Abstract
The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV "library". Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

Abstract
Multi-view has the potential to offer immersive viewing experiences to users, as an alternative to 360 degrees and Virtual Reality (VR) applications. In multi-view, a limited number of camera views are sent to the client and missing views are synthesised locally. Given the substantial complexity associated to view synthesis, considerable attention has been given to optimise the trade-off between bandwidth gains and computing resources, targeting smooth navigation and viewing quality. A still relatively unexplored field is the optimisation of the way navigation interactivity is achieved, i.e. how the user indicates to the system the selection of new viewpoints. In this article, we introduce SmoothMV, a multi-view system that uses a non-intrusive head tracking approach to enhance navigation and Quality of Experience (QoE) of the viewer. It relies on a novel Hot&Cold matrix concept to translate head positioning data into viewing angle selections. Streaming of selected views is done using MPEG-DASH, where a proposed extension to the standard descriptors enables to achieve consistent and flexible view identification.

Abstract
Introduction: Previous studies have shown that adverse childhood experiences negatively impact child development, with consequences throughout the lifespan. Some of these consequences include the exacerbation or onset of several pathologies and risk behaviors. Materials and Methods: A convenience sample of 398 individuals aged 20 years or older from the Porto metropolitan area, with quotas, was collected. The evaluation was conducted using an anonymous questionnaire that included sociodemographic questions about exposure to adverse childhood experiences, a list of current health conditions, questions about risk behaviors, the AUDIT-C test, the Fagerstrom test and the Childhood Trauma Questionnaire-brief form. Variables were quantified to measure adverse childhood experiences, pathologies, and risk behaviors in adult individuals for comparison purposes. Results: Individuals with different forms of adverse childhood experiences present higher rates of smoking dependence, self-harm behaviors, victimization of/aggression toward intimate partners, early onset of sexual life, sexually transmitted infections, multiple sexual partners, abortions, anxiety, depression, diabetes, arthritis, high cholesterol, hypertension, and stroke. Different associations are analyzed and presented. Discussion and Conclusions: The results show that individuals with adverse childhood experiences have higher total scores for more risk behaviors and health conditions than individuals without traumatic backgrounds. These results are relevant for health purposes and indicate the need for further research to promote preventive and protective measures.

Abstract
The FotoInMotion (FiM) project is building a novel media creation platform, leveraging the use of semi-automated analysis and editing tools to empower creators to easily transform static visual acquisitions of real-world events into rich, animated and engaging objects, distributable through common channels. FiM transforms the content creative chain into an integrated pipeline across which media and metadata seamlessly flow and are exploited to produce more complex media objects. One of the addressed challenges consists the need for a seamless and efficient communication across such pipeline and on how to preserve, in a structured manner, all of the involved media and metadata. Existing standardized metadata tools and content wrappers are limited in expressivity and scope and incapable of fully supporting the needs of the content creative pipeline. This paper describes FiM's new structured data object, i.e. the Digital Event (DE), which acts as a universal vehicle for media and metadata. It builds on well-established and emergent MPEG standards (MPEG-21, MPEG-V, MPEG-7 and MPEG HEIF), to support data diversity, interoperability, packaging and sharing, within complex, Machine Learning enhanced, creative pipelines. Our solution has been validated by creative professionals (photojournalism, fashion marketing and festivals), who have conducted experiments within the context of different creative workflows in real world scenarios. DE's employment revealed to be advantageous, particularly in the homogenization of the media and metadata representation and packaging and in the normalization of the interaction between different pipeline components.

Abstract
Heterogeneous platforms with FPGAs have started to be employed in the High-Performance Computing (HPC) field to improve performance and overall efficiency. These platforms allow the use of specialized hardware to accelerate software applications, but require the software to be adapted in what can be a prolonged and complex process. The main goal of this work is to describe and evaluate mechanisms that can transparently transfer the control flow between CPU and FPGA within the scope of HPC. Combining such a mechanism with transparent software profiling and accelerator configuration could lead to an automatic way of accelerating regular applications. In this work, a mechanism based on the ptrace system call is proposed, and its performance on the Intel Xeon+FPGA platform is evaluated. The feasibility of the proposed approach is demonstrated by a working prototype that performs the transparent control flow transfer of any function call to a matching hardware accelerator. This approach is more general than shared library interposition at the cost of a small time overhead in each accelerator use (about 1.3 ms in the prototype implementation).

Abstract
As applications move to the edge, efficiency in computing power and power/energy consumption is required. Heterogeneous computing promises to meet these requirements through application-specific hardware accelerators. Runtime adaptivity might be of paramount importance to realize the potential of hardware specialization, but further study is required on workload retargeting and offloading to reconfigurable hardware. This article presents our framework for the exploration of both offloading and hardware generation techniques. The framework is currently able to process instruction sequences from MicroBlaze, ARMv8, and riscv32imaf binaries, and to represent them as Control and Dataflow Graphs for transformation to implementations of hardware modules. We illustrate the framework's capabilities for identifying binary sequences for hardware translation with a set of 13 benchmarks.