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The four persistent radio sources in the northern sky with the highest flux density at metre wavelengths are Cassiopeia A, Cygnus A, Taurus A, and Virgo A; collectively they are called the A-team. Their flux densities at ultra-low frequencies (<100 MHz) can reach several thousands of janskys, and they often contaminate observations of the low-frequency sky by interfering with image processing. Furthermore, these sources are foreground objects for all-sky observations hampering the study of faint signals, such as the cosmological 21 cm line from the epoch of reionisation. We aim to produce robust models for the surface brightness emission as a function of frequency for the A-team sources at ultra-low frequencies. These models are needed for the calibration and imaging of wide-area surveys of the sky with low-frequency interferometers. This requires obtaining images at an angular resolution better than 15 arcsec with a high dynamic range and good image fidelity. We observed the A-team with the Low Frequency Array (LOFAR) at frequencies between 30 MHz and 77 MHz using the Low Band Antenna (LBA) system. We reduced the datasets and obtained an image for each A-team source. The paper presents the best models to date for the sources Cassiopeia A, Cygnus A, Taurus A, and Virgo A between 30 MHz and 77 MHz. We were able to obtain the aimed resolution and dynamic range in all cases. Owing to its compactness and complexity, observations with the long baselines of the International LOFAR Telescope will be required to improve the source model for Cygnus A further., Comment: 7 pages, 2 figures, accepted A&A, online data on A&A website

The ecosystem in which mobile applications run is highly heterogeneous and configurable. All layers upon which mobile apps are built offer wide possibilities of variations, from the device and the hardware, to the operating system and middleware, up to the user preferences and settings. Testing all possible configurations exhaustively, before releasing the app, is unaffordable. As a consequence, the app may exhibit different, including faulty, behaviours when executed in the field, under specific configurations. In this paper, we describe a framework that can be instantiated to support in-vivo testing of a mobile app. The framework monitors the configuration in the field and triggers in-vivo testing when an untested configuration is recognized. Experimental results show that the overhead introduced by monitoring is unnoticeable to negligible (i.e., 0-6%) depending on the device being used (high- vs. low-end). In-vivo test execution required on average 3s: if performed upon screen lock activation, it introduces just a slight delay before locking the device., Comment: Research paper accepted to ICST'20, 10+1 pages

Repeating fast radio bursts (FRBs) present excellent opportunities to identify FRB progenitors and host environments, as well as decipher the underlying emission mechanism. Detailed studies of repeating FRBs might also hold clues to the origin of FRBs as a population. We aim to detect the first two repeating FRBs: FRB 121102 (R1) and FRB 180814.J0422+73 (R2), and characterise their repeat statistics. We also want to significantly improve the sky localisation of R2. We use the Westerbork Synthesis Radio Telescope to conduct extensive follow-up of these two repeating FRBs. The new phased-array feed system, Apertif, allows covering the entire sky position uncertainty of R2 with fine spatial resolution in one pointing. We characterise the energy distribution and the clustering of detected R1 bursts. We detected 30 bursts from R1. Our measurements indicate a dispersion measure of 563.5(2) pc cm$^{-3}$, suggesting a significant increase in DM over the past few years. We place an upper limit of 8% on the linear polarisation fraction of the brightest burst. We did not detect any bursts from R2. A single power-law might not fit the R1 burst energy distribution across the full energy range or widely separated detections. Our observations provide improved constraints on the clustering of R1 bursts. Our stringent upper limits on the linear polarisation fraction imply a significant depolarisation, either intrinsic to the emission mechanism or caused by the intervening medium, at 1400 MHz that is not observed at higher frequencies. The non-detection of any bursts from R2 implies either a highly clustered nature of the bursts, a steep spectral index, or a combination of both. Alternatively, R2 has turned off completely, either permanently or for an extended period of time., Comment: 11 pages, 7 figures, submitted to A&A

The boosting on the need of security notably increased the amount of possible facial recognition applications, especially due to the success of the Internet of Things (IoT) paradigm. However, although handcrafted and deep learning-inspired facial features reached a significant level of compactness and expressive power, the facial recognition performance still suffers from intra-class variations such as ageing, facial expressions, lighting changes, and pose. These variations cannot be captured in a single acquisition and require multiple acquisitions of long duration, which are expensive and need a high level of collaboration from the users. Among others, self-update algorithms have been proposed in order to mitigate these problems. Self-updating aims to add novel templates to the users' gallery among the inputs submitted during system operations. Consequently, computational complexity and storage space tend to be among the critical requirements of these algorithms. The present paper deals with the above problems by a novel template-based self-update algorithm, able to keep over time the expressive power of a limited set of templates stored in the system database. The rationale behind the proposed approach is in the working hypothesis that a dominating mode characterises the features' distribution given the client. Therefore, the key point is to select the best templates around that mode. We propose two methods, which are tested on systems based on handcrafted features and deep-learning-inspired autoencoders at the state-of-the-art. Three benchmark data sets are used. Experimental results confirm that, by effective and compact feature sets which can support our working hypothesis, the proposed classification-selection approaches overcome the problem of manual updating and, in case, stringent computational requirements., Comment: This is an original manuscript of an article published by Elsevier in Pattern Recognition on 27 November 2019. Available online: https://doi.org/10.1016/j.patcog.2019.107121

Cloud systems are complex, large, and dynamic systems whose behavior must be continuously analyzed to timely detect misbehaviors and failures. Although there are solutions to flexibly monitor cloud systems, cost-effectively controlling the anomaly detection logic is still a challenge. In particular, cloud operators may need to quickly change the types of detected anomalies and the scope of anomaly detection, for instance based on observations. This kind of intervention still consists of a largely manual and inefficient ad-hoc effort. In this paper, we present Anomaly Detection as-a-Service (ADaaS), which uses the same as-a-service paradigm often exploited in cloud systems to declarative control the anomaly detection logic. Operators can use ADaaS to specify the set of indicators that must be analyzed and the types of anomalies that must be detected, without having to address any operational aspect. Early results with lightweight detectors show that the presented approach is a promising solution to deliver better control of the anomaly detection logic., Comment: Paper accepted at the Intl. Workshop on Governing Adaptive and Unplanned Systems of Systems (GAUSS)

Fingerprint Liveness detection, or presentation attacks detection (PAD), that is, the ability of detecting if a fingerprint submitted to an electronic capture device is authentic or made up of some artificial materials, boosted the attention of the scientific community and recently machine learning approaches based on deep networks opened novel scenarios. A significant step ahead was due thanks to the public availability of large sets of data; in particular, the ones released during the International Fingerprint Liveness Detection Competition (LivDet). Among others, the fifth edition carried on in 2017, challenged the participants in two more challenges which were not detailed in the official report. In this paper, we want to extend that report by focusing on them: the first one was aimed at exploring the case in which the PAD is integrated into a fingerprint verification systems, where templates of users are available too and the designer is not constrained to refer only to a generic users population for the PAD settings. The second one faces with the exploitation ability of attackers of the provided fakes, and how this ability impacts on the final performance. These two challenges together may set at which extent the fingerprint presentation attacks are an actual threat and how to exploit additional information to make the PAD more effective., Comment: Preprint version of a paper accepted at BioFor 2019

Galaxy clusters are the most massive gravitationally bound structures in the Universe. They grow by accreting smaller structures in a merging process that produces shocks and turbulence in the intra-cluster gas. We observed a ridge of radio emission connecting the merging galaxy clusters Abell 0399 and Abell 0401 with the Low Frequency Array (LOFAR) at 140 MHz. This emission requires a population of relativistic electrons and a magnetic field located in a filament between the two galaxy clusters. We performed simulations to show that a volume-filling distribution of weak shocks may re-accelerate a pre-existing population of relativistic particles, producing emission at radio wavelengths that illuminates the magnetic ridge., Comment: Published in Science

We present an overview of the LOFAR Tied-Array All-Sky Survey (LOTAAS) for radio pulsars and fast transients. The survey uses the high-band antennas of the LOFAR Superterp, the dense inner part of the LOFAR core, to survey the northern sky (dec > 0 deg) at a central observing frequency of 135 MHz. A total of 219 tied-array beams (coherent summation of station signals, covering 12 square degrees), as well as three incoherent beams (covering 67 square degrees) are formed in each survey pointing. For each ofthe 222 beams, total intensity is recorded at 491.52 us time resolution. Each observation integrates for 1 hr and covers 2592 channels from 119 to 151 MHz. This instrumental setup allows LOTAAS to reach a detection threshold of 1 to 5 mJy for periodic emission. Thus far, the LOTAAS survey has resulted in the discovery of 73 radio pulsars. Among these are two mildly recycled binary millisecond pulsars (P = 13 and 33 ms), as well as the slowest-spinning radio pulsar currently known (P = 23.5 s). The survey has thus far detected 311 known pulsars, with spin periods ranging from 4 ms to 5.0 s and dispersion measures from 3.0 to 217 pc/cc. Known pulsars are detected at flux densities consistent with literature values. We find that the LOTAAS pulsar discoveries have, on average, longer spin periods than the known pulsar population. This may reflect different selection biases between LOTAAS and previous surveys, though it is also possible that slower-spinning pulsars preferentially have steeper radio spectra. LOTAAS is the deepest all-sky pulsar survey using a digital aperture array; we discuss some of the lessons learned that can inform the approach for similar surveys using future radio telescopes such as the Square Kilometre Array., Comment: 22 pages, 9 figures, 3 tables. Accepted for publication in A&A

The International Fingerprint liveness Detection Competition (LivDet) is an open and well-acknowledged meeting point of academies and private companies that deal with the problem of distinguishing images coming from reproductions of fingerprints made of artificial materials and images relative to real fingerprints. In this edition of LivDet we invited the competitors to propose integrated algorithms with matching systems. The goal was to investigate at which extent this integration impact on the whole performance. Twelve algorithms were submitted to the competition, eight of which worked on integrated systems., Comment: Preprint version of a paper accepted at ICB 2019