12,869 research outputs found
The LDBC social network benchmark: Business intelligence workload
The Social Network Benchmarkâs Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBCâs âchoke pointâ-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of âparameter curationâ in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result â only audited results can use this trademarked term
Impact of plant growth promoting rhizobacteria (PGPR) on stress resistance of winter wheat (Triticum aestivum L.)
Wheat is one of the worldwide most cultivated crop and highly contribute to secure food production in different world regions. Although, it grows almost ubiquitous, its production is severely vulnerable to drought. Soil and rhizosphere microbial communities associated to plants come more and more into the focus of modern agrobiology research, as a solution to maintain productivity under drought, and reinforce sustainable production. Whereas numerous studies on wheat production and the beneficial influence of the soil microbiome under drought have been performed in arid and semiarid regions of the world, comparable studies in Central Europe are rare. This might change due to the ongoing climate crisis and expected less frequent precipitations during the vegetation season. So far, most studies that focus on acclimatization of the wheat rhizobiome to water deficit mostly consider, at best, two interacting factors, and lack to consider other biotic or abiotic drivers of rhizosphere microbial communities structure and function. Therefore, the aim of this thesis was to combine complementary analytical approaches to investigate drought-induced structural and functional changes in wheat rhizosphere bacterial communities and individual species in dependency of soil type, farming system, wheat cultivar and plant development stage, and to determine how these changes affect wheat performance as a consequence of possible climate change scenarios in Central Germany.
The presented thesis starts with a general introduction and presentation of the project, followed by three consecutive chapters containing the main findings published in peer-reviewed articles. Starting with an experiment performed in the greenhouse (Chapter 1) and then moving to a realistic climate scenario under field conditions (Chapter 2 and 3), the three chapters demonstrate the sole and interacting effects of drought and farming system (Chapter 1-3), soil type and wheat cultivar (Chapter 1), as well as plant growth stages (Chapter 2 and 3) on bacterial communities and individual taxa of the wheat rhizobiome. The methods used reach from traditional cultivation and in-vitro bioassays (Chapter 3), over extracellular enzyme activity potentials (Chapter 1 and 2) to more advanced technologies such as metabarcoding (Chapter 1 and 2) and computational tools (Chapter 1 and 2), addressing single bacterial taxa as well as community level. Finalizing the thesis, a concluding synopsis compiles and critically reviews the gained results and formulates future study perspectives.
In Chapter 1, we evaluated the impact of soil type (loamy vs. sandy), farming management (conventional vs. organic), wheat cultivar (non-demanding vs. demanding), and the interacting effects of these factors on wheat rhizobacterial community composition and function under extreme drought conditions. Water deficit exerted a strong pressure on rhizobacterial communities, and interacted with soil type and farming management, but not with the wheat cultivar types. In the sandy soil, we observed a strong drought-induced shift in community composition, with a decrease in species diversity and extracellulare enzyme production, while changes by drought were less prominent in the fertile loamy soil. A particular exception from this pattern was found for enzyme activities involved in carbon cycling in the sandy soil suggesting a positive plant-soil-feedback on enzyme activities by drought conditioning.
In Chapter 2, two individual, but interrelated aims were pursued. First, we used the platform of the Global Change Experimental Facility (GCEF) to explore the impact of two farming practices (conventional vs. organic) and two climate treatments (ambient vs. future) on bacterial community composition and activity profiles of extracellulare enzymes involved in C,N and P cycles in the wheat rhizosphere at two different plant growth stages. The climate treatment in the GCEF had no effect on the rhizobacterial communities. Rhizobacterial community composition and functions significantly differed between vegetative and mature growth stages of the plants, in both conventional and organic farming. In a second step, we reused the data to explore further the accuracy of computational approaches, like Tax4Fun and PanFP, to predict functional profiles of bacterial communities based on 16S rDNA abundance data. To this end, we compared the measured enzyme activities with respective gene abundances in the community under different climate and farming treatments, and at the two plant development stages. This analysis revealed qualitative, but not necessarily quantitative concordances, i.e. we found effects of the different treatments on the measured enzyme activities reflected in the gene abundances.
Chapter 3 is a complementary approach to Chapter 2 with a focus on individual bacterial species level. Culture-dependent methods were used to specifically isolate strong P-solubilizing bacteria from the rhizosphere of wheat, which were tested for their in-vitro drought tolerance. Among the more than 800 isolated species, Phyllobacterium, Pseudomonas and Streptomyces species dominated. While farming management and climate treatment had only minor effects on composition and functions of the isolates, the wheat growth stages had an impact, whereby a dominance of Pseudomonas species at the vegetative growth phase was replaced by dominance of Phyllobacterium species at the mature growth phase. Since P-solubilizing potential was paralleled by a high in vitro drought tolerance, Phyllobacterium species were characterized as promising plant growth promoting rhizobacteria (PGPR) of wheat under future drought conditions.
In the synopsis part, we evaluated the multifactorial and multidisciplinary approaches and investigated to what extent the adaptations of bacterial communities in field and pot experiments coincided or differed.
Overall, we found common and distinct adaptation processes of bacterial communities and individual species in the rhizosphere of wheat to drought, whereby single factors, but also interacting effects exerted a strong impact on these processes. This study underlines the importance of multifactorial approaches to reveal community- or species-specific plant-soil-feedbacks.:Contents 3
Preface 5
Bibliographic description 6
Zusammenfassung 9
Summary 13
Introduction 16
When extreme events become the new normal 17
Feedback to agricultural production and need for management adaptation 20
Difficulties in exploring the soil microbiome and identification of plant beneficial microbial taxa 22
Our approach with wheat 24
Bibliography 27
Ö Chapter 1 31
Interactions Between Soil Properties, Agricultural Management and Cultivar Type Drive Structural and Functional Adaptations of the Wheat Rhizosphere Microbiome To Drought 31
Supplemental Tables 51
Supplemental Figures 55
⏠Chapter 2 59
Can We Estimate Functionality of Soil Microbial Communities from Structure-Derived Predictions? A Reality Test in Agricultural Soils 59
Supplementary Tables 79
Supplemental Figures 84
Supplemental Material 1: 87
Variation in edaphic parameters according to experimental factors 87
Supplemental Material 2 88
Effect of abiotic soil parameters on bacterial community structure and function 88
Supplemental Material 3 90
Indicator species analysis 90
Û Chapter 3 95
Shifts Between and Among Populations of Wheat Rhizosphere Pseudomonas, Streptomyces and Phyllobacterium Suggest Consistent Phosphate Mobilization at Different Wheat Growth Stages Under Abiotic Stress 95
Supplementary Figures 112
Supplementary Tables 117
Synopsis 152
Multidisciplinary approaches combine advantages of cultivation-based and high throughput community-based methods 155
Multifactorial approaches to gain a more holistic understanding of plant-microbe interactions in pot experiments 157
Transferability of findings gained in the pot experiment to field conditions 159
Towards a wheat core microbiome? 161
Study limitations and outlook 163
Bibliography 164
Acknowledgements 169
Curriculum Vitae 171
Personal details 171
Education 171
Work experience 172
Research and Mentoring experience 172
Extracurricular activities 173
List of publications and Presentations 174
Publications in peer-reviewed journals: 174
Oral Presentations: 175
Poster Presentations: 175
Statutory declaration 176
Eidesstattliche ErklÀrung 177
Author contributions 178Weizen ist eine der weltweit am hĂ€ufigsten angebauten Kulturpflanzen und trĂ€gt zur Sicherung der Nahrungsmittelproduktion in verschiedenen Regionen der Welt bei. Obwohl er fast ĂŒberall angebaut werden kann, ist die Produktion durch Trockenheit limitiert. Daher rĂŒcken mehr und mehr die mikrobiellen Gemeinschaften im Boden und in der RhizosphĂ€re in den Mittelpunkt der modernen agrarbiologischen Forschung, um die ProduktivitĂ€t bei Trockenheit aufrechtzuerhalten und eine nachhaltige Produktion zu fördern. WĂ€hrend bereits zahlreiche Studien ĂŒber die Weizenproduktion und den positiven Einfluss des Bodenmikrobioms in ariden und semiariden Regionen der Welt durchgefĂŒhrt wurden, sind vergleichbare Studien in Mitteleuropa selten. Dies könnte sich aufgrund der anhaltenden Klimakrise und der zu erwartenden ausbleibenden SommerniederschlĂ€ge Ă€ndern. Dabei haben die meisten Studien, die sich mit der Akklimatisierung des Weizenrhizobioms an Wasserdefizite befasst haben, bestenfalls den Einfluss von Trockenheit und ein oder zwei weiteren biotischen oder abiotischen Einflussfaktoren, die zudem miteinander interagieren können, auf die Struktur und Funktion der mikrobiellen Gemeinschaften in der RhizosphĂ€re untersucht. Ziel dieser Arbeit war es daher, verschiedene komplementĂ€re Analysemethoden zu kombinieren, um trockenheitsbedingte strukturelle und funktionelle VerĂ€nderungen in den bakteriellen Gemeinschaften und auch einzelner Arten in der WeizenrhizosphĂ€re, in AbhĂ€ngigkeit von Bodentyp, Landnutzungssystem, Weizensorte und Pflanzenentwicklungsstadium zu untersuchen, und zu ermitteln, wie sich diese VerĂ€nderungen auf die ProduktivitĂ€t des Weizens als Folge möglicher Szenarien des Klimawandels in Mitteldeutschland auswirken.
Die vorliegende Arbeit leitet mit einer allgemeinen EinfĂŒhrung und Vorstellung des Projekts ein, gefolgt von drei aufeinanderfolgenden Kapiteln, die die wichtigsten Ergebnisse enthalten, die in von Fachleuten begutachteten Artikeln veröffentlicht wurden. Beginnend mit einem Experiment im GewĂ€chshaus (Kapitel 1) und weiterfĂŒhrend zu einem realistischen Klimaszenario unter Feldbedingungen (Kapitel 2 und 3), beschreiben die drei Kapitel die alleinigen und interagierenden Auswirkungen von Trockenheit und Anbausystem (Kapitel 1-3), Bodentyp und Weizensorte (Kapitel 1), sowie Pflanzenwachstumsstadien (Kapitel 2 und 3) auf Bakteriengemeinschaften und einzelne Taxa des Weizenrhizobioms. Die verwendeten Methoden reichen dabei von der traditionellen Kultivierung und In-vitro-Bioassays (Kapitel 3), ĂŒber extrazellulĂ€re EnzymaktivitĂ€tspotenziale (Kapitel 1 und 2), bis hin zu fortschrittlicheren Technologien, wie Metabarcoding (Kapitel 1 und 2) und computergestĂŒtzten Vorhersagen (Kapitel 1 und 2). Zum Abschluss der Arbeit werden in einer abschlieĂenden Synopsis die gewonnenen Ergebnisse zusammengetragen und kritisch betrachtet, sowie Ideen fĂŒr zukĂŒnftige Studien formuliert.
In Kapitel 1 untersuchten wir die Auswirkungen des Bodentyps (lehmig vs. sandig), der Bewirtschaftung (konventionell vs. ökologisch), der Weizensorte (anspruchslos vs. anspruchsvoll) und die Wechselwirkungen zwischen diesen Faktoren auf die Zusammensetzung und Funktion der Bakteriengemeinschaft in der RhizosphĂ€re von Weizen unter extremen Trockenheitsbedingungen. Das Wasserdefizit ĂŒbte einen starken Druck auf die RhizosphĂ€renbakteriengemeinschaften aus und stand in Wechselwirkung mit dem Bodentyp und der Bewirtschaftung, nicht aber mit den Weizensorten. In den Sandböden beobachteten wir eine starke trockenheitsbedingte VerĂ€nderung der Zusammensetzung der Gemeinschaft mit einem RĂŒckgang der Artenvielfalt und der extrazellulĂ€ren Enzymproduktion, wĂ€hrend die VerĂ€nderungen durch die Trockenheit in den fruchtbaren Lehmböden weniger stark ausgeprĂ€gt waren. Eine besondere Ausnahme von diesem Muster wurde fĂŒr EnzymaktivitĂ€ten gefunden, die am Kohlenstoffkreislauf im Sandboden beteiligt sind, was auf eine positive RĂŒckkopplung zwischen Pflanze und Bodengemeinschaften unter Trockenheit hindeutet.
In Kapitel 2 wurden zwei einzelne, jedoch miteinander verknĂŒpfte Ziele verfolgt. Erstens nutzten wir die Plattform der Global Change Experimental Facility (GCEF), um die Auswirkungen von zwei Anbaupraktiken (konventionell vs. ökologisch) und zwei Klimabehandlungen (ambient vs. zukĂŒnftig) auf die Zusammensetzung der Bakteriengemeinschaft und die AktivitĂ€tsprofile extrazellulĂ€rer Enzyme, die an den C-, N- und P-Zyklen in der RhizosphĂ€re von Weizen beteiligt sind, in zwei verschiedenen Pflanzenwachstumsstadien zu untersuchen. Die Klimabehandlung in der GCEF hatte keinen Einfluss auf die RhizosphĂ€renbakteriengemeinschaften. Die Zusammensetzung und die Funktionen der RhizosphĂ€renbakteriengemeinschaften unterschieden sich signifikant zwischen dem vegetativen und dem generativen Wachstumsstadium der Pflanzen, sowohl im konventionellen als auch im ökologischen Landbau. In einem zweiten Schritt nutzten wir die gewonnenen Daten, um die Genauigkeit rechnerischer AnsĂ€tze wie Tax4Fun und PanFP zur Vorhersage funktioneller Profile von Bakteriengemeinschaften auf der Grundlage von 16S rDNA-Daten zu ĂŒberprĂŒfen. Zu diesem Zweck verglichen wir die gemessenen EnzymaktivitĂ€ten mit den jeweiligen GenhĂ€ufigkeiten in der Gemeinschaft unter verschiedenen Klima- und Anbaubedingungen und in den beiden Entwicklungsstadien der Pflanzen. Diese Analyse ergab qualitative, aber nicht unbedingt quantitative Ăbereinstimmungen, d. h. wir fanden Auswirkungen der verschiedenen Behandlungen auf die gemessenen EnzymaktivitĂ€ten, die sich auch in den GenhĂ€ufigkeiten widerspiegeln.
Kapitel 3 stellt einen ergĂ€nzenden Ansatz zu Kapitel 2 dar, wobei der Schwerpunkt auf einzelnen Bakterienarten liegt. Mit kulturabhĂ€ngigen Methoden wurden gezielt stark Phosphat-solubilisierende Bakterien aus der RhizosphĂ€re von Weizen isoliert und auf ihre In-vitro-Trockenheitstoleranz getestet. Unter den mehr als 800 isolierten Arten dominierten Phyllobacterium-, Pseudomonas- und Streptomyces-Arten. WĂ€hrend Anbaumanagement und Klimabehandlung nur geringe Auswirkungen hatten, wirkten sich die Wachstumsstadien des Weizens signifikant auf die Zusammensetzung und Funktionen der Isolate aus, wobei eine Dominanz von Pseudomonas-Arten in der vegetativen Wachstumsphase durch eine Dominanz von Phyllobacterium-Arten in der generativen Wachstumsphase ersetzt wurde. Da das Potenzial zur P-Solubilisierung mit einer hohen in vitro-Trockenheitstoleranz einherging, wurden Phyllobacterium-Arten als vielversprechende pflanzenwachstumsfördernde Rhizobakterien (PGPR) fĂŒr Weizen unter zukĂŒnftigen Trockenheitsbedingungen charakterisiert.
In der Synopsis dieser Arbeit bewerteten wir die multifaktoriellen und multidisziplinĂ€ren AnsĂ€tze, und untersuchten, inwieweit die Anpassungen der Bakteriengemeinschaften in Feld- und Topfversuchen ĂŒbereinstimmen oder sich unterscheiden.
Insgesamt fanden wir allgemeine, aber auch differenzielle Anpassungsprozesse von Bakteriengemeinschaften und einzelnen Arten in der RhizosphĂ€re von Weizen an die Trockenheit, wobei einzelne Faktoren, aber auch interagierende Effekte einen starken Einfluss auf diese Prozesse ausĂŒbten. Diese Studie unterstreicht damit die Bedeutung multifaktorieller AnsĂ€tze, um gemeinschafts- oder artspezifische RĂŒckkopplungen zwischen Pflanze und Boden zu untersuchen.:Contents 3
Preface 5
Bibliographic description 6
Zusammenfassung 9
Summary 13
Introduction 16
When extreme events become the new normal 17
Feedback to agricultural production and need for management adaptation 20
Difficulties in exploring the soil microbiome and identification of plant beneficial microbial taxa 22
Our approach with wheat 24
Bibliography 27
Ö Chapter 1 31
Interactions Between Soil Properties, Agricultural Management and Cultivar Type Drive Structural and Functional Adaptations of the Wheat Rhizosphere Microbiome To Drought 31
Supplemental Tables 51
Supplemental Figures 55
⏠Chapter 2 59
Can We Estimate Functionality of Soil Microbial Communities from Structure-Derived Predictions? A Reality Test in Agricultural Soils 59
Supplementary Tables 79
Supplemental Figures 84
Supplemental Material 1: 87
Variation in edaphic parameters according to experimental factors 87
Supplemental Material 2 88
Effect of abiotic soil parameters on bacterial community structure and function 88
Supplemental Material 3 90
Indicator species analysis 90
Û Chapter 3 95
Shifts Between and Among Populations of Wheat Rhizosphere Pseudomonas, Streptomyces and Phyllobacterium Suggest Consistent Phosphate Mobilization at Different Wheat Growth Stages Under Abiotic Stress 95
Supplementary Figures 112
Supplementary Tables 117
Synopsis 152
Multidisciplinary approaches combine advantages of cultivation-based and high throughput community-based methods 155
Multifactorial approaches to gain a more holistic understanding of plant-microbe interactions in pot experiments 157
Transferability of findings gained in the pot experiment to field conditions 159
Towards a wheat core microbiome? 161
Study limitations and outlook 163
Bibliography 164
Acknowledgements 169
Curriculum Vitae 171
Personal details 171
Education 171
Work experience 172
Research and Mentoring experience 172
Extracurricular activities 173
List of publications and Presentations 174
Publications in peer-reviewed journals: 174
Oral Presentations: 175
Poster Presentations: 175
Statutory declaration 176
Eidesstattliche ErklÀrung 177
Author contributions 17
Recommended from our members
Effects of Particle Swarm Optimisation on a Hybrid Load Balancing Approach for Resource Optimisation in Internet of Things
This article belongs to the Special Issue Emerging Machine Learning Techniques in Industrial Internet of ThingsCopyright © 2023 by the authors. The internet of things, a collection of diversified distributed nodes, implies a varying choice of activities ranging from sleep monitoring and tracking of activities, to more complex activities such as data analytics and management. With an increase in scale comes even greater complexities, leading to significant challenges such as excess energy dissipation, which can lead to a decrease in IoT devicesâ lifespan. Internet of thingsâ (IoT) multiple variable activities and ample data management greatly influence devicesâ lifespan, making resource optimisation a necessity. Existing methods with respect to aspects of resource management and optimisation are limited in their concern of devices energy dissipation. This paper therefore proposes a decentralised approach, which contains an amalgamation of efficient clustering techniques, edge computing paradigms, and a hybrid algorithm, targeted at curbing resource optimisation problems and life span issues associated with IoT devices. The decentralised topology aimed at the resource optimisation of IoT places equal importance on resource allocation and resource scheduling, as opposed to existing methods, by incorporating aspects of the static (round robin), dynamic (resource-based), and clustering (particle swarm optimisation) algorithms, to provide a solid foundation for an optimised and secure IoT. The simulation constructs five test-case scenarios and uses performance indicators to evaluate the effects the proposed model has on resource optimisation in IoT. The simulation results indicate the superiority of the PSOR2B to the ant colony, the current centralised optimisation approach, LEACH, and C-LBCA.This research received no external funding
Multitenant Containers as a Service (CaaS) for Clouds and Edge Clouds
Cloud computing, offering on-demand access to computing resources through the
Internet and the pay-as-you-go model, has marked the last decade with its three
main service models; Infrastructure as a Service (IaaS), Platform as a Service
(PaaS), and Software as a Service (SaaS). The lightweight nature of containers
compared to virtual machines has led to the rapid uptake of another in recent
years, called Containers as a Service (CaaS), which falls between IaaS and PaaS
regarding control abstraction. However, when CaaS is offered to multiple
independent users, or tenants, a multi-instance approach is used, in which each
tenant receives its own separate cluster, which reimposes significant overhead
due to employing virtual machines for isolation. If CaaS is to be offered not
just at the cloud, but also at the edge cloud, where resources are limited,
another solution is required. We introduce a native CaaS multitenancy
framework, meaning that tenants share a cluster, which is more efficient than
the one tenant per cluster model. Whenever there are shared resources,
isolation of multitenant workloads is an issue. Such workloads can be isolated
by Kata Containers today. Besides, our framework esteems the application
requirements that compel complete isolation and a fully customized environment.
Node-level slicing empowers tenants to programmatically reserve isolated
subclusters where they can choose the container runtime that suits application
needs. The framework is publicly available as liberally-licensed, free,
open-source software that extends Kubernetes, the de facto standard container
orchestration system. It is in production use within the EdgeNet testbed for
researchers
FairGen: Towards Fair Graph Generation
There have been tremendous efforts over the past decades dedicated to the
generation of realistic graphs in a variety of domains, ranging from social
networks to computer networks, from gene regulatory networks to online
transaction networks. Despite the remarkable success, the vast majority of
these works are unsupervised in nature and are typically trained to minimize
the expected graph reconstruction loss, which would result in the
representation disparity issue in the generated graphs, i.e., the protected
groups (often minorities) contribute less to the objective and thus suffer from
systematically higher errors. In this paper, we aim to tailor graph generation
to downstream mining tasks by leveraging label information and user-preferred
parity constraint. In particular, we start from the investigation of
representation disparity in the context of graph generative models. To mitigate
the disparity, we propose a fairness-aware graph generative model named
FairGen. Our model jointly trains a label-informed graph generation module and
a fair representation learning module by progressively learning the behaviors
of the protected and unprotected groups, from the `easy' concepts to the `hard'
ones. In addition, we propose a generic context sampling strategy for graph
generative models, which is proven to be capable of fairly capturing the
contextual information of each group with a high probability. Experimental
results on seven real-world data sets, including web-based graphs, demonstrate
that FairGen (1) obtains performance on par with state-of-the-art graph
generative models across six network properties, (2) mitigates the
representation disparity issues in the generated graphs, and (3) substantially
boosts the model performance by up to 17% in downstream tasks via data
augmentation
Colour technologies for content production and distribution of broadcast content
The requirement of colour reproduction has long been a priority driving the development of new colour imaging systems that maximise human perceptual plausibility. This thesis explores machine learning algorithms for colour processing to assist both content production and distribution. First, this research studies colourisation technologies with practical use cases in restoration and processing of archived content. The research targets practical deployable solutions, developing a cost-effective pipeline which integrates the activity of the producer into the processing workflow. In particular, a fully automatic image colourisation paradigm using Conditional GANs is proposed to improve content generalisation and colourfulness of existing baselines. Moreover, a more conservative solution is considered by providing references to guide the system towards more accurate colour predictions. A fast-end-to-end architecture is proposed to improve existing exemplar-based image colourisation methods while decreasing the complexity and runtime. Finally, the proposed image-based methods are integrated into a video colourisation pipeline. A general framework is proposed to reduce the generation of temporal flickering or propagation of errors when such methods are applied frame-to-frame. The proposed model is jointly trained to stabilise the input video and to cluster their frames with the aim of learning scene-specific modes. Second, this research explored colour processing technologies for content distribution with the aim to effectively deliver the processed content to the broad audience. In particular, video compression is tackled by introducing a novel methodology for chroma intra prediction based on attention models. Although the proposed architecture helped to gain control over the reference samples and better understand the prediction process, the complexity of the underlying neural network significantly increased the encoding and decoding time. Therefore, aiming at efficient deployment within the latest video coding standards, this work also focused on the simplification of the proposed architecture to obtain a more compact and explainable model
Recommended from our members
Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
The Viability and Potential Consequences of IoT-Based Ransomware
With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested.
As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed.
For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim.
Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research
RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments
Resource sharing between multiple workloads has become a prominent practice
among cloud service providers, motivated by demand for improved resource
utilization and reduced cost of ownership. Effective resource sharing, however,
remains an open challenge due to the adverse effects that resource contention
can have on high-priority, user-facing workloads with strict Quality of Service
(QoS) requirements. Although recent approaches have demonstrated promising
results, those works remain largely impractical in public cloud environments
since workloads are not known in advance and may only run for a brief period,
thus prohibiting offline learning and significantly hindering online learning.
In this paper, we propose RAPID, a novel framework for fast, fully-online
resource allocation policy learning in highly dynamic operating environments.
RAPID leverages lightweight QoS predictions, enabled by
domain-knowledge-inspired techniques for sample efficiency and bias reduction,
to decouple control from conventional feedback sources and guide policy
learning at a rate orders of magnitude faster than prior work. Evaluation on a
real-world server platform with representative cloud workloads confirms that
RAPID can learn stable resource allocation policies in minutes, as compared
with hours in prior state-of-the-art, while improving QoS by 9.0x and
increasing best-effort workload performance by 19-43%
Bayesian Optimization with Conformal Prediction Sets
Bayesian optimization is a coherent, ubiquitous approach to decision-making
under uncertainty, with applications including multi-arm bandits, active
learning, and black-box optimization. Bayesian optimization selects decisions
(i.e. objective function queries) with maximal expected utility with respect to
the posterior distribution of a Bayesian model, which quantifies reducible,
epistemic uncertainty about query outcomes. In practice, subjectively
implausible outcomes can occur regularly for two reasons: 1) model
misspecification and 2) covariate shift. Conformal prediction is an uncertainty
quantification method with coverage guarantees even for misspecified models and
a simple mechanism to correct for covariate shift. We propose conformal
Bayesian optimization, which directs queries towards regions of search space
where the model predictions have guaranteed validity, and investigate its
behavior on a suite of black-box optimization tasks and tabular ranking tasks.
In many cases we find that query coverage can be significantly improved without
harming sample-efficiency.Comment: For code, see
https://www.github.com/samuelstanton/conformal-bayesopt.gi
- âŠ