Ernesto Damiani

Ernesto Damiani
Khalifa University | KU · Artificial Intelligence and Intelligent Systems Institute

PhD

About

858
Publications
163,952
Reads
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12,618
Citations
Introduction
Ernesto Damiani holds a PhD from Università di Milano and received a doctorate "honoris causa" from INSA-Lyon. He is a full professor at Universita' di Milano (Italy). On shared appointment with Milan, Ernesto leads the Artificial Intelligence and Intelligent Systems Institute at Khalifa University, UAE. Ernesto is the President of the Italian Inter-University Consourtium on Informatics 'CINI - http://www.consorzio-cini.it/.' His research interests include Big Data Analytics and Cyber Security.
Additional affiliations
July 2019 - present
Khalifa University
Position
  • Managing Director
May 2019 - present
Consorzio Nazionale Interuniversitario per l'Informatica
Position
  • CEO
June 2015 - present
Khalifa University
Position
  • Director, Information Security Research Center

Publications

Publications (858)
Article
Full-text available
This paper presents a counseling (ro)bot called Visual Counseling Agent (VICA) which focuses on remote mental healthcare. It is an agent system leveraging artificial intelligence (AI) to aid mentally distressed persons through speech conversation. The system terminals are connected to servers by the Internet exploiting Cloud-nativeness, so that any...
Article
Full-text available
Global and local whole genome sequencing of SARS-CoV-2 enables the tracing of domestic and international transmissions. We sequenced Viral RNA from 37 sampled Covid-19 patients with RT-PCR-confirmed infections across the UAE and developed time-resolved phylogenies with 69 local and 3,894 global genome sequences. Furthermore, we investigated specifi...
Article
Full-text available
With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection framew...
Preprint
Full-text available
Machine learning models have been widely adopted in several fields. However, most recent studies have shown several vulnerabilities from attacks with a potential to jeopardize the integrity of the model, presenting a new window of research opportunity in terms of cyber-security. This survey is conducted with a main intention of highlighting the mos...
Article
Full-text available
Mobile Edge Computing (MEC) has recently emerged as a promising paradigm for Mobile Crowdsensing (MCS) environments. In a given Area of Interest (AoI), the sensing process is performed based on task requirements, which usually ask for a specific quality of the sensing outcome. In this work, a two-stage Data-Driven Decision-making Mechanism using sm...
Article
It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision- making tools tailored to it. This is because the management of health conditions and their consequen...
Article
Traditional Process Mining offers batch analysis of business processes but does not transpose smoothly into online environments due to specific design constraints. Techniques adapted to support online analysis require peculiar adjustments that inherently restrict their focus to a single task. In this work, we extend the Concept Drift in Event Strea...
Chapter
Over the last few years, the interest in blockchain platforms has fostered the implementation of a number of distributed ledger-based solutions for the exchange of information, assets and digitized goods in both the private and the public sectors. While proposing promising alternatives to the original Bitcoin protocol is an important goal that the...
Article
This study discusses the general overview of Timed Efficient Stream Loss-tolerant Authentication (TESLA) protocol, including its properties, key setups, and improvement protocols. The discussion includes a new proposed two-level infinite $\mu $ TESLA (TLI $\mu $ TESLA) protocol that solves the authentication delay and synchronization issues. We the...
Article
Data privacy regulations like the EU GDPR allow the use of hashing techniques to anonymize data that may contain personal information. However, cryptographic hashing is well-known to destroy any possibility of performing analytics. Homomorphic crypto-systems allow computing analytics over encrypted data, but cannot guarantee privacy compliance with...
Article
Large-scale adoption of Artificial Intelligence and Machine Learning (AI-ML) models fed by heterogeneous, possibly untrustworthy data sources has spurred interest in estimating degradation of such models due to spurious, adversarial, or low-quality data assets. We propose a quantitative estimate of the severity of classifiers’ training set degradat...
Preprint
Full-text available
Detection of illegal and threatening items in baggage is one of the utmost security concern nowadays. Even for experienced security personnel, manual detection is a time-consuming and stressful task. Many academics have created automated frameworks for detecting suspicious and contraband data from X-ray scans of luggage. However, to our knowledge,...
Chapter
Security assurance is a discipline aiming to demonstrate that a target system holds some non/functional properties and behaves as expected. These techniques have been recently applied to the cloud, facing some critical issues especially when integrated within existing security processes and executed in a programmatic way. Furthermore, they pose sig...
Preprint
div>Visible light communication is envisaged as a promising enabling technology for sixth generation (6G) and beyond networks. It was introduced as a key enabler for reliable massive-scale connectivity, mainly thanks to its simple and low-cost implementation which require minor variations to the existing indoor lighting systems. The key features of...
Article
Drone flight controls and ground stations are known to be vulnerable to attacks. Besides posing a threat to integrity and confidentiality of drone data, their vulnerabilities endanger safety. Onboard continuous authentication is a vital countermeasure to hijacking attempts. Motivated by the success of Machine Learning (ML) techniques in the field o...
Preprint
Full-text available
Trace clustering has been extensively used to preprocess event logs. By grouping similar behavior, these techniques guide the identification of sub-logs, producing more understandable models and conformance analytics. Nevertheless, little attention has been posed to the relationship between event log properties and clustering quality. In this work,...
Article
Domain Adaptation (DA), i.e. reusing Machine Learning (ML) pre-trained models across related domains, is increasingly favored, especially in applications domains where training data is scarce. However, the adaptation process still requires data and time to adjust the model to the new domain. In this work, we propose a data selection mechanism to mi...
Article
Since the first version of the Entity–Relationship (ER) model proposed by Peter Chen over forty years ago, both the ER model and conceptual modeling activities have been key success factors for modeling computer-based systems. During the last decade, conceptual modeling has been recognized as an important research topic in academia, as well as a ne...
Article
Industrial insider threat detection has consistently been a popular field of research. To help detect potential insider threats, the emotional states of humans are identified through a wide range of physiological signals including the galvanic skin response, electrocardiogram, and electroencephalogram (EEG). This paper presents an insider risk asse...
Conference Paper
Emerging technologies are facilitating our daily activities and drive the digital transformation. The Internet of Things (IoT) and 5G communications will provide a wide range of new applications and business opportunities, but with a wide and quite complex attack surface. Several users are not aware of the underlying threats and most of them do not...
Conference Paper
We propose a security methodology for Machine Learning (ML) pipelines, supporting the definition of key security properties of ML assets, the identification of threats to them as well as the selection, test and verification of security controls. Our proposal is based on STRIDE, a widely used approach to threat modeling originally developed by Micro...
Article
We live in an interconnected and pervasive world where huge amount of data are collected every second. Fully exploiting data through advanced analytics, machine learning and artificial intelligence, becomes crucial for businesses, from micro to large enterprises, resulting in a key advantage (or shortcoming) in the global market competition, as wel...
Chapter
There is a general lack of trust in existing Services’ descriptions and directories, and public repositories and aggregators of such Services are currently missing. Frequent variations in exposed interfaces and lack of proper descriptions hinder interoperability and portability of applications. The present work aims at entrusting the current Cloud...
Preprint
Visible light communication (VLC) technology was introduced as a key enabler for the next generation of wireless networks, mainly thanks to its simple and low-cost implementation. However, several challenges prohibit the realization of the full potentials of VLC, namely, limited modulation bandwidth, ambient light interference, optical diffuse refl...
Article
Full-text available
In this paper, we present a smart connected parking lots solution to automatically count and notify drivers about empty parking spots in major cities. As its name implies, the proposed smart IoT system has two operating phases: (i) continuous counting of empty spots in the monitored far-apart parking lots, and (ii) instantaneous driver notification...
Article
Full-text available
Cross-Device Federated Learning (CDFL) systems enable fully decentralized training networks whereby each participating device can act as a model-owner and a model-producer. CDFL systems need to ensure fairness, trustworthiness, and high-quality model availability across all the participants in the underlying training networks. This paper presents a...
Chapter
Full-text available
Automatic detection of prohibited items in passenger baggage is a challenging task, especially in cluttered and occluded concealment scenarios. In this paper, we present a deep fusion driven semantic segmentation network that leverages multi-scale feature representations (extracted via CNN backbone) to generate highly accurate segmentation masks of...
Article
Full-text available
This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access enviro...
Preprint
Full-text available
Process discovery methods have obtained remarkable achievements in Process Mining, delivering comprehensible process models to enhance management capabilities. However, selecting the suitable method for a specific event log highly relies on human expertise, hindering its broad application. Solutions based on Meta-learning (MtL) have been promising...
Preprint
Full-text available
With the advent of Mobile Crowd Sourcing (MCS) systems and its applications, theselectionof the right crowd is gaining utmostimportance. The increasing variability in the context of MCS tasks makes the selection of not only the capable, but also the willingworkers crucial for high task completion rate. Most of the existing MCS selection frameworks...
Article
Full-text available
Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Netwo...
Chapter
Information system’s technologies increase rapidly and continuously due to the huge traffic and volume of data. Stored data need to be secured adequately and transferred safely through the computer network. Therefore the data transaction mechanism still exposed to the intrusion attack of which consequences remain unlikable. An intrusion can be unde...
Article
Full-text available
Reputation expresses the beliefs or opinions about someone or something that are held by an individual or by a community. Reputation Management Systems (RMSs) handle representation, computation, and storage of reputation in some quantitative form, suitable for grounding trust relations among parties. Quantifying reputation is important in situation...
Chapter
Full-text available
We study in a quantitative way the efficacy of a social intelligence scheme that is an extension of Extreme Learning Machine paradigm. The key question we investigate is whether and how a collection of elementary learning parcels can replace a single algorithm that is well suited to learn a relatively complex function. Per se, the question is defin...
Article
Managing information quality has become important in cyber-physical systems dealing with big data. In this regard, different models have been proposed, mainly in flat peer-to-peer networks, in which exchanging information efficiently is a key aspect due to scarce resources. However, little research has been conducted on information quality metrics...
Article
Internet of Things (IoT) is composed of physical devices, communication networks, and services provided by edge systems and over-the-top applications. IoT connects billions of devices that collect data from the physical environment, which are pre-processed at the edge and then forwarded to processing services at the core of the infrastructure, on t...
Preprint
Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We developed a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R...
Article
Full-text available
The loss or compromise of any safety critical industrial infrastructure can seriously impact the confidentiality, integrity, or delivery of essential services. Research has shown that such threats often come from malicious insiders. To identify these insiders, survey- and electrocardiogram-based approaches have been proposed; however, these approac...
Article
Full-text available
Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vul...
Chapter
Cyber ranges are virtual environments used in several contexts to enhance the awareness and preparedness of users to cybersecurity threats. Effectiveness of cyber ranges strongly depends on how much realistic are the training scenarios provided to trainees and on an efficient mechanism to monitor and evaluate trainees’ activities. In the context of...
Article
Full-text available
Mobile Crowd Sourcing (MCS) has been an enabler in the development of artificial intelligence (AI) in general, and machine learning in particular. From collecting data to giving meaning to the data, there has been considerable work supporting the use of MCS in AI. While successful , current MCS solutions still suffer from limitations such as worker...
Article
Full-text available
As now well established, the world population is aging rapidly and, according to World Health Organization (WHO), the amount of people aged 60 years and older is expected to total 2 billion in 2050. For this reason, an emerging important issue is the definition of a new generation of healthcare platforms capable of monitoring people's quality of li...
Conference Paper
Full-text available
Encoding methods affect the performance of process mining tasks but little work in the literature focused on quantifying their impact. In this paper, we compare 10 different encoding methods from three different families (trace replay and alignment, graph embeddings, and word embeddings) using measures to evaluate the overlaps in the feature space,...
Article
Assuring anomaly-free business process executions is a key challenge for many organizations. Traditional techniques address this challenge using prior knowledge about anomalous cases that is seldom available in real-life. In this work, we propose the usage of word2vec encoding and One-Class Classification algorithms to detect anomalies by relying o...
Conference Paper
Full-text available
Real-time response is crucial in many business process scenarios, however, few tools support the online processing of Process Mining tasks. In this paper, we present Concept Drift in Event Stream Framework (CDESF), a tool focused on concept drift detection that also supports several online Process Mining tasks. CDESF highlights the process model ev...
Conference Paper
Assuring anomaly-free business process executions is a key challenge for many organizations. Traditional techniques address this challenge using prior knowledge about anomalous cases that is seldom available in real-life. In this work, we propose the usage of word2vec encoding and One-Class Classification algorithms to detect anomalies by relying o...
Conference Paper
Distributed Ledgers (DLs) like Blockchain have become a popular technique to build collective trust in digital records. The rationale is that any agent wishing to append a block to a DL needs to provide proof of holding some property/asset or having performed some costly activity. Thus, “poisoning” a DL with spurious content requires much more effo...
Article
Full-text available
The increasing velocity of multilateral trade today is followed by a growing complexity of international, inter-regional and inter-continental collaborative arrangements. These unfold in a context defined by spectacular technological advancements, ground-breaking shifts in politics worldwide and an accompanying transformation of our societies. As t...
Article
Full-text available
In this paper, we propose adding the traditional Japanese nodding behavior to the repertoire of social movements to be used in the context of human–robot interaction. Our approach is motivated by the notion that in many cultures, trust-building can be boosted by small body gestures. We discuss the integration of a robot capable of such movements wi...