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Projeto de investigação
Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
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Protecting Hybrid ITS Networks
Publication . Severino, Ricardo; Simão, José; Datia, Nuno; Serrador, António; NOVALincs; MDPI - Multidisciplinary Digital Publishing Institute
Cooperative intelligent transport systems (C-ITS) continue to be developed to enhance transportation safety and sustainability. However, the communication of vehicle-to-everything (V2X) systems is inherently open, leading to vulnerabilities that attackers can exploit. This represents a threat to all road users, as security failures can lead to privacy violations or even fatalities. Moreover, a high fatality rate is correlated with soft-mobility road users. Therefore, when developing C-ITS systems, it is important to broaden the focus beyond connected vehicles to include soft-mobility users and legacy vehicles. This work presents a new approach developed in the context of emerging hybrid networks, combining intelligent transport systems operating in 5.9 GHz (ITS-G5) and radio-mobile cellular technologies. Two protocols were implemented and evaluated to introduce security guarantees (such as privacy and integrity) in communications within the developed C-ITS hybrid environment. As a result, this work securely integrates G5-connected ITS stations and soft-mobility users through a smartphone application via cellular networks. Commercial equipment was used for this goal, including on-board and roadside units. Computational, transmission and end-to-end latency were used to assess the system’s performance. Implemented protocols introduce an additional 11% end-to-end latency in hybrid communications. Moreover, workflows employing hybrid communications impose, on average, an extra 28.29 ms of end-to-end latency. The proposal shows promise, as it reaches end-to-end times below the latency requirements imposed in most C-ITS use cases.
On the challenges of predicting treatment response in Hodgkin’s Lymphoma using transcriptomic data
Publication . Patrício, André; Costa, Rafael S.; Henriques, Rui; LAQV@REQUIMTE; DQ - Departamento de Química; BioMed Central (BMC)
Background: Despite the advancements in multiagent chemotherapy in the past years, up to 10% of Hodgkin’s Lymphoma (HL) cases are refractory to treatment and, after remission, patients experience an elevated risk of death from all causes. These complications are dependent on the treatment and therefore an increase in the prognostic accuracy of HL can help improve these outcomes and control treatment-related toxicity. Due to the low incidence of this cancer, there is a lack of works comprehensively assessing the predictability of treatment response, especially by resorting to machine learning (ML) advances and high-throughput technologies. Methods: We present a methodology for predicting treatment response after two courses of Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapy, through the analysis of gene expression profiles using state-of-the-art ML algorithms. We work with expression levels of tumor samples of Classical Hodgkin’s Lymphoma patients, obtained through the NanoString’s nCounter platform. The presented approach combines dimensionality reduction procedures and hyperparameter optimization of various elected classifiers to retrieve reference predictability levels of refractory response to ABVD treatment using the regulatory profile of diagnostic tumor samples. In addition, we propose a data transformation procedure to map the original data space into a more discriminative one using biclustering, where features correspond to discriminative putative regulatory modules.Results: Through an ensemble of feature selection procedures, we identify a set of 14 genes highly representative of the result of an fuorodeoxyglucose Positron Emission Tomography (FDG-PET) after two courses of ABVD chemotherapy. The proposed methodology further presents an increased performance against reference levels, with the proposed space transformation yielding improvements in the majority of the tested predictive models (e.g. Decision Trees show an improvement of 20pp in both precision and recall).Conclusions: Taken together, the results reveal improvements for predicting treatment response in HL disease by resorting to sophisticated statistical and ML principles. This work further consolidates the current hypothesis on the structural difficulty of this prognostic task, showing that there is still a considerable gap to be bridged for these technologies to reach the necessary maturity for clinical practice.
Financial Analysis of Household Photovoltaic Self-Consumption in the Context of the Vehicle-to-Home (V2H) in Portugal
Publication . Nagel, Rafael G.; Pires, Vítor Fernão; Silveira, Jony L.; Cordeiro, Armando; Foito, Daniel; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; MDPI - Multidisciplinary Digital Publishing Institute
This paper focuses on the purpose to see if it is possible to increase the earnings associated to the installation of PV systems in people’s homes. In accordance with this, a different way of thinking was adopted, namely the investment in batteries to maximize the energy earnings. The main problem of this classical approach is that the investment in those batteries is important. In this way, a different perspective was taken into account, namely the use of the electrical vehicles. This kind of vehicles is starting to become a real reality. In fact, the selling of these vehicles start to become a solution for the ordinary people, and it is expected in a very near future to be a reality for most of them. Thus, this study presents the use of a storage system based on the vehicle-to-home (V2H) technology for the people’s homes. The V2H availability varies among prosumers profile regarding the daily routines, weather conditions, and business aspects, besides other aspects. These profiles were combined with different power panels with and without injection into the grid. The costs of each configuration considering a residential consumer located in Portugal, as well as, their peak solar hours in a year were estimated. From this study, it will be possible to verify that the obtained economical results show that the usage of V2H as storage system based on batteries for modern homes is very attractive.
Multilingual Vision-Language Pre-training for the Remote Sensing Domain
Publication . Silva, João Daniel; Magalhães, João; Tuia, Devis; Martins, Bruno; Faculdade de Ciências e Tecnologia (FCT); NOVALincs
Methods based on Contrastive Language-Image Pre-training (CLIP) are nowadays extensively used in support of vision-and-language tasks involving remote sensing data, such as cross-modal retrieval. The adaptation of CLIP to this specific domain has relied on model fine-tuning with the standard contrastive objective, using existing human-labeled image-caption datasets, or using synthetic data corresponding to image-caption pairs derived from other annotations over remote sensing images (e.g., object classes). The use of different pre-training mechanisms has received less attention, and only a few exceptions have considered multilingual inputs. This work proposes a novel vision-and-language model for the remote sensing domain, exploring the fine-tuning of a multilingual CLIP model and testing the use of a self-supervised method based on aligning local and global representations from individual input images, together with the standard CLIP objective. Model training relied on assembling pre-existing datasets of remote sensing images paired with English captions, followed by the use of automated machine translation into nine additional languages. We show that translated data is indeed helpful, e.g. improving performance also on English. Our resulting model, which we named Remote Sensing Multilingual CLIP (RS-M-CLIP), obtains state-of-the-art results in a variety of vision-and-language tasks, including cross-modal and multilingual image-text retrieval, or zero-shot image classification.
Artificial intelligence development races in heterogeneous settings
Publication . Cimpeanu, Theodor; Santos, Francisco C.; Pereira, Luís Moniz; Lenaerts, Tom; Han, The Anh; NOVALincs; Nature Publishing Group
Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues. With the great benefits promised from being able to first supply such technologies, safety precautions and societal consequences might be ignored or shortchanged in exchange for speeding up the development, therefore engendering a racing narrative among the developers. Starting from a game-theoretical model describing an idealised technology race in a fully connected world of players, here we investigate how different interaction structures among race participants can alter collective choices and requirements for regulatory actions. Our findings indicate that, when participants portray a strong diversity in terms of connections and peer-influence (e.g., when scale-free networks shape interactions among parties), the conflicts that exist in homogeneous settings are significantly reduced, thereby lessening the need for regulatory actions. Furthermore, our results suggest that technology governance and regulation may profit from the world’s patent heterogeneity and inequality among firms and nations, so as to enable the design and implementation of meticulous interventions on a minority of participants, which is capable of influencing an entire population towards an ethical and sustainable use of advanced technologies.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/50021/2020