Abstract
The scoping review reported by this paper aimed to analyze and synthesize state-of-the-art studies focused on the application of machine learning methods to enhance the cyber resilience of cyber-physical systems. An electronic search was conducted, and 24 studies were included in this review after the selection process. The most representative application domains were computer networks and power systems, while in terms of cyber resilience functions, risk identification, risk mitigation or protection, and detection of anomalous situations were the most implemented functions. Moreover, the results of this scoping review show that the interest in the topic of cyber resilience and machine learning is quite recent, which justifies the heterogeneity of the included studies in terms of machine learning methods and datasets being used for the experimental validations, as well as in terms of outcomes being measured. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
This article aimed to analyse state-of-the-art empirical evidence of randomized controlled trials designed to assess preventive cognitive training interventions based on virtual reality for older adults without cognitive impairment, by identifying virtual reality setups and tasks, clinical outcomes and respective measurement instruments, and positive effects on outcome parameters. A systematic electronic search was performed, and six randomized controlled trials were included in the systematic review. In terms of results, the included studies pointed to significant positive impact of virtual reality-based cognitive training interventions on global cognition, memory, attention, information processing speed, walking variability, balance, muscle strength, and falls. However, further research is required to evaluate the adequacy of the virtual reality setups and tasks, to study the impact of the interventions’ duration and intensity, to understand how to tailor the interventions to the characteristics and needs of the individuals, and to compare face-to-face to remote interventions. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract
Cyber-resilience is a relatively recent concept that, in essence, adds risk management to the cybersecurity concept and extends the scope of its action to processes and people, in addition to the technological component. It aims to ensure that organizations, systems, and especially critical infrastructures of our society function properly regardless of their dependence on cybernetic resources that may be affected by adverse events. Considering that the ongoing digital transition increases the exposure of such infrastructures to physical and cyberspace threats, this article reports on an exploratory study supported by bibliographical research, which aimed to analyze recent scientific publications to determine the relevance of cyber-resilience in the context of national security and defense. Although the number of publications focused on cyber-resilience is still relatively reduced when compared to the number of publications related to cybersecurity, there is a growing interest in exploring cyber-resilience in areas such as international relations, internal security, and national defense, which are fundamental pillars of the security and defense of States. © 2024, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Abstract
Over the years, many solutions have been suggested in order to improve object detection in maritime environments. However, none of these approaches uses flight information, such as altitude, camera angle, time of the day, and atmospheric conditions, to improve detection accuracy and network robustness, even though this information is often available and captured by the UAV. This work aims to develop a network unaffected by image-capturing conditions, such as altitude and angle. To achieve this, metadata was integrated into the neural network, and an adversarial learning training approach was employed. This was built on top of the YOLOv7, which is a state-of-the-art realtime object detector. To evaluate the effectiveness of this methodology, comprehensive experiments and analyses were conducted. Findings reveal that the improvements achieved by this approach are minimal when trying to create networks that generalize more across these specific domains. The YOLOv7 mosaic augmentation was identified as one potential responsible for this minimal impact because it also enhances the model's ability to become invariant to these image-capturing conditions. Another potential cause is the fact that the domains considered (altitude and angle) are not orthogonal with respect to their impact on captured images. Further experiments should be conducted using datasets that offer more diverse metadata, such as adverse weather and sea conditions, which may be more representative of real maritime surveillance conditions. The source code of this work is publicly available at https://git hub.com/ipleiria-robotics/maritime-metadata-adaptation.

Abstract
This work addresses the contemporary challenges in computing, caused by the stagnation of Moore's Law and Dennard scaling. The shift towards heterogeneous architectures necessitates innovative compilation strategies, prompting initiatives like the Multi-Level Intermediate Representation (MLIR) project, where progressive code lowering can be achieved through the use of dialects. Our work focuses on developing an MLIR dialect capable of representing streaming data accesses to memory, and Single Instruction Multiple Data (SIMD) vector operations. We also propose our own Structured Representation Language (SRL), a Design Specific Language (DSL) to serve as a precursor into the MLIR layer and subsequent inter-operation between new and existing dialects. The SRL exposes the streaming and vector computational concepts to a higher-level, and serves as intermediate step to supporting code generation containing our proposed dialect from arbitrary input code, which we leave as future work. This paper presents the syntaxes of the SRL DSL and of the dialect, and illustrates how we aim to employ them to target both General-Purpose Processors (GPPs) with SIMD co-processors and custom hardware options such as Field-Programmable Gate Arrayss (FPGAs) and Coarse-Grained Re-configurable Arrays (CGRAs).

Abstract
Hardware specialization is seen as a promising venue for improving computing efficiency, with reconfigurable devices as excellent deployment platforms for application-specific architectures. One approach to hardware specialization is via the popular RISC-V, where Instruction Set Architecture (ISA) extensions for domains such as Edge Artifical Intelligence (AI) are already appearing. However, to use the custom instructions while maintaining a high (e.g., C/C++) abstraction level, the assembler and compiler must be modified. Alternatively, inline assembly can be manually introduced by a software developer with expert knowledge of the hardware modifications in the RISC-V core. In this paper, we consider a RISC-V core with a vectorization and streaming engine to support the Unlimited Vector Extension (UVE), and propose an approach to automatically transform annotated C loops into UVE compatible code, via automatic insertion of inline assembly. We rely on a source-to-source transformation tool, Clava, to perform sophisticated code analysis and transformations via scripts. We use pragmas to identify code sections amenable for vectorization and/or streaming, and use Clava to automatically insert inline UVE instructions, avoiding extensive modifications of existing compiler projects. We produce UVE binaries which are functionally correct, when compared to handwritten versions with inline assembly, and achieve equal and sometimes improved number of executed instructions, for a set of six benchmarks from the Polybench suite. These initial results are evidence towards that this kind of translation is feasible, and we consider that it is possible in future work to target more complex transformations or other ISA extensions, accelerating the adoption of hardware/software co-design flows for generic application cases.