​Department of Informatics, Systems and Communication (DISCo)

OWL2ASP stands for Ontology Web Language to Answering Set Programming. This Java tool permits translating an OWL 2 ontology to ASP format. The output of that translation is used by the WASP solver to obtain the justifications (MUSes) of a specific consequence WASP solver link http://alviano.github.io/wasp/

A new synthetic, multi-purpose dataset - called ENRICH - for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH offers higher resolution images also rendered with different lighting conditions, camera orientation, scales, and field of view. Specifically, ENRICH is composed of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each exhibiting different characteristics. The proposed dataset is useful for several photogrammetry and computer vision-related tasks, such as the evaluation of hand-crafted and deep learning-based local features, effects of ground control points (GCPs) configuration on the 3D accuracy, and monocular depth estimation. Each zip file in the root is relative to a specific dataset: - ENRICH-Aerial, is an aerial image block of the city of Launceston, Australia. The acquisition is performed by simulating a typical oblique aerial camera with five views (nadir and four oblique views). - ENRICH-Square, is a ground-level dataset of a square captured by four cameras, each one moving on a different path with different focal length, orientation, and lighting conditions. - ENRICH-Statue, is a ground-level dataset portraying a statue (placed in the center of the ENRICH-Square scene), acquired using four cameras moving on different paths with different focal lengths, orientations, and lighting conditions. Be sure to check the README file in the dataset root for information on folder structure and file contents. Please refer to the related paper (https://doi.org/10.1016/j.isprsjprs.2023.03.002) for information about the generation method and the purpose of ENRICH.

Supplementary Files of the article "Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity" by Graudenzi et al.

This dataset contains images of five 3D models that can be used to evaluate pipelines for 3D reconstruction from images. Each model is placed in a reference scene and is rendered under different lighting and camera conditions. For each of the five models, 8 scenes are created and for each scene 100 images are taken from different points of view.

We present a MATLAB function to compute the subdivision matrices of semi-regular univariate interpolatory RBF-based binary subdivision schemes. The construction is the adaptation of the one presented in "Stationary binary subdivision schemes using radial basis function interpolation", B.-G. Lee, Y. J. Lee, J. Yoon (Adv. Comput. Math, 2006) and "Analysis of stationary subdivision schemes for curve design based on radial basis function interpolation", Y. J. Lee, J. Yoon (Appl. Math. Comput., 2010), to the semi-regular case, i.e. when the starting mesh is formed by two different uniform mesh that meet eachother at 0. The main function, RBFs_semi.m, given the stepsizes of the two uniform mesh, the family of radial basis function, the number of points used for the local computation, the required polynomial reproduction and, eventually, further parameters, determines the subdivision matrix of the scheme in the form of the regular mask on the left, the regular mask on the right and the irregular part of the matrix around 0. The supported families of RBFs are (inverse) multi-quadric, Gaussian, Wendland's functions, Wu's functions, Buhmann's functions, polyharmonic functions and Euclid's hat functions (see e.g. "Meshfree approximation methods with MATLAB", G. E. Fasshauer). For further information about how to choose the parameters for each family see the files in the Aux folder.

We present filters for the irregular framelets of semi-regular Dubuc-Deslauriers 2n-point wavelet tight frames with mesh parameters h_\ell = 1 and h_r > 0 for the cases: n = 2, h_r = 1.5, 2, 2.5, 3 n = 3, h_r = 1.5, 2, 2.25, 2.5 n = 4, h_r = 1.5, 2, 2.15, 2.3 n = 5, h_r = 1.5, 2, 2.1, 2.2 These filters have been computed using the method described in "Semi-regular Dubuc-Deslaurier wavelet tight frames" submitted to Journal of Computational and Applied Mathematics Special Issue for SMART 2017. The filters are the columns of the matrix Q_irr where R_irr = Q_irr * transpose(Q_irr). To avoid numerical fluctuations Q_irr is computed via singular value decomposition, with threshold on the singular values set to 10^-8. The filters depend only on the ratio h_\ell over h_r and, when this ratio is inverted, it is sufficient to flip the filters. Therefore there is no loss of generality in considering h_\ell = 1 and h_r greater than or equal to 1 only. Moreover, for any fixed natural number n and h_\ell = 1, there is an interval of availability for h_r of the form ( 1/c, c ), where h_r = 1 reduces to the regular case. For n = 2, the exact value of c is 3.5 while for the other values of n the approximated values of c are 2.6225, 2.3591 and 2.2346 respectively for n = 3, 4 and 5. For the examples presented we choose two common values of h_r working for all n=2,...,5 and two values specifically chosen for each n spreaded out between 2 and c.

Raw Data

This is the Replication Package of the paper titled "Automated Detection of Software Performance Antipatterns in Java-based Applications" under revision.

This is the online appendix of the paper Alessandro Tundo, Marco Mobilio, Oliviero Riganelli and Leonardo Mariani. "Monitoring Probe Deployment Patterns for Cloud-Native Applications: Definition and Empirical Assessment." Submitted to IEEE Transactions of Services Computing. The abstract of the paper follows: Cloud computing facilities enable global connectivity of actors (e.g., humans, robots, devices and sensors) and services across many heterogeneous domains such as health care, mobile computing, and telecommunication. Actors and services need to reliably interact, despite the impossibility to fully anticipate the huge number of possible execution scenarios. In this context, monitoring is a key feature to enhance systems with the capability to anticipate, detect, predict, and mitigate failures, while providing Quality of Service (QoS) monitoring and Service Level Agreements (SLAs) guarantee. Monitoring frameworks can serve these purposes by deploying probes according to many possible patterns that have different features, for instance in terms of efficiency and privacy. So far, these probe deployment patterns have not been systematically defined, analyzed and assessed. Thus, engineers who design and configure their monitoring systems have to take decisions only based on partial knowledge and personal experience. This paper addresses this knowledge gap, by presenting a systematic analysis of 11 probe deployment patterns, their known uses, and implementations. Further, we assess these schema both quantitatively and qualitatively, distilling findings that can guide engineers in the configuration of their monitoring systems. We experimented with both virtual machines and containers, obtaining a total of 990 configurations and 166320 samples collected over 462 execution hours. Results show the resource consumption on targets is negligible and the probe holder consumption is significantly mainly in relation to memory consumption. Although on different scale values, both experiments with container-based and with VM-based applications resulted in similar trends.

This is the Replication Package of the paper titled "Automated Detection of Software Performance Antipatterns in Java-based Applications" under revision.