1,937 research outputs found
Online semi-supervised learning in non-stationary environments
Existing Data Stream Mining (DSM) algorithms assume the availability of labelled and
balanced data, immediately or after some delay, to extract worthwhile knowledge from the
continuous and rapid data streams. However, in many real-world applications such as
Robotics, Weather Monitoring, Fraud Detection Systems, Cyber Security, and Computer
Network Traffic Flow, an enormous amount of high-speed data is generated by Internet of
Things sensors and real-time data on the Internet. Manual labelling of these data streams
is not practical due to time consumption and the need for domain expertise. Another
challenge is learning under Non-Stationary Environments (NSEs), which occurs due to
changes in the data distributions in a set of input variables and/or class labels. The problem
of Extreme Verification Latency (EVL) under NSEs is referred to as Initially Labelled Non-Stationary Environment (ILNSE). This is a challenging task because the learning algorithms
have no access to the true class labels directly when the concept evolves. Several approaches
exist that deal with NSE and EVL in isolation. However, few algorithms address both issues
simultaneously. This research directly responds to ILNSEâs challenge in proposing two
novel algorithms âPredictor for Streaming Data with Scarce Labelsâ (PSDSL) and
Heterogeneous Dynamic Weighted Majority (HDWM) classifier. PSDSL is an Online Semi-Supervised Learning (OSSL) method for real-time DSM and is closely related to label
scarcity issues in online machine learning.
The key capabilities of PSDSL include learning from a small amount of labelled data in an
incremental or online manner and being available to predict at any time. To achieve this,
PSDSL utilises both labelled and unlabelled data to train the prediction models, meaning it
continuously learns from incoming data and updates the model as new labelled or
unlabelled data becomes available over time. Furthermore, it can predict under NSE
conditions under the scarcity of class labels. PSDSL is built on top of the HDWM classifier,
which preserves the diversity of the classifiers. PSDSL and HDWM can intelligently switch
and adapt to the conditions. The PSDSL adapts to learning states between self-learning,
micro-clustering and CGC, whichever approach is beneficial, based on the characteristics of
the data stream. HDWM makes use of âseedâ learners of different types in an ensemble to
maintain its diversity. The ensembles are simply the combination of predictive models
grouped to improve the predictive performance of a single classifier.
PSDSL is empirically evaluated against COMPOSE, LEVELIW, SCARGC and MClassification
on benchmarks, NSE datasets as well as Massive Online Analysis (MOA) data streams and real-world datasets. The results showed that PSDSL performed significantly better than
existing approaches on most real-time data streams including randomised data instances.
PSDSL performed significantly better than âStaticâ i.e. the classifier is not updated after it is
trained with the first examples in the data streams. When applied to MOA-generated data
streams, PSDSL ranked highest (1.5) and thus performed significantly better than SCARGC,
while SCARGC performed the same as the Static. PSDSL achieved better average prediction
accuracies in a short time than SCARGC.
The HDWM algorithm is evaluated on artificial and real-world data streams against existing
well-known approaches such as the heterogeneous WMA and the homogeneous Dynamic
DWM algorithm. The results showed that HDWM performed significantly better than WMA
and DWM. Also, when recurring concept drifts were present, the predictive performance of
HDWM showed an improvement over DWM. In both drift and real-world streams,
significance tests and post hoc comparisons found significant differences between
algorithms, HDWM performed significantly better than DWM and WMA when applied to
MOA data streams and 4 real-world datasets Electric, Spam, Sensor and Forest cover. The
seeding mechanism and dynamic inclusion of new base learners in the HDWM algorithms
benefit from the use of both forgetting and retaining the models. The algorithm also
provides the independence of selecting the optimal base classifier in its ensemble depending
on the problem.
A new approach, Envelope-Clustering is introduced to resolve the cluster overlap conflicts
during the cluster labelling process. In this process, PSDSL transforms the centroidsâ
information of micro-clusters into micro-instances and generates new clusters called
Envelopes. The nearest envelope clusters assist the conflicted micro-clusters and
successfully guide the cluster labelling process after the concept drifts in the absence of true
class labels. PSDSL has been evaluated on real-world problem âkeystroke dynamicsâ, and
the results show that PSDSL achieved higher prediction accuracy (85.3%) and SCARGC
(81.6%), while the Static (49.0%) significantly degrades the performance due to changes in
the users typing pattern. Furthermore, the predictive accuracies of SCARGC are found
highly fluctuated between (41.1% to 81.6%) based on different values of parameter âkâ
(number of clusters), while PSDSL automatically determine the best values for this
parameter
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Investigating Biointerfacial Interactions in the Development of Epidemic Thunderstorm Asthma
Epidemic thunderstorm asthma (ETSA) outbreaks are triggered by airborne pollen allergens combined with thunderstorm activity. ETSA can affect anyone, as observed in the worldâs largest ETSA event in Australia. Allergens from rye grass pollen affect the respiratory airways and the fundamental physicochemical causes, biochemical interactions, and the role of the thunderstorm in ETSA have been the source of much speculation.
In this thesis, the physicochemical interactions of thunderstorm-derived reactive oxygen nitrogen species (RONS) and pollen-derived molecules are examined. It is hypothesised that RONS from the plasma-activated water (PAW) react with the airborne pollen allergens, exerting physicochemical changes to enhance allergenicity and subsequently causing ETSA. Simple biomimetic models are demonstrated, examining the key biointerfacial interactions and the influences of the conditions of plasma formation, pH, and temperature, employing advanced interface-sensitive techniques including QCM-D and neutron reflectometry.
Firstly, cellulose-mucin interactions were analysed, mimicking the interactions between the walls of inhaled pollen (intine) and mucosa of the respiratory tract (mucin). Interaction with plasma-treated cellulose surfaces led to adsorption and conformational alterations to mucin, potentially indicating changes to the permeability of the mucosa.
Secondly, the effect of PAW on the interactions between a model-allergen plant protein and lipid monolayers mimicking alveolar surfactant was studied. The protein took up RONS and PAW-treated protein showed stronger adsorption to the lipid monolayers, implying PAW-treatment enhances transport of the protein into lung tissue.
Lastly, the effect of PAW on allergen penetration into epithelial bilayers was elucidated. Solid-supported model lipid bilayers were allowed to interact with model allergen and rye grass derived proteins to deduce the structural integrity of the membrane. PAW-treatment increased adsorption of the proteins to the lipid bilayers, and enabled the penetration into the membrane, corroborating the enhanced allergenicity of PAW-treated allergens. Overall, PAW was seen to enhance three relevant nonspecific biointerfacial interactions; these physicochemical studies complement extant in vitro cell studies in an effort to enable the development of effective monitoring platforms, diagnostics, and therapeutic interventions for the prevention and treatment of ETSA
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds 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 modiïŹed Proportional ConïŹict 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 classiïŹers, 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, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation.
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 classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well
Engineered Emulsions Stabilised by Thermoresponsive Branched Copolymers for Pharmaceutical Applications
This research work explored thermoresponsive emulsions and investigated their potential
in delivering drugs through in situ gelling pharmaceutical formulations. Employing
thermoresponsive branched copolymer surfactants (BCSs), this study established their
efficacy in creating stable emulsions with reversible gelation triggered by changes in
temperature. While previous research had shown BCSs' capacity to transition emulsions
to gels via pH alteration, this study innovatively proposed the concept of
thermoresponsive emulsions that respond at physiological temperatures.
The focus was on generating materials capable of shifting from a liquid to a gel state upon
warming, promising enhanced healthcare technologies like in situ gel-forming materials
for diverse drug delivery routes. The thermoresponsive BCSs used to stabilise the
emulsions that showed sol-gel transition upon heating were synthesised with a lower
critical solution temperature (LCST) monomer, a hydrophilic macromonomer, a crosslinker
and a hydrophobic chain transfer agent. All these components were proven to
contribute to the gelation behaviour.
The research investigated the interplay between temperature and BCS structure at both
macro and nanoscales, dissecting how these engineered emulsions react to temperature
shifts. Moreover, the emulsions held the potential for solubilisation of various drug
chemistries and explored their drug delivery activities via in situ gelation. This thesis
evaluated the rheology of the engineered emulsions based on polymer architecture,
branching, molecular weight, and hydrophobic end groups, influencing gel formation on
heating. Furthermore, poly(ethylene glycol) methyl ether methacrylateâs role in
controlling emulsion responsiveness was highlighted, with longer poly(ethylene glycol)
chains inducing thermogelation and shorter chains causing emulsion breakdown upon
mild heating. The ratio of LCST monomer to hydrophilic macromonomer tightly governed
gelation temperature.
Expanding these findings, the research explored various pharmaceutically relevant oils in
the emulsion system, along with additives to enhance stability. The addition of
methylcellulose significantly improved stability, and small-angle neutron scattering
(SANS) helped to understand the gelation mechanism and the nanoscale processes within BCS-stabilised emulsions. Furthermore, these emulsion systems were investigated as
pharmaceutical formulations, analysing drug release mechanisms and compatibility with
nasal spray devices. These advanced emulsions showed promise in controlled drug release
and nasal spray device compatibility.
In summary, this thesis showed a new frontier in drug delivery through temperatureresponsive
emulsions, offering smart dosage forms with transformative potential. The
work not only advances understanding in thermoresponsive engineered emulsions but
also lays the groundwork for personalised medicine and targeted drug delivery, promising
improved patient outcomes and reduced dosing frequency
Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.
Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation
The Realisation of syntactic principles in non-standard Afrikaans: the correspondence of Jan Jonker Afrikaner (1820-1889)
This study compares the syntax of nineteenth-century Orange River Afrikaans with Dutch and synchronic Afrikaans varieties, with particular attention to Griqua Afrikaans. It provides an account of the differences that are found between the earliest attestations of an extraterritorial variety of the Dutch language on southern African soil (the so-called Cape Dutch Vernacular) with the present-day outcome. The data collected for this study originate chiefly from an hitherto undisclosed corpus of letters kept in the Namibian State Archives by the so-called Oorlam-Nama, people of mixed descent who lived on the periphery of the nineteenth- century Cape colonial society. This thesis argues that nineteenth-century Orange River Afrikaans is a representative continuation of the earliest developments in the linguistic contact situation that existed at the Cape. The thesis advances that literacy and social class are important factors in the assessment of the written record from the Dutch colony at the Cape. The thesis centers around the letters by one author, Jan Jonker Afrikaner, written over a period of nearly twenty years in the second half of the nineteenth century. This legacy is a unique contribution to the diachronic data concerning the development of Afrikaans. From the data it is shown that this author had the command over different registers, fluctuating between a near perfect metropolitan Dutch and a Hollands that is classified as basilectal Afrikaans. The comparison of the data is set in a framework inspired by the concepts put forward in Generative Grammar. This has precipitated an exciting linguistic comparison of contemporary Afrikaans grammar with the diachronic material. This dissertation challenges the idea that the Khoesan Languages were of no or little influence in the development of Afrikaans. The linguistic analysis of the nineteenth-century data reveal that the developments which took place cannot be attributed to one single origin. It is demonstrated that the innovations and change that can be identified run parallel to regular patterns that are found in other languages generally classified as creole languages. It is argued that the syntax of the Khoesan languages is a major reinforcing factor in the development of the syntactic idiosyncrasies that are identified as un-Germanic characteristics of Afrikaans. Limited to nonstandard varieties of Afrikaans, in the concluding sections the question is raised how these findings are to be addressed in the larger context of language change
Spatiotemporal Event Graphs for Dynamic Scene Understanding
Dynamic scene understanding is the ability of a computer system to interpret and make sense of the visual information present in a video of a real-world scene. In this thesis, we present a series of frameworks for dynamic scene understanding starting from road event detection from an autonomous driving perspective to complex video activity detection, followed by continual learning approaches for the life-long learning of the models. Firstly, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicleâs ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. Due to the lack of datasets equipped with formally specified logical requirements, we also introduce the ROad event Awareness Dataset with logical Requirements (ROAD-R), the first publicly available dataset for autonomous driving with requirements expressed as logical constraints, as a tool for driving neurosymbolic research in the area.
Next, we extend event detection to holistic scene understanding by proposing two complex activity detection methods. In the first method, we present a deformable, spatiotemporal scene graph approach, consisting of three main building blocks: action tube detection, a 3D deformable RoI pooling layer designed for learning the flexible, deformable geometry of the constituent action tubes, and a scene graph constructed by considering all parts as nodes and connecting them based on different semantics. In a second approach evolving from the first, we propose a hybrid graph neural network that combines attention applied to a graph encoding of the local (short-term) dynamic scene with a temporal graph modelling the overall long-duration activity. Our contribution is threefold: i) a feature extraction technique; ii) a method for constructing a local scene graph followed by graph attention, and iii) a graph for temporally connecting all the local dynamic scene graphs.
Finally, the last part of the thesis is about presenting a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community. We also propose to formulate the continual semi-supervised learning problem as a latent-variable
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