393 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
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
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Computer audition for emotional wellbeing
This thesis is focused on the application of computer audition (i. e., machine listening) methodologies for monitoring states of emotional wellbeing. Computer audition is a growing field and has been successfully applied to an array of use cases in recent years. There are several advantages to audio-based computational analysis; for example, audio can be recorded non-invasively, stored economically, and can capture rich information on happenings in a given environment, e. g., human behaviour. With this in mind, maintaining emotional wellbeing is a challenge for humans and emotion-altering conditions, including stress and anxiety, have become increasingly common in recent years. Such conditions manifest in the body, inherently changing how we express ourselves. Research shows these alterations are perceivable within vocalisation, suggesting that speech-based audio monitoring may be valuable for developing artificially intelligent systems that target improved wellbeing. Furthermore, computer audition applies machine learning and other computational techniques to audio understanding, and so by combining computer audition with applications in the domain of computational paralinguistics and emotional wellbeing, this research concerns the broader field of empathy for Artificial Intelligence (AI). To this end, speech-based audio modelling that incorporates and understands paralinguistic wellbeing-related states may be a vital cornerstone for improving the degree of empathy that an artificial intelligence has.
To summarise, this thesis investigates the extent to which speech-based computer audition methodologies can be utilised to understand human emotional wellbeing. A fundamental background on the fields in question as they pertain to emotional wellbeing is first presented, followed by an outline of the applied audio-based methodologies. Next, detail is provided for several machine learning experiments focused on emotional wellbeing applications, including analysis and recognition of under-researched phenomena in speech, e. g., anxiety, and markers of stress. Core contributions from this thesis include the collection of several related datasets, hybrid fusion strategies for an emotional gold standard, novel machine learning strategies for data interpretation, and an in-depth acoustic-based computational evaluation of several human states. All of these contributions focus on ascertaining the advantage of audio in the context of modelling emotional wellbeing. Given the sensitive nature of human wellbeing, the ethical implications involved with developing and applying such systems are discussed throughout
Contributions and applications around low resource deep learning modeling
El aprendizaje profundo representa la vanguardia del aprendizaje automático en multitud de aplicaciones. Muchas de estas tareas requieren una gran cantidad de recursos computacionales, lo que limita su adopción en dispositivos integrados. El objetivo principal de esta tesis es estudiar métodos y algoritmos que permiten abordar problemas utilizando aprendizaje profundo con bajos recursos computacionales. Este trabajo también tiene como objetivo presentar aplicaciones de aprendizaje profundo en la industria.
La primera contribución es una nueva función de activación para redes de aprendizaje profundo: la función de módulo. Los experimentos muestran que la función de activación propuesta logra resultados superiores en tareas de visión artificial cuando se compara con las alternativas encontradas en la literatura.
La segunda contribución es una nueva estrategia para combinar modelos preentrenados usando destilación de conocimiento. Los resultados de este capÃtulo muestran que es posible aumentar significativamente la precisión de los modelos preentrenados más pequeños, lo que permite un alto rendimiento a un menor costo computacional.
La siguiente contribución de esta tesis aborda el problema de la previsión de ventas en el campo de la logÃstica. Se proponen dos sistemas de extremo a extremo con dos técnicas diferentes de aprendizaje profundo (modelos de secuencia a secuencia y transformadores). Los resultados de este capÃtulo concluyen que es posible construir sistemas integrales para predecir las ventas de múltiples productos individuales, en múltiples puntos de venta y en diferentes momentos con un único modelo de aprendizaje automático. El modelo propuesto supera las alternativas encontradas en la literatura.
Finalmente, las dos últimas contribuciones pertenecen al campo de la tecnologÃa del habla. El primero estudia cómo construir un sistema de reconocimiento de voz Keyword Spotting utilizando una versión eficiente de una red neuronal convolucional. En este estudio, el sistema propuesto es capaz de superar el rendimiento de todos los puntos de referencia encontrados en la literatura cuando se prueba contra las subtareas más complejas. El último estudio propone un modelo independiente de texto a voz de última generación capaz de sintetizar voz inteligible en miles de perfiles de voz, mientras genera un discurso con variaciones de prosodia significativas y expresivas. El enfoque propuesto elimina la dependencia de los modelos anteriores de un sistema de voz adicional, lo que hace que el sistema propuesto sea más eficiente en el tiempo de entrenamiento e inferencia, y permite operaciones fuera de lÃnea y en el dispositivo.Deep learning is the state of the art for several machine learning tasks. Many of these tasks require large amount of computational resources, which limits their adoption in embedded devices. The main goal of this dissertation is to study methods and algorithms that allow to approach problems using deep learning with restricted computational resources. This work also aims at presenting applications of deep learning in industry.
The first contribution is a new activation function for deep learning networks: the modulus function. The experiments show that the proposed activation function achieves superior results in computer vision tasks when compared with the alternatives found in the literature.
The second contribution is a new strategy to combine pre-trained models using knowledge distillation. The results of this chapter show that it is possible to significantly increase the accuracy of the smallest pre-trained models, allowing high performance at a lower computational cost.
The following contribution in this thesis tackles the problem of sales fore- casting in the field of logistics. Two end-to-end systems with two different deep learning techniques (sequence-to-sequence models and transformers) are pro- posed. The results of this chapter conclude that it is possible to build end-to-end systems to predict the sales of multiple individual products, at multiple points of sale and different times with a single machine learning model. The proposed model outperforms the alternatives found in the literature.
Finally, the last two contributions belong to the speech technology field. The former, studies how to build a Keyword Spotting speech recognition system using an efficient version of a convolutional neural network. In this study, the proposed system is able to beat the performance of all the benchmarks found in the literature when tested against the most complex subtasks.
The latter study proposes a standalone state-of-the-art text-to-speech model capable of synthesizing intelligible voice in thousands of voice profiles, while generating speech with meaningful and expressive prosody variations. The proposed approach removes the dependency of previous models on an additional voice system, which makes the proposed system more efficient at training and inference time, and enables offline and on-device operations
Measuring the Severity of Depression from Text using Graph Representation Learning
The common practice of psychology in measuring the severity of a patient's depressive symptoms is based on an interactive conversation between a clinician and the patient. In this dissertation, we focus on predicting a score representing the severity of depression from such a text. We first present a generic graph neural network (GNN) to automatically rate severity using patient transcripts. We also test a few sequence-based deep models in the same task. We then propose a novel form for node attributes within a GNN-based model that captures node-specific embedding for every word in the vocabulary. This provides a global representation of each node, coupled with node-level updates according to associations between words in a transcript. Furthermore, we evaluate the performance of our GNN-based model on a Twitter sentiment dataset to classify three different sentiments and on Alzheimer's data to differentiate Alzheimer’s disease from healthy individuals respectively. In addition to applying the GNN model to learn a prediction model from the text, we provide post-hoc explanations of the model's decisions for all three tasks using the model's gradients
Learning from Audio, Vision and Language Modalities for Affect Recognition Tasks
The world around us as well as our responses to worldly events are multimodal in nature. For intelligent machines to integrate seamlessly into our world, it is imperative that they can process and derive useful information from multimodal signals. Such capabilities can be provided to machines by employing multimodal learning algorithms that consider both the individual characteristics of unimodal signals as well as the complementariness provided by multimodal signals. Based on the number of modalities available during the training and testing phases, learning algorithms can be of three categories: unimodal trained and unimodal tested, multimodal trained and multimodal tested, and multimodal trained and unimodal tested algorithms. This thesis provides three contributions, one for each category and focuses on three modalities that are important for human-human and human-machine communication, namely, audio (paralinguistic speech), vision (facial expressions) and language (linguistic speech) signals. For several applications, either due to hardware limitations or deployment specifications, unimodal trained and tested systems suffice. Our first contribution, for the unimodal trained and unimodal tested category, is an end-to-end deep neural network that uses raw speech signals as input for a computational paralinguistic task, namely, verbal conflict intensity estimation. Our model, which uses a convolutional recurrent architecture equipped with attention mechanism to focus on task-relevant instances of the input speech signal, eliminates the need for task-specific meta data or domain knowledge based manual refinement of hand-crafted generic features. The second contribution, for the multimodal trained and multimodal tested category, is a multimodal fusion framework that exploits both cross (inter) and intra-modal interactions for categorical emotion recognition from audiovisual clips. We explore the effectiveness of two types of attention mechanisms, namely, intra and cross-modal attention by creating two versions of our fusion framework. In many applications, multimodal signals might be available during model training phase, yet we cannot expect the availability of all modality signals during testing phase. Our third contribution addresses this situation wherein we propose a framework for cross-modal learning where paired audio-visual instances are used during training to develop test-time stand-alone unimodal models
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
Security and Privacy for Modern Wireless Communication Systems
The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks
An overview of video recommender systems: state-of-the-art and research issues
Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of video recommender systems (VRS), exploring the algorithms used, their applications, and related aspects. In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation
Language variation, automatic speech recognition and algorithmic bias
In this thesis, I situate the impacts of automatic speech recognition systems in relation to sociolinguistic theory (in particular drawing on concepts of language variation, language ideology
and language policy) and contemporary debates in AI ethics (especially regarding algorithmic
bias and fairness). In recent years, automatic speech recognition systems, alongside other
language technologies, have been adopted by a growing number of users and have been embedded in an increasing number of algorithmic systems. This expansion into new application
domains and language varieties can be understood as an expansion into new sociolinguistic
contexts. In this thesis, I am interested in how automatic speech recognition tools interact
with this sociolinguistic context, and how they affect speakers, speech communities and their
language varieties.
Focussing on commercial automatic speech recognition systems for British Englishes, I first
explore the extent and consequences of performance differences of these systems for different user groups depending on their linguistic background. When situating this predictive bias
within the wider sociolinguistic context, it becomes apparent that these systems reproduce and
potentially entrench existing linguistic discrimination and could therefore cause direct and indirect harms to already marginalised speaker groups. To understand the benefits and potentials
of automatic transcription tools, I highlight two case studies: transcribing sociolinguistic data
in English and transcribing personal voice messages in isiXhosa. The central role of the sociolinguistic context in developing these tools is emphasised in this comparison. Design choices,
such as the choice of training data, are particularly consequential because they interact with existing processes of language standardisation. To understand the impacts of these choices, and
the role of the developers making them better, I draw on theory from language policy research
and critical data studies. These conceptual frameworks are intended to help practitioners and
researchers in anticipating and mitigating predictive bias and other potential harms of speech
technologies. Beyond looking at individual choices, I also investigate the discourses about language variation and linguistic diversity deployed in the context of language technologies. These
discourses put forward by researchers, developers and commercial providers not only have a
direct effect on the wider sociolinguistic context, but they also highlight how this context (e.g.,
existing beliefs about language(s)) affects technology development. Finally, I explore ways of
building better automatic speech recognition tools, focussing in particular on well-documented,
naturalistic and diverse benchmark datasets. However, inclusive datasets are not necessarily
a panacea, as they still raise important questions about the nature of linguistic data and language variation (especially in relation to identity), and may not mitigate or prevent all potential
harms of automatic speech recognition systems as embedded in larger algorithmic systems
and sociolinguistic contexts
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