2,538 research outputs found
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Mining Butterflies in Streaming Graphs
This thesis introduces two main-memory systems sGrapp and sGradd for performing the fundamental analytic tasks of biclique counting and concept drift detection over a streaming graph. A data-driven heuristic is used to architect the systems. To this end, initially, the growth patterns of bipartite streaming graphs are mined and the emergence principles of streaming motifs are discovered. Next, the discovered principles are (a) explained by a graph generator called sGrow; and (b) utilized to establish the requirements for efficient, effective, explainable, and interpretable management and processing of streams. sGrow is used to benchmark stream analytics, particularly in the case of concept drift detection.
sGrow displays robust realization of streaming growth patterns independent of initial conditions, scale and temporal characteristics, and model configurations. Extensive evaluations confirm the simultaneous effectiveness and efficiency of sGrapp and sGradd. sGrapp achieves mean absolute percentage error up to 0.05/0.14 for the cumulative butterfly count in streaming graphs with uniform/non-uniform temporal distribution and a processing throughput of 1.5 million data records per second. The throughput and estimation error of sGrapp are 160x higher and 0.02x lower than baselines. sGradd demonstrates an improving performance over time, achieves zero false detection rates when there is not any drift and when drift is already detected, and detects sequential drifts in zero to a few seconds after their occurrence regardless of drift intervals
Deteção de intrusões de rede baseada em anomalias
Dissertação de mestrado integrado em Eletrónica Industrial e ComputadoresAo longo dos últimos anos, a segurança de hardware e software tornou-se uma grande preocupação. À medida
que a complexidade dos sistemas aumenta, as suas vulnerabilidades a sofisticadas técnicas de ataque têm
proporcionalmente escalado. Frequentemente o problema reside na heterogenidade de dispositivos conectados ao
veĂculo, tornando difĂcil a convergĂŞncia da monitorização de todos os protocolos num Ăşnico produto de segurança.
Por esse motivo, o mercado requer ferramentas mais avançadas para a monitorizar ambientes crĂticos Ă vida
humana, tais como os nossos automĂłveis.
Considerando que existem várias formas de interagir com os sistemas de entretenimento do automóvel como
o Bluetooth, o Wi-fi ou CDs multimédia, a necessidade de auditar as suas interfaces tornou-se uma prioridade,
uma vez que elas representam um sério meio de aceeso à rede interna do carro. Atualmente, os mecanismos de
segurança de um carro focam-se na monitotização da rede CAN, deixando para trás as tecnologias referidas e não
contemplando os sistemas nĂŁo crĂticos. Como exemplo disso, o Bluetooth traz desafios diferentes da rede CAN,
uma vez que interage diretamente com o utilizador e está exposto a ataques externos.
Uma abordagem alternativa para tornar o automĂłvel num sistema mais robusto Ă© manter sob supervisĂŁo as
comunicações que com este são estabelecidas. Ao implementar uma detecção de intrusão baseada em anomalias,
esta dissertação visa analisar o protocolo Bluetooth no sentido de identificar interações anormais que possam
alertar para uma situação fora dos padrões de utilização. Em última análise, este produto de software embebido
incorpora uma grande margem de auto-aprendizagem, que é vital para enfrentar quaisquer ameaças desconhecidas
e aumentar os nĂveis de segurança globais. Ao longo deste documento, apresentamos o estudo do problema seguido
de uma metodologia alternativa que implementa um algoritmo baseado numa LSTM para prever a sequĂŞncia de
comandos HCI correspondentes a tráfego Bluetooth normal. Os resultados mostram a forma como esta abordagem
pode impactar a deteção de intrusões nestes ambientes ao demonstrar uma grande capacidade para identificar padrões anómalos no conjunto de dados considerado.In the last few years, hardware and software security have become a major concern. As the systems’ complexity
increases, its vulnerabilities to several sophisticated attack techniques have escalated likewise. Quite often, the
problem lies in the heterogeneity of the devices connected to the vehicle, making it difficult to converge the monitoring
systems of all existing protocols into one security product. Thereby, the market requires more refined tools to monitor
life-risky environments such as personal vehicles.
Considering that there are several ways to interact with the car’s infotainment system, such as Wi-fi, Bluetooth,
or CD player, the need to audit these interfaces has become a priority as they represent a serious channel to reach
the internal car network. Nowadays, security in car networks focuses on CAN bus monitoring, leaving behind the
aforementioned technologies and not contemplating other non-critical systems. As an example of these concerns,
Bluetooth brings different challenges compared to CAN as it interacts directly with the user, being exposed to external
attacks.
An alternative approach to converting modern vehicles and their set of computers into more robust systems
is to keep track of established communications with them. By enforcing anomaly-based intrusion detection this
dissertation aims to analyze the Bluetooth protocol to identify abnormal user interactions that may alert for a non conforming pattern. Ultimately, such embedded software product incorporates a self-learning edge, which is vital to
face newly developed threats and increasing global security levels. Throughout this document, we present the study
case followed by an alternative methodology that implements an LSTM based algorithm to predict a sequence of
HCI commands corresponding to normal Bluetooth traffic. The results show how this approach can impact intrusion
detection in such environments by expressing a high capability of identifying abnormal patterns in the considered
data
Adaptive vehicular networking with Deep Learning
Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networks’ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities.
In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects.
The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup.
The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios.
The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the “ideal” optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark
Optimising multimodal fusion for biometric identification systems
Biometric systems are automatic means for imitating the human brain’s ability of identifying and verifying other humans by their behavioural and physiological characteristics. A system, which uses more than one biometric modality at the same time, is known as a multimodal system. Multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. This thesis addresses some issues related to the implementation of multimodal biometric identity verification systems. The thesis assesses the feasibility of using commercial offthe-shelf products to construct deployable multimodal biometric system. It also identifies multimodal biometric fusion as a challenging optimisation problem when one considers the presence of several configurations and settings, in particular the verification thresholds adopted by each biometric device and the decision fusion algorithm implemented for a particular configuration. The thesis proposes a novel approach for the optimisation of multimodal biometric systems based on the use of genetic algorithms for solving some of the problems associated with the different settings. The proposed optimisation method also addresses some of the problems associated with score normalization. In addition, the thesis presents an analysis of the performance of different fusion rules when characterising the system users as sheep, goats, lambs and wolves. The results presented indicate that the proposed optimisation method can be used to solve the problems associated with threshold settings. This clearly demonstrates a valuable potential strategy that can be used to set a priori thresholds of the different biometric devices before using them. The proposed optimisation architecture addressed the problem of score normalisation, which makes it an effective “plug-and-play” design philosophy to system implementation. The results also indicate that the optimisation approach can be used for effectively determining the weight settings, which is used in many applications for varying the relative importance of the different performance parameters
International environmental cooperation and climate change laws: A quantitative analysis
The increasing number of IEAs has induced a complex web of interdependent relationships among countries. This thesis mainly studies the international environmental cooperation network created by IEAs and countries’ adoption of national climate change laws by combing theories and methods from network science, economic and political economics and international relations. Specifically, I will outline four projects concerned with IEAs and climate change laws. In the first project, I construct a statistically significant international environmental cooperation network among countries and study its emergency and evolution by investigating its structural properties. The results reveal that the popularity of environmental agreements led to the emergence of an environmental cooperation network and document how collaboration is accelerating. The second and third projects concern the meso-organisation of international environmental cooperation. Specifically, the second project studies the community structure of the environmental cooperation network. Community detection is conducted, and results show that environmental cooperation presents regionalisation. In the third project, I study the core-periphery structure of international environmental cooperation by investigating the nestedness and rich clubs arising from country-treaty relationships. Furthermore, the cooperation complexity is analysed based on methods from economic complexity to further assess country-treaty relationships. I develop a new measure to quantify the diversification of countries’ commitment to environmental treaties. Results show that European countries lie at the core of international environmental cooperation with the highest diversification of commitment. In addition, countries’ diversification of commitment is significantly iv correlated with environmental performance within countries. In the fourth project, I turn to national climate change laws to explore factors influencing the burst of countries’ adoption behaviours. I show that scientific consensus, COPs, and natural disasters are significantly and positively associated with the burst of countries’ adoption behaviours
Getting noncooperative agents to cooperate:nudging and dynamic interventions
Due to the strong interconnection between modern engineering systems and their users, performance of these systems heavily rely on the user behavior. Therefore, uncoordinated user behavior can deteriorate the overall performance and entail undesired outcomes. To address this problem, this thesis studies the problem of designing suitable interventions that provide coordination among noncooperative agents/players. We investigate the development of suitable interventions in several setups and propose mechanisms that achieve a desired outcome. The first part of the thesis focuses on altering the aggregative behavior of noncooperative price-taking agents towards a desired stationary or temporal behavior. We address this problem by introducing a nudge framework, where a system regulator modifies the behavior of the agents by providing a price prediction signal. In the second part of the thesis, we focus on designing intervention mechanisms that steer the actions of noncooperative players in network games to the social optimum. We investigate different cases based on the knowledge of the system regulator on the game as well as constraints on the actions and interventions. The third part of the thesis deals with the problem of Nash equilibrium seeking in aggregative games. We develop a distributed algorithm where the players communicate to their neighboring players. The robustness and privacy preserving properties of the algorithm are also analyzed
Application of network filtering techniques in finding hidden structures on the Johannesburg Stock Exchange
Dissertation (MSc (Financial Engineering))--University of Pretoria, 2023.Researchers from the field of econophysics have favoured the idea that financial markets are a complex adaptive system, consisting of entities that behave and interact in a diverse manner, leading to non-linear, emergent behaviour of the system. In the last twenty years, there has been an increasing focus on modelling complex adaptive systems using network theory. Correlation-based networks, where stocks are represented as entities in the network, and the relationships amongst the stocks are based on the strength of the co-movements of the stocks, have been widely studied. Network filtering tools, such as the Minimal Spanning Tree (MST), and the Planar Maximally Filtered Graph (PMFG), have been useful to attenuate the impact of noise in these networks, thereby allowing important macroscopic and mesoscopic structures to emerge. One of the main benefits of the PMFG is that it is accompanied by a hierarchical clustering algorithm called the Directed Bubble Hierarchical Tree (DBHT). This method has the benefit of being fully unsupervised in that it does not require the user to decide a priori on the number of clusters that the data should be split into.
These techniques have been applied here to analyse the complex interactions amongst stocks on the Johannesburg Stock Exchange. A structure emerged in which shares from similar ICB sectors tended to cluster together. However, the so-called Rand Hedge shares, and shares which exhibited low liquidity, tended to override the sector effect and clustered together. From a dynamic perspective, the MST and PMFG seemed to shrink during market crashes, while the Basic Materials sector was typically the most important or central sector over time. Over the long-term, the DBHT divided the stocks in the South African stock market into six clusters. This technique was compared to other popular hierarchical clustering algorithms, and the amount of economic information that each method extracted was quantified. The most recent PMFG and DBHT showed a changed structure as compared to the long-term data, highlighting that the way that market participants view South African shares can change over time.Mathematics and Applied MathematicsMSc (Financial Engineering)Unrestricte
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