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Books > Computing & IT
Deep Learning through Sparse Representation and Low-Rank Modeling
bridges classical sparse and low rank models-those that emphasize
problem-specific Interpretability-with recent deep network models
that have enabled a larger learning capacity and better utilization
of Big Data. It shows how the toolkit of deep learning is closely
tied with the sparse/low rank methods and algorithms, providing a
rich variety of theoretical and analytic tools to guide the design
and interpretation of deep learning models. The development of the
theory and models is supported by a wide variety of applications in
computer vision, machine learning, signal processing, and data
mining. This book will be highly useful for researchers, graduate
students and practitioners working in the fields of computer
vision, machine learning, signal processing, optimization and
statistics.
Brain-machine interfacing or brain-computer interfacing (BMI/BCI)
is an emerging and challenging technology used in engineering and
neuroscience. The ultimate goal is to provide a pathway from the
brain to the external world via mapping, assisting, augmenting or
repairing human cognitive or sensory-motor functions. In this book
an international panel of experts introduce signal processing and
machine learning techniques for BMI/BCI and outline their practical
and future applications in neuroscience, medicine, and
rehabilitation, with a focus on EEG-based BMI/BCI methods and
technologies. Topics covered include discriminative learning of
connectivity pattern of EEG; feature extraction from EEG
recordings; EEG signal processing; transfer learning algorithms in
BCI; convolutional neural networks for event-related potential
detection; spatial filtering techniques for improving individual
template-based SSVEP detection; feature extraction and
classification algorithms for image RSVP based BCI; decoding music
perception and imagination using deep learning techniques;
neurofeedback games using EEG-based Brain-Computer Interface
Technology; affective computing system and more.
Securing Delay-Tolerant Networks with BPSec One-stop reference on
how to secure a Delay-Tolerant Network (DTN), written by
experienced industry insiders Securing Delay-Tolerant Networks with
BPSec answers the question, "How can delay-tolerant networks be
secured when operating in environments that would otherwise break
many of the common security approaches used on the terrestrial
Internet today?" The text is composed of three sections: (1)
security considerations for delay-tolerant networks, (2) the
design, implementation, and customization of the BPSec protocol,
and (3) how this protocol can be applied, combined with other
security protocols, and deployed in emerging network environments.
The text includes pragmatic considerations for deploying BPSec in
both regular and delay-tolerant networks. It also features a
tutorial on how to achieve several important security outcomes with
a combination of security protocols, BPSec included. Overall, it
covers best practices for common security functions, clearly
showing designers how to prevent network architecture from being
over-constrained by traditional security approaches. Written by the
lead author and originator of the BPSec protocol specification,
Securing Delay-Tolerant Networks (DTNs) with BPSec includes
information on: The gap between cryptography and network security,
how security requirements constrain network architectures, and why
we need something different DTN stressing conditions, covering
intermittent connectivity, congested paths, partitioned topologies,
limited link state, and multiple administrative controls Securing
the terrestrial internet, involving a layered approach to security,
the impact of protocol design on security services, and securing
the internetworking and transport layers A delay-tolerant security
architecture, including desirable properties of a DTN secure
protocol, fine-grained security services, and protocol augmentation
Securing Delay-Tolerant Networks (DTNs) with BPSec is a one-stop
reference on the subject for any professional operationally
deploying BP who must use BPSec for its security, including
software technical leads, software developers, space flight mission
leaders, network operators, and technology and product development
leaders in general.
More individuals than ever are utilizing internet technologies to
work from home, teach and learn, shop, interact with peers, review
medical records, and more. While it is certainly convenient to
conduct such tasks via the internet, this increased internet
presence has also led to a rise in the search and availability of
personal information, which in turn is resulting in more
cyber-attacks, privacy breaches, and information leaks. Cyber
criminals are using such opportunities to attack governments,
organizations, and individuals, making it necessary to anticipate,
assess, and mitigate privacy and security threats during this
infodemic. The Handbook of Research on Technical, Privacy, and
Security Challenges in a Modern World discusses the design and
development of different machine learning systems, including next
generation applications, in order to mitigate cyber-attacks and
address security challenges in everyday technologies. It further
explores select methods and algorithms of learning for implementing
better security methods in fields such as business and healthcare.
It recognizes the future of privacy and the importance of
preserving data through recommended practice, feedback loops, and
smart agents. Covering topics such as face mask detection, gesture
recognition, and botnet attacks and detection, this major reference
work is a dynamic resource for medical professionals, healthcare
administrators, government officials, business executives and
managers, IT managers, students and faculty of higher education,
librarians, researchers, and academicians.
With new technologies, such as computer vision, internet of things,
mobile computing, e-governance and e-commerce, and wide
applications of social media, organizations generate a huge volume
of data and at a much faster rate than several years ago. Big data
in large-/small-scale systems, characterized by high volume,
diversity, and velocity, increasingly drives decision making and is
changing the landscape of business intelligence. From governments
to private organizations, from communities to individuals, all
areas are being affected by this shift. There is a high demand for
big data analytics that offer insights for computing efficiency,
knowledge discovery, problem solving, and event prediction. To
handle this demand and this increase in big data, there needs to be
research on innovative and optimized machine learning algorithms in
both large- and small-scale systems. Applications of Big Data in
Large- and Small-Scale Systems includes state-of-the-art research
findings on the latest development, up-to-date issues, and
challenges in the field of big data and presents the latest
innovative and intelligent applications related to big data. This
book encompasses big data in various multidisciplinary fields from
the medical field to agriculture, business research, and smart
cities. While highlighting topics including machine learning, cloud
computing, data visualization, and more, this book is a valuable
reference tool for computer scientists, data scientists and
analysts, engineers, practitioners, stakeholders, researchers,
academicians, and students interested in the versatile and
innovative use of big data in both large-scale and small-scale
systems.
This open access book provides a comprehensive overview of the
state of the art in research and applications of Foundation Models
and is intended for readers familiar with basic Natural Language
Processing (NLP) concepts. Over the recent years, a
revolutionary new paradigm has been developed for training models
for NLP. These models are first pre-trained on large collections of
text documents to acquire general syntactic knowledge and semantic
information. Then, they are fine-tuned for specific tasks, which
they can often solve with superhuman accuracy. When the models are
large enough, they can be instructed by prompts to solve new tasks
without any fine-tuning. Moreover, they can be applied to a wide
range of different media and problem domains, ranging from image
and video processing to robot control learning. Because they
provide a blueprint for solving many tasks in artificial
intelligence, they have been called Foundation Models. After
a brief introduction to basic NLP models the main pre-trained
language models BERT, GPT and sequence-to-sequence transformer are
described, as well as the concepts of self-attention and
context-sensitive embedding. Then, different approaches to
improving these models are discussed, such as expanding the
pre-training criteria, increasing the length of input texts, or
including extra knowledge. An overview of the best-performing
models for about twenty application areas is then presented, e.g.,
question answering, translation, story generation, dialog systems,
generating images from text, etc. For each application area, the
strengths and weaknesses of current models are discussed, and an
outlook on further developments is given. In addition, links are
provided to freely available program code. A concluding chapter
summarizes the economic opportunities, mitigation of risks, and
potential developments of AI.
The emergent phenomena of virtual reality, augmented reality, and
mixed reality is having an impact on ways people communicate with
technology and with each other. Schools and higher education
institutions are embracing these emerging technologies and
implementing them at a rapid pace. The challenge, however, is to
identify well-defined problems where these innovative technologies
can support successful solutions and subsequently determine the
efficacy of effective virtual learning environments. Emerging
Technologies in Virtual Learning Environments is an essential
scholarly research publication that provides a deeper look into 3D
virtual environments and how they can be developed and applied for
the benefit of student learning and teacher training. This book
features a wide range of topics in the areas of science,
technology, engineering, arts, and math to ensure a blend of both
science and humanities research. Therefore, it is ideal for
curriculum developers, instructional designers, teachers, school
administrators, higher education faculty, professionals,
researchers, and students studying across all academic disciplines.
Practical Guide for Biomedical Signals Analysis Using Machine
Learning Techniques: A MATLAB Based Approach presents how machine
learning and biomedical signal processing methods can be used in
biomedical signal analysis. Different machine learning applications
in biomedical signal analysis, including those for
electrocardiogram, electroencephalogram and electromyogram are
described in a practical and comprehensive way, helping readers
with limited knowledge. Sections cover biomedical signals and
machine learning techniques, biomedical signals, such as
electroencephalogram (EEG), electromyogram (EMG) and
electrocardiogram (ECG), different signal-processing techniques,
signal de-noising, feature extraction and dimension reduction
techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and
other statistical measures, and more. This book is a valuable
source for bioinformaticians, medical doctors and other members of
the biomedical field who need a cogent resource on the most recent
and promising machine learning techniques for biomedical signals
analysis.
Safety and security are crucial to the operations of nuclear power
plants, but cyber threats to these facilities are increasing
significantly. Instrumentation and control systems, which play a
vital role in the prevention of these incidents, have seen major
design modifications with the implementation of digital
technologies. Advanced computing systems are assisting in the
protection and safety of nuclear power plants; however, significant
research on these computational methods is deficient. Cyber
Security and Safety of Nuclear Power Plant Instrumentation and
Control Systems is a pivotal reference source that provides vital
research on the digital developments of instrumentation and control
systems for assuring the safety and security of nuclear power
plants. While highlighting topics such as accident monitoring
systems, classification measures, and UAV fleets, this publication
explores individual cases of security breaches as well as future
methods of practice. This book is ideally designed for engineers,
industry specialists, researchers, policymakers, scientists,
academicians, practitioners, and students involved in the
development and operation of instrumentation and control systems
for nuclear power plants, chemical and petrochemical industries,
transport, and medical equipment.
Recent advances in information and communication technologies have
enhanced the standards of metropolitan planning and development.
With the increase in mobile communication, this will help to
deliver innovative new services and apps in the field of urban
e-planning. New Approaches, Methods, and Tools in Urban E-Planning
is a key resource for the latest academic research on recent
innovations in urban e-planning, citizen e-participation, the use
of social media, and new forms of data collection and idea
generation for urban planning. Presenting broad coverage among a
variety of pertinent views and themes such as ethnography,
e-consultation, and civic engagement, this book is ideally designed
for planners, policymakers, researchers, and graduate students
interested in how recent technological advancements are enhancing
the traditional practices in e-planning.
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