of SIFT for robust facial recognition in uncontrolled lighting conditions

The scale invariant feature transform (SIFT), which was proposed by David Lowe, is a powerful method that extracts and describes local features called keypoints from images. These keypoints are invariant to scale, translation, and rotation, and partially invariant to image illumination variation. Despite their robustness against these variations, strong lighting variation is a difficult challenge for SIFT‐based facial recognition systems, where significant degradation of performance has been reported. To develop a robust system under these conditions, variation in lighting must be first eliminated. Additionally, SIFT parameter default values that remove unstable keypoints and inadequately matched keypoints are not well‐suited to images with illumination variation. SIFT keypoints can also be incorrectly matched when using the original SIFT matching method. To overcome this issue, the authors propose propose a method for removing the illumination variation in images and correctly setting SIFT's main parameter values (contrast threshold, curvature threshold, and match threshold) to enhance SIFT feature extraction and matching. The proposed method is based on an estimation of comparative image lighting quality, which is evaluated through an automatic estimation of gamma correction value. Through facial recognition experiments, the authors find significant results that clearly illustrate the importance of the proposed robust recognition system