440 research outputs found

    Developing IncidentUI -- A Ride Comfort and Disengagement Evaluation Application for Autonomous Vehicles

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    This report details the design, development, and implementation of IncidentUI, an Android tablet application designed to measure user-experienced ride comfort and record disengagement data for autonomous vehicles (AV) during test drives. The goal of our project was to develop an Android application to run on a peripheral tablet and communicate with the Drive Pegasus AGX, the AI Computing Platform for Nvidia's AV Level 2 Autonomy Solution Architecture [1], to detect AV disengagements and report ride comfort. We designed and developed an Android XML-based intuitive user interface for IncidentUI. The development of IncidentUI required a redesign of the system architecture by redeveloping the system communications protocol in Java and implementing the Protocol Buffers (Protobufs) in Java using the existing system Protobuf definitions. The final iteration of IncidentUI yielded the desired functionality while testing on an AV test drive. We also received positive feedback from Nvidia's AV Platform Team during our final IncidentUI demonstration.Comment: Previously embargoed by Nvidia. Nvidia owns the right

    From Human Behavior to Machine Behavior

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    A core pursuit of artificial intelligence is the comprehension of human behavior. Imbuing intelligent agents with a good human behavior model can help them understand how to behave intelligently and interactively in complex situations. Due to the increase in data availability and computational resources, the development of machine learning algorithms for duplicating human cognitive abilities has made rapid progress. To solve difficult scenarios, learning-based methods must search for solutions in a predefined but large space. Along with implementing a smart exploration strategy, the right representation for a task can help narrow the search process during learning. This dissertation tackles three important aspects of machine intelligence: 1) prediction, 2) exploration, and 3) representation. More specifically we develop new algorithms for 1) predicting the future maneuvers or outcomes in pilot training and computer architecture applications; 2) exploration strategies for reinforcement learning in game environments and 3) scene representations for autonomous driving agents capable of handling large numbers of dynamic entities. This dissertation makes the following research contributions in the area of representation learning. First, we introduce a new time series representation for flight trajectories in intelligent pilot training simulations. Second, we demonstrate a method, Temporally Aware Embedding (TAE) for learning an embedding that leverages temporal information extracted from data retrieval series. Third, the dissertation introduces GRAD (Graph Representation for Autonomous Driving) that incorporates the future location of neighboring vehicles into the decision-making process. We demonstrate the usage of our models for pilot training, cache usage prediction, and autonomous driving; however, believe that our new time series representations can be applied to many other types of modeling problems

    NEGOTIATING THE SACRED: UNDERSTANDING IMPACTS TO IKS AND ITEK FROM USE OF REMOTE SENSING AND GIS TECHNOLOGIES WITHIN TRIBAL LANDSCAPES

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    How we see the world and ourselves in relation to it is largely achieved by the lens we are looking through and associated experiences within this relationship. This is additionally true when considering the acknowledged fact that Indigenous Knowledges are derived from natural and cultural sources and these assist in constituting the cultural identities of those Peoples associated with these sources. Presently there is a hunger for access and use of Indigenous Knowledges (IK) as never before seen in public ways, through a national Call for collaborative means to apply these knowledges to such as the issues we globally face as a result of Climate Change. What are Indigenous Knowledges? How are they created? Who holds these and can utilize them in public ways? These questions are an embedded aspect of this Call that requires attention. Further, what impacts exist that benefit, but also challenge, the endeavor to utilize Indigenous Knowledges outside local areas where they are derived? What of these sacred ways of knowing are being negotiated to attain their use? Five areas of concern were identified in response to these questions through application of An Indigenous Research Way (AIRW), a novel continuous improvement model for implementing Indigenous Research Methodologies and Methods, within research design and practice. Synthesizing these concerns into three themes, Education, Technology, and Tribal Leader Decision-Making, awareness was revealed of these as first level and gateway impacts. Indigenous ways of knowing, being, and doing operationalizes Indigenous worldviews about relationality and this as central to how Indigenous Knowledges Systems (IKS) are created and in turn create Indigenous Traditional Ecological Knowledges (ITEK). Understanding how we “see” ourselves in relation to this process is imperative. A burgeoning method for seeing landscapes, and they as sources of IK, is through use of remote sensing and Geographical Information Systems (GIS). This Phase I study, through a Kin-based Case Study and mixed-methods approach, sought to understand impacts to IKS and ITEK from use of these technologies within tribal landscapes through review and assessment of 73 ESRI tribal GIS public StoryMap projects, led by tribal practitioners, accomplished in 2017 - 2021. Assessment provides there exists an assumption that identifying as being Indigenous includes being a holder of cultural knowledges and that these are utilized at will and regularly. The data troubles this assumption with respect to tribal individuals trained as practitioners of these technologies and their use of ITEK then provided through public digital media. Impacts to IKS and ITEK reveal enhancements and also replacement of the “seeing” accomplished by Indigenous People through technological means and the public perceptions of their cultural lifeways and persona of being Holders of Indigenous Knowledges. These impacts are broad in their implications as they attend to not only understandings of past and present access to ITEK but also future applications that brings the conversation into the realms of understanding being Indigenous off-earth

    Development,Validation, and Integration of AI-Driven Computer Vision System and Digital-twin System for Traffic Safety Dignostics

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    The use of data and deep learning algorithms in transportation research have become increasingly popular in recent years. Many studies rely on real-world data. Collecting accurate traffic data is crucial for analyzing traffic safety. Still, traditional traffic data collection methods that rely on loop detectors and radar sensors are limited to collect macro-level data, and it may fail to monitor complex driver behaviors like lane changing and interactions between road users. With the development of new technologies like in-vehicle cameras, Unmanned Aerial Vehicle (UAV), and surveillance cameras, vehicle trajectory data can be collected from the recorded videos for more comprehensive and microscopic traffic safety analysis. This research presents the development, validation, and integration of three AI-driven computer vision systems for vehicle trajectory extraction and traffic safety research: 1) A.R.C.I.S, an automated framework for safety diagnosis utilizing multi-object detection and tracking algorithm for UAV videos. 2)N.M.E.D.S., A new framework with the ability to detect and predict the key points of vehicles and provide more precise vehicle occupying locations for traffic safety analysis. 3)D.V.E.D.S applied deep learning models to extract information related to drivers\u27 visual environment from the Google Street View (GSV) images. Based on the drone video collected and processed by A.R.C.I.S at various locations, CitySim: a new drone recorded vehicle trajectory dataset that aim to facilitate safety research was introduced. CitySim has vehicle interaction trajectories extracted from 1140- minutes of video recordings, which provide a large-scale naturalistic vehicle trajectory that covers a variety of locations, including basic freeway segments, freeway weaving segments, expressway segments, signalized intersections, stop-controlled intersections, and unique intersections without sign/signal control. The advantage of CitySim over other datasets is that it contains more critical safety events in quantity and severity and provides supporting scenarios for safety-oriented research. In addition, CitySim provides digital twin features, including the 3D base maps and signal timings, which enables a more comprehensive testing environment for safety research, such as autonomous vehicle safety. Based on these digital twin features provided by CitySim, we proposed a Digital Twin framework for CV and pedestrian in-the-loop simulation, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). The proposed framework is expected to guide the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development

    Smart Technology Adoption’s Impact on the Value of Logistics Service Providers’ Firms

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    Although it took a pandemic to raise awareness about supply chain issues in the minds of the public at large, industry players have long understood supply chain complexities—particularly in the face of continually evolving technologies and ever-more interconnected global enterprises. With Logistics 4.0 and the rapid developments in smart technologies, these complexities make the ongoing need for technology adoption even more complicated for logistics providers. While the literature regularly reports on the adoption of specific technologies, there is little research on the adoption process and even less that might guide providers in prioritizing their technology targets. This research examined the literature for drivers and consequences of technology adoption among providers, then tested those concepts through in-depth interviews with 40 senior-level executives at global logistics provider firms. Among the study’s findings are that the drivers and consequences of smart technology adoption are similar among logistics providers. However, firm size, business tenure, and client relationships moderate the adoption of these innovations. The study identifies incumbent people, processes, and systems as “excess baggage” that slows adoption because of adjustments needed to accommodate new technologies and creates bottlenecks for these firms. However, when combined with new competencies, streamlined processes, and proper change management, this baggage may improve firm performance because of the legacy processes integrated with customers’ supply chains. The study also developed a framework to inform practitioners’ adoption efforts. The framework addresses the research questions. It also recommends that to realize quicker revenue gains when adopting smart technology. Providers focus on two key drivers: customer relationships and market demands. This research also suggests that providers adopting smart technology leverage their incumbent human resources, processes, and technologies to deliver customer value and improve firm performance

    Monitoring Indonesian online news for COVID-19 event detection using deep learning

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    Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners

    Automatic Data Extraction Utilizing Structural Similarity From A Set of Portable Document Format (PDF) Files

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    Instead of storing data in databases, common computer-aided office workers often choose to keep data related to their work in the form of document or report files that they can conveniently and comfortably access with popular off-the-shelf softwares, such as in Portable Document Format (PDF) format files. Their workplaces may actually use databases but they usually do not possess the privilege nor the proficiency to fully utilize them. Said workplaces likely have front-end systems such as Management Information System (MIS) from where workers get their data containing reports or documents.These documents are meant for immediate or presentational uses but workers often keep these files for the data inside which may come to be useful later on. This way, they can manipulate and combine data from one or more report files to suit their work needs, on the occasions that their MIS were not able to fulfill such needs. To do this, workers need to extract data from the report files. However, the files also contain formatting and other contents such as organization banners, signature placeholders, and so on. Extracting data from these files is not easy and workers are often forced to use repeated copy and paste actions to get the data they want. This is not only tedious but also time-consuming and prone to errors. Automatic data extraction is not new, many existing solutions are available but they typically require human guidance to help the data extraction before it can become truly automatic. They may also require certain expertise which can make workers hesitant to use them in the first place. A particular function of an MIS can produce many report files, each containing distinct data, but still structurally similar. If we target all PDF files that come from such same source, in this paper we demonstrated that by exploiting the similarity it is possible to create a fully automatic data extraction system that requires no human guidance. First, a model is generated by analyzing a small sample of PDFs and then the model is used to extract data from all PDF files in the set. Our experiments show that the system can quickly achieve 100% accuracy rate with very few sample files. Though there are occasions where data inside all the PDFs are not sufficiently distinct from each other resulting in lower than 100% accuracy, this can be easily detected and fixed with slight human intervention. In these cases, total no human intervention may not be possible but the amount needed can be significantly reduced.

    Reducing the labeling effort for entity resolution using distant supervision and active learning

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    Entity resolution is the task of identifying records in one or more data sources which refer to the same real-world object. It is often treated as a supervised binary classification task in which a labeled set of matching and non-matching record pairs is used for training a machine learning model. Acquiring labeled data for training machine learning models is expensive and time-consuming, as it typically involves one or more human annotators who need to manually inspect and label the data. It is thus considered a major limitation of supervised entity resolution methods. In this thesis, we research two approaches, relying on distant supervision and active learning, for reducing the labeling effort involved in constructing training sets for entity resolution tasks with different profiling characteristics. Our first approach investigates the utility of semantic annotations found in HTML pages as a source of distant supervision. We profile the adoption growth of semantic annotations over multiple years and focus on product-related schema.org annotations. We develop a pipeline for cleansing and grouping semantically annotated offers describing the same products, thus creating the WDC Product Corpus, the largest publicly available training set for entity resolution. The high predictive performance of entity resolution models trained on offer pairs from the WDC Product Corpus clearly demonstrates the usefulness of semantic annotations as distant supervision for product-related entity resolution tasks. Our second approach focuses on active learning techniques, which have been widely used for reducing the labeling effort for entity resolution in related work. Yet, we identify two research gaps: the inefficient initialization of active learning and the lack of active learning methods tailored to multi-source entity resolution. We address the first research gap by developing an unsupervised method for initializing and further assisting the complete active learning workflow. Compared to active learning baselines that use random sampling or transfer learning for initialization, our method guarantees high anytime performance within a limited labeling budget for tasks with different profiling characteristics. We address the second research gap by developing ALMSER, the first active learning method which uses signals inherent to multi-source entity resolution tasks for query selection and model training. Our evaluation results indicate that exploiting such signals for query selection alone has a varying effect on model performance across different multi-source entity resolution tasks. We further investigate this finding by analyzing the impact of the profiling characteristics of multi-source entity resolution tasks on the performance of active learning methods which use different signals for query selection

    Texas Register

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    A weekly publication, the Texas Register serves as the journal of state agency rulemaking for Texas. Information published in the Texas Register includes proposed, adopted, withdrawn and emergency rule actions, notices of state agency review of agency rules, governor's appointments, attorney general opinions, and miscellaneous documents such as requests for proposals. After adoption, these rulemaking actions are codified into the Texas Administrative Code

    Frasar til besvĂŠr? Studiar av norm og bruk i norsk fraseologi

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    The dissertation deals with set phrases in the two written standards of Norwegian, viz. BokmĂ„l and Nynorsk. Phraseology has not been a field of extensive research in Norwegian linguistics, and the present work deals with some of the fundamental aspects regarding norm, represented by dictionaries, and observable language use in the two varieties. The first half of the dissertation aims at establishing a background for the interpretation and explanation of phraseological differences in two written varieties of the same language. The second part of the dissertation consists of two empirical studies. The first study has a broad perspective and aims to survey variation in the form of 54 phrases. Their treatment in four Norwegian dictionaries is compared to their use as documented in corpora of written BokmĂ„l and Nynorsk. Due to the quite unique linguistic situation with two written varieties, both exhibiting considerable internal orthographical and morphological variation, phraseological variation in lexicon, orthography, morphology and syntax is widespread. Besides, one can observe a growing acceptance among many Norwegian language users, especially of Nynorsk, to exploit elements from their local dialects in their written texts – leading to even more phraseological variation. The second empirical work is an in-depth study of variation, creative modification and divergent use of kaste barnet ut med badevatnet (‘throw out the baby with the bathwater’). The study is based on 460 unique examples in BokmĂ„l and 373 examples in Nynorsk excerpted from 10 different text corpora and other digital text collections. The phrase is open to substitution of almost all of its components and allows most kinds of syntactic transformatio without losing its meaning. The differences between BokmĂ„l and Nynorsk in how the phrase is used have decreased through time, but the use of creative modification has increased in both varieties.Doktorgradsavhandlin
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