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Letteratura scientifica selezionata sul tema "Model annotation"

Autore: Grafiati

Pubblicato: 31 agosto 2024

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Articoli di riviste sul tema "Model annotation"

Liu, Zheng. "LDA-Based Automatic Image Annotation Model". Advanced Materials Research 108-111 (maggio 2010): 88–94. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.88.

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This paper presents LDA-based automatic image annotation by visual topic learning and related annotation extending. We introduce the Latent Dirichlet Allocation (LDA) model in visual application domain. Firstly, the visual topic which is most relevant to the unlabeled image is obtained. According to this visual topic, the annotations with highest likelihood serve as seed annotations. Next, seed annotations are extended by analyzing the relationship between seed annotations and related Flickr tags. Finally, we combine seed annotations and extended annotations to construct final annotation set. Experiments conducted on corel5k dataset demonstrate the effectiveness of the proposed model.

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Paun, Silviu, Bob Carpenter, Jon Chamberlain, Dirk Hovy, Udo Kruschwitz e Massimo Poesio. "Comparing Bayesian Models of Annotation". Transactions of the Association for Computational Linguistics 6 (dicembre 2018): 571–85. http://dx.doi.org/10.1162/tacl_a_00040.

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The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, majority voting was used for 1, and coefficients of agreement for 2 and 3. Lately, model-based analysis of corpus annotations have proven better at all three tasks. But there has been relatively little work comparing them on the same datasets. This paper aims to fill this gap by analyzing six models of annotation, covering different approaches to annotator ability, item difficulty, and parameter pooling (tying) across annotators and items. We evaluate these models along four aspects: comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four datasets with varying degrees of noise in the form of random (spammy) annotators. We conclude with guidelines for model selection, application, and implementation.

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Misirli, Goksel, Matteo Cavaliere, William Waites, Matthew Pocock, Curtis Madsen, Owen Gilfellon, Ricardo Honorato-Zimmer, Paolo Zuliani, Vincent Danos e Anil Wipat. "Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization". Bioinformatics 32, n. 6 (11 novembre 2015): 908–17. http://dx.doi.org/10.1093/bioinformatics/btv660.

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Abstract Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. Availability and implementation: The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo. The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf. Contact: anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk

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Li, Huadong, Ying Wei, Han Peng e Wei Zhang. "DiffuPrompter: Pixel-Level Automatic Annotation for High-Resolution Remote Sensing Images with Foundation Models". Remote Sensing 16, n. 11 (2 giugno 2024): 2004. http://dx.doi.org/10.3390/rs16112004.

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Instance segmentation is pivotal in remote sensing image (RSI) analysis, aiding in many downstream tasks. However, annotating images with pixel-wise annotations is time-consuming and laborious. Despite some progress in automatic annotation, the performance of existing methods still needs improvement due to the high precision requirements for pixel-level annotation and the complexity of RSIs. With the support of large-scale data, some foundational models have made significant progress in semantic understanding and generalization capabilities. In this paper, we delve deep into the potential of the foundational models in automatic annotation and propose a training-free automatic annotation method called DiffuPrompter, achieving pixel-level automatic annotation of RSIs. Extensive experimental results indicate that the proposed method can provide reliable pseudo-labels, significantly reducing the annotation costs of the segmentation task. Additionally, the cross-domain validation experiments confirm the powerful effectiveness of large-scale pseudo-data in improving model generalization performance.

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Chu, Zhendong, Jing Ma e Hongning Wang. "Learning from Crowds by Modeling Common Confusions". Proceedings of the AAAI Conference on Artificial Intelligence 35, n. 7 (18 maggio 2021): 5832–40. http://dx.doi.org/10.1609/aaai.v35i7.16730.

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Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost. However, the annotation quality of annotators varies considerably, which imposes new challenges in learning a high-quality model from the crowdsourced annotations. In this work, we provide a new perspective to decompose annotation noise into common noise and individual noise and differentiate the source of confusion based on instance difficulty and annotator expertise on a per-instance-annotator basis. We realize this new crowdsourcing model by an end-to-end learning solution with two types of noise adaptation layers: one is shared across annotators to capture their commonly shared confusions, and the other one is pertaining to each annotator to realize individual confusion. To recognize the source of noise in each annotation, we use an auxiliary network to choose from the two noise adaptation layers with respect to both instances and annotators. Extensive experiments on both synthesized and real-world benchmarks demonstrate the effectiveness of our proposed common noise adaptation solution.

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Rotman, Guy, e Roi Reichart. "Multi-task Active Learning for Pre-trained Transformer-based Models". Transactions of the Association for Computational Linguistics 10 (2022): 1209–28. http://dx.doi.org/10.1162/tacl_a_00515.

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Abstract Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predictions when the tasks are inter-related. This technique, however, requires annotating the same text with multiple annotation schemes, which may be costly and laborious. Active learning (AL) has been demonstrated to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model. Yet, multi-task active learning (MT-AL) has not been applied to state-of-the-art pre-trained Transformer-based NLP models. This paper aims to close this gap. We explore various multi-task selection criteria in three realistic multi-task scenarios, reflecting different relations between the participating tasks, and demonstrate the effectiveness of multi-task compared to single-task selection. Our results suggest that MT-AL can be effectively used in order to minimize annotation efforts for multi-task NLP models.1

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Luo, Yan, Tianxiu Lu, Weihan Zhang, Suiqun Li e Xuefeng Wang. "Augmenting Three-Dimensional Model Annotation System with Enhanced Reality". Journal of Computing and Electronic Information Management 12, n. 2 (30 marzo 2024): 1–7. http://dx.doi.org/10.54097/uv15ws76.

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This study proposes an augmented reality-based three-dimensional model annotation system, integrating cloud anchors, three-dimensional reconstruction, and augmented reality technology to achieve explicit three-dimensional annotations on models. Employing an improved ORB algorithm, the annotated model is persistently anchored in three-dimensional space through cloud anchors, presenting accurate spatial information and showcasing the depth of scenes and relationships between elements. The system supports multiple data types for annotations, such as text and images. Through a comparison with traditional two-dimensional annotation in a drone experiment, the system demonstrates higher experimental efficiency, providing more intuitive annotation guidance and enhancing remote guidance efficiency and user understanding of drones.

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Filali, Jalila, Hajer Baazaoui Zghal e Jean Martinet. "Ontology-Based Image Classification and Annotation". International Journal of Pattern Recognition and Artificial Intelligence 34, n. 11 (16 marzo 2020): 2040002. http://dx.doi.org/10.1142/s0218001420400029.

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With the rapid growth of image collections, image classification and annotation has been active areas of research with notable recent progress. Bag-of-Visual-Words (BoVW) model, which relies on building visual vocabulary, has been widely used in this area. Recently, attention has been shifted to the use of advanced architectures which are characterized by multi-level processing. Hierarchical Max-Pooling (HMAX) model has attracted a great deal of attention in image classification. To improve image classification and annotation, several approaches based on ontologies have been proposed. However, image classification and annotation remain a challenging problem due to many related issues like the problem of ambiguity between classes. This problem can affect the quality of both classification and annotation results. In this paper, we propose an ontology-based image classification and annotation approach. Our contributions consist of the following: (1) exploiting ontological relationships between classes during both image classification and annotation processes; (2) combining the outputs of hypernym–hyponym classifiers to lead to a better discrimination between classes; and (3) annotating images by combining hypernym and hyponym classification results in order to improve image annotation and to reduce the ambiguous and inconsistent annotations. The aim is to improve image classification and annotation by using ontologies. Several strategies have been experimented, and the obtained results have shown that our proposal improves image classification and annotation.

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Wu, Xian, Wei Fan e Yong Yu. "Sembler: Ensembling Crowd Sequential Labeling for Improved Quality". Proceedings of the AAAI Conference on Artificial Intelligence 26, n. 1 (20 settembre 2021): 1713–19. http://dx.doi.org/10.1609/aaai.v26i1.8351.

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Many natural language processing tasks, such as named entity recognition (NER), part of speech (POS) tagging, word segmentation, and etc., can be formulated as sequential data labeling problems. Building a sound labeler requires very large number of correctly labeled training examples, which may not always be possible. On the other hand, crowdsourcing provides an inexpensive yet efficient alternative to collect manual sequential labeling from non-experts. However the quality of crowd labeling cannot be guaranteed, and three kinds of errors are typical: (1) incorrect annotations due to lack of expertise (e.g., labeling gene names from plain text requires corresponding domain knowledge); (2) ignored or omitted annotations due to carelessness or low confidence; (3) noisy annotations due to cheating or vandalism. To correct these mistakes, we present Sembler, a statistical model for ensembling crowd sequential labelings. Sembler considers three types of statistical information: (1) the majority agreement that proves the correctness of an annotation; (2) correct annotation that improves the credibility of the corresponding annotator; (3) correct annotation that enhances the correctness of other annotations which share similar linguistic or contextual features. We evaluate the proposed model on a real Twitter and a synthetical biological data set, and find that Sembler is particularly accurate when more than half of annotators make mistakes.

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VanBerlo, Bennett, Delaney Smith, Jared Tschirhart, Blake VanBerlo, Derek Wu, Alex Ford, Joseph McCauley et al. "Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View". Diagnostics 12, n. 10 (28 settembre 2022): 2351. http://dx.doi.org/10.3390/diagnostics12102351.

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Background: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. Methods: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view. Results: In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in 55±6 additional relevant labels per hour. Conclusions: Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

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Più fonti

Tesi sul tema "Model annotation"

Hu, Rong (RongRong). "Image annotation with discriminative model and annotation refinement by visual similarity matching". Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/61311.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 65-67).
A large percentage of photos on the Internet cannot be reached by search engines because of the absence of textual metadata. Such metadata come from description and tags of the photos by their uploaders. Despite of decades of research, neither model based and model-free approaches can provide quality annotation to images. In this thesis, I present a hybrid annotation pipeline that combines both approaches in hopes of increasing the accuracy of the resulting annotations. Given an unlabeled image, the first step is to suggest some words via a trained model optimized for retrieval of images from text. Though the trained model cannot always provide highly relevant words, they can be used as initial keywords to query a large web image repository and obtain text associated with retrieved images. We then use perceptual features (e.g., color, texture, shape, and local characteristics) to match the retrieved images with the query photo and use visual similarity to rank the relevance of suggested annotations for the query photo.
by Rong Hu.
M.Eng.

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Balachandar, Shreerekha. "Back annotation for conceptual structures". Thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06112009-063732/.

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Elias, Mturi. "Design of Business Process Model Repositories : Requirements, Semantic Annotation Model and Relationship Meta-model". Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-117035.

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Business process management is fast becoming one of the most important approaches for designing contemporary organizations and information systems. A critical component of business process management is business process modelling. It is widely accepted that modelling of business processes from scratch is a complex, time-consuming and error-prone task. However the efforts made to model these processes are seldom reused beyond their original purpose. Reuse of business process models has the potential to overcome the challenges of modelling business processes from scratch. Process model repositories, properly populated, are certainly a step toward supporting reuse of process models. This thesis starts with the observation that the existing process model repositories for supporting process model reuse suffer from several shortcomings that affect their usability in practice. Firstly, most of the existing repositories are proprietary, therefore they can only be enhanced or extended with new models by the owners of the repositories. Secondly, it is difficult to locate and retrieve relevant process models from a large collection. Thirdly, process models are not goal related, thereby making it difficult to gain an understanding of the business goals that are realized by a certain model. Finally, process model repositories lack a clear mechanism to identify and define the relationship between business processes and as a result it is difficult to identify related processes. Following a design science research paradigm, this thesis proposes an open and language-independent process model repository with an efficient retrieval system to support process model reuse. The proposed repository is grounded on four original and interrelated contributions: (1) a set of requirements that a process model repository should possess to increase the probability of process model reuse; (2) a context-based process semantic annotation model for semantically annotating process models to facilitate effective retrieval of process models; (3) a business process relationship meta-model for identifying and defining the relationship of process models in the repository; and (4) architecture of a process model repository for process model reuse. The models and architecture produced in this thesis were evaluated to test their utility, quality and efficacy. The semantic annotation model was evaluated through two empirical studies using controlled experiments. The conclusion drawn from the two studies is that the annotation model improves searching, navigation and understanding of process models. The process relationship meta-model was evaluated using an informed argument to determine the extent to which it meets the established requirements. The results of the analysis revealed that the meta-model meets the established requirements. Also the analysis of the architecture against the requirements indicates that the architecture meets the established requirements.
Processhantering, också kallat ärendehantering, har blivit en av de viktigaste ansatserna för att utforma dagens organisationer och informationssystem. En central komponent i processhantering är processmodellering. Det är allmänt känt att modellering av processer kan vara en komplex, tidskrävande och felbenägen uppgift. Och de insatser som görs för att modellera processer kan sällan användas bortom processernas ursprungliga syfte. Återanvändning av processmodeller skulle kunna övervinna många av de utmaningar som finns med att modellera processer. En katalog över processmodeller är ett steg mot att stödja återanvändning av processmodeller. Denna avhandling börjar med observationen att befintliga processmodellkataloger för att stödja återanvändning av processmodeller lider av flera brister som påverkar deras användbarhet i praktiken. För det första är de flesta processmodellkatalogerna proprietära, och därför kan endast katalogägarna förbättra eller utöka dem med nya modeller. För det andra är det svårt att finna och hämta relevanta processmodeller från en stor katalog. För det tredje är processmodeller inte målrelaterade, vilket gör det svårt att få en förståelse för de affärsmål som realiseras av en viss modell. Slutligen så saknar processmodellkataloger ofta en tydlig mekanism för att identifiera och definiera förhållandet mellan processer, och därför är det svårt att identifiera relaterade processer. Utifrån ett designvetenskapligt forskningsparadigm så föreslår denna avhandling en öppen och språkoberoende processmodellkatalog med ett effektivt söksystem för att stödja återanvändning av processmodeller. Den föreslagna katalogen bygger på fyra originella och inbördes relaterade bidrag: (1) en uppsättning krav som en processmodellkatalog bejöver uppfylla för att öka möjligheterna till återanvändning av processmodeller; (2) en kontextbaserad semantisk processannoteringsmodell för semantisk annotering av processmodeller för att underlätta effektivt återvinnande av processmodeller; (3) en metamodell för processrelationer för att identifiera och definiera förhållandet mellan processmodeller i katalogen; och (4) en arkitektur av en processmodellkatalog för återanvändning av processmodeller. De modeller och den arkitektur som tagits fram i denna avhandling har utvärderats för att testa deras användbarhet, kvalitet och effektivitet. Den semantiska annotationsmodellen utvärderades genom två empiriska studier med kontrollerade experiment. Slutsatsen av de två studierna är att modellen förbättrar sökning, navigering och förståelse för processmodeller. Metamodellen för processrelationer utvärderades med hjälp av ett informerat argument för att avgöra i vilken utsträckning den uppfyllde de ställda kraven. Resultaten av analysen visade att metamodellen uppfyllde dessa krav. Även analysen av arkitekturen indikerade att denna uppfyllde de fastställda kraven.

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Jones, Martin Robert. "Deep metabolome annotation of the freshwater model species, Daphnia magna". Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7984/.

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In the 21st century - the era of big data science - chemical risk assessment procedures remain woefully dependent upon a suite of basic toxicological assays that offer little, if any, biochemical information pertaining to the underlying mechanism of toxicity. Metabolomics, defined as the holistic study of all naturally occurring, low molecular weight metabolites present within a biological system, holds huge potential as a tool to fill this knowledge gap, and thereby, to revolutionise the chemical risk assessment process through provision of rich molecular information . Owing to on-going challenges in the area of metabolite identification, however, which ultimately serves to impede derivation of biological knowledge from metabolomics data sets, the full potential of the metabolomics platform has yet to be realised in the context of (eco-)toxicological research. In this thesis, I present the experiments undertaken in establishing a bespoke bioanalytical workflow specifically designed and optimised to resolve this bottleneck. Ultimately, I demonstrate application of select components of this workflow in the characterisation of the metabolome of D. magna, a model organism for eco-toxicological research.

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Khalili, Ali. "A Semantics-based User Interface Model for Content Annotation, Authoring and Exploration". Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-159956.

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The Semantic Web and Linked Data movements with the aim of creating, publishing and interconnecting machine readable information have gained traction in the last years. However, the majority of information still is contained in and exchanged using unstructured documents, such as Web pages, text documents, images and videos. This can also not be expected to change, since text, images and videos are the natural way in which humans interact with information. Semantic structuring of content on the other hand provides a wide range of advantages compared to unstructured information. Semantically-enriched documents facilitate information search and retrieval, presentation, integration, reusability, interoperability and personalization. Looking at the life-cycle of semantic content on the Web of Data, we see quite some progress on the backend side in storing structured content or for linking data and schemata. Nevertheless, the currently least developed aspect of the semantic content life-cycle is from our point of view the user-friendly manual and semi-automatic creation of rich semantic content. In this thesis, we propose a semantics-based user interface model, which aims to reduce the complexity of underlying technologies for semantic enrichment of content by Web users. By surveying existing tools and approaches for semantic content authoring, we extracted a set of guidelines for designing efficient and effective semantic authoring user interfaces. We applied these guidelines to devise a semantics-based user interface model called WYSIWYM (What You See Is What You Mean) which enables integrated authoring, visualization and exploration of unstructured and (semi-)structured content. To assess the applicability of our proposed WYSIWYM model, we incorporated the model into four real-world use cases comprising two general and two domain-specific applications. These use cases address four aspects of the WYSIWYM implementation: 1) Its integration into existing user interfaces, 2) Utilizing it for lightweight text analytics to incentivize users, 3) Dealing with crowdsourcing of semi-structured e-learning content, 4) Incorporating it for authoring of semantic medical prescriptions.

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Banks, Russell K. "Annotation Tools for Multivariate Gene Set Testing of Non-Model Organisms". DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4515.

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Many researchers across a wide range of disciplines have turned to gene expression anal- ysis to aid in predicting and understanding biological outcomes and mechanisms. Because genes are known to work in a dependent manner, it’s common for researchers to first group genes in biologically meaningful sets and then test each gene set for differential expression. Comparisons are made across different treatment/condition groups. The meta-analytic method for testing differential activity of gene sets, termed multi-variate gene set testing (mvGST), will be used to provide context for two persistent and problematic issues in gene set testing. These are: 1) gathering organism specific annotation for non-model organisms and 2) handling gene annotation ambiguities. The primary purpose of this thesis is to explore different gene annotation gathering methods in the building of gene set lists and to address the problem of gene annotation ambiguity. Using an example study, three different annotation gathering methods are proposed to construct GO gene set lists. These lists are directly compared, as are the subsequent results from mvGST analysis. In a separate study, an optimization algorithm is proposed as a solution for handling gene annotation ambiguities.

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Harmon, Trev R. "On-Line Electronic Document Collaboration and Annotation". Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1589.pdf.

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Basu, Roy Sreeya. "Automated Annotation of Simulink Generated C Code Based on the Simulink Model". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284564.

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There has been a wave of transformation in the automotive industry in recent years, with most vehicular functions being controlled electron- ically instead of mechanically. This has led to an exponential increase in the complexity of software functions in vehicles, making it essential for manufactures to guarantee their correctness. Traditional software testing is reaching its limits, consequently pushing the automotive in- dustry to explore other forms of quality assurance. One such technique that has been gaining momentum over the years is a set of verification techniques based on mathematical logic called formal verification tech- niques. Although formal techniques have not yet been adopted on a large scale, these methods offer systematic and possibly more exhaus- tive verification of the software under test, since their fundamentals are based on the principles of mathematics.In order to be able to apply formal verification, the system under test must be transformed into a formal model, and a set of proper- ties over such models, which can then be verified using some of the well-established formal verification techniques, such as model check- ing or deductive verification. This thesis focuses on formal verification of automatically generated C code based on Simulink models using deductive verification techniques. More specifically, the aim is to ex- plore whether the generated code can be automatically annotated using the underlying Simulink model as an executable specification, thereby making it suitable for verification using state-of-the-art tools. Our in- vestigation of Simulink generated C code shows that the same can be annotated using the corresponding Simulink model as an executable specification. Consequently, we propose an algorithm that automates the annotation generation and their injection into C code for a specific class of Simulink models and code generated under specific conditions. Successful verification would mean that the code satisfies all functional properties of the model irrespective of the code generator used.We validate our approach on a prototype implementation of a Brake-by-Wire (BBW) functionality of heavy load vehicles. Most of the functional properties of the generated code were satisfied.
Det har skett en våg av omvandling inom fordonsindustrin i de senas- te åren, där de flesta fordonsfunktioner nu styrs elektroniskt istället för mekaniskt. Detta har lett till en exponentiell ökning i komplexite- ten av programvarufunktioner i fordon, vilket gör det väsentligt för tillverkare att kunna garantera deras korrekthet. Traditionell testning av programvara är nära sina gränser och driver därför bilindustrin att utforska andra former av kvalitetssäkring. En teknik som har fått fart genom åren är en uppsättning verifieringstekniker baserade på matematisk logik som kallas formella verifikationstekniker. Även om formella tekniker ännu inte har införts på en stor skala erbjuder dessa metoder systematisk och möjligen mer genomgripande verifiering av programvaran som testas, eftersom dess grund är baserad på mate- matikens principer. För att kunna tillämpa formell verifiering behöver systemet som testas omvandlas till en formell modell och en upp- sättning egenskaper över sådana modeller, som sedan kan verifieras med hjälp av några väletablerade formella verifieringstekniker, såsom modellkontroll eller deduktiv verifiering. Denna avhandling fokuse- rar på formell verifiering av automatiskt genererad C-kod baserad på Simulink-modeller med hjälp av deduktiva verifieringstekniker. Mer specifikt är syftet att undersöka om den genererade koden automa- tiskt kan antecknas med den underliggande Simulink-modellen som en körbar specifikation, vilket då skulle göra den lämplig för verifiering med toppmoderna verktyg. Vår undersökning av Simulink-genererad C-kod visar att samma sak kan kommenteras när den motsvarande Simulink-modellen används som en körbar specifikation. Följaktligen föreslår vi en algoritm som automatiserar anteckningsgenereringen och dess injektion i C-kod för en specifik klass av Simulink-modeller och kod genererad under specifika förhållanden. Lyckad verifiering skulle betyda att koden uppfyller alla funktionella egenskaper av mo- dellen oavsett vilken kodgenerator som används.Vi validerar vår strategi med en prototypimplementering av enBrake-by-Wire (BBW) -funktionalitet för tunga lastbilar. De mesta funk- tionella egenskaperna för den genererade koden var uppfyllda.

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Dorribo, Camba Jorge. "ANNOTATION MECHANISMS TO MANAGE DESIGN KNOWLEDGE IN COMPLEX PARAMETRIC MODELS AND THEIR EFFECTS ON ALTERATION AND REUSABILITY". Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/45997.

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El proyecto de investigación propuesto se enmarca dentro del área de diseño de producto con aplicaciones de modelado sólido CAD/CAM (Computer Aided Design/Computer Aided Manufacturing). Concretamente, se pretende hacer un estudio de las herramientas de anotación asociativas disponibles en las aplicaciones comerciales de modelado CAD con el fin de analizar su uso, viabilidad, eficiencia y efectos en la modificación y reutilización de modelos digitales 3D, así como en la gestión y comunicación del conocimiento técnico vinculado al diseño. La idea principal de esta investigación doctoral es establecer un método para representar y evaluar el conocimiento implícito de los ingenieros de diseño acerca de un modelo digital, así como la integración dinámica de dicho conocimiento en el propio modelo CAD, a través de anotaciones, con el objetivo de poder almacenar y comunicar eficientemente la mayor cantidad de información útil acerca del modelo, y reducir el tiempo y esfuerzo requeridos para su alteración y/o reutilización.
Dorribo Camba, J. (2014). ANNOTATION MECHANISMS TO MANAGE DESIGN KNOWLEDGE IN COMPLEX PARAMETRIC MODELS AND THEIR EFFECTS ON ALTERATION AND REUSABILITY [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/45997
TESIS

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Li, Honglin. "Hierarchical video semantic annotation the vision and techniques /". Connect to this title online, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1071863899.

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Thesis (Ph. D.)--Ohio State University, 2003.
Title from first page of PDF file. Document formatted into pages; contains xv, 146 p.; also includes graphics. Includes bibliographical references (p. 136-146).

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Libri sul tema "Model annotation"

Oliver J. Bear Don't Walk. Inferring Race and Ethnicity from Clinical Notes: Annotation, Model Auditing, and Ethical Implications. [New York, N.Y.?]: [publisher not identified], 2022.

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Wallace, Don. Model foreign investment law: With annotations. Washington, D.C: International Law Institute, 1996.

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F, Del Duca Louis, e American Bar Association. Section of Urban, State, and Local Government Law., a cura di. Annotations to the Model procurement code for state and local governments, with analytical summary of state enactments. 3^a ed. Chicago, Ill: Section of State and Local Government Law, American Bar Association, 1996.

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Padó, Sebastian. Cross-lingual annotation projection models for role-semantic information. Saarbrücken: Saarland University, 2007.

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American Bar Association. Section of Business Law, a cura di. Model business corporation act annotated: Model business corporation act with official comments and reporter's annotations. 2^a ed. Chicago, IL: Section of Business Law, American Bar Association, 2013.

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American Bar Association. Committee on Corporate Laws. Model business corporation act annotated: Model business corporation act with official comment and reporter's annotations : model close corporation supplement : model professional corporation supplement. Chicago, IL: The Section, 2008.

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American Bar Association. Committee on Corporate Laws. e American Bar Foundation, a cura di. Model Business Corporation Act annotated: Model Business Corporation Act with official comment and reporter's annotations : Model Close Corporation supplement, Model Professional Corporation supplement. 3^a ed. Chicago: Section of Business Law, American Bar Association, 1998.

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J, Falvey Patrick, Adler Theodore A e American Bar Association. Section of Urban, State, and Local Government Law., a cura di. Annotations to the Model procurement code for state and local governments. [Chicago, IL]: Section of Urban, State, and Local Government Law, American Bar Association, 1987.

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Brants, Thorsten. Tagging and parsing with cascaded Markov models: Automation of corpus annotation. Saarbrücken: DFKI, 1999.

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Ronnberg, Erik A. R. Pilot schooner Hesper of Boston, 1884: Plans with annotations and descriptive keys. Rockport, Mass. (P.O. Box 410, Rockport 01966-0410): E.A.R. Ronnberg, Jr., 1994.

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Pustejovsky, James, Harry Bunt e Annie Zaenen. "Designing Annotation Schemes: From Theory to Model". In Handbook of Linguistic Annotation, 21–72. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-0881-2_2.

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Ide, Nancy, Christian Chiarcos, Manfred Stede e Steve Cassidy. "Designing Annotation Schemes: From Model to Representation". In Handbook of Linguistic Annotation, 73–111. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-0881-2_3.

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Kralj Novak, Petra, Teresa Scantamburlo, Andraž Pelicon, Matteo Cinelli, Igor Mozetič e Fabiana Zollo. "Handling Disagreement in Hate Speech Modelling". In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 681–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08974-9_54.

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AbstractHate speech annotation for training machine learning models is an inherently ambiguous and subjective task. In this paper, we adopt a perspectivist approach to data annotation, model training and evaluation for hate speech classification. We first focus on the annotation process and argue that it drastically influences the final data quality. We then present three large hate speech datasets that incorporate annotator disagreement and use them to train and evaluate machine learning models. As the main point, we propose to evaluate machine learning models through the lens of disagreement by applying proper performance measures to evaluate both annotators’ agreement and models’ quality. We further argue that annotator agreement poses intrinsic limits to the performance achievable by models. When comparing models and annotators, we observed that they achieve consistent levels of agreement across datasets. We reflect upon our results and propose some methodological and ethical considerations that can stimulate the ongoing discussion on hate speech modelling and classification with disagreement.

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Zhao, Yong, Michael Wilde e Ian Foster. "Applying the Virtual Data Provenance Model". In Provenance and Annotation of Data, 148–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_16.

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Mannai, Zayneb, Anis Kalboussi e Ahmed Hadj Kacem. "An Approach for Modeling Annotation in the e-Health Domain". In Digital Health Transformation, Smart Ageing, and Managing Disability, 269–81. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43950-6_24.

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AbstractIn our research, we established a medical annotation model in the form of an ontology in an effort to ensure data interchange amongst medical annotation systems. We employ the “patient partner” approach to involve the patient in the medical annotative activity. In fact, the patient will be able to register, annotate, and comprehend the comments made in his medical file utilizing this new paradigm.

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Buneman, Peter, Adriane Chapman, James Cheney e Stijn Vansummeren. "A Provenance Model for Manually Curated Data". In Provenance and Annotation of Data, 162–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_17.

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Cohen, K. Bretonnel, Karin Verspoor, Karën Fort, Christopher Funk, Michael Bada, Martha Palmer e Lawrence E. Hunter. "The Colorado Richly Annotated Full Text (CRAFT) Corpus: Multi-Model Annotation in the Biomedical Domain". In Handbook of Linguistic Annotation, 1379–94. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-0881-2_53.

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Aldinucci, Marco, Sonia Campa, Peter Kilpatrick, Fabio Tordini e Massimo Torquati. "An Abstract Annotation Model for Skeletons". In Formal Methods for Components and Objects, 257–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35887-6_14.

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Bowers, Shawn, Timothy McPhillips, Bertram Ludäscher, Shirley Cohen e Susan B. Davidson. "A Model for User-Oriented Data Provenance in Pipelined Scientific Workflows". In Provenance and Annotation of Data, 133–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_15.

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Leung, Man-Kit, Thomas Mandl, Edward A. Lee, Elizabeth Latronico, Charles Shelton, Stavros Tripakis e Ben Lickly. "Scalable Semantic Annotation Using Lattice-Based Ontologies". In Model Driven Engineering Languages and Systems, 393–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04425-0_31.

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Atti di convegni sul tema "Model annotation"

Dorribo-Camba, Jorge, Gerardo Alducin-Quintero, Pascual Perona e Manuel Contero. "Enhancing Model Reuse Through 3D Annotations: A Theoretical Proposal for an Annotation-Centered Design Intent and Design Rationale Communication". In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64595.

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The long term goals of this research are to study the effectiveness of CAD 3D annotation techniques to support the explicit communication of design intent and rationale, and to analyze the impact of the annotations in the alteration and reutilization of 3D models in a product design context. Towards these goals, we are initially examining the formal annotation practices defined by model-based standards such as ASME Y14.41-2012 and ISO 16792:2006, and their implementation in current CAD systems. This paper presents a prototype implementation of a module to automatically extract textual information from annotated 3D CAD models. Automated extraction of data annotation can be used to analyze both the content and the quality of the annotations with the purpose of determining what makes annotations effective and ultimately communicating design intent. The architecture of a system designed to manage and manipulate this information is also described and analyzed.

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Bao, Zhenshan, Yuezhang Wang e Wenbo Zhang. "Low-Resource Named Entity Recognition without Human Annotation". In 2nd International Conference on Machine Learning Techniques and NLP (MLNLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111427.

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Most existing approaches to named entity recognition (NER) rely on a large amount of highquality annotations or a more complete specific entity lists. However, in practice, it is very expensive to obtain manually annotated data, and the list of entities that can be used is often not comprehensive. Using the entity list to automatically annotate data is a common annotation method, but the automatically annotated data is usually not perfect under low-resource conditions, including incomplete annotation data or non-annotated data. In this paper, we propose a NER system for complex data processing, which could use an entity list containing only a few entities to obtain incomplete annotation data, and train the NER model without human annotation. Our system extracts semantic features from a small number of samples by introducing a pre-trained language model. Based on the incomplete annotations model, we relabel the data using a cross-iteration approach. We use the data filtering method to filter the training data used in the iteration process, and re-annotate the incomplete data through multiple iterations to obtain high-quality data. Each iteration will do corresponding grouping and processing according to different types of annotations, which can improve the model performance faster and reduce the number of iterations. The experimental results demonstrate that our proposed system can effectively perform low-resource NER tasks without human annotation.

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Chen, Zhijun, Huimin Wang, Hailong Sun, Pengpeng Chen, Tao Han, Xudong Liu e Jie Yang. "Structured Probabilistic End-to-End Learning from Crowds". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/210.

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End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced annotations. It models the relationship between true labels and annotations with a specific type of neural layer, termed as the crowd layer, which can be trained using pure backpropagation. Parameters of the crowd layer, however, can hardly be interpreted as annotator reliability, as compared with the more principled probabilistic approach. The lack of probabilistic interpretation further prevents extensions of the approach to account for important factors of annotation processes, e.g., instance difficulty. This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks. Moreover, we propose SpeeLFC-D, which further takes into account instance difficulty. Extensive validation on real-world datasets shows that our methods improve the state-of-the-art.

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Zhang, Dabin, Meng Chen, Weiming Huang, Yongshun Gong e Kai Zhao. "Exploring Urban Semantics: A Multimodal Model for POI Semantic Annotation with Street View Images and Place Names". In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/280.

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Semantic annotation for points of interest (POIs) is the process of annotating a POI with a category label, which facilitates many services related to POIs, such as POI search and recommendation. Most of the existing solutions extract features related to POIs from abundant user-generated content data (e.g., check-ins and user comments). However, such data are often difficult to obtain, especially for newly created POIs. In this paper, we aim to explore semantic annotation for POIs with limited information such as POI (place) names and geographic locations. Additionally, we have found that the street view images provide extensive visual clues about POI attributes and could be an essential supplement to limited information of POIs that enables semantic annotation. To this end, we propose a novel multimodal model for POI semantic annotation, namely M3PA, which achieves enhanced semantic annotation through fusing a POI’s textual and visual representations. Specifically, M3PA extracts visual features from street view images using a pre-trained image encoder and integrates these features to generate the visual representation of a targeted POI based on a geographic attention mechanism. Furthermore, M3PA utilizes the contextual information of neighboring POIs to extract textual features and captures their spatial relationships through geographical encoding to generate the textual representation of a targeted POI. Finally, the visual and textual representations of a POI are fused for semantic annotation. Extensive experiments with POI data from Amap validate the effectiveness of M3PA for POI semantic annotation, compared with several competitive baselines.

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Cheng, Zehua, e Lianlong Wu. "Hybrid Learning System for Large-scale Medical Image Analysis". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/824.

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Adequate annotated data cannot always be satisfied in medical imaging applications. To address such a challenge, we would explore ways to reduce the quality and quantity of annotations requirements of the deep learning model by developing a hybrid learning system. We combined self-supervised learning, semi-supervised learning and weak-supervised learning to improve annotation utilization. Our primary research work on 2D medical image detection under poor annotation conditions has found that better regularization and adversarial loss can improve the robustness and performance with poor annotation conditions.

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Chamberlain, Jon. "The annotation-validation (AV) model". In the First International Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2594776.2594779.

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Rahayu, Syarifah Bahiyah, Shahrul Azman Noah e Andrianto Arfan Wardhana. "Semantic Document Annotation Ranking Model". In 2010 Second International Conference on Advances in Computing, Control and Telecommunication Technologies (ACT). IEEE, 2010. http://dx.doi.org/10.1109/act.2010.56.

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Hejabi, Parsa, Akshay Kiran Padte, Preni Golazizian, Rajat Hebbar, Jackson Trager, Georgios Chochlakis, Aditya Kommineni et al. "CVAT-BWV: A Web-Based Video Annotation Platform for Police Body-Worn Video". In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/1006.

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We introduce an open-source platform for annotating body-worn video (BWV) footage aimed at enhancing transparency and accountability in policing. Despite the widespread adoption of BWVs in police departments, analyzing the vast amount of footage generated has presented significant challenges. This is primarily due to resource constraints, the sensitive nature of the data, which limits widespread access, and consequently, lack of annotations for training machine learning models. Our platform, called CVAT-BWV, offers a secure, locally hosted annotation environment that integrates several AI tools to assist in annotating multimodal data. With features such as automatic speech recognition, speaker diarization, object detection, and face recognition, CVAT-BWV aims to reduce the manual annotation workload, improve annotation quality, and allow for capturing perspectives from a diverse population of annotators. This tool aims to streamline the collection of annotations and the building of models, enhancing the use of BWV data for oversight and learning purposes to uncover insights into police-civilian interactions.

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Tran, Thu Hoai, e Seungjin Choi. "Supervised multi-modal topic model for image annotation". In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854751.

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Baechler, Gilles, Srinivas Sunkara, Maria Wang, Fedir Zubach, Hassan Mansoor, Vincent Etter, Victor Carbune, Jason Lin, Jindong Chen e Abhanshu Sharma. "ScreenAI: A Vision-Language Model for UI and Infographics Understanding". In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/339.

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Screen user interfaces (UIs) and infographics, sharing similar visual language and design principles, play important roles in human communication and human-machine interaction. We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. Our model improves upon the PaLI architecture with the flexible patching strategy of pix2struct and is trained on a unique mixture of datasets. At the heart of this mixture is a novel screen annotation task in which the model has to identify the type and location of UI elements. We use these text annotations to describe screens to Large Language Models and automatically generate question-answering (QA), UI navigation, and summarization training datasets at scale. We run ablation studies to demonstrate the impact of these design choices. At only 5B parameters, ScreenAI achieves new state-of-the-art results on UI- and infographics-based tasks (Multipage DocVQA, WebSRC, and MoTIF), and new best-in-class performance on others (ChartQA, DocVQA, and InfographicVQA) compared to models of similar size. Finally, we release three new datasets: one focused on the screen annotation task and two others focused on question answering.

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Rapporti di organizzazioni sul tema "Model annotation"

Mbani, Benson, Valentin Buck e Jens Greinert. Megabenthic Fauna Detection with Faster R-CNN (FaunD-Fast) Short description of the research software. GEOMAR, 2023. http://dx.doi.org/10.3289/sw_1_2023.

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This is an A.I. - based workflow for detecting megabenthic fauna from a sequence of underwater optical images. The workflow (semi) automatically generates weak annotations through the analysis of superpixels, and uses these (refined and semantically labeled) annotations to train a Faster R-CNN model. Currently, the workflow has been tested with images of the Clarion-Clipperton Zone in the Pacific Ocean

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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor e Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, gennaio 2016. http://dx.doi.org/10.32747/2016.7600047.bard.

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Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.

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Or, Etti, David Galbraith e Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, dicembre 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.

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