Relevant bibliographies by topics / Augmentation de tables

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Augmentation de tables'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Augmentation de tables"

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Wu, Junyi, Chen Ye, Haoshi Zhi, and Shihao Jiang. "Column-Type Prediction for Web Tables Powered by Knowledge Base and Text." Mathematics 11, no. 3 (January 20, 2023): 560. http://dx.doi.org/10.3390/math11030560.

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Web tables are essential for applications such as data analysis. However, web tables are often incomplete and short of some critical information, which makes it challenging to understand the web table content. Automatically predicting column types for tables without metadata is significant for dealing with various tables from the Internet. This paper proposes a CNN-Text method to deal with this task, which fuses CNN prediction and voting processes. We present data augmentation and synthetic column generation approaches to improve the CNN’s performance and use extracted text to get better predictions. The experimental result shows that CNN-Text outperforms the baseline methods, demonstrating that CNN-Text is well qualified for the table column type prediction.
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Wang, Jiayi, Chengliang Chai, Nan Tang, Jiabin Liu, and Guoliang Li. "Coresets over multiple tables for feature-rich and data-efficient machine learning." Proceedings of the VLDB Endowment 16, no. 1 (September 2022): 64–76. http://dx.doi.org/10.14778/3561261.3561267.

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Successful machine learning (ML) needs to learn from good data. However, one common issue about train data for ML practitioners is the lack of good features. To mitigate this problem, feature augmentation is often employed by joining with (or enriching features from) multiple tables, so as to become feature-rich ML. A consequent problem is that the enriched train data may contain too many tuples, especially if the feature augmentation is obtained through 1 (or many)-to-many or fuzzy joins. Training an ML model with a very large train dataset is data-inefficient. Coreset is often used to achieve data-efficient ML training, which selects a small subset of train data that can theoretically and practically perform similarly as using the full dataset. However, coreset selection over a large train dataset is also known to be time-consuming. In this paper, we aim at achieving both feature-rich ML through feature augmentation and data-efficient ML through coreset selection. In order to avoid time-consuming coreset selection over a feature augmented (or fully materialized) table, we propose to efficiently select the coreset without materializing the augmented table. Note that coreset selection typically uses weighted gradients of the subset to approximate the full gradient of the entire train dataset. Our key idea is that the gradient computation for coreset selection of the augmented table can be pushed down to partial feature similarity of tuples within each individual table, without join materialization. These partial feature similarity values can be aggregated to estimate the gradient of the augmented table, which is upper bounded with provable theoretical guarantees. Extensive experiments show that our method can improve the efficiency by nearly 2 orders of magnitudes, while keeping almost the same accuracy as training with the fully augmented train data.
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Dobra, Adrian, Claudia Tebaldi, and Mike West. "Data augmentation in multi-way contingency tables with fixed marginal totals." Journal of Statistical Planning and Inference 136, no. 2 (February 2006): 355–72. http://dx.doi.org/10.1016/j.jspi.2004.07.002.

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Chen, Zhiyu. "Dataset Search and Augmentation." ACM SIGIR Forum 56, no. 1 (June 2022): 1–2. http://dx.doi.org/10.1145/3582524.3582544.

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Data has become an indispensable part of our life. However, current mainstream commercial search engines do not support specialized functions for dataset search. A dataset usually consists of both metadata and data content. Existing information retrieval models designed for Web search cannot efficiently extract semantic information inside structured datasets, even when they contain textual content. Developing new algorithms for next-generation search engines to efficiently find datasets can benefit data practitioners in their data discovery experience. In this dissertation, we consider how to effectively perform dataset search and augmentation. We start by providing an end-to-end description of a dataset search engine following the lifecycle of datasets. Our review includes web dataset acquisition techniques, dataset profiling and augmentation methods, and dataset search tasks and corresponding methods. In order to extract datasets from research articles, we present an information extraction framework to determine triples of interest which can be used for academic dataset search. We propose a feature-based method to augment tabular datasets with additional schema labels to help users and systems to better understand the datasets. We develop three methods for tabular dataset search: the first utilizes generated schema labels to enhance the search results; the second adopts pretrained language models to learn matching features; the third models the complex relations in the datasets as one or more graphs and uses graph neural networks to learn representations of queries and tables. To support dataset search in which a query is also a dataset, we propose universal dataset encoders which regard a dataset as a point set so that the encoded dataset representations can be used to search for similar datasets. Extensive experiments across multiple tasks demonstrate the superiority of our proposed methods over the state of the art. Awarded by: Lehigh University, Bethlehem, USA on 10 May 2022. Supervised by: Brian D. Davison. Available at: https://github.com/Zhiyu-Chen/Dissertation/blob/main/Dissertation_Dataset_Search.pdf.
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Bussotti, Jean-Flavien, Enzo Veltri, Donatello Santoro, and Paolo Papotti. "Generation of Training Examples for Tabular Natural Language Inference." Proceedings of the ACM on Management of Data 1, no. 4 (December 8, 2023): 1–27. http://dx.doi.org/10.1145/3626730.

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Tabular data is becoming increasingly important in Natural Language Processing (NLP) tasks, such as Tabular Natural Language Inference (TNLI). Given a table and a hypothesis expressed in NL text, the goal is to assess if the former structured data supports or refutes the latter. In this work, we focus on the role played by the annotated data in training the inference model. We introduce a system, Tenet, for the automatic augmentation and generation of training examples for TNLI. Given the tables, existing approaches are either based on human annotators, and thus expensive, or on methods that produce simple examples that lack data variety and complex reasoning. Instead, our approach is built around the intuition that SQL queries are the right tool to achieve variety in the generated examples, both in terms of data variety and reasoning complexity. The first is achieved by evidence-queries that identify cell values over tables according to different data patterns. Once the data for the example is identified, semantic-queries describe the different ways such data can be identified with standard SQL clauses. These rich descriptions are then verbalized as text to create the annotated examples for the TNLI task. The same approach is also extended to create counterfactual examples, i.e., examples where the hypothesis is false, with a method based on injecting errors in the original (clean) table. For all steps, we introduce generic generation algorithms that take as input only the tables. For our experimental study, we use three datasets from the TNLI literature and two crafted by us on more complex tables. Tenet generates human-like examples, which lead to the effective training of several inference models with results comparable to those obtained by training the same models with manually-written examples.
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Ahmed, Naveed, Umar Khan, Syed Tauseef Mohyud-Din, and Saeed Ullah Jan. "Non-linear radiative squeezed flow in a rotating frame." Engineering Computations 34, no. 8 (November 6, 2017): 2450–62. http://dx.doi.org/10.1108/ec-04-2017-0158.

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Purpose In this current study, the authors aim to analyze non-linear radiative squeezed flow in a rotating frame of viscous fluid. Design/methodology/approach The Radioactive nature of the fluid is taken into consideration. The reduced form of equations governing the flow are developed by the implementation of similarity transformations. The coupled system thus obtained is solved by using the homotopy analysis method (HAM). Findings Augmentation in velocity and temperature profiles is discussed graphically by varying various involved parameters. The total error of the system is discussed in Table I. The cases of linear radiation and non-linear radiation are also discussed in Tables II and III. Originality/value The study presented in this paper is original and it has not been submitted to any other journal for publication purpose. The contents are original and proper references have been provided wherever applicable.
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Sajid, Tanveer, Muhammad Sagheer, Shafqat Hussain, and Faisal Shahzad. "Impact of double-diffusive convection and motile gyrotactic microorganisms on magnetohydrodynamics bioconvection tangent hyperbolic nanofluid." Open Physics 18, no. 1 (May 2, 2020): 74–88. http://dx.doi.org/10.1515/phys-2020-0009.

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AbstractThe double-diffusive tangent hyperbolic nanofluid containing motile gyrotactic microorganisms and magnetohydrodynamics past a stretching sheet is examined. By adopting the scaling group of transformation, the governing equations of motion are transformed into a system of nonlinear ordinary differential equations. The Keller box scheme, a finite difference method, has been employed for the solution of the nonlinear ordinary differential equations. The behaviour of the working fluid against various parameters of physical nature has been analyzed through graphs and tables. The behaviour of different physical quantities of interest such as heat transfer rate, density of the motile gyrotactic microorganisms and mass transfer rate is also discussed in the form of tables and graphs. It is found that the modified Dufour parameter has an increasing effect on the temperature profile. The solute profile is observed to decay as a result of an augmentation in the nanofluid Lewis number.
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Li, Xiangge, Hong Luo, and Yan Sun. "WordBlitz: An Efficient Hard-Label Textual Adversarial Attack Method Jointly Leveraging Adversarial Transferability and Word Importance." Applied Sciences 14, no. 9 (April 30, 2024): 3831. http://dx.doi.org/10.3390/app14093831.

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Existing textual attacks mostly perturb keywords in sentences to generate adversarial examples by relying on the prediction confidence of victim models. In practice, attackers can only access the prediction label, meaning that the victim model can easily defend against such hard-label attacks by denying access based on the attack’s frequency. In this paper, we propose an efficient hard-label attack approach, called WordBlitz. First, based on the adversarial transferability, we train a substitute model to initialize the attack parameter set, including a candidate pool and two weight tables of keywords and candidate words. Then, adversarial examples are generated and optimized under the guidance of the two weight tables. During optimization, we design a hybrid local search algorithm with word importance to find the globally optimal solution while updating the two weight tables according to the attack results. Finally, the non-adversarial text generated during perturbation optimization is added to the training of the substitute model as data augmentation to improve the adversarial transferability. Experimental results show that WordBlitz surpasses the baseline in terms of better effectiveness, higher efficiency, and lower cost. Its efficiency is especially pronounced in scenarios with broader search spaces, and its attack success rate on a Chinese dataset is higher than on baselines.
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Ismail, Nur Hilwani, Siti Fatimah Ibrahim, Farah Hanan Fathihah Jaffar, Mohd Helmy Mokhtar, Kok Yong Chin, and Khairul Osman. "Augmentation of the Female Reproductive System Using Honey: A Mini Systematic Review." Molecules 26, no. 3 (January 27, 2021): 649. http://dx.doi.org/10.3390/molecules26030649.

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Phytochemical contents of honey are presumed to be beneficial to the female reproductive system (FRS). However, the biological effects of honey supplementation (HS) in vivo on the FRS remain unclear. This review aims to investigate the current literature on the effects of HS on the FRS, particularly on the sex hormone profile and reproductive organs (uterus and vagina). A systematic literature search using Scopus, MEDLINE via Ovid and Cochrane Library databases was conducted. Records were screened and identified for preclinical and clinical studies addressing the effects of HS on the FRS. Data on populations, interventions, outcomes and methodological quality were extracted. Studies were synthesised using tables and written summaries. Of the 198 identified records, six fulfilled the inclusion criteria. All six records were used for data extraction: two experimental studies using rats as the model organism and four human clinical studies of honey on female reproductive health. HS elevated the progesterone levels, restrained body weight increase, prevented uterine and vaginal atrophies in ovariectomised rats, attenuated symptoms of candidiasis and improved oxidative status in patients. Current evidence shows that short-term HS following surgical or physiological menopause exerts an oestrogenic, antioxidant and anti-inflammatory effect on the FRS. However, insufficient long-term studies preclude any definitive conclusions.
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Castelo, Sonia, Rémi Rampin, Aécio Santos, Aline Bessa, Fernando Chirigati, and Juliana Freire. "Auctus." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2791–94. http://dx.doi.org/10.14778/3476311.3476346.

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The large volumes of structured data currently available, from Web tables to open-data portals and enterprise data, open up new opportunities for progress in answering many important scientific, societal, and business questions. However, finding relevant data is difficult. While search engines have addressed this problem for Web documents, there are many new challenges involved in supporting the discovery of structured data. We demonstrate how the Auctus dataset search engine addresses some of these challenges. We describe the system architecture and how users can explore datasets through a rich set of queries. We also present case studies which show how Auctus supports data augmentation to improve machine learning models as well as to enrich analytics.
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Dissertations / Theses on the topic "Augmentation de tables"

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Liu, Jixiong. "Semantic Annotations for Tabular Data Using Embeddings : Application to Datasets Indexing and Table Augmentation." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS529.

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Avec le développement de l'Open Data, un grand nombre de sources de données sont mises à disposition des communautés (notamment les data scientists et les data analysts). Ces données constituent des sources importantes pour les services numériques sous réserve que les données soient nettoyées, non biaisées, et combinées à une sémantique explicite et compréhensible par les algorithmes afin de favoriser leur exploitation. En particulier, les sources de données structurées (CSV, JSON, XML, etc.) constituent la matière première de nombreux processus de science des données. Cependant, ces données proviennent de différents domaines pour lesquels l'expertise des consommateurs des données peut être limitée (knowledge gap). Ainsi, l'appropriation des données, étape critique pour la création de modèles d'apprentissage automatique de qualité, peut être complexe.Les modèles sémantiques (en particulier, les ontologies) permettent de représenter explicitement le sens des données en spécifiant les concepts et les relations présents dans les données. L'association d'étiquettes sémantiques aux ensembles de données facilite la compréhension et la réutilisation des données en fournissant une documentation sur les données qui peut être facilement utilisée par un non-expert. De plus, l'annotation sémantique ouvre la voie à des modes de recherche qui vont au-delà de simples mots-clés et permettent l'expression de requêtes d'un haut niveau conceptuel sur le contenu des jeux de données mais aussi leur structure tout en surmontant les problèmes d'hétérogénéité syntaxique rencontrés dans les données tabulaires. Cette thèse introduit un pipeline complet pour l'extraction, l'interprétation et les applications de tableaux de données à l'aide de graphes de connaissances. Nous rappelons tout d'abord la définition des tableaux du point de vue de leur interprétation et nous développons des systèmes de collecte et d'extraction de tableaux sur le Web et dans des fichiers locaux. Nous proposons ensuite trois systèmes d'interprétation de tableaux basés sur des règles heuristiques ou sur des modèles de représentation de graphes, afin de relever les défis observés dans la littérature. Enfin, nous présentons et évaluons deux applications d'augmentation des tables tirant parti des annotations sémantiques produites: l'imputation des données et l'augmentation des schémas
With the development of Open Data, a large number of data sources are made available to communities (including data scientists and data analysts). This data is the treasure of digital services as long as data is cleaned, unbiased, as well as combined with explicit and machine-processable semantics in order to foster exploitation. In particular, structured data sources (CSV, JSON, XML, etc.) are the raw material for many data science processes. However, this data derives from different domains for which consumers are not always familiar with (knowledge gap), which complicates their appropriation, while this is a critical step in creating machine learning models. Semantic models (in particular, ontologies) make it possible to explicitly represent the implicit meaning of data by specifying the concepts and relationships present in the data. The provision of semantic labels on datasets facilitates the understanding and reuse of data by providing documentation on the data that can be easily used by a non-expert. Moreover, semantic annotation opens the way to search modes that go beyond simple keywords and allow the use of queries of a high conceptual level on the content of the datasets but also their structure while overcoming the problems of syntactic heterogeneity encountered in tabular data. This thesis introduces a complete pipeline for the extraction, interpretation, and applications of tables in the wild with the help of knowledge graphs. We first refresh the exiting definition of tables from the perspective of table interpretation and develop systems for collecting and extracting tables on the Web and local files. Three table interpretation systems are further proposed based on either heuristic rules or graph representation models facing the challenges observed from the literature. Finally, we introduce and evaluate two table augmentation applications based on semantic annotations, namely data imputation and schema augmentation
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Lehmberg, Oliver [Verfasser], and Christian [Akademischer Betreuer] Bizer. "Web table integration and profiling for knowledge base augmentation / Oliver Lehmberg ; Betreuer: Christian Bizer." Mannheim : Universitätsbibliothek Mannheim, 2019. http://d-nb.info/1197143866/34.

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Heyder, Jakob Wendelin. "Knowledge Base Augmentation from Spreadsheet Data : Combining layout inference with multimodal candidate classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278824.

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Spreadsheets compose a valuable and notably large dataset of documents within many enterprise organizations and on the Web. Although spreadsheets are intuitive to use and equipped with powerful functionalities, extraction and transformation of the data remain a cumbersome and mostly manual task. The great flexibility they provide to the user results in data that is arbitrarily structured and hard to process for other applications. In this paper, we propose a novel architecture that combines supervised layout inference and multimodal candidate classification to allow knowledge base augmentation from arbitrary spreadsheets. In our design, we consider the need for repairing misclassifications and allow for verification and ranking of ambiguous candidates. We evaluate the performance of our system on two datasets, one with single-table spreadsheets, another with spreadsheets of arbitrary format. The evaluation result shows that the proposed system achieves similar performance on single-table spreadsheets compared to state-of-the-art rule-based solutions. Additionally, the flexibility of the system allows us to process arbitrary spreadsheet formats, including horizontally and vertically aligned tables, multiple worksheets, and contextualizing metadata. This was not possible with existing purely text-based or table-based solutions. The experiments demonstrate that it can achieve high effectiveness with an F1 score of 95.71 on arbitrary spreadsheets that require the interpretation of surrounding metadata. The precision of the system can be further increased by applying candidate schema-matching based on semantic similarity of column headers.
Kalkylblad består av ett värdefullt och särskilt stort datasätt av dokument inom många företagsorganisationer och på webben. Även om kalkylblad är intuitivt att använda och är utrustad med kraftfulla funktioner, utvinning och transformation av data är fortfarande en besvärlig och manuell uppgift. Den stora flexibiliteten som de ger användaren resulterar i data som är godtyckligt strukturerade och svåra att bearbeta för andra applikationer. I det här förslaget föreslår vi en ny arkitektur som kombinerar övervakad layoutinferens och multimodal kandidatklassificering för att tillåta kunskapsbasförstärkning från godtyckliga kalkylblad. I vår design överväger vi behovet av att reparera felklassificeringar och möjliggöra verifiering och rangordning av tvetydiga kandidater. Vi utvärderar systemets utförande på två datasätt, en med singeltabellkalkylblad, en annan med kalkylblad av godtyckligt format. Utvärderingsresultatet visar att det föreslagna systemet uppnår liknande prestanda på singel-tabellkalkylblad jämfört med state-of-the-art regelbaserade lösningar. Dessutom tillåter systemets flexibilitet oss att bearbeta godtyckliga kalkylark format, inklusive horisontella och vertikala inriktade tabeller, flera kalkylblad och sammanhangsförande metadata. Detta var inte möjligt med existerande rent textbaserade eller tabellbaserade lösningar. Experimenten visar att det kan uppnå hög effektivitet med en F1-poäng på 95.71 på godtyckliga kalkylblad som kräver tolkning av omgivande metadata. Systemets precision kan ökas ytterligare genom att applicera schema-matchning av kandidater baserat på semantisk likhet mellan kolumnrubriker.
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Books on the topic "Augmentation de tables"

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United States. National Aeronautics and Space Administration., ed. Experimental and computational investigation of lift-enhancing tabs on a multi-element airfoil. [Stanford, Calif.]: Joint Institute for Aeronautics and Acoustics, National Aeronautics and Space Administration, Ames Research Center, 1996.

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United States. National Aeronautics and Space Administration., ed. Experimental and computational investigation of lift-enhancing tabs on a multi-element airfoil. [Stanford, Calif.]: Joint Institute for Aeronautics and Acoustics, National Aeronautics and Space Administration, Ames Research Center, 1996.

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Book chapters on the topic "Augmentation de tables"

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Del Bimbo, Davide, Andrea Gemelli, and Simone Marinai. "Data Augmentation on Graphs for Table Type Classification." In Lecture Notes in Computer Science, 242–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-23028-8_25.

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Khan, Umar, Sohaib Zahid, Muhammad Asad Ali, Adnan Ul-Hasan, and Faisal Shafait. "TabAug: Data Driven Augmentation for Enhanced Table Structure Recognition." In Document Analysis and Recognition – ICDAR 2021, 585–601. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_38.

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Chen, Bangdong, Dezhi Peng, Jiaxin Zhang, Yujin Ren, and Lianwen Jin. "Complex Table Structure Recognition in the Wild Using Transformer and Identity Matrix-Based Augmentation." In Frontiers in Handwriting Recognition, 545–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_37.

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Mehl-Schneider, Toby B. "Recent Advances in Augmentative and Alternative Communication." In Advances in Medical Technologies and Clinical Practice, 128–40. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8395-2.ch006.

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With the increased development of mobile technologies, such as smartphones and tablets (i.e. iPhone, iPad), the field of augmentative and alternative communication (AAC) has changed rapidly over the last few years. Recent advances in technology have introduced applications (apps) for AAC purposes. These novel technologies could provide numerous benefits to individuals with complex communication needs. Nevertheless, introducing mobile technology apps is not without risk. Since these apps can be purchased and retrieved with relative ease, AAC assessments and collaborative evaluations have been circumvented in favor of the “quick fix”-simply ordering a random app for a potential user, without fully assessing the individual's needs and abilities. There is a paucity of research pertaining to mobile technology use in AAC. Therapists, parents and developers of AAC applications must work collaboratively to expand the research pertaining to the assessment and treatment of children who utilize AAC mobile technologies for communication purposes.
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Zhu, Wei, Charles B. Owen, Hairong Li, and Joo-Hyun Lee. "Design of the PromoPad." In Advances in End User Computing, 193–205. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-687-7.ch011.

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Augmented-reality technologies as a new way of human-computer interaction make possible real-time modification of our perception of reality without active user interference. This article introduces the prototype of an augmented-reality shopping-assistant device, the PromoPad, based on a handheld tablet PC allowing see-through vision with augmentations. While this new interaction utilizing augmented reality that places products into contextual settings can enhance shopping experience and suggest complementary products, it also has challenges and issues when used in a public environment such as a store setting. This article discusses the design and implementation of the PromoPad, and addresses the issues and possible solutions. The concept of dynamic contextualization is further investigated in this setting with a list of possible context modifications and their relation to advertising and the psychology of consumer purchasing.
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"Faithful extension of a relation, bivalent tableau, faithful augmentation, Szpilrajn chain." In Theory of Relations, 223–39. Elsevier, 2000. http://dx.doi.org/10.1016/s0049-237x(00)80055-5.

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Wunder, Iris, and Ruth Maloszek. "Perspective Chapter: iPEAR-MOOC." In Massive Open Online Courses - Current Practice and Future Trends [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1001463.

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Augmented Reality (AR) has been developing rapidly in the past years, and the acceptance of this technology is growing amongst the users of smartphones and tablets. Educational applications and resources that use AR technology are also growing in both number and quality. However, most educators lack sufficient digital skills and are unaware of the pedagogical approaches necessary to use the available digital tools to advance their teaching practices and their professional development. An international team of researchers from the Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany), NTNU (Norway), AETMALab (Greece), Akto (Greece) and Eucen (Spain) started an EU-funded project to explore the possibilities of engaging higher education students by combining AR And peer learning. As part of the project, a MOOC started in October 2022 (https://imoox.at/course/ipear) to share the iPEAR approach created by the project group. In this 4-week course, students learn about AR tools, pear learning, and how to implement the combination of both into their seminars. Participants work in peer groups to create a first augmentation and provide a pedagogical background for their projects. The chapter will describe the experiences with and results from the MOOC.
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Plattard, Serge. "L’ONU et l’espace." In Annuaire français de relations internationales, 887–903. Éditions Panthéon-Assas, 2023. http://dx.doi.org/10.3917/epas.ferna.2023.01.0887.

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Les fondements du droit spatial établis dans les années 1960 par les Nations Unies ont permis l’essor des activités spatiales. Toutefois, la transformation de celles-ci ces vingt dernières années montre les limites de ces actes fondateurs. En effet, les risques accrus de collisions orbitales dues aux débris spatiaux en constante augmentation, la congestion des orbites basses, la compromission des communications et les risques d’une « arsenalisation » de l’espace nécessitent la prise rapide de nouvelles initiatives par l’Organisation des Nations Unies (ONU). Si des lignes directrices ont récemment bien été proposées sur la viabilité des activités spatiales à long terme, leur portée reste très en deçà de ce qui est désormais nécessaire. L’ONU doit agir en prenant à bras-le-corps toute la nature duale de l’activité spatiale pour proposer des normes, des règles et des principes de comportement responsable, au risque de laisser des initiatives à d’autres acteurs agissant dans leur propre intérêt.
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Konstantinou, Gerasimos, and Mohamed Attia. "Perspective Chapter: From the Boom to Gen Z – Has Depression Changed across Generations?" In Depression - What Is New and What Is Old in Human Existence [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1003091.

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The chapter delineates the intricate tableau of depression, scrutinizing its generational disparities and spotlighting salient elements such as stigma, resilience, awareness, the impact of the pandemic, and the ambivalent role of technology. Historically, the pervasive stigma surrounding mental health has obfuscated transparent dialogues and deterred help-seeking behaviors. Presently, generational shifts reveal an augmentation in awareness, predominantly among younger demographics, fervently advocating for destigmatization and transparent discussions. Resilience, manifesting divergently across age brackets, demonstrates that older adults typically exhibit amplified resilience, attributed to cumulative life experiences and substantial support networks. In contrast, younger individuals navigate through unique stressors like academic duress and the high-velocity digital epoch. Enhanced awareness of depression, fostered by targeted campaigns across demographics, may underpin early identification and interventions, mitigating the severity and chronic implications of depression. The COVID-19 pandemic has universally magnified feelings of despair and isolation, with technology proffering a double-edged sword, particularly for tech-dependent younger generations, by facilitating communication while potentially intensifying depressive symptoms through its excessive use and resultant social comparison. Hence, acknowledging generational distinctions in depression is imperative for sculpting efficacious interventions, aiming to foster a societal framework that staunchly supports mental well-being and adequately equips individuals to navigate their mental health challenges.
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D., Aju, Anil Kumar Kakelli, Ashwin Suresh Varma, and Kishore Rajendiran. "A Comprehensive Perspective on Mobile Forensics." In Advances in Digital Crime, Forensics, and Cyber Terrorism, 1–28. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4900-1.ch001.

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The modern-day smartphones are the result of the technological progression that is happening in this digital world. This technological advancement has brought an incremental augmentation where these were not perceived as critical by the smartphone users. Also, the computational capability and networking competence has been dragooned constantly to maintain the momentum with the ever-expanding workload demands. This scenario has endorsed the smart gadgets such as smartphones and tablets to accomplish the growing complex challenges. In this digital era, the next generation users are substituting the conventional way of preference such as the personal computers and laptops with smartphone for the social connectedness, e-commerce, financial transaction, market updates, latest news, or even editing images. Users willingly install various mobile apps on to their smartphone and consequently providing their valuable and sensitive personal information to their service providers without thinking and knowing the security lapses and repercussions. Considering the fact, the smartphones' size and its portability, these devices are much more susceptible of being stolen, becoming jeopardized, or being exploited for various cyber-attacks and other malevolent activities. Essentially, the hackers look forward to the new mobile vulnerabilities so that they exploit the revealed vulnerability once a newer edition of the respective mobile operating system is released. In view of the fact that the smartphones are too vulnerable to various exploits, the necessity for a digital investigation entrained to establish a separate domain named mobile forensics. This established forensic domain is specialized in acquiring, extracting, analyzing, and reporting the evidence that is obtained from the smartphone devices so that the exploiting artifacts and its respective actions are determined and located. This chapter puts forward the various processes involved with the mobile forensics that can be employed for examining the evidences of various cyber incidents. Furthermore, it discusses the various vulnerabilities with the iOS and Android mobile operating systems and how they are being exploited in detail. The chapter also discusses the various approaches of data extraction and the respective industry standard for the tools that are being utilized for the same.
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Conference papers on the topic "Augmentation de tables"

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Weger, Marian, Thomas Hermann, and Robert Höldrich. "AltAR/Table: A Platform for Plausible Auditory Augmentation." In ICAD 2022: The 27th International Conference on Auditory Display. icad.org: International Community for Auditory Display, 2022. http://dx.doi.org/10.21785/icad2022.005.

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Auditory feedback from everyday interactions can be augmented to project digital information in the physical world. For that purpose, auditory augmentation modulates irrelevant aspects of already existing sounds while at the same time preserving relevant ones. A strategy for maintaining a certain level of plausibility is to metaphorically modulate the physical object itself. By mapping information to physical parameters instead of arbitrary sound parameters, it is assumed that even untrained users can draw on prior knowledge. Here we present AltAR/table, a hard- and software platform for plausible auditory augmentation of flat surfaces. It renders accurate augmentations of rectangular plates by capturing the structure-borne sound, feeding it through a physical sound model, and playing it back through the same object in real time. The implementation solves basic problems of equalization, active feedback control, spatialization, hand tracking, and low-latency signal processing. AltAR/table provides the technical foundations of object-centered auditory augmentations, for embedding sonifications into everyday objects such as tables, walls, or floors.
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Dreossi, Tommaso, Shromona Ghosh, Xiangyu Yue, Kurt Keutzer, Alberto Sangiovanni-Vincentelli, and Sanjit A. Seshia. "Counterexample-Guided Data Augmentation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/286.

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We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key components of our framework include a \textit{counterexample generator}, which produces data items that are misclassified by the model and error tables, a novel data structure that stores information pertaining to misclassifications. Error tables can be used to explain the model's vulnerabilities and are used to efficiently generate counterexamples for augmentation. We show the efficacy of the proposed framework by comparing it to classical augmentation techniques on a case study of object detection in autonomous driving based on deep neural networks.
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Kavitha, K. M., Vaishnavi Naik, Sahana Angadi, Sandra Satish, and Suman Nayak. "Hybrid Approaches for Augmentation of Translation Tables for Indian Languages." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00157.

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Chen, Haipeng, Sushil Jajodia, Jing Liu, Noseong Park, Vadim Sokolov, and V. S. Subrahmanian. "FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/287.

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In many cases, an organization wishes to release some data, but is restricted in the amount of data to be released due to legal, privacy and other concerns. For instance, the US Census Bureau releases only 1% of its table of records every year, along with statistics about the entire table. However, the machine learning (ML) models trained on the released sub-table are usually sub-optimal. In this paper, our goal is to find a way to augment the sub-table by generating a synthetic table from the released sub-table, under the constraints that the generated synthetic table (i) has similar statistics as the entire table, and (ii) preserves the functional dependencies of the released sub-table. We propose a novel generative adversarial network framework called ITS-GAN, where both the generator and the discriminator are specifically designed to satisfy these two constraints. By evaluating the augmentation performance of ITS-GAN on two representative datasets, the US Census Bureau data and US Bureau of Transportation Statistics (BTS) data, we show that ITS-GAN yields high quality classification results, and significantly outperforms various state-of-the-art data augmentation approaches.
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Cao, Jianhao, Tamara Munzner, and Rachel Pottinger. "Visualizing a Tabular Data Repository to Facilitate Descriptive Tag Augmentation for New Tables." In SIGMOD/PODS '23: International Conference on Management of Data. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3597465.3605226.

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Pham, Minh, Craig A. Knoblock, Muhao Chen, Binh Vu, and Jay Pujara. "SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/488.

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Error detection is one of the most important steps in data cleaning and usually requires extensive human interaction to ensure quality. Existing supervised methods in error detection require a significant amount of training data while unsupervised methods rely on fixed inductive biases, which are usually hard to generalize, to solve the problem. In this paper, we present SPADE, a novel semi-supervised probabilistic approach for error detection. SPADE introduces a novel probabilistic active learning model, where the system suggests examples to be labeled based on the agreements between user labels and indicative signals, which are designed to capture potential errors. SPADE uses a two-phase data augmentation process to enrich a dataset before training a deep learning classifier to detect unlabeled errors. In our evaluation, SPADE achieves an average F1-score of 0.91 over five datasets and yields a 10% improvement compared with the state-of-the-art systems.
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Frame, Mary, Jessica Armstrong, and Bradley Schlessman. "Decision Support Systems for Route Planning: Impacts on Performance and Trust." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001562.

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Decision Support Systems (DSS) and other performance augmentation tools are increasingly leveraged by the military to recommend courses of action and improve analyst performance on critical tasks. This is particularly important for path planning operations, where analysts must consider complex tradeoffs and contingencies based on available assets, distance, and target priority. To emulate this environment in a more general applied context, we developed a path planning task that emulated long-range delivery truck dispatch. Participants conducted a quality control check on four scenarios, each with a simulated DSS that recommended truck allocations, which ranged from perfect (100%) accuracy to subpar (40%) accuracy. Each participant also received one of four explanations for how the DSS algorithm would determine where trucks should be allocated: (1) no explanation, (2) a simple written explanation, (3) a flowchart, or (4) annotated tables. Participants demonstrated appropriately lower trust of DSS that had lower accuracy. Despite this appropriate trust calibration, their quality control performance was significantly lower when exposed to a DSS that had below perfect accuracy. Further, participants who self-reported higher levels of experience with path planning and AI algorithms demonstrated lower quality control accuracy. This demonstrates that while participants were able to successfully calibrate trust in their DSS, they nevertheless experienced performance decrements, possibly due to anchoring on the DSS’s incorrect result. Participants demonstrated the greatest understanding, strongest trust, and highest subjective preference for the simple written explanation of the DSS’s algorithm over more complex presentations, including a flowchart or annotated tables. The findings of this study provide the groundwork to understand the relationship between automation-reliance, trust, and performance, to determine when it is most appropriate to allow automation to make recommendations to analysts in operational environments and when DSS under-reliability may impact or increase human error.
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Pillai, Prashanth, and Purnaprajna Mangsuli. "Document Layout Analysis Using Detection Transformers." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207266-ms.

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Abstract In the O&G (Oil & Gas) industry, unstructured data sources such as technical reports on hydrocarbon production, daily drilling, well construction, etc. contain valuable information. This information however is conveyed through various formats such as tables, forms, text, figures, etc. Detecting these different entities in documents is essential for building a structured representation of the information within and for automated processing of documents at scale. Our work presents a document layout analysis workflow to detect/localize different entities based on a deep learning-based framework. The workflow comprises of a deep learning-based object-detection framework based on transformers to identify the spatial location of entities in a document page. The key elements of the object-detection pipeline include a residual network backbone for feature extraction and an encoder-decoder transformer based on the latest detection transformers (DETR) to predict object-bounding boxes and category labels. The object detection is formulated as a direct set prediction task using bipartite matching while also eliminating conventional operations like anchor box generation and non-maximal suppression. The availability of sufficient publicly available document layout data sets that incorporate the artifacts observed in historical O&G technical reports is often a major challenge. We attempt to address this challenge by using a novel training data augmentation methodology. The dense occurrence of elements in a page can often introduce uncertainties resulting in bounding boxes cutting through text content. We adopt a bounding box post-processing methodology to refine the bounding box coordinates to minimize undercuts. The proposed document layout analysis pipeline was trained to detect entity types such as headings, text blocks, tables, forms, and images/charts in a document page. A wide range of pages from lithology, stratigraphy, drilling, and field development reports were used for model training. The reports also included a considerable number of historical scanned reports. The trained object-detection model was evaluated on a test data set prepared from the O&G reports. DETR demonstrated superior performance when compared with the Mask R-CNN on our dataset.
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Dai, Chaofan, Qideng Tang, Wubin Ma, Yahui Wu, Haohao Zhou, and Huahua Ding. "PromptER: Prompt Contrastive Learning for Generalized Entity Resolution." In 11th International Conference on Artificial Intelligence and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140102.

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Entity resolution (ER), which aims to identify whether data records from various sources refer to the same real-world entity, is a crucial part of data integration systems. Traditional ER solutions assumes that data records are stored in relational tables with an aligned schema. However, in practical applications, it is common that data records to be matched may have different formats (e.g., relational, semi-structured, or textual types). In order to support ER for data records with varying formats, Generalized Entity Resolution has been proposed and has recently gained much attention. In this paper, we propose PromptER, a model based on pre-trained language models that offers an efficient and effective approach to accomplish Generalized Entity Resolution tasks. PromptER starts with a supervised contrastive learning process to train a Transformer encoder, which is afterward used for blocking and fine-tuned for matching. Specially, in the record embedding process, PromptER uses the proposed prompt embedding technique to better utilized the pre-trained language model layers and avoid embedding bias. Morever, we design a novel data augmentation method and an evaluation method to enhance the performance of the proposed model. We conduct experiments on the Generalized Entity Resolution dataset Machamp and the results show that PromptER significantly outperforms other state-of-art methods in the blocking and matching tasks.
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Weger, Marian, Thomas Hermann, and Robert Höldrich. "Plausible Auditory Augmentation of Physical Interaction." In The 24th International Conference on Auditory Display. Arlington, Virginia: The International Community for Auditory Display, 2018. http://dx.doi.org/10.21785/icad2018.024.

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Interactions with physical objects usually evoke sounds, i.e., auditory feedback that depends on the interacting objects (e.g., table, hand, or pencil) and interaction type (e.g., tapping or scratching). The continuous real-time adaptation of sound during interaction enables the manipulation/refinement of perceived characteristics (size, material) of physical objects. Furthermore, when controlled by unrelated external data, the resulting ambient sonifications can keep users aware of changing data. This article introduces the concept of plausibility to the topic of auditory augmentations of physical interactions, aiming at providing an experimentation platform for investigating surface-based physical interactions, understanding relevant acoustic cues, redefining these via auditory augmentation / blended sonification and particularly to empirically measure the plausibility limits of such auditory augmentations. Besides conceptual contributions along the trade-off between plausibility and usability, a practical experimentation system is introduced, together with a very first qualitative pilot study.
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Reports on the topic "Augmentation de tables"

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DEPARTMENT OF THE ARMY WASHINGTON DC. Army Policies and Procedures for Establishing Multiple Component Modification Table of Organization and Equipment (MTOE) and Augmentation Tables of Distribution (AUGTDAs) Units. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada402529.

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