449 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
Reshaping the Museum of Zoology in Rome by Visual Storytelling and Interactive Iconography
This article summarizes the concept of a new immersive and interactive setting for the Zoology Museum in Rome, Italy. The concept, co-designed with all the museumâs curators, is aimed at enhancing the experiential involvement of the visitors by visual storytelling and interactive iconography. Thanks to immersive and interactive technologies designed by Centro Studi Logos, developed by Logosnet and known as e-REALâ and MirrorMeä, zoological findings and memoirs come to life and interact directly with the visitors in order to deepen their understanding, visualize stories and live experiences, and interact with the founder of the Museum (Mr. Arrigoni degli Oddi) who is now a virtualized avatar, or digital human, able to talk with the visitors. All the interactions are powered through simple hand gestures and, in a few cases, vocal inputs that transform into recognized commands from multimedia systems
Mixed Reality Interfaces for Augmented Text and Speech
While technology plays a vital role in human communication, there still remain many significant challenges when using them in everyday life. Modern computing technologies, such as smartphones, offer convenient and swift access to information, facilitating tasks like reading documents or communicating with friends. However, these tools frequently lack adaptability, become distracting, consume excessive time, and impede interactions with people and contextual information. Furthermore, they often require numerous steps and significant time investment to gather pertinent information. We want to explore an efficient process of contextual information gathering for mixed reality (MR) interfaces that provide information directly in the userâs view. This approach allows for a seamless and flexible transition between language and subsequent contextual references, without disrupting the flow of communication. âAugmented Languageâ can be defined as the integration of language and communication with mixed reality to enhance, transform, or manipulate language-related aspects and various forms of linguistic augmentations (such as annotation/referencing, aiding social interactions, translation, localization, etc.). In this thesis, our broad objective is to explore mixed reality interfaces and their potential to enhance augmented language, particularly in the domains of speech and text. Our aim is to create interfaces that offer a more natural, generalizable, on-demand, and real-time experience of accessing contextually relevant information and providing adaptive interactions. To better address this broader objective, we systematically break it down to focus on two instances of augmented language. First, enhancing augmented conversation to support on-the-fly, co-located in-person conversations using embedded references. And second, enhancing digital and physical documents using MR to provide on-demand reading support in the form of different summarization techniques. To examine the effectiveness of these speech and text interfaces, we conducted two studies in which we asked the participants to evaluate our system prototype in different use cases. The exploratory usability study for the first exploration confirms that our system decreases distraction and friction in conversation compared to smartphone search while providing highly useful and relevant information. For the second project, we conducted an exploratory design workshop to identify categories of document enhancements. We later conducted a user study with a mixed-reality prototype to highlight five board themes to discuss the benefits of MR document enhancement
Northeastern Illinois University, Academic Catalog 2023-2024
https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp
The Creative Surrogate
It is not just manufacturing jobs that are being replaced by digital automation: creative careers now face this specter. As generative techniques advance, both for images and text, the application of expert systems will not stop at replacing mundane tasks. Instead âsmartâ software is making incursions into intellectual fields as diverse as art, design, photography, and authorship. Systematized applications of artificial intelligence are beginning to play new roles in the creative process. Intellectual surrogates are becoming a new front in the centuries-long cultural transformation brought about by technical innovation and automation. Production trends in digital culture suggest that we will be treated, increasingly, to âautomagicalâ software authorship and artistry. If past is prolog, the degree of intellectual dependence on software will be a guarded secret. Human input into creative products may begin to resemble the fruit content of packaged juices, as for example âContains 2% human input.â The time has come to evaluate the likely consequences of the systematized generation of (formerly) creative products
Green Cities Artificial Intelligence
119 pagesIn an era defined by rapid urbanization, the effective planning and
management of cities have become paramount to ensure sustainable
development, efficient resource allocation, and enhanced quality of life
for residents. Traditional methods of urban planning and management
are grappling with the complexities and challenges presented by modern
cities. Enter Artificial Intelligence (AI), a disruptive technology that holds
immense potential to revolutionize the way cities are planned, designed,
and operated.
The primary aim of this report is to provide an in-depth exploration of the
multifaceted role that Artificial Intelligence plays in modern city planning
and management. Through a comprehensive analysis of key AI
applications, case studies, challenges, and ethical considerations, the
report aims to provide resources for urban planners, City staff, and
elected officials responsible for community planning and development.
These include a model City policy, draft informational public meeting
format, AI software and applications, implementation actions, AI
timeline, glossary, and research references. This report represents the
cumulative efforts of many participants and is sponsored by the City of
Salem and Sustainable City Year Program. The Green Cities AI project
website is at: https://blogs.uoregon.edu/artificialintelligence/.
As cities continue to evolve into complex ecosystems, the integration of
Artificial Intelligence stands as a pivotal force in shaping their
trajectories. Through this report, we aim to provide a comprehensive
understanding of how AI is transforming the way cities are planned,
operated, and experienced. By analyzing the tools, applications, and
ethical considerations, we hope to equip policymakers, urban planners,
and stakeholders with the insights needed to navigate the AI-driven
urban landscape effectively and create cities that are not only smart but
also sustainable, resilient, and regenerative.This year's SCYP partnership is possible in part due to support from U.S. Senators Ron Wyden and Jeff Merkley, as well as former Congressman Peter DeFazio, who secured federal funding for SCYP through Congressionally Directed Spending. With additional funding from the city of Salem, the partnerships will allow UO students and faculty to study and make recommendations on city-identified projects and issues
Metaverse. Old urban issues in new virtual cities
Recent years have seen the arise of some early attempts to build virtual cities,
utopias or affective dystopias in an embodied Internet, which in some respects appear to
be the ultimate expression of the neoliberal city paradigma (even if virtual). Although
there is an extensive disciplinary literature on the relationship between planning and
virtual or augmented reality linked mainly to the gaming industry, this often avoids design
and value issues. The observation of some of these early experiences - Decentraland,
Minecraft, Liberland Metaverse, to name a few - poses important questions and problems
that are gradually becoming inescapable for designers and urban planners, and allows
us to make some partial considerations on the risks and potentialities of these early virtual
cities
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Learning from Sequential User Data: Models and Sample-efficient Algorithms
Recent advances in deep learning have made learning representation from ever-growing datasets possible in the domain of vision, natural language processing (NLP), and robotics, among others. However, deep networks are notoriously data-hungry; for example, training language models with attention mechanisms sometimes requires trillions of parameters and tokens. In contrast, we can often access a limited number of samples in many tasks. It is crucial to learn models from these `limited\u27 datasets. Learning with limited datasets can take several forms. In this thesis, we study how to select data samples sequentially such that downstream task performance is maximized. Moreover, we study how to introduce prior knowledge in the deep networks to maximize prediction performance. We focus on four sequential tasks: computerized adaptive testing in psychometrics, sketching in recommender systems, knowledge tracing in computer-assisted education, and career path modeling in the labor market.
In the first two tasks, we devise novel sample-efficient algorithms to query a minimal number of sequential samples to improve future predictions. We propose a Bilevel Optimization-Based framework for computerized adaptive testing to learn a data-driven question selection algorithm that improves existing data selection policies. We also tackle the sketching problem in the recommender system, with the task of recommending the next item using a stored subset of prior data samples. In this setting, we develop a data-driven sequential selection algorithm that tackles evolving downstream task distribution. In the last two tasks, we devise novel neural models to introduce prior knowledge exploiting limited data samples. For knowledge tracing, we propose a novel neural architecture, inspired by cognitive and psychometric models, to improve the prediction of students\u27 future performance and utilize the labeled data samples efficiently. For career path modeling, we propose a novel and interpretable monotonic nonlinear state-space model to analyze online user professional profiles and provide actionable feedback and recommendations to users on how they can reach their career goals.
The data-driven differentiable data selection algorithms for the first two tasks open up future directions to query (a non-differentiable operation) a minimal number of samples optimally to maximize prediction performance. The structures, introduced in the neural architecture for the models in the last two tasks using prior knowledge, open up future directions to learn deep models augmented with prior knowledge using limited data samples
Exploring Digital Portfolios and their Effects on Test Scores
Many educators in the field are looking for ways to grade their students. Many of our youth can learn new concepts and attain academic growth but have difficulties in the traditional lecture-based classroom. Digital portfolio incorporation in our secondary classrooms may be a solution to engaging learners through various interactions using online learning tools, interaction with peers, and their teachers. This exploratory study investigated the current value of digital portfolios in improving academic performance in todayâs classroom. Much of the trend was popular ten years ago. The research explored how current educators in the secondary school setting feel about the incorporation of digital portfolios, and if the learning tool effectively prepares their studentsâ subject understanding prior to an assessment. Data were collected using a research survey that obtained the responses of teachers who volunteered to be a part of this study. Three main questions were directed to educators by this study. Do e-portfolios affect student academic performance? Does the tool improve student subject-matter efficacy? Third, do technical skills have an impact on academic performance and curriculum pacing while using e-portfolios? The collected data had mixed results, with many responses to the survey questions produced data that were inconclusive regarding the effectiveness of digital portfolios. Although the study did not provide enough evidence of digital portfolios as a tool that greatly improves test scores in classes, it is sufficient to say that there is a positive direction from the scores analyzed in the one-month data-collection
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