Abstract
The sharing of cyberthreat information within a community or group of entities is possible due to solutions such as the Malware Information Sharing Platform (MISP). However, the MISP was considered limited if its information was deemed as classified or shared only for a given period of time. A solution using searchable encryption techniques that better control the sharing of information was previously proposed by the same authors. This paper describes a prototype implementation for two key functionalities of the previous solution, considering multiple entities sharing information with each other: the symmetric key generation of a sharing group and the functionality to update a shared index. Moreover, these functionalities are evaluated regarding their performance, and enhancements are proposed to improve the performance of the implementation regarding its execution time. As the main result, the duration of the update process was shortened from around 2922 s to around 302 s, when considering a shared index with 100,000 elements. From the security analysis performed, the implementation can be considered secure, thus confirming the secrecy of the exchanged nonces. The limitations of the current implementation are depicted, and future work is pointed out.

Abstract
The role of new technologies such as additive manufacturing and blockchain technology in designing and implementing circular economy ecosystems is not a trivial issue. This study aimed to understand if blockchain technology can be an enabler tool for developing additive symbiotic networks. A real case study was developed regarding a circular economy ecosystem in which a fused granular fabrication 3D printer is used to valorize polycarbonate waste. The industrial symbiosis network comprised four stakeholders: a manufacturing company that produces polycarbonate waste, a municipality service responsible for the city waste management, a start-up holding the 3D printer, and a non-profit store. It was identified a set of six requirements to adopt the blockchain technology in an additive symbiotic network, bearing in mind the need to have a database to keep track of the properties of the input material for the 3D printer during the exchanges, in addition to the inexistence of mechanisms of trust or cooperation between well-established industries and the additive manufacturing industry. The findings suggested a permissioned blockchain to support the implementation of the additive symbiotic network, namely, to enable the physical transactions (quantity and quality of waste material PC sheets) and monitoring and reporting (additive manufacturing technology knowledge and final product's quantity and price).Future research venues include developing blockchain-based systems that enhance the development of ad-ditive symbiotic networks.

Abstract
Advancements in semiconductor technology no longer occur at the pace the industry had been accustomed to. We have entered what is considered by many to be the post-Moore era. In order to continue scaling performance, increasingly heterogeneous architectures are being developed and the use of special purpose accelerators is on the rise. One notable example are Field-Programmable-Gate-Arrays (FPGAs), both in the data-center and embedded spaces. Advances in FPGA features and tools is allowing for critical kernels to be accelerated on specialized hardware without fabrication costs. However, re-targeting code to such heterogeneous platforms still requires significant refactoring of the compute intensive kernels, as well as knowledge of parallel compute and hardware design concepts for maximization of performance. We present Tribble, a source-to-source framework under active development, capable of transforming regular C/C++ programs for execution on heterogeneous architectures. This includes transforming the target kernel source code so that it is amenable for circuit generation while keeping the original version for software execution, inserting code for task and memory management and injecting a scheduler algorithm.

Abstract
The C/C++ compilation stack (Intermediate Representations (IRs), compilation passes and backends) is encumbered by a steep learning curve, which we believe can be lowered by complementing it with approaches such as source-to-source compilation. Source-to-source compilation is a technology that is widely used and quite mature in certain programming environments, such as JavaScript, but that faces a low adoption rate in others. In the particular case of C and C++ some of the identified factors include the high complexity of the languages, increased difficulty in building and maintaining C/C++ parsers, or limitations on using source code as an intermediate representation. Additionally, new technologies such as Multi-Level Intermediate Representation (MLIR) have appeared as potential competitors to source-to-source compilers at this level. In this paper, we present what we have identified as current challenges of source-to-source compilation of C and C++, as well as what we consider to be opportunities and possible directions forward. We also present several examples, implemented on top of the Clava source-to-source compiler, that use some of these ideas and techniques to raise the abstraction level of compiler research on complex compiled languages such as C or C++. The examples include automatic parallelization of for loops, high-level synthesis optimisation, hardware/software partitioning with run-time decisions, and automatic insertion of inline assembly for fast prototyping of custom instructions.

Abstract
Semantic segmentation consists of classifying each pixel according to a set of classes. This process is particularly slow for high-resolution images, which are present in many applications, ranging from biomedicine to the automotive industry. In this work, we propose an algorithm targeted to segment high-resolution images based on two stages. During stage 1, a lower-resolution interpolation of the image is the input of a first neural network, whose low-resolution output is resized to the original resolution. Then, in stage 2, the probabilities resulting from stage 1 are divided into contiguous patches, with less confident ones being collected and refined by a second neural network. The main novelty of this algorithm is the aggregation of the low-resolution result from stage 1 with the high-resolution patches from stage 2. We propose the U-Net architecture segmentation, evaluated in six databases. Our method shows similar results to the baseline regarding the Dice coefficient, with fewer arithmetic operations.

Abstract