5,559 research outputs found

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Christian Global Citizenship Education in Korea

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    This practice-based research thesis employs theological action research as a methodology to explore the intersection of Global Citizenship Education (GCED) and public theology, with a specific focus on the contributions of Protestant Christianity. The research objectives are to investigate the relevance of Christianity in public life, its potential influence on GCED within the context of South Korea, and to develop practical tools for promoting the application of Christian values in global citizenship education.GCED is widely promoted by international and national governments, but its implementation faces challenges due to diverse perspectives, historicity, and varying economic and political statuses worldwide. Bridging these differences requires dialogue among different groups to determine the knowledge, skills, attitudes, and values necessary for effective engagement. Within this context, Protestant Christianity offers unique insights and contributions.Through key informant interviews with experienced practitioners in educational settings in South Korea, this practice-based research thesis gathers perspectives on integrating Protestant Christian values in GCED and the role of public theology. Employing theological action research practices, the study develops a refined study guide for Christian Global Citizenship Education, incorporating feedback from key informants to ensure its relevance and alignment with the needs of engaged faith communities. The study guide serves as a practical tool for promoting the application of Christian values in global citizenship education.Overall, this practice-based research underscores the importance of integrating biblical values drawn from reflection on scripture and perspectives from Christianity into global education efforts and encourages active participation in discussions on global citizenship. The research aims to make an original contribution to scholarship in this area by contribute by creating a nuanced approach to GCED rooted in the teachings of Protestant Christianity to bridge gaps between different regions and communities, fostering a more inclusive and nuanced approach to GCED rooted in the teachings of Protestant Christianity. The findings of this research have practical implications for educators, policymakers, and faith communities seeking to incorporate spiritual values into the global citizenship education discourse.<br/

    Communicating a Pandemic

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    This edited volume compares experiences of how the Covid-19 pandemic was communicated in the Nordic countries – Denmark, Finland, Iceland, Norway, and Sweden. The Nordic countries are often discussed in terms of similarities concerning an extensive welfare system, economic policies, media systems, and high levels of trust in societal actors. However, in the wake of a global pandemic, the countries’ coping strategies varied, creating certain question marks on the existence of a “Nordic model”. The chapters give a broad overview of crisis communication in the Nordic countries during the first year of the Covid-19 pandemic by combining organisational and societal theoretical perspectives and encompassing crisis response from governments, public health authorities, lobbyists, corporations, news media, and citizens. The results show several similarities, such as political and governmental responses highlighting solidarity and the need for exceptional measures, as expressed in press conferences, social media posts, information campaigns, and speeches. The media coverage relied on experts and was mainly informative, with few critical investigations during the initial phases. Moreover, surveys and interviews show the importance of news media for citizens’ coping strategies, but also that citizens mostly trusted both politicians and health authorities during the crisis. This book is of interest to all who are looking to understand societal crisis management on a comprehensive level. The volume contains chapters from leading experts from all the Nordic countries and is edited by a team with complementary expertise on crisis communication, political communication, and journalism, consisting of Bengt Johansson, Øyvind Ihlen, Jenny Lindholm, and Mark Blach-Ørsten. Publishe

    Mapping the Focal Points of WordPress: A Software and Critical Code Analysis

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    Programming languages or code can be examined through numerous analytical lenses. This project is a critical analysis of WordPress, a prevalent web content management system, applying four modes of inquiry. The project draws on theoretical perspectives and areas of study in media, software, platforms, code, language, and power structures. The applied research is based on Critical Code Studies, an interdisciplinary field of study that holds the potential as a theoretical lens and methodological toolkit to understand computational code beyond its function. The project begins with a critical code analysis of WordPress, examining its origins and source code and mapping selected vulnerabilities. An examination of the influence of digital and computational thinking follows this. The work also explores the intersection of code patching and vulnerability management and how code shapes our sense of control, trust, and empathy, ultimately arguing that a rhetorical-cultural lens can be used to better understand code\u27s controlling influence. Recurring themes throughout these analyses and observations are the connections to power and vulnerability in WordPress\u27 code and how cultural, processual, rhetorical, and ethical implications can be expressed through its code, creating a particular worldview. Code\u27s emergent properties help illustrate how human values and practices (e.g., empathy, aesthetics, language, and trust) become encoded in software design and how people perceive the software through its worldview. These connected analyses reveal cultural, processual, and vulnerability focal points and the influence these entanglements have concerning WordPress as code, software, and platform. WordPress is a complex sociotechnical platform worthy of further study, as is the interdisciplinary merging of theoretical perspectives and disciplines to critically examine code. Ultimately, this project helps further enrich the field by introducing focal points in code, examining sociocultural phenomena within the code, and offering techniques to apply critical code methods

    Editing and Advocacy

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    Good editors don’t just see the sentence that was written. They see the sentence that might have been written. They know how to spot words that shouldn’t be included and summon up ones that haven’t yet appeared. Their value comes not just from preventing mistakes but from discovering new ways to improve a piece of writing’s style, structure, and overall impact. This book— which is based on a popular course taught at the University of Chicago Law School, the University of Michigan Law School, and the UCLA School of Law— is designed to help you become one of those editors. You’ll learn how to edit with empathy. You’ll learn how to edit with statistics. You’ll learn, in short, a wide range of compositional skills you can use to elevate your advocacy and better champion the causes you care about the most. An All-American soccer player in college who holds both a PhD in English and a JD, Professor Patrick Barry joined the University of Michigan Law School after clerking for two federal judges and working in legal clinics devoted to combatting human trafficking and reforming the foster care system. He is the author of several books on advocacy—including Good with Words: Writing and Editing, The Syntax of Sports, and Notes on Nuance—and regularly puts on workshops for law firms, state governments, and nonprofit organizations. He also teaches at the University of Chicago Law School and has developed a series of online courses for the educational platform Coursera.https://repository.law.umich.edu/books/1116/thumbnail.jp

    Declarative Specification of Intraprocedural Control-flow and Dataflow Analysis

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    Static program analysis plays a crucial role in ensuring the quality and security of software applications by detecting and fixing bugs, and potential security vulnerabilities in the code. The use of declarative paradigms in dataflow analysis as part of static program analysis has become increasingly popular in recent years. This is due to its enhanced expressivity and modularity, allowing for a higher-level programming approach, resulting in easy and efficient development.The aim of this thesis is to explore the design and implementation of control-flow and dataflow analyses using the declarative Reference Attribute Grammars formalism. Specifically, we focus on the construction of analyses directly on the source code rather than on an intermediate representation.The main result of this thesis is our language-agnostic framework, called IntraCFG. IntraCFG enables efficient and effective dataflow analysis by allowing the construction of precise and source-level control-flow graphs. The framework superimposes control-flow graphs on top of the abstract syntax tree of the program. The effectiveness of IntraCFG is demonstrated through two case studies, IntraJ and IntraTeal. These case studies showcase the potential and flexibility of IntraCFG in diverse contexts, such as bug detection and education. IntraJ supports the Java programming language, while IntraTeal is a tool designed for teaching program analysis for an educational language, Teal.IntraJ has proven to be faster than and as precise as well-known industrial tools. The combination of precision, performance, and on-demand evaluation in IntraJ leads to low latency in querying the analysis results. This makes IntraJ a suitable tool for use in interactive tools. Preliminary experiments have also been conducted to demonstrate how IntraJ can be used to support interactive bug detection and fixing.Additionally, this thesis presents JFeature, a tool for automatically extracting and summarising the features of a Java corpus, including the use of different Java features (e.g., use of Lambda Expressions) across different Java versions. JFeature provides researchers and developers with a deeper understanding of the characteristics of corpora, enabling them to identify suitable benchmarks for the evaluation of their tools and methodologies

    Microcredentials to support PBL

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    Efficient Deep Learning for Real-time Classification of Astronomical Transients

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    A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will raise the big-data bar for time- domain astronomy, with an expected 10 million alerts per-night, and generating many petabytes of data over the lifetime of the survey. Fast and efficient classification algorithms that can operate in real-time, yet robustly and accurately, are needed for time-critical events where additional resources can be sought for follow-up analyses. In order to handle such data, state-of-the-art deep learning architectures coupled with tools that leverage modern hardware accelerators are essential. The work contained in this thesis seeks to address the big-data challenges of LSST by proposing novel efficient deep learning architectures for multivariate time-series classification that can provide state-of-the-art classification of astronomical transients at a fraction of the computational costs of other deep learning approaches. This thesis introduces the depthwise-separable convolution and the notion of convolutional embeddings to the task of time-series classification for gains in classification performance that are achieved with far fewer model parameters than similar methods. It also introduces the attention mechanism to time-series classification that improves performance even further still, with significant improvement in computational efficiency, as well as further reduction in model size. Finally, this thesis pioneers the use of modern model compression techniques to the field of photometric classification for efficient deep learning deployment. These insights informed the final architecture which was deployed in a live production machine learning system, demonstrating the capability to operate efficiently and robustly in real-time, at LSST scale and beyond, ready for the new era of data intensive astronomy

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
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