Parthenope University of Naples
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Digital Skills and Maturity in the Public Sector: A Review of EU Guidelines
No full textThe development and application of digital technology have a strong impact on the way the public sector operates today and require public servants to develop and strengthen their skills. This study provides a scoping review examining grey literature around digitalization practices and digital skills in the public sector in EU member countries and proposes a framework for analyzing twenty-first-century digital skills in the public sector as per different stages of digitalization maturity. To uncover relevant “grey” literature, an established methodology was used including a search on Google.com using keywords identified from existing literature on digitalization HRM practices in the public sector. Out of 630 sources, seven relevant documents related to the relevant guidelines were included in the analysis. Then, two additional documents were added, making a total of nine documents. These were analyzed qualitatively with the help of an integrated framework of twenty-first-century digital skills, organizational dimensions, and digitalization maturity stages. This review contributes to the body of knowledge on digital transformation in the public sector by proposing a framework that can serve as a guide for understanding what digital competencies are essential and applicable at different phases of the digital transformation process. It demonstrates the importance of comprehensive policy guidelines that are linked with digital skills development. This study also suggests future research directions, emphasizing the need for continuous adaptation and advancement of digital skills in public sector training and development programs
Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control
No full textHyperspectral (HS) pansharpening has received much attention in recent years due to technological and methodological advances that open the door to new application scenarios. However, research on this topic is only now gaining momentum. The most popular methods are still borrowed from the more mature field of multispectral pansharpening and often overlook the unique challenges posed by HS data fusion, such as: 1) the very large number of bands; 2) the overwhelming noise in selected spectral ranges; 3) the significant spectral mismatch between panchromatic (PAN) and HS components; and 4) a typically high resolution ratio. Imprecise data modeling especially affects spectral fidelity. Even state-of-the-art (SotA) methods perform well in certain spectral ranges and much worse in others, failing to ensure consistent quality across all bands, with the risk of generating unreliable results. Here, we propose an HS pansharpening method that explicitly addresses this problem and ensures uniform spectral quality. To this end, a single lightweight neural network is used, with weights that adapt on the fly to each band. During fine-tuning, the spatial loss is turned on and off to ensure a fast convergence of the spectral loss to the desired level, according to a hysteresis-like dynamic. Furthermore, the spatial loss itself is appropriately redefined to account for nonlinear dependencies between PAN and spectral bands. Overall, the proposed method is fully unsupervised, with no prior training on external data, flexible, and low-complexity. Experiments on a recently published benchmarking toolbox show that it ensures excellent sharpening quality, competitive with the SotA, consistently across all bands
Disentangling the S and the G in the European banking industry: the role of climate risk and opportunity awareness.
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LE GARANZIE PROCEDURALI DEL DSA E I LIMITI EURO-UNITARI AL POTERE PRIVATO DELLE PIATTAFORME DIGITALI
No full textIl saggio analizza il Digital Services Act(Regolamento UE n. 2022/2065) quale paradigma innovativo di regolamentazione delle piattaforme digitali, esaminandone le implicazioni costituzionali e amministrative nel contesto del costituzionalismo digitale europeo. L'analisi evidenzia come il regolamento introduca significative garanzie procedurali mutuate dal diritto amministrativo: trasparenza, motivazione, non discriminazione e spiegabilità delle decisioni algoritmiche. Il sistema di co-regolazione attraverso codici di condotta costituisce un elemento caratterizzante del DSA, configurandosi come strumento di enforcement complementare alla normativa, con particolare attenzione ai rischi sistemici quali disinformazione, hate speeche manipolazione. Altro elemento caratterizzante è il sistema di vigilanza, improntato ad una logica di networking multilivello, sintomo di una progressiva amministrativizzazione del sistema dell’UE. Il DSA, pur rappresentando un punto di partenza piuttosto che di arrivo nella regolazione delle piattaforme digitali, è inteso come un tentativo di riequilibrio del rapporto asimmetrico tra utenti e Big Techattraverso la proceduralizzazione delle garanzie.This essay analyzes the Digital Services Act (EU Regulation no. 2022/2065) as an innovative paradigm for regulating digital platforms, examining its constitutional and administrative implications in the context of European digital constitutionalism. The analysis highlights how the regulation introduces significant procedural guarantees borrowed from administrative law: transparency, motivation, non-discrimination and explainability of algorithmic decisions. The system of co-regulation through codes of conduct is a defining feature of the DSA, acting as a complementary enforcement tool to the legislation, with a particular focus on systemic risks such as disinformation, hate speech, and manipulation. At the same time, the supervision system, based on a logic of multilevel networking, has to be noticed. It appears to be the symptom of a EU gradual «administrativization». The DSA, while representing a starting point rather than an end point in the regulation of digital platforms, is intended as an attempt to rebalance the asymmetrical relationship between users and Big Tech through the proceduralization of safeguards
Correct by design, complete by iteration: A graph-based framework for automated security assessment
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Deep Learning for Regular Raster Spatio-Temporal Prediction: An Overview
No full textThe raster is the most common type of spatio-temporal data, and it can be either regularly or irregularly spaced. Spatio-temporal prediction on regular raster data is crucial for modelling and understanding dynamics in disparate realms, such as environment, traffic, astronomy, remote sensing, gaming and video processing, to name a few. Historically, statistical and classical machine learning methods have been used to model spatio-temporal data, and, in recent years, deep learning has shown outstanding results in regular raster spatio-temporal prediction. This work provides a self-contained review about effective deep learning methods for the prediction of regular raster spatio-temporal data. Each deep learning technique is described in detail, underlining its advantages and drawbacks. Finally, a discussion of relevant aspects and further developments in deep learning for regular raster spatio-temporal prediction is presented