Alessandro Druetto

The representation of binary data in a compact, printable, efficient, and often human-readable format is essential in numerous computing applications, mainly driven by the limitations of systems and communication protocols not designed to handle arbitrary 8-bit binary data. This paper provides a comprehensive survey and an extensive characterization of printable encoding schemes, tracing their evolution from historical methods to contemporary solutions for representing, storing, and transmitting binary data using restricted character sets. The review includes a foundational analysis of fundamental character encodings, proposes a layered model for the classification of printable encodings, and examines various schemes based on their numerical bases, alphabets, and functional characteristics. Algorithms, key design trade-offs, the impact of relevant standards, security implications, performance considerations, and human factors are systematically discussed, aiming to offer a detailed understanding of the current context and open challenges.

This paper extends and improves the performance of a digital reversible watermarking algorithm based on histogram shifting presented in previous works. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. In particular, radiographic images exhibit this structure after contrast enhancement during the acquisition process. The previous work suggested performing gray-level histogram shifting according to a local optimization technique. In this paper, we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs using a non-linear objective function transformed to fit a linear optimization algorithm. The obtained results show a meaningful improvement in the payload capacity of the original data-hiding method. A mild Peak Signal-to-Noise Ratio (PSNR) reduction is still acceptable for a qualitative preview of the images, which can be completely restored to their original cover form thanks to the reversibility of the method.

We consider the problem of scheduling non preemptively a set of jobs on parallel identical machines with prior setup operations on a single shared server, where the objective is to minimise the makespan. We develop an arc-flow formulation to the problem with two multigraphs, one for the machines and one for the server, with a same set of nodes representing points in time, and arcs associated with job execution, and with machines or server idleness. The resulting Flow-Flow formulation (FFF) and its tuned version (FFT) are compared with the best existing model in the literature, TIV I, on benchmark instances with up to 200 jobs and 7 machines. Computational results showed that our Flow-Flow models outperformed TIV I for instances with 100 jobs and produced optimal solutions to the vast majority of problems with 150 and 200 jobs for which TIV I failed to even find integer solutions within the standard limited runtime of 3600 s. When non optimal, solutions provided by our models exhibited very low gaps to best bound.

Base45 encodes pairs of octets using 3 characters from an alphabet of 45 printable symbols. Previous works showed the ability to hide payload data into encoded strings by exploiting the unused configurations of the Base45 encoding. In this paper, we present Hide45, an algorithm for hiding data into Base45 encoded strings that optimizes the embedding capacity, according to the frequency distribution of the input data. Experimental tests show that an optimal assignment of bit configurations to the most frequent pairs of octets allows to reach a payload capacity very close to the theoretical capacity of the method, improving over a baseline assignment by up to 53%.

This paper extends and improves the performance of a digital reversible watermarking algorithm presented in a previous work. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. The previous work suggested to perform some gray level histogram shifting according to a local optimization technique. In this paper we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs, improving the payload capacity of the reversible data hiding method.

Multicore architectures provide the increased performance required by modern embedded real-time systems. Most platforms exhibit a non-uniform memory access (NUMA). In NUMA, memory banks with different access time can be explicitly addressed. Such an architecture, however, is challenging predictability given the significant impact of the allocation of variables on the execution times. At software level, real-world embedded applications (e.g. automotive) are composed by thousands of functions often communicating through shared variables stored in memory, with a variable access time because of NUMA. This paper addresses the mapping of complex embedded applications onto NUMA multicore architectures. The developed problem formulation offers a solution to the following problems: (i) allocating variables (called labels in the automotive context) over memories of different characteristics, (ii) mapping functionalities (called runnables) onto CPUs, (iii) creating OS tasks from runnables, and (iv) assigning priorities to tasks. Our developed implementation is capable to handle an application composed by 1K+ runnables, all sharing 10K+ labels and finds a solution in at most 3 minutes on a standard laptop, enabling interactive design space exploration.

Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science https://doi.org/10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT.