Abstract

Abstract
Occupational safety is a major concern these days because it is an important social issue promoting financial implications for organisations, employees, and society. But while occupational safety is an important concern, the top management of organizations usually prioritize waste and cost reduction. Therefore, there is a need for a technique that reduces waste and simultaneously improves occupational safety. Lean has been effective in reducing waste and costs. Some researchers have shown that Lean can also improve occupational safety. The objective of this work was to determine wether, in organizations where Lean tools were implemented, if there was an improvement in occupational safety conditions, namely in the reduction of accident rates, and to verify which Lean tools contributed the most to that improvement. A survey was conducted by sending a questionnaire to Portuguese organizations, from north to south and islands, who had potentially Lean tools implemented. In total, 189 answers have been obtained from organizations, 59 of which had Lean tools implemented, being considered valid answers for the study. Through statistical analysis of the data obtained, it was found that no organisations had worsened their safety indicators, some had maintained the same level and a reasonable number stated that their indicators had improved. Of these, the vast majority said that their accident rates had decreased by 20%, this being the figure that statistically showed the best results in terms of change.

Abstract
A sensor based on a balloon-like interferometer and a spring-shaped structure for micro curvature measurement is proposed and experimentally demonstrated. The sensor is composed by singlemode fiber inserted into a capillary tube. The experimental results show micro-curvature sensitivities of -35.04 pm/mu m, -28.07 pm/mu m e -18.7 pm/mu m in the range from 0 to 200 mu m for three resonants dips lambda(1), lambda(2) and lambda(3), respectively. In addition, the sensor has advantages of easy fabrication, low cost, and satisfactory sensitivity, which shows good results of sensing of micro curvature in some applications.

Abstract
Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. In practice this can lead to low detection rates and high false positive rates, which characterise for example anti-money laundering systems. In fact, it is estimated that 1.7-4 trillion euros are laundered annually and go undetected. We propose The GANfather, a method to generate samples with properties of malicious activity, without label requirements. We propose to reward the generation of malicious samples by introducing an extra objective to the typical Generative Adversarial Networks (GANs) loss. Ultimately, our goal is to enhance the detection of illicit activity using the discriminator network as a novel and robust defence system. Optionally, we may encourage the generator to bypass pre-existing detection systems. This setup then reveals defensive weaknesses for the discriminator to correct. We evaluate our method in two real-world use cases, money laundering and recommendation systems. In the former, our method moves cumulative amounts close to 350 thousand dollars through a network of accounts without being detected by an existing system. In the latter, we recommend the target item to a broad user base with as few as 30 synthetic attackers. In both cases, we train a new defence system to capture the synthetic attacks.

Abstract
Semantic measures evaluate and compare the strength of relations between entities. To assess their accuracy, semantic measures are compared against human-generated gold standards. Existing semantic gold standards are mainly focused on concepts. Nevertheless, semantic measures are frequently applied both to concepts and instances. Games with a purpose are used to offload to humans computational or data collection needs, improving results by using entertainment as motivation for higher engagement. We present Grettir, a system which allows the creation of crowdsourced semantic relations datasets for named entities through a game with a purpose where participants are asked to compare pairs of entities. We describe the system architecture, the algorithms and implementation decisions, the first implemented instance – dedicated to the comparison of music artists – and the results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
We present Jay, a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.