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SDN-enabled Workload Offloading Schemes for IoT Video Analytics Applications
Get PDFIncreasing demand for using IoT applications, such as video analytics, leverages the importance of developing an architecture to meet the requirements in terms of the latency, reliability, and energy consumption. IoT video cameras combined with the power of machine learning algorithms introduce real-time video analytics applications that can be used in diverse domains, such as security surveillance, sports, and retail stores. However, processing captured video frames using machine
learning algorithms needs resources that are beyond the capability of these IoT devices.
IoT task offloading is a new paradigm to aim IoT applications to deliver processing intensive applications to their users. IoT devices, which have limited resources by nature, offload their tasks to more powerful servers, i.e., edge/cloud servers . Nonetheless, selecting an appropriate destination for offloading the tasks is the first incoming problem for the IoT task offloading. There are some criteria which needs to be considered when it comes to IoT task offloading, for example transmission latency, queuing delay, as well as processing latency. Although edge servers have limited resources compared to cloud servers, the end-to-end latency for sending the packets to the edge servers is less than the cloud servers. On the other hand, because of the limited available resources in the edge servers, distributing the offloaded tasks between these devices is necessary to avoid overloaded servers.
Considering the above mentioned facts, in this thesis, we present load-balancing algorithms benefits from Software Defined Networking (SDN) to distribute offloaded tasks to reduce the chance of using overloaded servers and processing latency of offloaded packets of IoT video analytics applications. Taking into account the aforementioned facts, we propose a scoring metric to balance the incoming offloaded packets between edge servers. The introduced algorithm takes advantage of underlying SDN to collect information about the load of each edge server in the network. Then, the SDN controller uses the scoring metric and sorts the edge servers accordingly. The offloaded task will be directed to the edge server with the lowest processing load to avoid overloaded edge servers.
Since the number of IoT devices in the network is not predictable, increasing number of IoT devices will lead to overloaded edge servers. Hence, offloading a part of the IoT tasks to the cloud server might be a better option, even though the packets should pass through the core network. In this regard, we developed a hierarchical edge/cloud system for IoT task offloading. We modeled each of edge/cloud servers by M/M/1 queue model. By benefiting from SDN as an underlying network, the SDN calculates the processing latency and transmission latency to edge and cloud servers, and decides the best destination in terms of the minimum latency that directs the offloaded tasks to one of the desired servers.
We have conducted extensive performance evaluation to demonstrate the out-performance of the developed solutions compared with other related approaches in terms of total experienced latency and load distribution between the available servers. The results are comprehensively discussed in their related chapters to clarify the performance of the developed solution
The Poetics of Fieldwork: Geographies of Difference and Togetherness.
Get PDFThis thesis examines various logics of extraction in the Indigenous P'urhépecha community in Cherán, in the Mexican state of Michoacán. It comprises field-based artistic research in the Cherán forest, investigating various articulations of colonial violence manifested in diverse forms of resource extraction, state-sponsored violence, appropriation of land, enforced disappearance, and unevenly distributed visual rights. The experiences of the Indigenous communities in Michoacán are rooted in longstanding histories of exclusion, disappearance, colonialism, and genocide.
In 2015, I initiated a long-term investigation in the P'urhépecha landscape, examining social relations and tensions following the 2011 P'urhépecha uprising in Cherán. In 2011, the people in Cherán locked down the town and took up arms, engaging against organized criminal cartels, whose drug-related violence and illegal logging had plagued the area for decades. In 2012, the P'urhépecha legally took political control of the town, expelling the police and other state institutions. An autonomous Indigenous government has been consolidated, without a mayor, police, or political parties. Cherán is the first autonomous Indigenous community with a new governance system built on P'urhépecha traditions to be recognized officially by the Mexican government.
The Indigenous autonomous government in Cherán stands as a successful case of political emancipation and environmental protection against extractivist practices. Through an extensive process of community-based research, I combine personal, political, and theoretical, in order to grapple with the complex relationship between culture, positionality, ethnicity, and class. This thesis builds upon three moving image artworks, investigating the impermanence and malleability of spatialities of memory, exception, erasure, and disappearance
Memories of Salluit: An Oral History
Get PDFInuit elders hold a wealth of knowledge about the history and formation of their communities in what is now called Nunavik, as many of them have seen these communities from their genesis, and through development, to the present day. As Qallunaat started establishing trading posts or military bases around the early twentieth century (1920s), most settlements were established by the mid century (1950s), before formally inaugurating or legally incorporating towards the end of the century (1980s). My grandmother Alacie Naluiyuk was born on Pujjunaq in 1946, an island that is now a part of the Nunavut Territory. She was born on the land in an iglu and raised in a semi-nomadic manner with her family before eventually settling with other families in the area today called Salluit. She lived in a Western-style wooden house for the first time later in her youth and witnessed the first Western houses being built in her community, and also observed the introduction of governmental public service establishments built and led by Qallunaat. She however also saw the emergence of Inuit-led organizations and initiatives as a response to colonialism and structural violence. Alacie Naluiyuk has seen and experienced firsthand the social and cultural shifts that Inuit have faced since the temporary and permanent settlement of Qallunaat and Ui-Uiit (non-Inuit and/or anglophone and francophone European settlers and their descendants, respectively). In this thesis I draw on Alacie’s oral history and lived experiences to explore the ways in which Inuit have navigated and adapted to the intricate colonial cultural and infrastructural fabric that constitutes the community today
Machine Learning for Fault Detection in Clouds
No full textCloud computing is recognized with several important benefits, including elastic architecture, easy accessibility, scalability, and flexibility. These benefits enable a wide variety of services and applications. These services, however, are prone to faults. A fault is a
condition where the system fails to perform its required functionality. Once a fault occurs, it may propagate through cloud environments, and it can result in service interruption, which can eventually lead to the unavailability of cloud services. Given the large-scale cloud environments and the diversity of faults that may occur in clouds, automated fault detection with limited or no human intervention is crucial. Machine Learning (ML) is the key enabler technology that can deliver an automated means to provide a more reliable
network by detecting faults. However, the data that is gathered from cloud computing environments limits the applicability of ML-based solutions due to several reasons. Firstly, the cloud data typically includes several features, of which some features may have higher
impacts on the accuracy of the fault detection system than other features. Second, some features may be missing/unknown, which can happen due to the absence of traffic, maintenance, and/or errors in collecting information about the state of network devices (e.g.,
faults in monitoring equipment or applications). The values of such missing features may be necessary in order to build an accurate fault detection method, as missing informative feature values can affect the ultimate fault detection outcome. In addition, even if the values of all features are known, calculating the required number of features for fault detection is important. In fact, if the number of the selected features is too large, there may be many overlapping features, which can lead to the so-called over-fitting issue. On the other hand, if the number of features is too small, it causes accuracy degradation. To address the above-mentioned challenges, a proper feature selection method is required not only to estimate the values of the unknown feature values but also to select the most important features with the aim of improving fault detection accuracy. In this thesis, we propose a context-aware feature selection method, which exploits a sensitivity analysis to
measure the impact of each feature on the output prediction. Also, our feature selection method is able to estimate the value of missing feature values and their sensitivity using time-series, multivariate and incomplete feature vectors as input. Our proposed feature selection method comprises Recurrent Neural Network (RNN) and Denoising Auto-Encoder (DAE) stacked with an RNN and Discriminative Model (DM). The RNN and DAE are responsible for dealing with time-series data and missing feature values, respectively, while RNN DM is used for predicting the system status. We have evaluated the performance
of our proposed framework on real-world Google cluster data. Based on the simulation results, it is evident that our proposed feature selection method plays a significant role in providing accurate fault detection in terms of F1-score
Applications of Equilibrium Modeling and Game Theory in Biomass Supply Chain Management
Get PDFThe increasing attention towards renewable energies as solutions to environmental problems and future energy security has made biomass-based energy an attractive option. Biomass energy not only reduces dependence on fossil fuels but also helps mitigate environmental impacts. Effective biomass supply chain management is essential for bioenergy production, covering the entire process from feedstock harvesting to energy conversion facilities. Despite its advantages, biomass-based energy faces challenges such as low energy density, seasonal availability, and variable costs. Moreover, inefficient interactions and conflicting interests among supply chain participants hinder its development.
To address these challenges, efficient decision-making structures and coordination among supply chain entities are crucial. This PhD thesis focuses on coordination in biomass supply chains using game theoretical tools, which are well-suited for addressing conflicting objectives. The research encompasses three main attempts:
1. Evaluation of the impact of power distribution on supply chain efficiency through game theoretic modeling, considering various leadership schemes.
2. Assessment of the role of government incentives using game theoretic analysis to determine the most effective approach for incentivizing biomass development.
3. Design of game theoretic contract approaches for coordinating biomass supply chains while considering environmental impacts, including revenue sharing and quantity discounts.
Non-cooperative approaches, particularly Stackelberg game and equilibrium models, are emphasized within the game theoretic framework. A case study of northern Canadian communities is proposed to validate the feasibility of replacing diesel with bioenergy for heat and electricity consumption.
Preliminary work on modeling supply chains with different leaders using Stackelberg games has shown promising results, demonstrating the dominant role of communities in supply chain efficiency. The outcomes of this research have been published in peer-reviewed journals, including Sustainable Cities and Society and Clean Technologies and Environmental Policy. Additionally, a coordinated approach involving quantity discounts and revenue sharing has been proposed to evaluate the economic and environmental impact of bioenergy development. This approach has shown potential for improved economic performance and significant reductions in environmental impact. By employing game theory and coordination strategies, this thesis contributes to the understanding and optimization of biomass supply chains, promoting sustainable energy systems and addressing the challenges faced in the bioenergy sector
Towards the Improvement of Therapeutic Oligonucleotides: Synthesis, Biophysical and Antisense Properties of Arabinonucleic Acids Containing C5-Propynyl Pyrimidines
No full textNucleic acids (NA) have emerged as a promising class of therapeutics with potential for the development of drug candidates aimed at treating genetic disorders. The clinical success of these platforms is attributable to several factors including their ease of synthesis, biocompatibility, and highly specific target binding via programmable hydrogen bonding patterns. Early investigation into the use of synthetic oligonucleotides (ONs) revealed that these molecules face a host of challenges when delivered in vivo, including susceptibility towards nucleases, rapid clearance, poor cellular uptake, and off-target effects. As a result, decades of research have focused on the development of methodologies to introduce chemical modification along the ON to enhance their therapeutic characteristics. However, no single ON design that addresses all these challenges has emerged, and as such, the development of potent chemical modifications is necessary for their continued success. Towards this end, here we focus on the development of new chemical modifications along the arabinonucleic acid (ANA) scaffold. These NA analogues have been shown to increase nuclease resistance and act as a substrate for RNase H1, an enzyme capable of cleaving the RNA strand of a DNA:RNA hybrid and the basis of the antisense approach. However, ANA forms duplexes of low thermal stability with their RNA targets relative to DNA and as such, in Chapters 2-3, we aimed to address these issues via installation of propynyl groups at the C5 position of pyrimidines using the phosphoramidite approach. These modifications improved the thermal stability of duplexes formed between ANA and target RNA when placed consecutively while reducing affinity for DNA targets in several ON systems. Moreover, modified ONs were compatible with E. coli RNase H mediated cleavage of RNA including in uniformly modified systems, while simultaneously enhancing nuclease stability. Furthermore, in Chapter 4 C5-propynyl-2'-deoxy-2'-fluoro analogs of ANA (FANA) were investigated in similar ON systems. Duplexes containing uracil analogs displayed greater stability of duplexes formed with RNA but not DNA targets and cytosine analogs were exceedingly stabilizing towards RNA targets. Similarly, all modified ONs were substrates for E. coli RNase H with rates comparable to DNA however, only minor improvements in nuclease stability were observed for these analogs. With the goal of exploring these modifications in vitro, we explored approaches to conjugate modified ONs to a ligand for enhanced cellular internalization. During our investigation, we developed a novel linker phosphoramidite containing a disulfide bond which would allow for reductive responsive release of the ON upon cellular internalization. In Chapter 5 we describe the synthesis of this linker, and how it can be used to incorporate several 5'- functional handles for bioconjugation. The results of this thesis contribute towards developing new chemical modalities which can be harnessed to tune the therapeutic properties of ONs for their continued success in the clinic
Enhancing Anomaly Detection with Flexible Distribution Models
Get PDFThe performance of an anomaly detection task depends on the modeling of the input data. In the case of proportional data, Dirichlet and its general form distributions are a convenient choice to effectively capture the underlying characteristics of this kind of data.
In this thesis, we propose a normality score approach based on transformations that consist of learning a normality function. We suggest geometric transformations for image data and transformation-based neural networks for non-image data. Then, we propose an approximation of the softmax output vector of a classifier with generalized Dirichlet (GD), scaled Dirichlet (SD), shifted scaled Dirichlet (SSD), and Beta-Liouville (BL) distributions. We use a technique based on likelihood to determine its parameters.
Motivated by the salient characteristics of Liouville and Libby-Novick Beta distributions, we expand the Beta-Liouville distribution and build a new distribution called the Libby-Novick Beta-Liouville distribution. We demonstrate the efficiency of our proposed distribution through three challenging approaches. First, we develop generative models, namely finite mixture models of
Libby-Novick Beta-Liouville distributions. Then, we propose two discriminative techniques: normality scores based on selecting the given distribution to approximate the softmax output vector of
a deep classifier, and an improved version of the Support Vector Machine (SVM) by suggesting a feature mapping method. We test the efficiency of our suggested techniques for anomaly detection
tasks using several experimental settings and five data sets: three image data sets and two non-image data sets
On the Convergence of Three Applied Stochastic Models related to Reflected Jump Diffusions, Fast-Slow Dynamical Systems, and Optimistic Policy Iteration
Get PDFThis dissertation explores three stochastic models: additive functionals of reflected jump-diffusion processes, two-time scale dynamical systems forced by α-stable Lévy noise, and a variant of the Optimistic Policy Iteration algorithm in Reinforcement Learning. The connecting thread between all three projects is in showing convergence of these objects whose results have direct applied implications.
A large deviation principle is established for general additive processes of reflected jump-diffusions on a bounded domain, both in the normal and oblique setting. A characterization of the large deviation rate function, which quantifies the rate of exponential decay for the rare event probabilities of the additive processes, is provided. This characterization relies on a solution of a partial integro-differential equation with boundary constraints that is numerically solved with its implementation provided. It is then applied to a few practical examples, in particular, a reflected jump-diffusion arising from applications to biochemical reactions.
We derive the weak convergence of the functional central limit theorem for a fast-slow dynamical system driven by two independent, symmetric, and multiplicative α-stable noise processes. To do this, a strong averaging principle is established by solving an auxiliary Poisson equation where the regularity properties of the solution are essential to the proof. The latter allow for the order of convergence to the averaged process of 1-1/α to be established and subsequently used to show weak convergence of the scaled deviations of the slow process from its average. The theory is then applied to a Monte Carlo simulation of an illustrative example.
In the Optimistic Policy Iteration algorithm, Monte Carlo simulations of trajectories for some known environment are used to evaluate a value function and greedily update the policy which we show converges to its optimal value almost surely. This is done for undiscounted costs and without restricting which states are used for updating. We employ the greedy lookahead policies used in previous results thereby extending current research to discount factor α=1. The first-visit variation of this algorithm follows as a corollary and we further extend previous known results when the first state is picked for updating
Risk and Regulation: A Longitudinal Perspective on Parenting, Psychosocial Risk, and the Development of Emotion Regulation from Infancy to Preschool
Get PDFThe early development of emotion regulation behaviours is predictive of lifelong socioemotional functioning and risk of psychopathology. Traditional developmental models of emotion regulation tend to categorize early regulatory strategies as “adaptive” or “maladaptive,” with the assumption that certain strategies are superior to others. This is in contrast to recent research in adult emotion regulation, which contends that the benefits of any given strategy vary across individuals and situations (Bonanno & Burton, 2013).
In the current set of studies, observational coding systems were used to capture the range of regulatory behaviours employed across early development in contexts of both normative interaction with mothers and brief periods of mild distress. Rather than categorizing regulatory behaviours as helpful or unhelpful, strategies were examined individually (Study 1) or in co-occurring behavioural patterns (Study 2) to understand their associations with risk and protective factors, including positive and negative parenting behaviours (Studies 1 and 2), maternal depression (Study 1), and child temperament (Study 2).
Study 1 included a sample of depressed and non-depressed mothers and their four-month-old infants (N=35). Infant emotion regulation and maternal sensitivity and hostility were observationally coded during the Still-Face procedure. Results indicated that maternal sensitivity moderated the association between maternal depression and infant emotion regulation during the Still-Face period, such that maternal depressive symptoms negatively predicted infant gaze aversion when sensitivity was low and positively predicted gaze aversion when sensitivity was high. Maternal hostility did not moderate the association during either the Normal or Still-Face periods but did have a direct effect on emotion regulation during the Still-Face period. Higher maternal hostility was associated with increased self-soothing and decreased gaze aversion. Results suggest that parenting both mitigates the effects of maternal depression and directly impacts regulatory behaviour use.
In Study 2, mother-infant dyads (N=167) were followed at four time points from infancy to preschool (6 months, 12 months, 18 months, 4.5 years). Emotion regulation behaviours were observationally coded during periods of mild distress at each time point, as well as during normative dyadic interaction periods as a comparison point. Latent profile analyses were used to identify patterns of co-occurring behaviours. The associations of the identified profiles with parenting (maternal sensitivity, non-hostility) and, at one time point, child temperament (emotionality, sociability) were explored. For a subset of the sample, the longitudinal associations between regulatory profiles and socioemotional outcomes (emotion regulation, depression, anxiety, aggression) in middle childhood (9-12 years) were also tested. Results indicated that more optimal parenting (higher sensitivity and non-hostility) was associated with more complex profiles of regulation during periods of induced mild distress, and that such profiles were predictive of positive socioemotional outcomes in later childhood. Findings suggest that attaining a diverse repertoire of regulatory behaviours in early life may be as important as the specific behaviours used.
The present dissertation makes an important contribution to the emotion regulation literature by considering the function and adaptiveness of a variety of regulatory behaviours, challenging current conceptualizations that behaviours fall into categories of “adaptive” or maladaptive,” and including risk and protective factors at multiple levels of influence, including parenting, child characteristics, and maternal mental health. Findings from both studies suggest that aspects of regulatory flexibility may be emerging in early life, such that infants are adjusting their behaviours to meet situational demands, and that this flexibility may promote healthy socioemotional development. Results have implications for the design of early intervention to prevent future difficulties with dysregulation
Using ChatGPT to Augment Software Engineering Chatbots Datasets
Get PDFChatbots are envisioned to bring about a significant shift in the realm of Software Engineering (SE), enabling practitioners to engage in conversations and interact with various services using natural language. At the heart of each chatbot is a Natural Language Understanding (NLU) component that enables the chatbots to comprehend the user's queries. However, the NLU requires extensive, high-quality training data (examples) to accurately interpret user queries. Prior work shows that the creation and augmentation of SE datasets are resource-intensive and time-consuming. To address this gap, we explore the potential of using ChatGPT to augment the SE chatbot training dataset. Specifically, we evaluate the impact of retraining the NLU on ChatGPT’s augmented dataset on the NLU's performance using four widely used SE datasets. Moreover, we assess the syntactic and semantic aspects of the generated examples compared to human-written examples. Additionally, we conduct an ablation study to investigate the impact of each component in the prompt on the NLU's performance and the diversity of the generated examples. The results show that ChatGPT significantly improves the NLU's performance, with F1-score improvements ranging from 3.9% to 11.6%. Moreover, we find that ChatGPT-generated examples exhibit syntactic diversity while maintaining consistent semantics (2.2% on average) across all datasets. Additionally, the results indicate that including a few human-written examples and a description of the intent’s objective in the prompt impacts the quality of the generated examples. Finally, we provide implications for practitioners and researchers of SE chatbots