Papers by Gianni D'Angelo
Acta Astronautica, 2018
Nowadays, satellites are becoming increasingly software dependent. Satellite Flight Software (FSW... more Nowadays, satellites are becoming increasingly software dependent. Satellite Flight Software (FSW), that is to say, the application software running on the satellite main On-Board Computer (OBC), plays a relevant role in implementing complex space mission requirements. In this paper, we examine relevant technical approaches and programmatic strategies adopted for the development of the Meteosat Third Generation Satellite (MTG) FSW. To begin with, we present its layered model-based architecture, and the means for ensuring a robust and reliable interaction among the FSW components. Then, we focus on the selection of an effective software development life cycle model. In particular, by combining plan-driven and agile approaches, we can fulfill the need of having preliminary SW versions. They can be used for the elicitation of complex system-level requirements as well as for the initial satellite integration and testing activities. Another important aspect can be identified in the testi...
Neural Computing & Applications, 2021
With the emergence of COVID-19, mobile health applications have increasingly become crucial in co... more With the emergence of COVID-19, mobile health applications have increasingly become crucial in contact tracing, information dissemination, and pandemic control in general. Apps warn users if they have been close to an infected person for sufficient time, and therefore potentially at risk. The distance measurement accuracy heavily affects the probability estimation of being infected. Most of these applications make use of the electromagnetic field produced by Bluetooth Low Energy technology to estimate the distance. Nevertheless, radio interference derived from numerous factors, such as crowding, obstacles, and user activity can lead to wrong distance estimation, and, in turn, to wrong decisions. Besides, most of the social distance-keeping criteria recognized worldwide plan to keep a different distance based on the activity of the person and on the surrounding environment. In this study, in order to enhance the performance of the COVID-19 tracking apps, a human activity classifier b...
Resource Planning Optimization (RPO) is a common task that many companies need to face to obtain ... more Resource Planning Optimization (RPO) is a common task that many companies need to face to obtain several benefits, like budget improvements and run-time analyses. It is often addressed by using several software products and tools, based on sophisticated mathematical artifacts. However, these tools are not able to provide a practical solution because they are often expensive and time-consuming. On the other hand, Artificial Intelligence-based approaches have been increasingly used in many industrial and scientific fields in last decades, and have demonstrated to be a valid alternative to the classical mathematical-based methods. For this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the pr...
This study deals with the growing complexity brought on by the recent phenomenon of digitization ... more This study deals with the growing complexity brought on by the recent phenomenon of digitization associated with the pervasive computing paradigm. The large diffusion of intelligent machines along with the huge amount of data produced by such machines, often in an autonomous manner without the explicit knowledge or voluntary consent of the user, places human beings in front of a new model of life, one that is based on a new concept of the cognitive process. In such a view, the way people interact with information and technological systems in general is being affected by radical changes. The human cognition, through its mental actions such as memory, senses, reasoning, and attention is no longer the main engine which allows individuals to represent the external world, but it is integrated in the world itself as a component, and it acts through a complex human-computer interaction. In modern life relationships, humans and nonhuman agents, through their interaction, create a system of ...
International Journal of Intelligent Systems, 2020
SARS‐CoV‐2 is a novel severe acute respiratory syndrome‐like coronavirus (SARS‐CoV), which is res... more SARS‐CoV‐2 is a novel severe acute respiratory syndrome‐like coronavirus (SARS‐CoV), which is responsible of the ongoing world pandemic of COVID‐19 disease. Although many approaches are being investigated to address this issue, nowaday there are no vaccines available and there is little evidence supporting the efficiency of potential therapeutic agents. Moreover, the high mutation rate of this virus heavily affects the understanding of its evolution and diffusion mechanisms, and, in turn, the development of effective solutions. In this study, two novel algorithms are provided for finding out recurrent patterns of nucleotide subsequences of different SARS‐CoV‐2 genomes as a unique signature capable of identifying the most peculiar features of the pathogen. In particular, we provide several subsequence patterns related to the Spike glycoprotein, which is believed to be the main target for developing effective drugs and vaccines against the COVID‐19 disease because of its role in the e...
2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace)
This paper investigates the use of the Eddy Current Testing (ECT) for detecting gaps between carb... more This paper investigates the use of the Eddy Current Testing (ECT) for detecting gaps between carbon-fiber composite materials, caused by overlapping of assembly parts with geometrical variations. To this purpose, we use two overlapped carbon-fiber reinforced plastic (CFRP) tapes, while an increasing number of PVC sheets are placed between these tapes to vary the thickness of the gaps. Several experiments are carried out. For everyone, and for each considered gap, three ECT response parameters are extracted. The obtained data are used to train different machine learning-based classifiers to distinguish the gaps. Their validation, assessed through the 10-fold cross validation technique, proves the effectiveness of ECT as gaps detector for CFRP materials.
Connection Science
Due to their open nature and popularity, Android-based devices have attracted several end-users a... more Due to their open nature and popularity, Android-based devices have attracted several end-users around the World and are one of the main targets for attackers. Because of the reasons given above, it is necessary to build tools that can reliably detect zero-day malware on these devices. At the moment, many of the frameworks that have been proposed to detect malware applications leverage Machine Learning (ML) techniques. However, an essential requirement to build these frameworks consists of using very large and sophisticated datasets for model construction and training purposes. Their success, indeed, strongly depends on the choice of the right features used for building a classification model providing adequate generalisation capability. Furthermore, the creation of a training dataset that well represents the malware properties and behaviour is one of the most critical challenges in malware analysis. Therefore, the main aim of this paper is proposing a new dataset called Unisa Malware Dataset (UMD) available on http://antlab.di.unisa.it/malware/, which is based on the extraction of static and dynamic features characterising the malware activities. Additionally, we will show some experiments concerning common ML tools to demonstrate how it is possible to build efficient ML-based malware classification frameworks using the proposed dataset.
2017 IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace)
Spacecraft on-board autonomy is an important topic in currently developed and future space missio... more Spacecraft on-board autonomy is an important topic in currently developed and future space missions. In this study, we present a robust approach to the optimal policy of autonomous space systems modeled via Markov Decision Process (MDP) from the values assigned to its transition probability matrix. After addressing the curse of dimensionality in solving the formulated MDP problem via Approximate Dynamic Programming, we use an Apriori-based Association Classifier to infer a specific optimal policy. Finally, we also assess the effectiveness of such optimal policy in fulfilling the spacecraft autonomy requirements.
International Journal of Intelligent Systems
Modern interconnected power grids are a critical target of many kinds of cyber‐attacks, potential... more Modern interconnected power grids are a critical target of many kinds of cyber‐attacks, potentially affecting public safety and introducing significant economic damages. In such a scenario, more effective detection and early alerting tools are needed. This study introduces a novel anomaly detection architecture, empowered by modern machine learning techniques and specifically targeted for power control systems. It is based on stacked deep neural networks, which have proven to be capable to timely identify and classify attacks, by autonomously eliciting knowledge about them. The proposed architecture leverages automatically extracted spatial and temporal dependency relations to mine meaningful insights from data coming from the target power systems, that can be used as new features for classifying attacks. It has proven to achieve very high performance when applied to real scenarios by outperforming state‐of‐the‐art available approaches.
Applied Soft Computing
Abstract Emerging malware pose increasing challenges to detection systems as their variety and so... more Abstract Emerging malware pose increasing challenges to detection systems as their variety and sophistication continue to increase. Malware developers use complex techniques to produce malware variants, by removing, replacing, and adding useless API calls to the code, which are specifically designed to evade detection mechanisms, as well as do not affect the original functionality of the malicious code involved. In this work, a new recurring subsequences alignment-based algorithm that exploits associative rules has been proposed to infer malware behaviors. The proposed approach exploits the probabilities of transitioning from two API invocations in the call sequence, as well as it also considers their timeline, by extracting subsequence of API calls not necessarily consecutive and representative of common malicious behaviors of specific subsets of malware. The resulting malware classification scheme, capable to operate within dynamic analysis scenarios in which API calls are traced at runtime, is inherently robust against evasion/obfuscation techniques based on the API call flow perturbation. It has been experimentally compared with two detectors based on Markov chain and API call sequence alignment algorithms, which are among the most widely adopted approaches for malware classification. In such experimental assessment the proposed approach showed an excellent classification performance by outperforming its competitors.
Journal of Network and Computer Applications
Information Sciences
Abstract In the last few decades, genetic algorithms (GAs) demonstrated to be an effective approa... more Abstract In the last few decades, genetic algorithms (GAs) demonstrated to be an effective approach for solving real-world optimization problems. However, it is known that, in presence of a huge solution space and many local optima, GAs cannot guarantee the achievement of global optimality. In this work, in order to make GAs more effective in finding the global optimal solution, we propose a hybrid GA which combines the classical genetic mechanisms with the gradient-descent (GD) technique for local searching and constraints management. The basic idea is to exploit the GD capability in finding local optima to refine search space exploration and to place individuals in areas that are more favorable for achieving convergence. This confers to GAs the capability of escaping from the discovered local optima, by progressively moving towards the global solution. Experimental results on a set of test problems from well-known benchmarks showed that our proposal is competitive with other more complex and notable approaches, in terms of solution precision as well as reduced number of individuals and generations.
Sensors
This Special Issue aims at collecting several original state-of-the-art research experiences in t... more This Special Issue aims at collecting several original state-of-the-art research experiences in the area of intelligent applications in the IoT and Sensor networks environment, by analyzing several open issues and perspectives associated with such scenarios, in order to explore novel potentialities and solutions and face with the emerging challenges.
Concurrency and Computation: Practice and Experience
Journal of Parallel and Distributed Computing
Abstract Due to their open nature and popularity, Android-based devices represent one of the main... more Abstract Due to their open nature and popularity, Android-based devices represent one of the main targets for malware attacks that may adversely affect the privacy of their users. Considering the huge Android market share, it is necessary to build effective tools able to reliably detect zero-day malware on these platforms. Therefore, several static and dynamic analysis methods based on Neural Networks and Deep Learning have been proposed in the literature. Despite machine learning can be considered the most promising approach for classifying applications into malware or legitimate ones, its success strongly depends on the choice of the right features used for building the detection model. This is definitely not an easy task that requires a systematic solution. Accordingly, this work represents the sequences of API calls invoked by apps during their execution as sparse matrices looking like images (API-images), which can be used as fingerprints of the apps’ behavior over time. We also used autoencoders to autonomously extract the most representative and discriminating features from these matrices, that, once provided to an artificial neural network-based classifier have shown to be effective in detecting malware, also when the network is trained on a reduced number of samples. Experimental results show that the resulting framework is able to outperform more complex and sophisticated machine learning approaches in malware classification.
Future Generation Computer Systems
Abstract In non-destructive testing of aerospace structures’ defects, the tests reliability is a ... more Abstract In non-destructive testing of aerospace structures’ defects, the tests reliability is a crucial issue for guaranteeing security of both aircrafts and passengers. Most of the widely recognized approaches rely on precision and reliability of testing equipment, but also the methods and techniques used for processing measurement results, in order to detect defects, may heavily influence the overall quality of the testing process. The effectiveness of such methods strongly depends on specific field knowledge that is definitely not easy to be formalized and codified within the results processing practices. Although many studies have been conducted in this direction, such issue is yet an open-problem. This work describes the use of Genetic Programming for the diagnosis and modeling of aerospace structural defects. The resulting approach aims at extracting such knowledge by providing a mathematical model of the considered defects, which can be used for recognizing other similar ones. Eddy-Current Testing has been selected as a case study in order to assess both the performance and functionality of the whole framework, and a publicly available dataset of specific measures for aircraft structures has been considered. The experimental results put into evidence the effectiveness of the proposed approach in building reliable models of the aforementioned defects, so that it can be considered a successful option for building the knowledge needed by tools for controlling the quality of critical aerospace systems.
Soft Computing
Malignant pleural effusion is diagnostically challenging in presence of negative cytology. The as... more Malignant pleural effusion is diagnostically challenging in presence of negative cytology. The assessment of tumor markers in serum has become a standard tool in cancer diagnosis, while pleural fluid sampling has not met universal consensus. The evaluation of a panel of markers both in serum and pleural fluid may be crucial to improve the diagnostic accuracy. Using a machine learning-based approach, we provide a mathematical formula capable to express the complex relation existing among the expressed markers in serum and pleural effusion and the presence of lung cancer. The formula indicates CEA and CYFRA21-1 in pleural fluid as the best diagnostic markers, with 97% accuracy, 98% sensitivity, 95% specificity, 96% area under curve, 98% positive predictive value, and 92% MCC (Matthews correlation coefficient).
Information Sciences
Abstract Dynamic programming (DP) and Markov Decision Process (MDP) offer powerful tools for form... more Abstract Dynamic programming (DP) and Markov Decision Process (MDP) offer powerful tools for formulating, modeling, and solving decision making problems under uncertainty. In real-world applications, the applicability of DP is limited by severe scalability issues. These issues can be addressed by Approximate Dynamic Programming (ADP) techniques. ADP methods are based on the assumption of having either a proper estimation of the underlying state transition probability distributions or a simulation mechanism with the capability of generating samples according to such probability distributions. In this paper, we present a data-driven ADP-based approach, which can offer an alternative in case such assumption cannot be guaranteed. In particular, when varying the set-up of the MDP state transition probability matrix, different policies can be calculated through exact DP or ADP methods. Such policies are then processed by an Apriori-based algorithm to find frequent association rules within them. A pruning procedure is used to select the most suitable association rules, and finally an Association Classifier infers the optimal policy in all the possible circumstances. We show a detailed application of the proposed approach for the calculation of a proper mission operations plan for spacecrafts with a high level of on-board autonomy.
Thoracic Surgery
Background: Malignant pleural effusion (MPE) is diagnostically challenging in presence of negativ... more Background: Malignant pleural effusion (MPE) is diagnostically challenging in presence of negative cytology. The assessment of tumor markers in serum has become a standard tool in cancer diagnosis while pleural fluid sampling has not met universal consensus. The evaluation of a panel of markers both in serum and pleural fluid may be crucial to improve the diagnostic accuracy. Aims: Identify best expressed markers in serum and pleural effusion of lung cancer patients by a Machine Learning-based approach and a mathematical formula to retrieve their link with cancer. Methods: Carcinoembryonic antigen (CEA), Carbohydrate Antigen 125 (Ca 125), Carbohydrate Antigen 19.9 (Ca 19.9), Cytokeratin fragment 21-1 (CYFRA 21.1) and Neuron-specific Enolase (NSE) were assessed by chemiluminescence and immunofluorescence assay in serum and pleural fluid from 168 patients (124 malignant, 44 non-malignant). Video assisted thoracoscopic biopsy obviated pathology. Receiver operating curves (ROC) analysis selected markers. Genetic programming algorithm mined the link between the expression of markers in MPE and pathological outcome. Relationship was expressed by a mathematical formula. Results: CEA and CYFRA 21-1 in pleural fluid achieved the best diagnostic accuracy (92%). Machine Learning-based approach indicated that combination of CEA and CYFRA 21-1 in MPE reached a 97% sensitivity, 93% specificity, 95 % area under curve (AUC), 98% positive predictive value (PPV), and 90% MCC (Matthews correlation coefficient). Conclusions: The proposed formula indicates CEA and CYFRA 21-1 as the most efficient tumor markers in the diagnosis of cytologically negative lung cancer associated MPE.
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Papers by Gianni D'Angelo