Abstract
Pedestrian behavior is an essential subject of study when developing or enhancing urban infrastructure. However, most behavior elicitation techniques are inherently bound to be biased by either the observer, the subject, or the environment. The SIMUSAFE project aims at collecting road users’ behavioral data in naturalistic and realistic scenarios to produce more accurate decision-making models. Using video captured from a monocular camera worn by a pedestrian, we employ machine learning and computer vision techniques to identify areas of interest surrounding a pedestrian. Namely, we use object detection and depth estimation to generate a map of obstacles that may influence the pedestrian’s actions. Our methods have shown to be successful in detecting free and occupied areas from monocular video. © 2020, Springer Nature Switzerland AG.

Abstract
Complex data management on healthcare institutions makes very hard to identify illegitimate accesses which is a serious issue. We propose to develop a system to detect accesses with suspicious behavior for further investigation. We modeled use cases (UC) and sequence diagrams (SD) showing the data flow between users and systems. The algorithms represented by activity diagrams apply rules based on professionals' routines, use data from an audit trail (AT) and classify accesses as suspicious or normal. The algorithms were evaluated between 23rd and 31st July 2019. The results were analyzed using absolute and relative frequencies and dispersion measures. Access classification was in accordance to rules applied. "Check time of activity" UC had 64,78% of suspicious classifications, being 55% of activity period shorter and 9,78% longer than expected, "Check days of activity" presented 2,27% of suspicious access and "EHR read access" 79%, the highest percentage of suspicious accesses. The results show the first picture of HIS accesses. Deeper analysis to evaluate algorithms sensibility and specificity should be done. Lack of more detailed information about professionals' routines and systems. and low quality of systems logs are some limitations. Although we believe this is an important step in this field.

Abstract
Promotional 360 degrees videos are now widely used to promote touristic sites, giving consumers a more immersive glimpse of what they can expect from those places. However, these 360 degrees videos often comprise so much information that it overloads the users, not allowing them to benefit from such a rich multimedia experience. To overcome this issue, we propose and evaluate a novel method that allows the experience of immersive 360 degrees promotional videos to be more interactive and informative without overloading users. The evaluation study focuses on how the proposed interaction method performs versus the non-interactive method in terms of user satisfaction, presence, and cybersickness in both a low-immersive (computer monitor) setup and an immersive platform (head-mounted display (HMD)). Our sample (N = 50) was randomly divided into four groups: 360 degrees (computer monitor without interaction), 360 degrees Interaction (computer monitor with interaction), IVR360 degrees (HMD without interaction) and IVR360 degrees Interaction (HMD with interaction). The results show that the novel proposed method is preferred by users over the non-interactive approach regardless of the setup (low-immersive or immersive). For cybersickness, there were no differences across all the experimental scenarios. We conclude that our method has the potential to bring added value to touristic promotion when compared to conventional promotional approaches.

Abstract
The breakdown of Dennard scaling has resulted in a decade-long stall of the maximum operating clock frequencies of processors. To mitigate this issue, computing shifted to multi-core devices. This introduced the need for programming flows and tools that facilitate the expression of workload parallelism at high abstraction levels. However, not all workloads are easily parallelizable, and the minor improvements to processor cores have not significantly increased single-threaded performance. Simultaneously, Instruction Level Parallelism in applications is considerably underexplored. This article reviews notable approaches that focus on exploiting this potential parallelism via automatic generation of specialized hardware from binary code. Although research on this topic spans over more than 20 years, automatic acceleration of software via translation to hardware has gained new importance with the recent trend toward reconfigurable heterogeneous platforms. We characterize this kind of binary acceleration approach and the accelerator architectures on which it relies. We summarize notable state-of-the-art approaches individually and present a taxonomy and comparison. Performance gains from 2.6x to 5.6x are reported, mostly considering bare-metal embedded applications, along with power consumption reductions between 1.3x and 3.9x. We believe the methodologies and results achievable by automatic hardware generation approaches are promising in the context of emergent reconfigurable devices.

Abstract
This paper introduces a sort of automata and associated languages, often arising in modelling natural phenomena, in which both vagueness and simultaneity are taken as first class citizens. This requires a fuzzy semantics assigned to transitions and a precise notion of a synchronous product to enforce the simultaneous occurrence of actions. The expected relationships between automata and languages are revisited in this setting; in particular it is shown that any subset of a fuzzy synchronous language with the suitable signature forms a synchronous Kleene algebra.

Abstract