4,647 research outputs found
Deep generative models for network data synthesis and monitoring
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
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
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
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
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
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
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
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
- …