282 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
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Alice Benchmarks: Connecting Real World Object Re-Identification with the Synthetic
For object re-identification (re-ID), learning from synthetic data has become
a promising strategy to cheaply acquire large-scale annotated datasets and
effective models, with few privacy concerns. Many interesting research problems
arise from this strategy, e.g., how to reduce the domain gap between synthetic
source and real-world target. To facilitate developing more new approaches in
learning from synthetic data, we introduce the Alice benchmarks, large-scale
datasets providing benchmarks as well as evaluation protocols to the research
community. Within the Alice benchmarks, two object re-ID tasks are offered:
person and vehicle re-ID. We collected and annotated two challenging real-world
target datasets: AlicePerson and AliceVehicle, captured under various
illuminations, image resolutions, etc. As an important feature of our real
target, the clusterability of its training set is not manually guaranteed to
make it closer to a real domain adaptation test scenario. Correspondingly, we
reuse existing PersonX and VehicleX as synthetic source domains. The primary
goal is to train models from synthetic data that can work effectively in the
real world. In this paper, we detail the settings of Alice benchmarks, provide
an analysis of existing commonly-used domain adaptation methods, and discuss
some interesting future directions. An online server will be set up for the
community to evaluate methods conveniently and fairly.Comment: 9 pages, 4 figures, 4 table
Object Detection and Classification in the Visible and Infrared Spectrums
The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark detection is presented. The third scenario addresses near-infrared cross-spectral periocular recognition with a coupled conditional generative adversarial network guided by auxiliary synthetic loss functions. Finally, a deep sparse feature selection and fusion is proposed to detect the presence of textured contact lenses prior to near-infrared iris recognition
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
Graphic Design in the Age of Artificial Intelligence : A Speculative Co-design Investigation into the Possibilities and Challenges of Artificial Intelligence on the Field of Graphic Design in Saudi Arabia
The potential impact of artificial intelligence on graphic design has, in recent years, stimulated a range of questions and concerns from design practitioners and academics about the future of AI-driven designs. This impact has prompted researchers, academics and practitioners alike to rethink the new implication of AI on the role of the graphic designer in this progression. It has also led to consideration of a plethora of issues and challenges around academia and practice, addressing questions associated with the definition of creativity, cultural acceptance, and ethical issues, besides possibilities that AI can imply in having autonomous AI-driven designs. In this research, I investigate the impact of AI from the graphic designers' perspective measuring the impact on their roles as designers in the design process, including an assessment of how to use AI as a self-governed system to generate visual designs autonomously rather than having AI as an application tool. This investigation will propose a new literature of theory and practice into the process of designing, particularly exploring and speculating upon new opportunities associated with combining data and algorithms with graphic design, in practice as well as in education. Co-design activities and semi-structured interviews were used in this research to initiate speculative provocative discussions and debates. In addition, this thesis presents the ADI card toolkit, a speculative design toolkit designed to help initiate collaboration in brainstorming and generate solutions to these challenges by using the gameplay approach. The ADI card toolkit was tested as part of conducting the research in Saudi Arabia, a country undergoing a transformation in terms of employing the 4th industrial revolution of technology and innovation towards building their infrastructure, economy, and quality of life under the government’s Vision 2030. The findings suggest many actions to consider when using AI in education and practice one of which is equipping graphic designers with knowledge, skills and qualifications to be furtherly open and aware of the broad spectrum of AI potentials which allows for proactive collaboration as a self-driven system with graphic designers. The research also suggests using gameplay as an approach when introducing AI tools in academia which can aid in exploring opportunities to alternate the human–machine entanglements and enable designer and academics alike to explore self-generated designs and alternative futures in this field
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