7,432 research outputs found

    Mobile Device Background Sensors: Authentication vs Privacy

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    The increasing number of mobile devices in recent years has caused the collection of a large amount of personal information that needs to be protected. To this aim, behavioural biometrics has become very popular. But, what is the discriminative power of mobile behavioural biometrics in real scenarios? With the success of Deep Learning (DL), architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), have shown improvements compared to traditional machine learning methods. However, these DL architectures still have limitations that need to be addressed. In response, new DL architectures like Transformers have emerged. The question is, can these new Transformers outperform previous biometric approaches? To answers to these questions, this thesis focuses on behavioural biometric authentication with data acquired from mobile background sensors (i.e., accelerometers and gyroscopes). In addition, to the best of our knowledge, this is the first thesis that explores and proposes novel behavioural biometric systems based on Transformers, achieving state-of-the-art results in gait, swipe, and keystroke biometrics. The adoption of biometrics requires a balance between security and privacy. Biometric modalities provide a unique and inherently personal approach for authentication. Nevertheless, biometrics also give rise to concerns regarding the invasion of personal privacy. According to the General Data Protection Regulation (GDPR) introduced by the European Union, personal data such as biometric data are sensitive and must be used and protected properly. This thesis analyses the impact of sensitive data in the performance of biometric systems and proposes a novel unsupervised privacy-preserving approach. The research conducted in this thesis makes significant contributions, including: i) a comprehensive review of the privacy vulnerabilities of mobile device sensors, covering metrics for quantifying privacy in relation to sensitive data, along with protection methods for safeguarding sensitive information; ii) an analysis of authentication systems for behavioural biometrics on mobile devices (i.e., gait, swipe, and keystroke), being the first thesis that explores the potential of Transformers for behavioural biometrics, introducing novel architectures that outperform the state of the art; and iii) a novel privacy-preserving approach for mobile biometric gait verification using unsupervised learning techniques, ensuring the protection of sensitive data during the verification process

    Authentication enhancement in command and control networks: (a study in Vehicular Ad-Hoc Networks)

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    Intelligent transportation systems contribute to improved traffic safety by facilitating real time communication between vehicles. By using wireless channels for communication, vehicular networks are susceptible to a wide range of attacks, such as impersonation, modification, and replay. In this context, securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything (V2X) communication, constitutes a technological challenge that needs to be addressed. Hence, message authentication is crucial to safeguard vehicular ad-hoc networks (VANETs) from malicious attacks. The current state-of-the-art for authentication in VANETs relies on conventional cryptographic primitives, introducing significant computation and communication overheads. In this challenging scenario, physical (PHY)-layer authentication has gained popularity, which involves leveraging the inherent characteristics of wireless channels and the hardware imperfections to discriminate between wireless devices. However, PHY-layerbased authentication cannot be an alternative to crypto-based methods as the initial legitimacy detection must be conducted using cryptographic methods to extract the communicating terminal secret features. Nevertheless, it can be a promising complementary solution for the reauthentication problem in VANETs, introducing what is known as “cross-layer authentication.” This thesis focuses on designing efficient cross-layer authentication schemes for VANETs, reducing the communication and computation overheads associated with transmitting and verifying a crypto-based signature for each transmission. The following provides an overview of the proposed methodologies employed in various contributions presented in this thesis. 1. The first cross-layer authentication scheme: A four-step process represents this approach: initial crypto-based authentication, shared key extraction, re-authentication via a PHY challenge-response algorithm, and adaptive adjustments based on channel conditions. Simulation results validate its efficacy, especially in low signal-to-noise ratio (SNR) scenarios while proving its resilience against active and passive attacks. 2. The second cross-layer authentication scheme: Leveraging the spatially and temporally correlated wireless channel features, this scheme extracts high entropy shared keys that can be used to create dynamic PHY-layer signatures for authentication. A 3-Dimensional (3D) scattering Doppler emulator is designed to investigate the scheme’s performance at different speeds of a moving vehicle and SNRs. Theoretical and hardware implementation analyses prove the scheme’s capability to support high detection probability for an acceptable false alarm value ≀ 0.1 at SNR ≄ 0 dB and speed ≀ 45 m/s. 3. The third proposal: Reconfigurable intelligent surfaces (RIS) integration for improved authentication: Focusing on enhancing PHY-layer re-authentication, this proposal explores integrating RIS technology to improve SNR directed at designated vehicles. Theoretical analysis and practical implementation of the proposed scheme are conducted using a 1-bit RIS, consisting of 64 × 64 reflective units. Experimental results show a significant improvement in the Pd, increasing from 0.82 to 0.96 at SNR = − 6 dB for multicarrier communications. 4. The fourth proposal: RIS-enhanced vehicular communication security: Tailored for challenging SNR in non-line-of-sight (NLoS) scenarios, this proposal optimises key extraction and defends against denial-of-service (DoS) attacks through selective signal strengthening. Hardware implementation studies prove its effectiveness, showcasing improved key extraction performance and resilience against potential threats. 5. The fifth cross-layer authentication scheme: Integrating PKI-based initial legitimacy detection and blockchain-based reconciliation techniques, this scheme ensures secure data exchange. Rigorous security analyses and performance evaluations using network simulators and computation metrics showcase its effectiveness, ensuring its resistance against common attacks and time efficiency in message verification. 6. The final proposal: Group key distribution: Employing smart contract-based blockchain technology alongside PKI-based authentication, this proposal distributes group session keys securely. Its lightweight symmetric key cryptography-based method maintains privacy in VANETs, validated via Ethereum’s main network (MainNet) and comprehensive computation and communication evaluations. The analysis shows that the proposed methods yield a noteworthy reduction, approximately ranging from 70% to 99%, in both computation and communication overheads, as compared to the conventional approaches. This reduction pertains to the verification and transmission of 1000 messages in total

    Single-cell time-series analysis of metabolic rhythms in yeast

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    The yeast metabolic cycle (YMC) is a biological rhythm in budding yeast (Saccharomyces cerevisiae). It entails oscillations in the concentrations and redox states of intracellular metabolites, oscillations in transcript levels, temporal partitioning of biosynthesis, and, in chemostats, oscillations in oxygen consumption. Most studies on the YMC have been based on chemostat experiments, and it is unclear whether YMCs arise from interactions between cells or are generated independently by each cell. This thesis aims at characterising the YMC in single cells and its response to nutrient and genetic perturbations. Specifically, I use microfluidics to trap and separate yeast cells, then record the time-dependent intensity of flavin autofluorescence, which is a component of the YMC. Single-cell microfluidics produces a large amount of time series data. Noisy and short time series produced from biological experiments restrict the computational tools that are useful for analysis. I developed a method to filter time series, a machine learning model to classify whether time series are oscillatory, and an autocorrelation method to examine the periodicity of time series data. My experimental results show that yeast cells show oscillations in the fluorescence of flavins. Specifically, I show that in high glucose conditions, cells generate flavin oscillations asynchronously within a population, and these flavin oscillations couple with the cell division cycle. I show that cells can individually reset the phase of their flavin oscillations in response to abrupt nutrient changes, independently of the cell division cycle. I also show that deletion strains generate flavin oscillations that exhibit different behaviour from dissolved oxygen oscillations from chemostat conditions. Finally, I use flux balance analysis to address whether proteomic constraints in cellular metabolism mean that temporal partitioning of biosynthesis is advantageous for the yeast cell, and whether such partitioning explains the timing of the metabolic cycle. My results show that under proteomic constraints, it is advantageous for the cell to sequentially synthesise biomass components because doing so shortens the timescale of biomass synthesis. However, the degree of advantage of sequential over parallel biosynthesis is lower when both carbon and nitrogen sources are limiting. This thesis thus confirms autonomous generation of flavin oscillations, and suggests a model in which the YMC responds to nutrient conditions and subsequently entrains the cell division cycle. It also emphasises the possibility that subpopulations in the culture explain chemostat-based observations of the YMC. Furthermore, this thesis paves the way for using computational methods to analyse large datasets of oscillatory time series, which is useful for various fields of study beyond the YMC

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    A probabilistic approach for acoustic emission based monitoring techniques: with application to structural health monitoring

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    It has been demonstrated that acoustic-emission (AE), inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an increased sensitivity to damage, as well as an ability to localise its source. There are, however, numerous challenges associated with the analysis of AE data. One issue is the high sampling frequencies required to capture AE activity. In just a few seconds, a recording can generate very high volumes of data, of which a significant portion may be of little interest for analysis. Identifying the individual AE events in a recorded time-series is therefore a necessary procedure for reducing the size of the dataset and projecting out the influence of background noise from the signal. In this paper, a state-of-the-art technique is presented that can automatically identify cluster the AE events from a probabilistic perspective. A nonparametric Bayesian approach, based on the Dirichlet process (DP), is employed to overcome some of the challenges associated with this task. Additionally, the developed model is applied for damage detection using AE data collected from an experimental setup. Two main sets of AE data are considered in this work: (1) from a journal bearing in operation, and (2) from an Airbus A320 main landing gear subjected to fatigue testing

    The organisation and function of the gut microbiota in cystic fibrosis

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    People with cystic fibrosis (pwCF) suffer from a range of gastrointestinal manifestations of disease, which has increased in prevalence as respiratory outcomes improve and life expectancy raises. This translates to the occurrence of daily GI symptoms for many pwCF, which is a top research priority to alleviate. The gut microbiota is altered in CF and has been shown to associate with intestinal abnormalities, therefore offers a potential avenue of therapeutic intervention by its modulation. This thesis investigated relationships between the microbiota and associated functions, intestinal outcomes, and cystic fibrosis transmembrane regulator (CFTR) modulator usage. Initially, relationships between altered intestinal function and physiology in CF were revealed with microbiota, by combining 16S rRNA gene sequencing data with magnetic resonance imaging (MRI) results and clinical metadata across pwCF and healthy controls. Significant differences in diversity and composition were observed between groups, which further associated with clinical factors and markers of intestinal function. To understand how microbiota function might be compromised in CF, a sensitive method to profile and quantify faecal short-chain fatty acids (SCFAs) using gas chromatography-mass spectrometry (GC-MS) was validated. This was subsequently used to demonstrate that overall SCFA compositional differences persist between healthy controls pwCF receiving Tezacaftor/Ivacaftor CFTR modulator therapy, further extending to microbiota compositional differences, which were also not significantly altered by treatment. Finally, microbiota composition and function were assessed across pwCF receiving more efficacious Elexacaftor/Tezacaftor/Ivacaftor (ETI) therapy. Subtle differences were observed following extended ETI administration, yet microbiota and SCFA compositions remained significantly different from controls. Interestingly, there were no differences across the most abundant SCFAs, indicating possible functional redundancy in the CF microbiota. Overall, the results obtained in this thesis advocate for further investigation of microbiota function through more sophisticated metagenomic and untargeted metabolomic approaches, to unravel the complex relationships between the microbiota, gastrointestinal (GI) manifestations, and patient symptoms in CF

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Ecology of methanotrophs in a landfill methane biofilter

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    Decomposing landfill waste is a significant anthropogenic source of the potent climate-active gas methane (CH₄). To mitigate fugitive methane emissions Norfolk County Council are trialling a landfill biofilter, designed to harness the methane oxidizing potential of methanotrophic bacteria. These methanotrophs can convert CH₄ to CO₂ or biomass and act as CH₄ sinks. The most active CH₄ oxidising regions of the Strumpshaw biofilter were identified from in-situ temperature, CH₄, O₂ and CO₂ profiles. While soil CH₄ oxidation potential was estimated and used to confirm methanotroph activity and determine optimal soil moisture conditions for CH₄ oxidation. It was observed that most CH₄ oxidation occurs in the top 60cm of the biofilter (up to 50% of CH4 input) at temperatures around 50ÂșC, optimal soil moisture was 10-27.5%. A decrease in in-situ temperature following CH₄ supply interruption suggested the high biofilter temperatures were driven by CH₄ oxidation. The biofilter soil bacterial community was profiled by 16S rRNA gene analysis, with methanotrophs accounting for ~5-10% of bacteria. Active methanotrophs at a range of different incubation temperatures were identified by ÂčÂłCH₄ DNA stable-isotope probing coupled with 16S rRNA gene amplicon and metagenome analysis. These methods identified Methylocella, Methylobacter, Methylocystis and Crenothrix as potential CH₄ oxidisers at the lower temperatures (30ÂșC/37ÂșC) observed following system start-up or gas-feed interruption. At higher temperatures typical of established biofilter operation (45ÂșC/50ÂșC), Methylocaldum and an unassigned Methylococcaceae species were the dominant active methanotrophs. Finally, novel methanotrophs Methylococcus capsulatus (Norfolk) and Methylocaldum szegediense (Norfolk) were isolated from biofilter soil enrichments. Methylocaldum szegediense (Norfolk) may be very closely related to or the same species as one of the most abundant active methanotrophs in a metagenome from a 50ÂșC biofilter soil incubation, based on genome-to-MAG similarity. This isolate was capable of growth over a broad temperature range (37-62ÂșC) including the higher (in-situ) biofilter temperatures (>50ÂșC)

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
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