8,011 research outputs found
Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.
Mobile heritage apps have become one of the most popular means for audience
engagement and curation of museum collections and heritage contexts. This
raises practical and ethical questions for both researchers and practitioners, such
as: what kind of audience engagement can be built using mobile apps? what are
the current approaches? how can audience engagement with these experience
be evaluated? how can those experiences be made more resilient, and in turn
sustainable? In this thesis I explore experience design scholarships together with
personal professional insights to analyse digital heritage practices with a view to
accelerating thinking about and critique of mobile apps in particular. As a result,
the chapters that follow here look at the evolution of digital heritage practices,
examining the cultural, societal, and technological contexts in which mobile
heritage apps are developed by the creative media industry, the academic
institutions, and how these forces are shaping the user experience design
methods. Drawing from studies in digital (critical) heritage, Human-Computer
Interaction (HCI), and design thinking, this thesis provides a critical analysis of
the development and use of mobile practices for the heritage. Furthermore,
through an empirical and embedded approach to research, the thesis also
presents auto-ethnographic case studies in order to show evidence that mobile
experiences conceptualised by more organic design approaches, can result in
more resilient and sustainable heritage practices. By doing so, this thesis
encourages a renewed understanding of the pivotal role of these practices in the
broader sociocultural, political and environmental changes.AHRC REAC
Deep generative models for network data synthesis and monitoring
Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network.
Although networks inherently
have abundant amounts of monitoring data, its access and effective measurement is
another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset
without leaking commercial sensitive information. Second, it could be very expensive
to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of
flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources
in the network element that can be applied to support the measurement function are
too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex
structure. Various emerging optimization-based solutions (e.g., compressive sensing)
or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet
meet the current network requirements.
The contributions made in this thesis significantly advance the state of the art in
the domain of network measurement and monitoring techniques. Overall, we leverage
cutting-edge machine learning technology, deep generative modeling, throughout the
entire thesis. First, we design and realize APPSHOT , an efficient city-scale network
traffic sharing with a conditional generative model, which only requires open-source
contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system â GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we
design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time
network telemetry system with latent GANs and spectral-temporal networks. Finally,
we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through
this research are summarized, and interesting topics are discussed for future work in
this domain. All proposed solutions have been evaluated with real-world datasets and
applied to support different applications in real systems
Authentication enhancement in command and control networks: (a study in Vehicular Ad-Hoc Networks)
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 âcross-layer authentication.â 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 schemeâs performance at different speeds of a moving vehicle and SNRs. Theoretical and hardware implementation analyses prove the schemeâs capability to support high detection probability for an acceptable false alarm value †0.1 at SNR â„ 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 Ă 64 reflective units. Experimental results show a significant improvement in the Pd, increasing from 0.82 to 0.96 at SNR = â 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 Ethereumâs 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
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (âAIâ) 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 â 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 CatĂłlica 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
Testing pALPIDE sensors for particle detection and Characterization of a Laser beam using a webcam CMOS sensor
The upgrade program of the Large Hadron Collider (LHC) was implemented during the second Long Shutdown program (2019/2020). For this program, the ALICE Collaboration (A Large Ion Collider Experiment) proposed, among others, a new detector called Muon Forward Tracker (MFT). The primary goal of the MFT detector, installed on December 2021 and located between the Inner Tracker System (ITS) and the Muon Spectrometer, is to improve the capability of vertex reconstruction. The MFT is equipped with the same pixel sensors used for the ITS upgrade. These sensors are the ALICE Pixel Detectors (ALPIDE), a kind of monolithic active pixel sensor. The MFT is composed of five arrays of pixel sensors which are configured as parallel discs covering â3.6 < η < â2.45. Some prototypes were designed in order to achieve the final version of the ALPIDE, such as the pALPIDE family, which was divided into three versions (i.e., pALPIDE-1,2,3). The ALICE upgrade also included a new system for the data taking and simulation called Online-offline (O2) to replace AliRoot. We designed the geometry of two non-active parts of the MFT and included them in the O2 system. The first goal of this thesis is focused on the characterization of the pALPIDE-2. This sensor is segmented into four groups corresponding to four types of pixels. This characterization includes the test of analogue and digital. According to these tests, we identified a group of pixels that do not work correctly. The threshold scan tests showed the threshold level in each pixel is influenced by the input capacitance according to its n-well size and the surrounding area. Also, we studied the response of the pALPIDE-2 when it was exposed to a soft x-ray source, varying the distance between them. This test showed that the hit count changed according to the inverse square of the distance. iv The second goal of this thesis was to implement a low-cost tool based on a CMOS sensor to characterize laser beams. This tool comprises a Raspberry, a Pi Camera with a pitch size of 1.4 ”m, and an optical system. To test the accuracy of the results of this tool, we made similar measurements with other sensors. A photodiode and a light-dependent resistor performed these measurements, which showed the spot radius size compatibility. However, the CMOS sensor expressed the highest precision and is a more affordable tool than commercial devices
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire
Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the UniversitĂ© de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent rĂ©alisĂ©e Ă lâaide dâun capteur situĂ© au bout dâune perche que lâopĂ©rateur introduit dans le chantier, depuis une zone sĂ©curisĂ©e. Le capteur Ă©met des faisceaux laser qui fournissent la distance Ă un mur dĂ©tectĂ©, crĂ©ant ainsi une carte en 3D. Ceci produit des zones dâombres et une faible densitĂ© de points sur les parois Ă©loignĂ©es. Pour relever ces dĂ©fis, une Ă©quipe de recherche de lâUniversitĂ© de Sherbrooke conçoit un drone filaire Ă©quipĂ© dâun LiDAR rotatif pour cette mission, bĂ©nĂ©ficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimitĂ©, un partage de calcul et une communication en temps rĂ©el. Pour une compatibilitĂ© avec le mouvement du drone lors des coincements du fil, la longueur excĂ©dante est intĂ©grĂ©e dans une bobine embarquĂ©e, qui contribue Ă la charge utile du drone. Lors dâun pilotage manuel, le facteur humain entraĂźne des problĂšmes de perception et comprĂ©hension dâun environnement 3D virtuel, et dâexĂ©cution dâune mission optimale. Cette thĂšse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et lâexploration. Le systĂšme doit calculer une trajectoire qui cartographie lâenvironnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquĂ©e. La planification de trajectoire Ă lâaide dâun Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin rĂ©alisable, mais lâoptimisation est coĂ»teuse en calcul et la performance est variable et imprĂ©visible. Lâexploration par la mĂ©thode des frontiĂšres est reprĂ©sentative de lâespace Ă explorer et le chemin peut ĂȘtre optimisĂ© en rĂ©solvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considĂšrent que le cas 2D et nâoptimisent pas le chemin global. Pour relever ces dĂ©fis, cette thĂšse prĂ©sente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin Ă©gal ou plus court que les algorithmes existants en un temps de calcul jusquâĂ 70% plus court que le deuxiĂšme meilleur algorithme dans un environnement reprĂ©sentatif. Une version modifiĂ©e de RRT-connect calcule un chemin rĂ©alisable, raccourci avec une technique dĂ©terministe qui tire profit des noeuds intermĂ©diaires prĂ©alablement ajoutĂ©s. Le deuxiĂšme algorithme, TAPE, est la premiĂšre mĂ©thode dâexploration de cavitĂ©s en 3D qui minimise le temps de mission et la longueur du fil dĂ©roulĂ©. En moyenne, le trajet global est 4% plus long que la mĂ©thode qui rĂ©sout le TSP, mais le fil reste sous la longueur autorisĂ©e dans 100% des cas simulĂ©s, contre 53% avec la mĂ©thode initiale. Lâapproche utilise une architecture hiĂ©rarchique Ă 2 niveaux : la planification globale rĂ©sout un TSP aprĂšs extraction des frontiĂšres, et la planification locale minimise le coĂ»t du chemin et la longueur de fil via une fonction de dĂ©cision. LâintĂ©gration de ces deux outils dans le NetherDrone produit un systĂšme intelligent pour lâexploration autonome, dotĂ© de fonctionnalitĂ©s semi-autonomes pour une interaction avec lâopĂ©rateur. Les travaux rĂ©alisĂ©s ouvrent la porte Ă de nouvelles approches de navigation dans le domaine des missions dâinspection, de cartographie et de recherche et sauvetage
Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation
The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and
challenging task that is important in many practical applications. Classical
model-based approaches to this problem require an accurate model to capture how
robot motions affect the deformation of the DLO. Nowadays, data-driven models
offer the best tradeoff between quality and computation time. This paper
analyzes several learning-based 3D models of the DLO and proposes a new one
based on the Transformer architecture that achieves superior accuracy, even on
the DLOs of different lengths, thanks to the proposed scaling method. Moreover,
we introduce a data augmentation technique, which improves the prediction
performance of almost all considered DLO data-driven models. Thanks to this
technique, even a simple Multilayer Perceptron (MLP) achieves close to
state-of-the-art performance while being significantly faster to evaluate. In
the experiments, we compare the performance of the learning-based 3D models of
the DLO on several challenging datasets quantitatively and demonstrate their
applicability in the task of shaping a DLO.Comment: Under review for IEEE Robotics and Automation Letter
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