1,747 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 â 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
Robust interventions in network epidemiology
Which individual should we vaccinate to minimize the spread of a disease? Designing optimal interventions of this kind can be formalized as an optimization problem on networks, in which we have to select a budgeted number of dynamically important nodes to receive treatment that optimizes a dynamical outcome. Describing this optimization problem requires specifying the network, a model of the dynamics, and an objective for the outcome of the dynamics. In real-world contexts, these inputs are vulnerable to misspecification---the network and dynamics must be inferred from data, and the decision-maker must operationalize some (potentially abstract) goal into a mathematical objective function. Moreover, the tools to make reliable inferences---on the dynamical parameters, in particular---remain limited due to computational problems and issues of identifiability. Given these challenges, models thus remain more useful for building intuition than for designing actual interventions. This thesis seeks to elevate complex dynamical models from intuition-building tools to methods for the practical design of interventions.
First, we circumvent the inference problem by searching for robust decisions that are insensitive to model misspecification.If these robust solutions work well across a broad range of structural and dynamic contexts, the issues associated with accurately specifying the problem inputs are largely moot. We explore the existence of these solutions across three facets of dynamic importance common in network epidemiology.
Second, we introduce a method for analytically calculating the expected outcome of a spreading process under various interventions. Our method is based on message passing, a technique from statistical physics that has received attention in a variety of contexts, from epidemiology to statistical inference.We combine several facets of the message-passing literature for network epidemiology.Our method allows us to test general probabilistic, temporal intervention strategies (such as seeding or vaccination). Furthermore, the method works on arbitrary networks without requiring the network to be locally tree-like .This method has the potential to improve our ability to discriminate between possible intervention outcomes.
Overall, our work builds intuition about the decision landscape of designing interventions in spreading dynamics. This work also suggests a way forward for probing the decision-making landscape of other intervention contexts. More broadly, we provide a framework for exploring the boundaries of designing robust interventions with complex systems modeling tools
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
Predicting Paid Certification in Massive Open Online Courses
Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as âthe Year of the MOOCsâ, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5â13% and 1â3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learnerâs activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learnersâ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar
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classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learnersâ discussion forums to predict paid certification after annotating learnersâ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches â specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learnersâ interaction in discussion forums can predict learnersâ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learnersâ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
Transformer is a deep neural network that employs a self-attention mechanism
to comprehend the contextual relationships within sequential data. Unlike
conventional neural networks or updated versions of Recurrent Neural Networks
(RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in
handling long dependencies between input sequence elements and enable parallel
processing. As a result, transformer-based models have attracted substantial
interest among researchers in the field of artificial intelligence. This can be
attributed to their immense potential and remarkable achievements, not only in
Natural Language Processing (NLP) tasks but also in a wide range of domains,
including computer vision, audio and speech processing, healthcare, and the
Internet of Things (IoT). Although several survey papers have been published
highlighting the transformer's contributions in specific fields, architectural
differences, or performance evaluations, there is still a significant absence
of a comprehensive survey paper encompassing its major applications across
various domains. Therefore, we undertook the task of filling this gap by
conducting an extensive survey of proposed transformer models from 2017 to
2022. Our survey encompasses the identification of the top five application
domains for transformer-based models, namely: NLP, Computer Vision,
Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze
the impact of highly influential transformer-based models in these domains and
subsequently classify them based on their respective tasks using a proposed
taxonomy. Our aim is to shed light on the existing potential and future
possibilities of transformers for enthusiastic researchers, thus contributing
to the broader understanding of this groundbreaking technology
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students
In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies.
To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on).
The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts.
This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution.
The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials.
The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote studentsâ understanding and acceptance of quantum physics as a personal reliable description of the world
AI: Limits and Prospects of Artificial Intelligence
The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
University bulletin 2023-2024
This catalog for the University of South Carolina at Beaufort lists information about the college, the academic calendar, admission policies, degree programs, faculty and course descriptions
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