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Recent Submissions
Perspectives of Graph Diffusion: Computation, Local Partitioning, Statistical Recovery, and Applications
(University of Waterloo, 2025-03-06) Yang, Shenghao
Diffusion describes the process of mass moving from one region to another. In the con- text of graph, the diffusing mass spreads from nodes to nodes along the edges of the graph. Broadly speaking, this includes a number of stochastic and deterministic processes such as random walk, heat diffusion, network flow and electrical flow on graphs. Graph diffusion is a highly important primitive, and has been shown to have a variety of surprising properties both theoretically and practically. In this thesis, we present several new perspectives of graph diffusion, with an emphasis on how diffusion algorithms uncover the local clustering structure of the input data without necessarily exploring the entire graph.
In the first two parts of the thesis, we introduce a new class of graph diffusion methods that are provably better at extracting the local clustering structure of a graph or a hy- pergraph. Here, diffusion is formulated as a pair of primal and dual convex optimization problems, based on the idea of spreading mass in the graph while minimizing a p-norm net- work flow cost. The primal solution of the diffusion problem provides an intuitive physical interpretation where paint (i.e. mass) spills from the source nodes, spreads over the graph, and there is a sink at each node where up to a certain amount of paint can settle. The dual solution embeds the nodes on the non-negative real line and is considered as the output of diffusion. We will show that the dual variables nicely encode the local clustering structure around a given set of seed nodes. In particular, assume the existence of a cluster C of low conductance Φ(C), the sweep cut procedure on the dual variables returns a cluster whose conductance is not too much larger than Φ(C).
In the next two parts of the thesis, we introduce a weighted diffusion mechanism which allows any existing diffusion method to take into account additional node information such as node attributes and labels. The method weighs the edges of the graph based on the attributes or the labels of each node. Depending on the nature and availability of additional node information, two simple yet effective edge-weighting schemes are introduced and analyzed. Over contextual random graphs generated by a local variant of the stochastic block model with noisy node information, we will show that, if the additional information contains enough signal about the ground-truth cluster, then employing existing diffusion algorithms in the weighted graph can more accurately recover the ground-truth cluster than employing diffusion in the original graph without edge weights. In particular, statistical recovery guarantees in terms of precision and F1 score will be derived and compared.
All of the results are supplemented with extensive experiments on both synthetic and real-world data to illustrate the technical results and the effectiveness of the new methods in practice. The code is open-source on GitHub.
Introducing the INSPIRE Framework Guidelines From Expert Librarians for Search and Selection in HCI Literature
(Oxford University Press on behalf of The British Computer Society., 2025-02-01) Joseph Tu; Lennart Nacke; Katja Rogers
Formalized literature reviews are crucial in human–computer interaction (HCI) because they synthesize research and identify unsolved problems. However, current practices lack transparency when reporting details of a literature search. This restricts replicability. This paper introduces the INSPIRE framework for HCI research. It focuses on the search stage in literature reviews to support a search that prioritizes transparency and quality-of-fit to a research question. It was developed based on guiding principles for successful searches and precautions advised by librarian experts in HCI (n=8) for search strategies in (primarily systematic) literature reviews. We discuss how their advice aligns with the HCI field and their concerns about computational AI tools assisting or automating these reviews. Based on their advice, the framework outlines pivotal stages in conducting a literature search. These essential stages are: (1) defining research goals, (2) navigating relevant databases and (3) using searching techniques (like divergent and convergent searching) to identify a set of relevant studies. The framework also emphasizes the importance of team involvement, transparent reporting, and a flexible, iterative approach to refining the search terms.
The Relationship Between Mental Health and Mobile Banking Adoption: Evidence from Canada
(Springer, 2024) Amirkhalili, Yekta; Cozzarin, Brian P.; Dimitrov, Stanko
Mobile banking (m-banking) is the use of a mobile device such as a smartphone to do banking tasks. We investigate the direct and moderated effect of mental health on m-banking adoption. Moderators in our study are extracted from theories in technology adoption paradigm or from literature related to mental health. These variables are relationship satisfaction (RS), smartphone dependency (SD), and social networking/social media (SNS) use. We use the Canadian Internet Usage Survey conducted by Statistics Canada in 2020-21 as the main source of data. The impact of mental health on m-banking adoption is analyzed across levels of RS, SD, and SNS use. A fixed effect logistic regression model is utilized to investigate the relationship of variables, considering t he grouping based on province following the cluster sampling design of the dataset. Our results indicate that mental health significantly negatively affects m-banking adoption: better mental health outcomes are associated with lower likelihood of m-banking adoption. We observe that social media users and those that are more dependent on their smartphones are more likely to adopt m-banking, therefore one suggestion for banks is to use social media platforms as marketing channels. We do not find sufficient evidence that significant differences exist for the effect of mental health on m-banking adoption across levels of SNS, SD and RS.
Operating Systems are a Service
(University of Waterloo, 2025-03-05) Hancock, Kenneth
OS containers have set the standard for the deployment of applications in modern
systems. OS containers are combined sandboxes/manifests of applications that isolate
the running applications and its dependencies from other applications running on top of
the same kernel. Containers make it easy to provide multi-tenancy and control over the
application, making it ideal for use within cloud architectures such as serverless.
This thesis explores and develops novel systems to address three problems faced by
containers and the services that use them. First, OS containers currently lack a fast
checkpoint-restore mechanism. Second, container security is still inadequate due to its
underlying security mechanisms, which provide coarse-grained policies that are abused.
Third, the lack of a benchmark for serverless clouds, one of the largest consumers of
containers, and specifically checkpoint-restore.
This thesis outlines solutions to these problems. First, ObjSnap, a storage system
designed and built for two modern single-level store systems, Aurora and MemSnap, which
enable checkpoint restore for container systems. ObjSnap is a transactional copy-on-write
object store that can outperform other storage systems by up to 4×. Second, we introduce
SlimSys, a framework that tackles security issues found within containers by binding a
policy to kernel resources. Lastly, we introduce Orcbench, the first benchmark used to
evaluate serverless orchestrators.
A Comprehensive Process for Addressing Market Power in Decentralized ADN Electricity Markets
(University of Waterloo, 2025-03-05) AboAhmed, Yara
Electric power systems have transformed globally, with distribution grids evolving into active distribution networks (ADNs), altering their characteristics and operations. Traditional centralized market structures have become inadequate for the complexities of the ADNs, leading to inefficiencies and challenges in reliable operation and energy pricing. ADN electricity markets offer a solution by leveraging smart grid features to integrate distributed energy resources (DERs), allowing non-utility entities, such as producers, consumers and prosumers, to participate directly, enhancing market efficiency, reducing monopoly power, and limiting utility control over prices. However, with the increasing penetration of DERs, there is a growing risk of market concentration and manipulation by entities owning large shares of DERs in ADN electricity markets. This poses a potential threat to market fairness, as some participants may exploit market power, leading to an uneven playing field, reducing the integrity and efficiency of ADN electricity markets.
From this standpoint, this thesis investigates and adapts the concept of market power within ADN electricity markets, considering the unique characteristics of the market and the system. The investigation is structured around six central questions: (1) Can non-utility entities exercise market power in ADN electricity markets? (2) Is there a comprehensive framework for accurately monitoring, evaluating, and mitigating market power in decentralized ADN markets? (3) If such a framework exists, can it manage the complexity of monitoring the large number of ADN market participants? (4) If market power manipulation exists, are current investigations adequate, considering the decentralized market structure, the physical characteristics of the system, DER operational constraints, and the interplay between active and reactive power markets? (5) What types of decentralized market structures and frameworks—such as fully decentralized, community-based, or network-based peer-to-peer (P2P)—are appropriate for addressing market power in ADN electricity markets? (6) Are traditional market power mitigation methods applicable and effective in the context of ADN electricity markets considering the decentralized nature of the ADN and the dispersed DERs?.
The primary objective of this thesis is to develop a fair and decentralized energy trading platform that limits monopoly power and mitigates market power abuse in ADN electricity markets. To achieve this goal, the thesis proposes an innovative comprehensive process for monitoring, evaluating, and mitigating market power, specially designed for the decentralized structure of ADNs and their market frameworks. This process considers the shifts in network configuration as well as the physical and operational characteristics of ADNs and their components. The process begins by monitoring market power of dominant market participants through introducing the zoning concept. These operational zones narrow down the number of market participants within each zone, addressing the challenge of monitoring a large number of market participants with widely distributed DERs and improving the identification and control of potential market power exercisers, thus minimizing their potential market power. These operational zones serve as decentralized interfaces between the zonal market participants and their corresponding zonal market operators, establishing a decentralized platform for energy trading.
The second stage of the process focuses on evaluating market power through investigating and analyzing the strategic offering behavior of the potential market power exercisers identified in stage one. This analysis is conducted within the framework of a community-based P2P decentralized ADN electricity market, considering the physical and operational characteristics of both the system and DERs, along with the coupled active and reactive power markets. A comparative evaluation of market outcomes under competitive and strategic conditions is performed to identify strategic manipulators. In this context, the study also examines the applicability and effectiveness of conventional market power mitigation techniques used for the centralized market and assesses their impact on the strategic offering behavior of identified manipulators. While some traditional market power mitigation techniques may demonstrate efficiency, a new approach is necessary to address the unique decentralization characteristic of ADN electricity markets.
A novel market power mitigation technique is proposed in the third stage of the process, targeting the root cause of market power: market concentration. This approach introduces an innovative market zoning concept, dynamically partitioning the system into "Market-Zones" to reduce market concentration while adapting to different system operational conditions, considering the uncertainties in system demand and generation, thereby aligning with the decentralized nature of ADNs and their markets. The proposed innovative zoning approach offers a robust solution for mitigating market power in decentralized ADN electricity markets. Within these Market-Zones, each player can actively engage and participate in the market and obtain the benefit without being overtaken by entities with large market shares. Consequently, the market power of the dominant players is subsided and diluted by utilizing the proposed Market-Zones, establishing a fair energy trading platform.