Toward a Formal Semantics for Autonomic Components

Autonomic management can improve the QoS provided by parallel/ distributed applications. Within the CoreGRID Component Model, the autonomic management is tailored to the automatic - monitoring-driven - alteration of the component assembly and, therefore, is defined as the effect of (distributed) management code. This work yields a semantics based on hypergraph rewriting suitable to model the dynamic evolution and non-functional aspects of Service Oriented Architectures and component-based autonomic applications. In this regard, our main goal is to provide a formal description of adaptation operations that are typically only informally specified. We contend that our approach makes easier to raise the level of abstraction of management code in autonomic and adaptive applications.Comment: 11 pages + cover pag

Slope stability analyses and geological risk reduction: two case studies, from engineering-geological characterization to rockfall runout modeling with intervention proposal

Rock slope instability is a major risk to human life, often leading to economic losses, property damage and maintenance costs, as well as injuries or death. Because the rock mass behavior is significantly governed by the presence of joints or other discontinuities, several types of slope failure such as plane failure, toppling failure, wedge failure, buckling failure and circular failure are often observed. These failures may be gradual with very slow movement of the sliding block or instantaneous without much warning. To understand this process, it is important to study the rock slope (geological data collections, geotechnical collections, data kinematic stability analysis, runout analyses…) This work is divided in two cases of studies, which are both complementary to study a rock slope stability: 1. The first case of study is an underground quarry of marble located in Levigliani (Luca, Italy), which we did a classification of the rockmass based on the empirical method of Bieniawski (RMR) and also a kinematic analysis of the conditions of stability with the software Rocscience Dips, after a 3D stability analysis was used by the software Rocscience Unwedge which was developed specifically for the use in underground rock mining; 2. For the second case of study is an ex open pit quarry of limestone located in Vecchiano (Pisa, Italy), in which we calculated the trajectories of falling blocks with an advanced numerical method (Rockyfor3D): rigid body approach, capable of analyzing the propagation phase of the volumes detached from the slope; methodology that allowed to simulate the rockfall phenomena through the production of block rebound mechanisms during the descent towards the slope and also we gave an intervention proposal to mitigate the risk; the localization and sizing of the blocks was done by a Digital Terrain Model (DTM)

The Use of Remote Sensing Techniques for Monitoring and Characterization of Slope Instability

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Understanding changes in slope geometry and knowledge of underlying engineering properties of the rock mass are essential for the safe design of man-made slopes and to reduce the significant risks associated with slope failure. Recent advances in the geomatics industry have provided the capability to obtain accurate, fully geo-referenced three-dimensional datasets that can be subsequently interrogated to provide engineering-based solutions for monitoring of deformation processes, rock mass characterization and additional insight into any underlying failure mechanisms. Importantly, data can also be used to spatially locate and map geological features and provide displacement or deformation rate information relating to movement of critical sections or regions of a slope. This paper explores the benefits that can be obtained by incorporating different remote sensing techniques and conventional measurement devices to provide a comprehensive database required for development of an effective slope monitoring and risk management program. The integration of different techniques, such as high accuracy discrete point measurement at critical locations, which can be used to complement larger scale less dense three-dimensional survey will be explored. Case studies using a combination of aerial and terrestrial laser scanning, unmanned aerial vehicle and hand-held scanning devices will demonstrate their ability to provide spatial data for informing decision making processes and ensuring compliance with Regulations

A Study of Generalization and Fitness Landscapes for Neuroevolution

Rodrigues, N. M., Silva, S., & Vanneschi, L. (2020). A Study of Generalization and Fitness Landscapes for Neuroevolution. IEEE Access, 8, 108216-108234. [9113453]. https://doi.org/10.1109/ACCESS.2020.3001505Fitness landscapes are a useful concept for studying the dynamics of meta-heuristics. In the last two decades, they have been successfully used for estimating the optimization capabilities of different flavors of evolutionary algorithms, including genetic algorithms and genetic programming. However, so far they have not been used for studying the performance of machine learning algorithms on unseen data, and they have not been applied to studying neuroevolution landscapes. This paper fills these gaps by applying fitness landscapes to neuroevolution, and using this concept to infer useful information about the learning and generalization ability of the machine learning method. For this task, we use a grammar-based approach to generate convolutional neural networks, and we study the dynamics of three different mutations used to evolve them. To characterize fitness landscapes, we study autocorrelation, entropic measure of ruggedness, and fitness clouds. Also, we propose the use of two additional evaluation measures: density clouds and overfitting measure. The results show that these measures are appropriate for estimating both the learning and the generalization ability of the considered neuroevolution configurations.publishersversionpublishe

A Study of Fitness Landscapes for Neuroevolution

Rodrigues, N. M., Silva, S., & Vanneschi, L. (2020). A Study of Fitness Landscapes for Neuroevolution. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020: Conference Proceedings [9185783] (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185783Fitness landscapes are a useful concept to study the dynamics of meta-heuristics. In the last two decades, they have been applied with success to estimate the optimization power of several types of evolutionary algorithms, including genetic algorithms and genetic programming. However, so far they have never been used to study the performance of machine learning algorithms on unseen data, and they have never been applied to neuroevolution. This paper aims at filling both these gaps, applying for the first time fitness landscapes to neuroevolution and using them to infer useful information about the predictive ability of the method. More specifically, we use a grammar-based approach to generate convolutional neural networks, and we study the dynamics of three different mutations to evolve them. To characterize fitness landscapes, we study autocorrelation and entropic measure of ruggedness. The results show that these measures are appropriate for estimating both the optimization power and the generalization ability of the considered neuroevolution configurations.preprintpublishe

Investigation and modeling of direct toppling using a three-dimensional distinct element approach with incorporation of point cloud geometry

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordBlock toppling instability can be a common problem in natural rock masses, especially in mining environments where excavation activity may trigger discontinuity-controlled instability by modifying the natural slope geometry. Traditional investigations of block toppling failure consider classic kinematic analyses and simplified two-dimensional limit equilibrium methods. This approach is still the most commonly adopted, but the simple two-dimensional conceptual model may often oversimplify the instability mechanisms, ignoring potential critical factors specifically related to the three-dimensional geometry. This paper uses a three-dimensional distinct element method approach applied to an example case study, identifying the critical parameters that influence direct toppling instability in an open pit environment. Terrestrial laser scanning was used to obtain detailed three-dimensional geometrical information of the slope face geometry for subsequent stability analyses. A series of sensitivity analyses on critical parameters such as friction angle, discontinuity shear and normal stiffness, discontinuity spacing, and orientation was performed, using simple conceptual three-dimensional numerical modeling. Results of the analyses revealed the importance of undertaking three-dimensional analyses for direct toppling investigations that allow identification of critical parameters. A three-dimensional distinct element analysis was then performed using a more realistic complex volumetric mesh model of the case study slope which confirmed the previous modeling results but also identified unstable blocks in high slope angle areas, providing useful information for life of mine design. The paper highlights the importance of slope geometry and fracture network orientation on potential slope instability mechanisms.European CommissionEuropean Commissio

Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming

Azzali, I., Vanneschi, L., & Giacobini, M. (2020). Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming. In T. Hu, N. Lourenço, E. Medvet, & F. Divina (Eds.), Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings (pp. 52-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12101 LNCS). Springer. https://doi.org/10.1007/978-3-030-44094-7_4 ------- This work was partially supported by FCT, Portugal through funding of LASIGE Research Unit (UID/CEC/00408/2019), and projects PREDICT (PTDC/CCI-IF/29877/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DSAIPA/DS/0022/2018) and AICE (DSAIPA/DS/0113/2019).Vectorial Genetic Programming (VE_GP) is a new GP approach for panel data forecasting. Besides permitting the use of vectors as terminal symbols to represent time series and including aggregation functions to extract time series features, it introduces the possibility of evolving the window of aggregation. The local aggregation of data allows the identification of meaningful patterns overcoming the drawback of considering always the previous history of a series of data. In this work, we investigate the use of geometric semantic operators (GSOs) in VE_GP, comparing its performance with traditional GP with GSOs. Experiments are conducted on two real panel data forecasting problems, one allowing the aggregation on moving windows, one not. Results show that classical VE_GP is the best approach in both cases in terms of predictive accuracy, suggesting that GSOs are not able to evolve efficiently individuals when time series are involved. We discuss the possible reasons of this behaviour, to understand how we could design valuable GSOs for time series in the future.authorsversionpublishe

Supporting medical decisions for treating rare diseases through genetic programming

Bakurov, I., Castelli, M., Vanneschi, L., & Freitas, M. J. (2019). Supporting medical decisions for treating rare diseases through genetic programming. In P. Kaufmann, & P. A. Castillo (Eds.), Applications of Evolutionary Computation: 22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Proceedings (pp. 187-203). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11454 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-16692-2_13. ISBN: 978-3-030-16691-5; Online ISBN: 978-3-030-16692-2Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centers like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases.authorsversionpublishe

A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI

Rodrigues, N. M., Silva, S., Vanneschi, L., & Papanikolaou, N. (2023). A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI. Cancers, 15(5), 1-21. [1467]. https://doi.org/10.3390/cancers15051467 --- Funding: The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952159 (ProCAncer-I). This work was partially supported by FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020), and under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Nuno Rodrigues was supported by PhD Grant 2021/05322/BD.Prostate cancer is one of the most common forms of cancer globally, affecting roughly one in every eight men according to the American Cancer Society. Although the survival rate for prostate cancer is significantly high given the very high incidence rate, there is an urgent need to improve and develop new clinical aid systems to help detect and treat prostate cancer in a timely manner. In this retrospective study, our contributions are twofold: First, we perform a comparative unified study of different commonly used segmentation models for prostate gland and zone (peripheral and transition) segmentation. Second, we present and evaluate an additional research question regarding the effectiveness of using an object detector as a pre-processing step to aid in the segmentation process. We perform a thorough evaluation of the deep learning models on two public datasets, where one is used for cross-validation and the other as an external test set. Overall, the results reveal that the choice of model is relatively inconsequential, as the majority produce non-significantly different scores, apart from nnU-Net which consistently outperforms others, and that the models trained on data cropped by the object detector often generalize better, despite performing worse during cross-validation.publishersversionpublishe

Open-addressing hashing with unequal-probability keys

This paper describes the use of a drone in collecting data for mapping discontinuities within a marble quarry. A topographic survey was carried out in order to guarantee high spatial accuracy in the exterior orientation of images. Photos were taken close to the slopes and at different angles, depending on the orientation of the quarry walls. This approach was used to overcome the problem of shadow areas and to obtain detailed information on any feature desired. Dense three-dimensional (3D) point clouds obtained through image processing were used to rebuild the quarry geometry. Discontinuities were then mapped deterministically in detail. Joint attitude interpretation was not always possible due to the regular shape of the cut walls; for every discontinuity set we therefore also mapped the uncertainty. This, together with additional fracture characteristics, was used to build 3D discrete fracture network models. Preliminary results reveal the advantage of modern photogrammetric systems in producing detailed orthophotos; the latter allow accurate mapping in areas difficult to access (one of the main limitations of traditional techniques). The results highlight the benefits of integrating photogrammetric data with those collected through classical methods: the resulting knowledge of the site is crucially important in instability analyses involving numerical modelling.Part of the present study was undertaken within the framework of the Italian National Research Project PRIN2009, funded by the Ministry of Education, Universities and Research, which involves the collaboration between the University of Siena, ‘La Sapienza’ University of Rome, and USL1 of Massa and Carrara (Mining Engineering Operative Unit – Department of Prevention). The authors acknowledge M. Pellegri and D. Gullì (USL1, Mining Engineering Operative Unit – Department of Prevention), M. Ferrari, M. Profeti and V. Carnicelli (Cooperativa Cavatori Lorano), X. Chaoshui and P.A. Dowd (School of Civil, Environmental and Mining Engineering, University of Adelaide, South Australia) and M. Bocci (Geographike) for their support of this research