Abstract
Vulnerability detection and repair is a demanding and expensive part of the software development process. As such, there has been an effort to develop new and better ways to automatically detect and repair vulnerabilities. DifFuzz is a state-of-the-art tool for automatic detection of timing side-channel vulnerabilities, a type of vulnerability that is particularly difficult to detect and correct. Despite recent progress made with tools such as DifFuzz, work on tools capable of automatically repairing timing side-channel vulnerabilities is scarce. In this paper, we propose DifFuzzAR, a new tool for automatic repair of timing side-channel vulnerabilities in Java code. The tool works in conjunction with DifFuzz and it is able to repair 56% of the vulnerabilities identified in DifFuzz's dataset. The results show that the tool can indeed automatically correct timing side-channel vulnerabilities, being more effective with those that are controlflow based.

Abstract
Reliable detection of gait events is important to ensure accurate assessment of gait. While it is usually performed resorting to force platforms, methods based uniquely on kinematic analysis have also been proposed. These methods place no restrictions on the number of steps that can be analysed, simplifying setup and complexity of assessments. They also replace the need of annotating events manually when force platforms are not available. Although few methods have been proposed in literature, validation studies are relatively scarce. In this study we present multiple methods for the detection of heel strike (HS) and toe off (TO) in normal walking, and validate the detection against annotated events using three different datasets. The best performing candidates are based on the evaluation of heel vertical velocity (for HS) and toe vertical acceleration (for TO), resulting in relative errors of -12.4 +/- 32.9 ms for HS and of -15.5 +/- 24.9 ms for TO. The method is compatible with barefoot and shod walking, constituting a convenient, fast and reliable alternative to automatic gait event detection using kinematic data.

Abstract
The main goal of this systematic review is to synthesize existing evidence on the use of immersive virtual reality (IVR) to train professionals as well as to identify the main gaps and challenges that still remain and need to be addressed by future research. Following a comprehensive search, 66 documents were identified, assessed for relevance, and analysed. The main areas of application of IVR-based training were identified. Moreover, we identified the stimuli provided, the hardware used and information regarding training evaluation. The results showed that the areas in which a greater number of works were published were those related to healthcare and elementary occupations. In hardware, the most commonly used equipment was head mounted displays (HMDs), headphones included in the HMDs and handheld controllers. Moreover, the results indicated that IVR training systems are often evaluated manually, the most common metric being questionnaires applied before and after the experiment, and that IVR training systems have a positive effect in training professionals. We conclude that the literature is insufficient for determining the effect of IVR in the training of professionals. Although some works indicated promising results, there are still relevant themes that must be explored and limitations to overcome before virtual training replaces real-world training.

Abstract
This paper proposes a camera-to-3D Light Detection And Ranging calibration framework through the optimization of atomic transformations. The system is able to simultaneously calibrate multiple cameras with Light Detection And Ranging sensors, solving the problem of Bundle. In comparison with the state-of-the-art, this work presents several novelties: the ability to simultaneously calibrate multiple cameras and LiDARs; the support for multiple sensor modalities; the calibration through the optimization of atomic transformations, without changing the topology of the input transformation tree; and the integration of the calibration framework within the Robot Operating System (ROS) framework. The software pipeline allows the user to interactively position the sensors for providing an initial estimate, to label and collect data, and visualize the calibration procedure. To test this framework, an agricultural robot with a stereo camera and a 3D Light Detection And Ranging sensor was used. Pairwise calibrations and a single calibration of the three sensors were tested and evaluated. Results show that the proposed approach produces accurate calibrations when compared to the state-of-the-art, and is robust to harsh conditions such as inaccurate initial guesses or small amount of data used in calibration. Experiments have shown that our optimization process can handle an angular error of approximately 20 degrees and a translation error of 0.5 meters, for each sensor. Moreover, the proposed approach is able to achieve state-of-the-art results even when calibrating the entire system simultaneously.

Abstract
Purpose The COVID-19 pandemic has significantly impacted the European Union (EU) through heavy pressure on health services, business activity and people's life. To mitigate these effects, government agencies, civil society and the private sector are working together in proposing innovative initiatives. In this sense, this study aims to characterize and explore the relevance of these projects to mitigate the effects of COVID-19. Design/methodology/approach The Observatory of Public Sector Innovation provided by the Organization for Economic Co-operation and Development was considered to enable the identification and exploration of innovative projects to combat COVID-19. A methodology based on mixed methods is adopted to initially identify quantitatively the distribution of these projects, followed by a qualitative approach based on thematic analysis that allows exploring their relevance. Findings A total of 206 initiatives in the EU have been identified. The distribution of these projects is quite asymmetric, with Portugal and Austria totaling 33.52% of these projects. Most of these projects focus on the areas of public health, infection detection and control, virtual education, local commerce, digital services literacy, volunteering and solidarity and hackathons. Originality/value This work is relevant to identifying and understanding the various areas in which COVID-19 initiatives have been developed. This information is of great relevance for the actors involved in this process to be able to replicate these initiatives in their national, regional and local contexts.

Abstract
Deep Learning (DL) training requires efficient access to large collections of data, leading DL frameworks to implement individual I/O optimizations to take full advantage of storage performance. However, these optimizations are intrinsic to each framework, limiting their applicability and portability across DL solutions, while making them inefficient for scenarios where multiple applications compete for shared storage resources. We argue that storage optimizations should be decoupled from DL frameworks and moved to a dedicated storage layer. To achieve this, we propose a new Software-Defined Storage architecture for accelerating DL training performance. The data plane implements self-contained, generally applicable I/O optimizations, while the control plane dynamically adapts them to cope with workload variations and multi-tenant environments. We validate the applicability and portability of our approach by developing and integrating an early prototype with the TensorFlow and PyTorch frameworks. Results show that our I/O optimizations significantly reduce DL training time by up to 54% and 63% for TensorFlow and PyTorch baseline configurations, while providing similar performance benefits to framework-intrinsic I/O mechanisms provided by TensorFlow.