691 research outputs found
iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems
To meet the demand of the world's largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols – that demand considerably high computational power and memory – can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts
CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context
When reading a scholarly article, inline citations help researchers
contextualize the current article and discover relevant prior work. However, it
can be challenging to prioritize and make sense of the hundreds of citations
encountered during literature reviews. This paper introduces CiteSee, a paper
reading tool that leverages a user's publishing, reading, and saving activities
to provide personalized visual augmentations and context around citations.
First, CiteSee connects the current paper to familiar contexts by surfacing
known citations a user had cited or opened. Second, CiteSee helps users
prioritize their exploration by highlighting relevant but unknown citations
based on saving and reading history. We conducted a lab study that suggests
CiteSee is significantly more effective for paper discovery than three
baselines. A field deployment study shows CiteSee helps participants keep track
of their explorations and leads to better situational awareness and increased
paper discovery via inline citation when conducting real-world literature
reviews
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Hybrid human-AI driven open personalized education
Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer.
In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer).
All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result
ML-based data-entry automation and data anomaly detection to support data quality assurance
Data playsacentralroleinmodernsoftwaresystems,whichare
very oftenpoweredbymachinelearning(ML)andusedincriticaldo-
mains ofourdailylives,suchasfinance,health,andtransportation.
However,theeffectivenessofML-intensivesoftwareapplicationshighly
depends onthequalityofthedata.Dataqualityisaffectedbydata
anomalies; dataentryerrorsareoneofthemainsourcesofanomalies.
The goalofthisthesisistodevelopapproachestoensuredataquality
by preventingdataentryerrorsduringtheform-fillingprocessandby
checking theofflinedatasavedindatabases.
The maincontributionsofthisthesisare:
1. LAFF, anapproachtoautomaticallysuggestpossiblevaluesofcat-
egorical fieldsindataentryforms.
2. LACQUER, anapproachtoautomaticallyrelaxthecompleteness
requirementofdataentryformsbydecidingwhenafieldshould
be optionalbasedonthefilledfieldsandhistoricalinputinstances.
3. LAFF-AD, anapproachtoautomaticallydetectdataanomaliesin
categorical columnsinofflinedatasets.
LAFF andLACQUERfocusmainlyonpreventingdataentryerrors
during theform-fillingprocess.Bothapproachescanbeintegratedinto
data entryapplicationsasefficientandeffectivestrategiestoassistthe
user duringtheform-fillingprocess.LAFF-ADcanbeusedofflineon
existing suspiciousdatatoeffectivelydetectanomaliesincategorical
data.
In addition,weperformedanextensiveevaluationofthethreeap-
proaches,assessingtheireffectivenessandefficiency,usingreal-world
datasets
Active learning based on computer vision and human-robot interaction for the user profiling and behavior personalization of an autonomous social robot
Social robots coexist with humans in situations where they have to exhibit proper communication skills. Since users may have different features and communicative procedures, personalizing human-robot interactions is essential for the success of these interactions. This manuscript presents Active Learning based on computer vision and human-robot interaction for user recognition and profiling to personalize robot behavior. The system identifies people using Intel-face-detection-retail-004 and FaceNet for face recognition and obtains users" information through interaction. The system aims to improve human-robot interaction by (i) using online learning to allow the robot to identify the users and (ii) retrieving users' information to fill out their profiles and adapt the robot's behavior. Since user information is necessary for adapting the robot for each interaction, we hypothesized that users would consider creating their profile by interacting with the robot more entertaining and easier than taking a survey. We validated our hypothesis with three scenarios: the participants completed their profiles using an online survey, by interacting with a dull robot, or with a cheerful robot. The results show that participants gave the cheerful robot a higher usability score (82.14/100 points), and they were more entertained while creating their profiles with the cheerful robot than in the other scenarios. Statistically significant differences in the usability were found between the scenarios using the robot and the scenario that involved the online survey. Finally, we show two scenarios in which the robot interacts with a known user and an unknown user to demonstrate how it adapts to the situation.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Spain Ministry of Science, Innovation and Universities; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spain Ministry of Science and Innovation. This publication is part of the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/5011000-11033 and by the European Union NextGenerationEU/PRTR
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Innovation of Tourism Mobility Systems in Historic City Centres: The Case of Austria
Fundamentally, tourism involves people on the move. Although controlled and well-managed tourism mobility can facilitate the sustainable touristic utilisation of places, uncontrolled touristic movement often creates significant challenges for host destinations. Developments in technology and digitalisation, such as the ubiquitous use of smartphones, are changing not only the way tourists move and behave while visiting historic cities, but also the evolution and management of tourism mobility systems in cities. Therefore, it is crucial to understand these changes and their effects on existing tourism mobility systems to benefit from digitalisation.
This thesis develops a detailed understanding of the configuration of existing tourism mobility systems to analyse and model digitally induced innovations in tourism mobility systems in tourist-historic cities in Europe.
This study employs the multi-level perspective (MLP) as an analytical tool. This approach enables a holistic analysis of innovation processes within tourism mobility by incorporating both internal and external factors that may influence system change.
A two-step empirical approach was adopted. First, a scoping study was employed to identify the current innovation status of tourism mobility systems in United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage City Centres in Europe. Based on these findings, in-depth expert interviews were then conducted for the Austrian case cities of Vienna, Salzburg and Graz to develop a detailed understanding of stepwise innovation within digitally penetrated tourism mobility systems.
The main contribution of this study is the development of an analytical five-phase innovation model of tourism mobility systems in tourist-historic cities. This model provides a detailed understanding of the general characteristics of each innovation phase of the tourism mobility system and the drivers and constraints of innovation. The five-phase model can be used as an assessment tool to establish the current innovation status of a local tourism mobility system and to evaluate the readiness of the system to innovate (further). In addition, for the tourism mobility systems investigated in the research, a detailed understanding of the actor configuration was revealed, including the roles and responsibilities of the actors. This thesis also contributes to the conceptual discussion of tourism mobility as a joint objective for research and will be of utility to practitioners in developing more sustainable tourism mobility systems
Cognitive Load Reduction in Commanding Heterogeneous Robotic Teams
With the proliferation of multi-robot systems, the interfaces required to operate them have become increasingly complex compared to those used for single robot systems. This can present challenges for operators who need to extract relevant information in order to make informed decisions about how to operate the robots. To address this issue, this thesis explores a variety of strategies aimed at improving the intuitiveness and usability of such systems. These strategies encompass a range of approaches, from designing user interfaces to integrating physical input devices, knowledge representations, and other modalities to assist operators. In this context, the thesis proposes a decision support system that provides operators with additional information in an intuitive way, focusing specifically on handling a set of distinct commands for a heterogeneous robotic team. A key constraint during the development of this system was the lack of historical data available to train the modules on. As a result, the proposed system was tested in a few-shot environment and was specifically designed for this circumstance. The support system comprises two modules: one that probabilistically classifies the next command using a data mining approach called sequence prediction, which is used to reorder the available commands in the interface; and a second that creates higher-level commands by mining frequent sequences from the historical dataset. These command sequences are presented to the operator, who can add them as additional executable commands. To evaluate the advantages and disadvantages of this novel approach, a user study was conducted, which showed that both modules increased the efficiency and usability of the system, while also identifying opportunities for further improvement
Arquitectura de percepción bioinspirada basada en atención para un robot social
La atención desempeña un papel fundamental, tanto para los seres humanos como para
los sistemas artificiales, ya que es una habilidad crucial que nos permite interactuar de manera
efectiva con nuestro entorno. Desde la infancia hasta la edad adulta, la atención nos ayuda a
concentrarnos en estímulos relevantes, procesar información de manera eficiente y responder a
estímulos emocionales y sociales. Además, de influir en aspectos importantes de nuestras vidas,
como el aprendizaje y las interacciones sociales.
La implementación de mecanismos de atención en sistemas artificiales tiene como objetivo
aprovechar los beneficios de esta habilidad fundamental. Esto se traduce en una mejora en el
procesamiento de información, la toma de decisiones y la interacción con el entorno. La atención
en sistemas artificiales es un área de investigación en constante desarrollo, con el propósito
de mejorar la capacidad de los sistemas inteligentes en diversas aplicaciones. Uno de los campos
donde más se ha estudiado el concepto de la atención es en visión artificial, en la cual se utiliza
para resaltar regiones relevantes en las imágenes, lo que mejora el análisis y el reconocimiento
de objetos, mientras que en la robótica, la atención permite a los robots enfocarse en objetos o
eventos específicos, mejorando su capacidad de reacción y ejecución de tareas.
Por este motivo, en este trabajo se propone un sistema de percepción bioinspirado basado
en atención diseñado para mejorar la interacción humano-robot. Este sistema está diseñado para
localizar el foco de atención del robot en cada momento teniendo en cuenta la tarea actual,
los estímulos disponibles y el estado interno del robot. El sistema integra fenómenos bioinspirados
como la inhibición al retorno, la relocalización del foco de atención dependiendo de los
estímulos, los conceptos de atención sostenida y puntual para el cambio en el foco de atención
y de agregación de estímulos de forma exógena y endógena de forma independiente. Además, se
ha integrado en una plataforma robótica y se ha validado su funcionamiento en diferentes aplicaciones.
Este trabajo se ha abordado desde dos perspectivas: la ampliación de las capacidades
perceptuales del robot y la mejora de la interacción gracias a la integración de la atención en la
arquitectura software de las plataformas robóticas. Para ello, en este trabajo se han investigado
los estímulos más relevantes para la atención en humanos y su integración en el ámbito de la
robótica y como realizar la agregación y fusión multisensorial de estos desde un punto de vista
basado en la atención, consiguiendo una representación del entorno y seleccionando la posición
del foco de atención en cada momento. Por otro lado, se ha investigado la relevancia de la
integración de este sistema artificial a una plataforma robótica en lo que respecta a la interacción
humano-robot, lo que ha dado lugar a un estudio que explora esta idea.Attention plays a fundamental role for both humans and artificial systems, as it is a crucial
skill that enables us to interact effectively with our environment. From childhood to adulthood,
attention helps us to focus on relevant stimuli, process information efficiently, and respond to
emotional and social stimuli. It also influences important aspects of our lives, such as learning
and social interactions.
The implementation of attention mechanisms in artificial systems aims to take advantage of
the benefits of this fundamental ability. This translates into improved information processing,
decision making and interaction with the environment. Attention in artificial systems is an area
of research in constant development, with the purpose of improving the capacity of intelligent
systems in various applications. The fields where the concept of attention has been most studied
are computer vision and robotics. In computer vision, attention is used to highlight relevant
areas in images, which improves object analysis and recognition, while in robotics, attention
allows robots to focus on specific objects or events, improving their ability to react and perform
tasks.
For this reason, this work proposes a bio-inspired attention-based perception system designed
to improve human-robot interaction. This system is designed to locate the focus of attention
of the robot at each moment, taking into account the current task, the available stimuli and
the internal state of the robot.Moreover, the architecture integrates bioinspired concepts such
as return inhibition, stimulus-dependent relocation of the focus of attention, the concepts of
sustained and punctual attention for the shift in the focus of attention and the aggregation of
exogenous and endogenous stimuli independently are integrated. In addition to this, it has been
integrated into a robotic platform, and its performance has been validated in different applications.
This work has been approached from two perspectives: the increase of the perceptual
capabilities of the robot and the improvement of the interaction thanks to the integration of
attention in the software architecture of robotic platforms. To this end, in this work, we have
investigated the most relevant stimuli for attention in humans and their integration in the robotics
environment, and how to perform the aggregation and multisensory fusion of these from
an attention-based point of view, achieving a representation of the environment and selecting
the position of the focus of attention at each moment. On the other hand, we have investigated
the relevance of the integration of this artificial system to a robotic platform in terms of
human-robot interaction, leading to a study that explores this idea.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Antonio Fernández Caballero.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Plinio Moreno Lópe
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