A network N is formed by a (multi)digraph D together with a capacity function u : A ( D ) → R + , and it is denoted by N = ( D , u ) . A flow on N is a function x : A ( D ) → R + such that x ( a ) ≤ u ( a ) for all a ∈ A ( D ), and it is said to be k -splittable if it can be decomposed into up to k paths. We say that a flow is λ -uniform if its value on each arc of the network with positive flow value is exactly λ , for some λ ∈ R + * . We consider the problem of decomposing a flow over an arc-coloured network with minimum cost, that is, with minimum sum of the cost of its paths, where the cost of each path is given by its number of colours. We show that this problem is NP -Hard for general flows on networks. When we restrict the problem to λ -uniform flows, we show that it can be solved in polynomial time for networks with at most two colours. Moreover, we prove that it is NP -Hard for general networks with three colours and for acyclic networks with at least five colours.

Trying to address the security challenges of a cloud-centric software deployment paradigm, silicon and cloud vendors are introducing confidential computing – an umbrella term aimed at providing hardware and software mechanisms for protecting cloud workloads from the cloud provider and its software stack. Today, Intel Software Guard Extensions (SGX), AMD secure encrypted virtualization (SEV), Intel trust domain extensions (TDX), etc., provide a way to shield cloud applications from the cloud provider through encryption of the application’s memory below the hardware boundary of the CPU, hence requiring trust only in the CPU vendor. Unfortunately, existing hardware mechanisms do not automatically enable the guarantee that a protected system was not tampered with during configuration and boot time. Such a guarantee relies on a hardware root of trust, i.e., an integrity-protected location that can store measurements in a trustworthy manner, extend them, and authenticate the measurement logs to the user (remote attestation).

Tutorial Education Program (TEP) is an initiative from Brazilian government to develop education quality in different areas of knowledge, as its main aim to decrease students' evasion and promotion of academic success. PET-Informatica group from Federal University of Pernambuco (UFPE) has invested in these activities, in particular, to teach programming, one of the disciplines in which students have lower grades in technology courses. In this context, this paper describes an experience of an extension course in Python, using the active learning approach Problem-Based Learning (PBL). This course, ministered in two weeks, made possible the construction of a PBL model to teach Python, based in the xPBL methodology described by Santos & Rodrigues (2016) and the lessons learned with this practical experiment. As the main contribution, this experience stands out the possibility to apply this course to different audiences with different levels of education, and also the dissemination of PBL approach in teaching programming.