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Publications

2024

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

Authors
Marques, P; Padua, L; Sousa, JJ; Fernandes Silva, A;

Publication
REMOTE SENSING

Abstract
This systematic review explores the role of remote sensing technology in addressing the requirements of sustainable olive growing, set against the backdrop of growing global food demands and contemporary environmental constraints in agriculture. The critical analysis presented in this document assesses different remote sensing platforms (satellites, manned aircraft vehicles, unmanned aerial vehicles and terrestrial equipment) and sensors (RGB, multispectral, thermal, hyperspectral and LiDAR), emphasizing their strategic selection based on specific study aims and geographical scales. Focusing on olive growing, particularly prominent in the Mediterranean region, this article analyzes the diverse applications of remote sensing, including the management of inventory and irrigation; detection/monitoring of diseases and phenology; and estimation of crucial parameters regarding biophysical parameters, water stress indicators, crop evapotranspiration and yield. Through a global perspective and insights from studies conducted in diverse olive-growing regions, this review underscores the potential benefits of remote sensing in shaping and improving sustainable agricultural practices, mitigating environmental impacts and ensuring the economic viability of olive trees.

2024

Enhancing IoT Security in Vehicles: A Comprehensive Review of AI-Driven Solutions for Cyber-Threat Detection

Authors
Abreu, R; Simao, E; Serôdio, C; Branco, F; Valente, A;

Publication
AI

Abstract
Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people's daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This connectivity allows for a better integration of the pervasive computing, making devices smart and capable of interacting with each other and with the corresponding users in a sublime way. However, the widespread adoption of IoT devices has introduced some security challenges, because these devices usually run in environments that have limited resources. As IoT technology becomes more integrated into critical infrastructure and daily life, the need for stronger security measures will increase. These devices are exposed to a variety of cyber-attacks. This literature review synthesizes the current research of artificial intelligence (AI) technologies to improve IoT security. This review addresses key research questions, including: (1) What are the primary challenges and threats that IoT devices face?; (2) How can AI be used to improve IoT security?; (3) What AI techniques are currently being used for this purpose?; and (4) How does applying AI to IoT security differ from traditional methods? Methods: We included a total of 33 peer-reviewed studies published between 2020 and 2024, specifically in journal and conference papers written in English. Studies irrelevant to the use of AI for IoT security, duplicate studies, and articles without full-text access were excluded. The literature search was conducted using scientific databases, including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. Results were synthesized through a narrative synthesis approach, with the help of the Parsifal tool to organize and visualize key themes and trends. Results: We focus on the use of machine learning, deep learning, and federated learning, which are used for anomaly detection to identify and mitigate the security threats inherent to these devices. AI-driven technologies offer promising solutions for attack detection and predictive analysis, reducing the need for human intervention more significantly. This review acknowledges limitations such as the rapidly evolving nature of IoT technologies, the early-stage development or proprietary nature of many AI techniques, the variable performance of AI models in real-world applications, and potential biases in the search and selection of articles. The risk of bias in this systematic review is moderate. While the study selection and data collection processes are robust, the reliance on narrative synthesis and the limited exploration of potential biases in the selection process introduce some risk. Transparency in funding and conflict of interest reporting reduces bias in those areas. Discussion: The effectiveness of these AI-based approaches can vary depending on the performance of the model and the computational efficiency. In this article, we provide a comprehensive overview of existing AI models applied to IoT security, including machine learning (ML), deep learning (DL), and hybrid approaches. We also examine their role in enhancing the detection accuracy. Despite all the advances, challenges still remain in terms of data privacy and the scalability of AI solutions in IoT security. Conclusion: This review provides a comprehensive overview of ML applications to enhance IoT security. We also discuss and outline future directions, emphasizing the need for collaboration between interested parties and ongoing innovation to address the evolving threat landscape in IoT security.

2024

Exposing and Explaining Fake News On-the-Fly

Authors
Arriba Pérez, Fd; Méndez, SG; Leal, F; Malheiro, B; Burguillo, JC;

Publication
CoRR

Abstract

2024

GRAVITY plus Wide: Towards hundreds of z ~ 2 AGN, larger throughput and improved vibrational control

Authors
Fabricius, M; Woillez, J; Abuter, R; Bourdarot, G; Bourget, P; Brandner, W; Brara, A; Defrère, D; Drescher, A; Eisenhauer, F; Feuchtgruber, H; Frahm, R; Genzel, R; Gillessen, S; Gonté, F; Gopinath, V; Graf, J; Hartl, M; Haussmann, F; Hönig, SF; Horrobin, M; Garcia, PJ; Jilg, T; Kreidberg, L; Laugier, R; Le Bouquin, JB; Bolzer, ML; Lutz, D; More, N; Ott, T; Özdemir, H; Paumard, T; Perraut, K; Perrin, G; Rau, C; Rehm, C; Sauter, J; Schuhler, N; Schuppe, D; Shangguan, JY; Shimizu, T; Straubmeier, C; Subroweit, M; Uysal, S; Wessely, P; Widmann, F; Wieprecht, E; Wimmer, L; Yazici, S; Prowatke, H; Böttcher, R;

Publication
OPTICAL AND INFRARED INTERFEROMETRY AND IMAGING IX

Abstract
In the GRAVITY+ project, GRAVITY is presently undergoing a series of upgrades to enhance its performance, add wide field capability and thereby expand its sky coverage. Some aspects of these improvements have already been implemented and commissioned by the end of 2021, making them accessible to the community. The augmentation of sky coverage involves increasing the maximum angular separation between the celestial science object and the fringe tracking object from the previous 2 arcseconds (limited by the field of view of the VLTI) to 20 - 30 arcseconds (constrained by atmospheric conditions during observation). Phase 1 of GRAVITY+ Wide utilizes the earlier PRIMA Differential Delay Lines to compensate for the optical path length variation between the science and fringe tracking beams throughout an observation. In phase 2, we are upgrading the existing beam compressors (BC) to integrate optical path length difference compensation directly into the BC. This modification eliminates five optical reflections per beam, thereby enhancing the optical throughput of the VLTI-GRAVITY [GRAPHICS] system and the bandwidth of the vibrational control. We will present the implementation of phase 2 and share preliminary results from our testing activities for GRAVITY+ Wide.

2024

How are Contracts Used in Android Mobile Applications?

Authors
Ferreira, DR; Mendes, A; Ferreira, JF;

Publication
2024 ACM/IEEE 44TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS, ICSE-COMPANION 2024

Abstract
Formal contracts and assertions are effective methods to enhance software quality by enforcing preconditions, postconditions, and invariants. However, the adoption and impact of contracts in the context of mobile application development, particularly of Android applications, remain unexplored. We present the first large-scale empirical study on the presence and use of contracts in Android applications, written in Java or Kotlin. We consider 2,390 applications and five categories of contract elements: conditional runtime exceptions, APIs, annotations, assertions, and other. We show that most contracts are annotation-based and are concentrated in a small number of applications.

2024

Optimal gas subset selection for dissolved gas analysis in power transformers

Authors
Pinto, J; Esteves, V; Tavares, S; Sousa, R;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE

Abstract
The power transformer is one of the key components of any electrical grid, and, as such, modern day industrialization activities require constant usage of the asset. This increases the possibility of failures and can potentially diminish the lifespan of a power transformer. Dissolved gas analysis (DGA) is a technique developed to quantify the existence of hydrocarbon gases in the content of the power transformer oil, which in turn can indicate the presence of faults. Since this process requires different chemical analysis for each type of gas, the overall cost of the operation increases with number of gases. Thus said, a machine learning methodology was defined to meet two simultaneous objectives, identify gas subsets, and predict the remaining gases, thus restoring them. Two subsets of equal or smaller size to those used by traditional methods (Duval's triangle, Roger's ratio, IEC table) were identified, while showing potentially superior performance. The models restored the discarded gases, and the restored set was compared with the original set in a variety of validation tasks.

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