4,647 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

    Information actors beyond modernity and coloniality in times of climate change:A comparative design ethnography on the making of monitors for sustainable futures in Curaçao and Amsterdam, between 2019-2022

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    In his dissertation, Mr. Goilo developed a cutting-edge theoretical framework for an Anthropology of Information. This study compares information in the context of modernity in Amsterdam and coloniality in Curaçao through the making process of monitors and develops five ways to understand how information can act towards sustainable futures. The research also discusses how the two contexts, that is modernity and coloniality, have been in informational symbiosis for centuries which is producing negative informational side effects within the age of the Anthropocene. By exploring the modernity-coloniality symbiosis of information, the author explains how scholars, policymakers, and data-analysts can act through historical and structural roots of contemporary global inequities related to the production and distribution of information. Ultimately, the five theses propose conditions towards the collective production of knowledge towards a more sustainable planet

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Evaluation Methodologies in Software Protection Research

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    Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 572 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks

    OpenLB User Guide: Associated with Release 1.6 of the Code

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    OpenLB is an object-oriented implementation of LBM. It is the first implementation of a generic platform for LBM programming, which is shared with the open source community (GPLv2). Since the first release in 2007, the code has been continuously improved and extended which is documented by thirteen releases as well as the corresponding release notes which are available on the OpenLB website (https://www.openlb.net). The OpenLB code is written in C++ and is used by application programmers as well as developers, with the ability to implement custom models OpenLB supports complex data structures that allow simulations in complex geometries and parallel execution using MPI, OpenMP and CUDA on high-performance computers. The source code uses the concepts of interfaces and templates, so that efficient, direct and intuitive implementations of the LBM become possible. The efficiency and scalability has been checked and proved by code reviews. This user manual and a source code documentation by DoxyGen are available on the OpenLB project website

    IMPROVING POPULATION HEALTH BY ADDRESSING SOCIAL DETERMINANTS OF MENTAL HEALTH

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    This study examined the social determinants of mental health as influential factors on health outcomes. Three research studies comprised the dissertation. The first study was a systematic review that identified factors linking common mental disorders to the incidence of the four most prevalent non-communicable diseases (NCDs). Interventions to prevent poor health should target smokers, the elderly, women, and individuals with fewer than 12 years of schooling, according to findings. The second mixed-method study found that the pandemic and its control measures negatively impacted social determinants of mental health and health outcomes, with women, children and informal workers in Gaza being most affected. Some of the strategies deployed by the United Nations for the Relief and Works Agency in the Near East (UNRWA), such as the use of telemedicine, warrant further investigation for efficiency and acceptability. The third study assessed UNRWA's mental health and psychosocial support (MHPSS) response addressing the social determinants of mental health during the COVID-19 pandemic. During Group Model Building (GMB) workshops, participants shared their perspectives on what UNRWA did and how it addressed the vulnerabilities of Palestine refugees in Gaza during the health crisis. Findings suggested improving community wellbeing and enhancing staff support for better future pandemic preparedness. The PhD concludes that addressing social determinants of mental health is a joint responsibility between state and non-state actors and that it is necessary to reduce health inequities to lessen the global burden of disease. In addition to rigorous testing and contact tracing, addressing these determinants during crises, for example by distributing financial aid to poor families and strengthening social services, should be bolstered. This is especially important because evidence suggests that enhancing the socioeconomic status of individuals reduces health inequities and improves health outcomes

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    A Structured Testing Framework for ADAS Software Development

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    A major task in the design of automated vehicles is the need to quickly and thoroughly validate a development teams algorithms. There currently exists no explicitly defined common standard for developers working on Advanced Driver Assisted Systems to adopt during their software testing process. Instead different teams customize their testing process specifically to their software systems current needs. Literature indicates that these processes can be comprehensive but convoluted, and not flexible to change as test requirements and the system itself does. This thesis introduces a test framework at the unit, integration, and system test levels with the objective of addressing these challenges through a complete test framework centered around rapid execution and modular test design. At the unit test level a recommendation guide is put forth that is largely aimed at new developers with concrete actionable items that can be integrated into a teams process. For integration and system level testing, a software solution for ROS based development referred to as University of Waterloo Structured Testing Framework (UW-STF) is described in regards to both the benefits it provides as well as its low level implementation details. This includes how to tie the framework into using data generated from the popular simulator CARLA for end-to-end testing of a system. Lastly the test framework is applied to the codebase of UWAFT for their development efforts related to connected and automated vehicles. The framework was shown to increase readability/clarity at the unit test level, facilitate robust automated testing at the integration level and provide transparency on the teams current algorithms performance at the system test level (average F1-score of 0.77 and average OSPA of 2.42). When compared to the standard ROS integration test framework, UW-STF executed the same test suite with 60%+ reduction in lines of code and meaningful differences in CPU and memory requirements
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