14,701 research outputs found
Security and Privacy Problems in Voice Assistant Applications: A Survey
Voice assistant applications have become omniscient nowadays. Two models that
provide the two most important functions for real-life applications (i.e.,
Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR)
models and Speaker Identification (SI) models. According to recent studies,
security and privacy threats have also emerged with the rapid development of
the Internet of Things (IoT). The security issues researched include attack
techniques toward machine learning models and other hardware components widely
used in voice assistant applications. The privacy issues include technical-wise
information stealing and policy-wise privacy breaches. The voice assistant
application takes a steadily growing market share every year, but their privacy
and security issues never stopped causing huge economic losses and endangering
users' personal sensitive information. Thus, it is important to have a
comprehensive survey to outline the categorization of the current research
regarding the security and privacy problems of voice assistant applications.
This paper concludes and assesses five kinds of security attacks and three
types of privacy threats in the papers published in the top-tier conferences of
cyber security and voice domain.Comment: 5 figure
Perceptual Requirements for World-Locked Rendering in AR and VR
Stereoscopic, head-tracked display systems can show users realistic,
world-locked virtual objects and environments. However, discrepancies between
the rendering pipeline and physical viewing conditions can lead to perceived
instability in the rendered content resulting in reduced realism, immersion,
and, potentially, visually-induced motion sickness. The requirements to achieve
perceptually stable world-locked rendering are unknown due to the challenge of
constructing a wide field of view, distortion-free display with highly accurate
head- and eye-tracking. In this work we introduce new hardware and software
built upon recently introduced hardware and present a system capable of
rendering virtual objects over real-world references without perceivable drift
under such constraints. The platform is used to study acceptable errors in
render camera position for world-locked rendering in augmented and virtual
reality scenarios, where we find an order of magnitude difference in perceptual
sensitivity between them. We conclude by comparing study results with an
analytic model which examines changes to apparent depth and visual heading in
response to camera displacement errors. We identify visual heading as an
important consideration for world-locked rendering alongside depth errors from
incorrect disparity
Diffusion Schr\"odinger Bridge Matching
Solving transport problems, i.e. finding a map transporting one given
distribution to another, has numerous applications in machine learning. Novel
mass transport methods motivated by generative modeling have recently been
proposed, e.g. Denoising Diffusion Models (DDMs) and Flow Matching Models
(FMMs) implement such a transport through a Stochastic Differential Equation
(SDE) or an Ordinary Differential Equation (ODE). However, while it is
desirable in many applications to approximate the deterministic dynamic Optimal
Transport (OT) map which admits attractive properties, DDMs and FMMs are not
guaranteed to provide transports close to the OT map. In contrast,
Schr\"odinger bridges (SBs) compute stochastic dynamic mappings which recover
entropy-regularized versions of OT. Unfortunately, existing numerical methods
approximating SBs either scale poorly with dimension or accumulate errors
across iterations. In this work, we introduce Iterative Markovian Fitting, a
new methodology for solving SB problems, and Diffusion Schr\"odinger Bridge
Matching (DSBM), a novel numerical algorithm for computing IMF iterates. DSBM
significantly improves over previous SB numerics and recovers as
special/limiting cases various recent transport methods. We demonstrate the
performance of DSBM on a variety of problems
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
An Orchestration Framework for Open System Models of Reconfigurable Intelligent Surfaces
To obviate the control of reflective intelligent surfaces (RISs) and the
related control overhead, recent works envisioned autonomous and
self-configuring RISs that do not need explicit use of control channels.
Instead, these devices, named hybrid RISs (HRISs), are equipped with receiving
radio-frequency (RF) chains and can perform sensing operations to act
independently and in parallel to the other network entities. A natural problem
then emerges: as the HRIS operates concurrently with the communication
protocols, how should its operation modes be scheduled in time such that it
helps the network while minimizing any undesirable effects? In this paper, we
propose an orchestration framework that answers this question revealing an
engineering trade-off, called the self-configuring trade-off, that
characterizes the applicability of self-configuring HRISs under the
consideration of massive multiple-input multiple-output (mMIMO) networks. We
evaluate our proposed framework considering two different HRIS hardware
architectures, the power- and signal-based HRISs that differ in their hardware
complexity. The numerical results show that the self-configuring HRIS can offer
significant performance gains when adopting our framework.Comment: 31 pages, 7 figures, submitted to an IEEE journa
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ‘problem of implementation’ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sector’s emergence
Discovering the hidden structure of financial markets through bayesian modelling
Understanding what is driving the price of a financial asset is a question that is currently mostly unanswered. In this work we go beyond the classic one step ahead prediction and instead construct models that create new information on the behaviour of these time series. Our aim is to get a better understanding of the hidden structures that drive the moves of each financial time series and thus the market as a whole.
We propose a tool to decompose multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving their underlying variability. The methodology we introduce goes beyond the direct model forecast. Indeed, since our model continuously adapts its variables and coefficients, we can study the time series of coefficients and selected variables. We also present a model to construct the causal graph of relations between these time series and include them in the exogenous factors.
Hence, we obtain a model able to explain what is driving the move of both each specific time series and the market as a whole. In addition, the obtained graph of the time series provides new information on the underlying risk structure of this environment. With this deeper understanding of the hidden structure we propose novel ways to detect and forecast risks in the market. We investigate our results with inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time. We also go in more details on the economic interpretation of the new variables and discuss the created graph structure of the market.Open Acces
Curriculum Subcommittee Agenda, April 7, 2022
Approval of 3 March 2022 Minutes Program Proposals Semester Course Approval Reviews https://usu.curriculog.com/ Other Business New Curriculum Subcommittee Chair appointment. Acceptance of membership for 2022-2023 academic year.
Program Proposals Request from the Department of Plants, Soils and Climate in the College of Agriculture and Applied Sciences to offer a new specialization (Bioinformatics and Computational Biology) to the MS and PhD degrees of Plant Science. Request from the Department of Theatre Arts in the Caine College of the Arts to change the name of the Theatre Arts Theatre Education Certification Option BFA to Theatre Arts Education BFA. Request from the Department of Mechanical and Aerospace Engineering in the College of Engineering to create a Center for the Design and Manufacturing of Advanced Materials (CDMAM). Request from the Department of Data Analytics and Information Systems in the Jon M. Huntsman School of Business to create a new post-baccalaureate certificate in Cybersecurity. Request from the Department of Data Analytics and Information Systems in the Jon M. Huntsman School of Business to create a new post-baccalaureate certificate in Data Analytics. Request from the Department of Data Analytics and Information Systems in the Jon M. Huntsman School of Business to create a new post-baccalaureate certificate in Data Engineering. Request from the Department of Data Analytics and Information Systems in the Jon M. Huntsman School of Business to create a new post-baccalaureate certificate in Data Technologies. Request from the Department of Data Analytics and Information Systems in the Jon M. Huntsman School of Business to restructure the existing Master of Management Information Systems program to require completion of two stackable post-baccalaureate certificates (24 credits) along with six credits of information technology strategy or management courses. Request from the Department of Data Analytics and Information Systems in the Jon M. Huntsman School of Business to create a new post-baccalaureate certificate in Web Development
Forested buffers in agricultural landscapes : mitigation effects on stream–riparian meta-ecosystems
Stream–riparian meta-ecosystems are strongly connected through exchanges of
energy, material and organisms. Land use can disrupt ecological connectivity by
affecting community composition directly and/or indirectly by altering the instream
and riparian habitats that support biological structure and function. Although
forested riparian buffers are increasingly used as a management intervention, our
understanding of their effects on the functioning of stream–riparian metaecosystems
is limited. This study assessed patterns in the longitudinal and lateral
profiles of streams in modified landscapes across Europe and Sweden using a pairedreach
approach, with upstream unbuffered reaches lacking woody riparian
vegetation and with downstream reaches having well-developed forested buffers.
The presence of buffers was positively associated with stream ecological status as
well as important attributes, which included instream shading and the provision of
suitable habitats for instream and riparian communities, thus supporting more
aquatic insects (especially EPT taxa). Emergence of aquatic insects is particularly
important because they mediate reciprocal flows of subsidies into terrestrial systems.
Results of fatty acid analysis and prey DNA from spiders further supported the
importance of buffers in providing more aquatic-derived quality food (i.e. essential
fatty acids) for riparian spiders. Findings presented in this thesis show that buffers
contribute to the strengthening of cross-ecosystem connectivity and have the
potential to affect a wide range of consumers in modified landscapes
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
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