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PubblicazioneFocused Test Generation for Autonomous Driving Systems( 2024)Testing Autonomous Driving Systems (ADSs) is crucial to ensure their reliability when navigating complex environments. ADSs may exhibit unexpected behaviours when presented, during operation, with driving scenarios containing features inadequately represented in the training dataset. To address this shift from development to operation, developers must acquire new data with the newly observed features. This data can be then utilised to fine tune the ADS, so as to reach the desired level of reliability in performing driving tasks. However, the resource-intensive nature of testing ADSs requires efficient methodologies for generating targeted and diverse tests.In this work, we introduce a novel approach, DeepAtash-LR, that incorporates a surrogate model into the focused test generation process. This integration significantly improves focused testing effectiveness and applicability in resource-intensive scenarios. Experimental results show that the integration of the surrogate model is fundamental to the success of DeepAtash-LR. Our approach was able to generate an average of up to 60× more targeted, failure-inducing inputs compared to the baseline approach. Moreover, the inputs generated by DeepAtash-LR were useful to significantly improve the quality of the original ADS through fine tuning.
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PubblicazioneSorry, Your HIT Is Overbooked - Investigating the Use of Crowdsourcing HIT Catchers( 2025)In microtask crowdsourcing, Human Intelligence Tasks (HITs) are commonly allocated on a first-come, first-served basis: they are published on the platform and the fastest workers select the most attractive ones first. This step has not received much attention from the scientific community yet, though it can become particularly taxing for workers when they compete to secure the most sought-after tasks. There are many strategies to ensure one's access to tasks and their effects on the labour process as a whole are not well understood. For instance, platforms with a sizeable task reservation queue allow workers to gain preferential access to a large number of tasks, which in turn may cause a shortage of work for the rest of the crowd. For the requesters, this means lower rates of completion and a lack of worker diversity. We explore workers' strategies for accessing and reserving tasks using monitoring techniques from both client and server sides. We investigate how these strategies affect task execution, in terms of availability, completion time, and answer quality, by deploying 1000 image annotation HITs in Amazon Mechanical Turk including objective and subjective tasks. We observe that workers who do not use automated catching techniques tend to have higher annotation quality, are more focused, spend more effort on text editing, and provide a higher diversity of output than workers using such tools. This study also reveals the tragedy of the commons effect among platform members due to the use of catching techniques: workers using automated catching techniques reserve and complete a substantially higher portion of the available tasks, but the over-reservation of HITs restricts all workers of reservation opportunities, and compromise their own future labour capacity as well. We observe a high inefficiency in job completions, as the majority of the times a task is being reserved by a worker, it will not get actually performed and will need to be republished for further allocation. Finally, we propose solutions to mitigate the negative effects of these phenomena on the labour process.
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PubblicazioneFrom Edge to Cloud: Securing Distributed Containerized Applications(Institute of Electrical and Electronics Engineers Inc., 2025)The adoption of containers in complex software systems is rapidly increasing, due to their flexibility that facilitates integration, scalability, and dynamic deployment. However, assessing the security of container-based applications remains challenging in distributed and heterogeneous environments: The scale and diversity of deployment scenarios call for sophisticated security evaluation and verification techniques. In this paper, we present Project SecCo (Securing Containers), whose aim is to develop an innovative framework for the systematic integration of security assessment services into the Continuous Integration and Continuous Delivery (CI/CD) DevOps pipeline. The framework orchestrates automatic services to prevent and reduce vulnerabilities in the design, implementation, and deployment phases, and to mitigate runtime attacks. This allows developers and IT operators to focus on integration and delivery, reducing security management tasks. Finally, the paper highlights the main research challenges for realizing this vision.
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PubblicazioneThe use of Pietra Piasentina stone for more sustainable cement( 2025)This research focuses on the use of waste materials from other production processes available within the national territory, obtaining the dual advantage of effectively reducing the CO2 released, but also of reusing substances that would otherwise have to be sent to landfill. In particular, the study concerns Piasentina Stone, which is a stone available in the Italian region of Friuli Venezia Giulia and which is used for flooring and decorative elements. To obtain the finished material ready for sale, the manufacturing process is characterized by more than 50% of waste products (large stone elements, crushed stone and cutting mud). The composition of the stone is characterized by a percentage of limestone greater than 95%, which makes it perfectly suitable both as a material to be used in the kilns for the production of clinker, and as a substitute for clinker in binary and composite cements. A campaign of chemical-physical investigations was conducted on the stone to evaluate its suitability with respect to the Codes limitations. Analyses on mortars obtained with partial replacement of clinker have been developed. The purpose of this research is to provide a possible alternative to obtain a more sustainable cement with a natural by-product widely available.
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