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Towards Automated Error analysis: Learning to Characterize Errors 35th

Towards Automated Error Analysis: Learning to Characterize E...

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35th International Florida Artificial Intelligence Research Society conference, FLAIRS-35 2022

作者： Gao, Tong Singh, Shivang Mooney, Raymond J. Department of Computer Science University of Texas at Austin United States

Characterizing the patterns of errors that a system makes helps researchers focus future development on increasing its accuracy and robustness. We propose a novel form of "meta learning" that automatically learns interpretable rules that characterize the types of errors that a system makes, and demonstrate these rules’ ability to help understand and improve two NLP systems. Our approach works by collecting error cases on validation data, extracting meta-features describing these samples, and finally learning rules that characterize errors using these features. We apply our approach to vilBERT, for visual Question Answering, and RoBERTa, for Common Sense Question Answering. Our system learns interpretable rules that provide insights into systemic errors these systems make on the given tasks. Using these insights, we are also able to "close the loop" and modestly improve performance of these systems. © 2021 by the authors. All rights reserved.

关键词： Machine learning

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Digitization's Influence on Organizational Processes

Digitization's Influence on Organizational Processes

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Portland International conference on Management of Engineering and Technology (PICMET)

作者： v.K. Narayanan Srivardhini Jha Strategy & Entrepreneurship LeBow College of Business Drexel University USA Indian Institute of Management Bangalore India

ISBN: (数字)9781890843458

ISBN: (纸本)9798350369137

In this study, we examine the organizational arrangements adopted by legacy firms in the manufacturing sector as they confront the challenges of digitalization. Our conclusions are based on five intensive case studies, developed out of interviews with C-suite executives (primary data) of each firm, and supplemented by documentary evidence. The interviews suggest that firms do not yet share a common vocabulary for digitalization, although data analytics and artificial intelligence form the centerpiece of each firm's view of digitalization. The firms have developed their own mind maps of what digitalization comprises, and it typically encompasses five distinct yet inter-related aspects of the organization beyond traditional IT: Product/Technology management, Process efficiencies in supply chain and manufacturing, Efficiencies in support functions, Customer facing processes, and new business model exploration. Because our sample consists of legacy firms, there is significant appreciation of the organizational change effort required for digitalization to work, and some recognition of the competitive threat presented by born digital firms in their market space. The interviews also underscored the transitory nature of organizational arrangements, the significant interfaces with all other functions in the organization, and the need for CEO engagement for digitization projects to be successful.

关键词： vocabulary data analysis Supply chains Organizations Manufacturing Interviews Artificial intelligence

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Ontology model for new functional software platform for oil and gas reservoir modeling 23

Ontology model for new functional software platform for oil ...

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23th conference on Oil and Gas Geological exploration and Development, Geomodel 2021

作者： Mityaev, M.Y. Popov, v.L. Gazpromneft-DS Gazpromnft STC

ISBN: (纸本)9789462823839

Ontology model was developed in order to describe data used in new functional software platform for oil and gas reservoir modelling. The main idea of this model is to compile data into universal analysis workflow. The analysis process model consists of four data categories: source data, models, scenarios, results. Source data is the information that are imported in project from external sources. Models are the combined with specific algorithms source data. Scenarios consist of properties and conditions of models, which is, determine system states in the prediction periods. The results consist of data, which is obtained by combining of the models with the scenarios. Each model consist of three or four nesting levels in order. First is physical objects (e.g. well or reservoir or core specimen). The second one is object research that combine similar data. The third level is series that consists of many repetitions of data (e.g. well trajectory is a series of depth, inclination and azimuth measurements) and the last level is the data. All data, which are used in reservoir modeling, were decomposed into developed ontology model that allows achieving, intuitive, and convenient representation of data in functional platform. © Geomodel 2021 - 23th Conf. on Oil and Gas Geo. Expl. and Dev. All rights reserved.

关键词： Petroleum reservoir engineering

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visual analysis of Graph Algorithm Dynamics 21

Visual Analysis of Graph Algorithm Dynamics

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14th International Symposium on visual Information Communication and Interaction, vINCI 2021

作者： Burch, Michael Wallner, Günter van De Wetering, Huub Rooks, Freek Morra, Olof Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9781450386470

In this paper we describe a visualization tool for representing the dynamics of graph algorithms. Toward this end, we designed a web-based framework which illustrates the dynamics as time-to-space mappings of dynamic graphs. Such static diagrams of dynamic data have the benefit of being able to display longer time spans in one view, hence supporting the observer with comparison tasks. The tool can show details about how an algorithm traverses a graph step-by-step in a static and animated fashion, for graph algorithm exploration as well as educational purposes. The animation together with the time-to-space mapping forms an overview-and-detail approach. By using flight carrier data from the U.S. Department of Transportation we show the usefulness of our interactive visualization for conveying graph algorithm dynamics. © 2021 ACM.

关键词： Mapping

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Revolutionizing Stock Price Prediction with Automated Facebook Prophet analysis

Revolutionizing Stock Price Prediction with Automated Facebo...

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International conference on Inventive Computation Technologies (ICICT)

作者： E Annapoorna Sreya v Sujil Sreepriya S S Abhishek Anjali T Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Making predictions, about stock prices has always been a challenge in research even though the Efficient Market Hypothesis suggests otherwise. While it’s widely believed that it’s impossible to predict stock prices with precision there have been studies in the literature showing that accurate predictions can be made by using predictor models and relevant variables. The complexity of predicting stock market volatility adds another layer of difficulty as stock prices are influenced by factors such as conditions, psychological factors, and logical considerations. In this study, the Facebook Prophet model is utilized to forecast stock prices by analyzing data from Yahoo Finance. The experimental results indicate that Facebook Prophet can be effectively used to predict stock prices overtime periods. The research work’s emphasis on Facebook Prophet provides not only accurate forecasts but also seamless visualization, allowing users to gain confidence in analyzing, visualizing, and forecasting stock prices. This comprehensive effort provides users with the knowledge to navigate the difficulties of stock price analysis, employing automation to quickly acquire solid forecasts and visual insights, regardless of the selected stock.

关键词： Analytical models visualization Social networking (online) Time series analysis data visualization Predictive models Market research

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A CLIP-based video Question Answering framework with Explainable AI

A CLIP-based Video Question Answering framework with Explain...

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Jeshmol P J Binsu C. Kovoor Division of Information Technology School of Engineering Cochin Univesity of Science and Technology Kochi Kerala

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

video analytics utilizing video Question Answering (videoQA) techniques to develop AI systems capable of comprehending and responding to user-specific questions from the content of the custom video datasets. The proposed system detects objects from the custom video datasets using YOLO v8, a deep learning-based object detection algorithm driven by the provided questions. The SORT algorithm helps to track detected objects throughout the entire video. Also, significant frames are extracted from the input questions utilizing a pre-trained model Contrastive Language-Image Pretraining (CLIP) specialized for analyzing video clips. The proposed model comprises video processing where the raw video data is analyzed and transformed for further analysis. Subsequently, questions are encoded and processed facilitating the integration of natural language understanding into the system. The proposed model integrates video comprehension with questions and enables it to correlate the content of the videos with the queries posed by the user. Finally, the model generates answers to the questions asked, accompanied by the selection of the most pertinent frame from the video based on similarity scores. The explainable LIME algorithm enhances the interpretability of the frame with the highest similarity score. This selection process ensures that the provided answers are grounded in the relevant visual context, enhancing the overall quality and relevance of the responses generated by the system. The model exhibited superior performance when applied to specialized custom video datasets.

关键词： YOLO Measurement Deep learning Computer science Analytical models Accuracy Explainable AI visual analytics Question answering (information retrieval) data models

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An Empirical Study of Task-Specific Limitations of the Overview+Detail Technique for Interactive Time Series analysis 2

An Empirical Study of Task-Specific Limitations of the Overv...

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2nd International conference on Industry 4.0 and Smart Manufacturing, ISM 2020

作者： Friedl, Judith Zimmer, Björn Perkhofer, Lisa Zenisek, Jan Hofer, Peter Jetter, Hans-Christian University of Applied Sciences Upper Austria Hagenberg Campus Softwarepark 11 HagenbergA-4232 Austria University of Applied Sciences Upper Austria Steyr Campus Wehrgrabengasse 1 SteyrA-4400 Austria University of Lübeck Ratzeburger Allee 160 LübeckD-23562 Germany

Interactive visualisation and analysis of time series data is a critical part of many data-driven optimisation processes, particularly in Industry 4.0 and Smart Manufacturing. Time series visualisation enables data analysts or domain experts to visually identify problems such as missing values, sensor drift, precision degradation, or faulty data, before or after algorithmic analysis. A common technique to support the visual exploration of large time series is the overview+detail (O+D) technique. O+D provides both detail and context information by displaying a detailed view showing the actual data and a thumbnail overview for showing its context. User studies have shown that users analyse and navigate data sets more efficently and effectively with than without O+D, but that this strongly depends on the task and the nature and amount of the data to be displayed. We present results of a quantitative user study that was performed on Amazon Mechanical Turk with 95 participants to identify scenarios in which O+D could not effectively solve the challenge of visualising large time series. By this, we identify potential usability issues of O+D for typical time series analysis tasks and discuss their origins. For each of these usability issues, we also propose alternative interaction and visualisation designs or other strategies to maintain good usability, even for challenging task types and data densities. © 2021 Elsevier B.v.. All rights reserved.

关键词： Time series analysis

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A-Map: Interactive visual exploration of Intercity Accessibility Dynamics Based on Railway Network data

A-Map: Interactive Visual Exploration of Intercity Accessibi...

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Pacific (formerly Asia-Pacific APvIS) visualization Symposium

作者： Kaichen Nie Hanning Shao Yuchu Luo Min Tian Hao Wu Wei Zeng Xin Fu Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) School of AI Peking University Hong Kong University of Science and Technology Guangzhou Chang’an University National Engineering Laboratory for Big Data Analysis and Application Peking University

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

Railway transportation is closely linked to everyday lives while also aiding domain experts in analyzing national or regional development. However, discrepancies between travel time reduced by railways and actual distance pose challenges in visualizing the accessibility. Existing methods either struggle to simultaneously depict the accessibility relationships among all cities or disregard genuine geographical positions, leading to spatial cognitive confusion. In this work, we propose a novel approach to balance between the geographical positions and travel time between cities. We construct linear cartograms to represent accessibility while preserving better local railway network structures compared with existing methods. We further propose the A-Map system, enabling users to interactively explore the extensive development of China’s railway system over decades. We validate the effectiveness of our proposed layout algorithm from qualitative metric evaluation and illustrate the applicability of our system with results.

关键词： Measurement visualization Heuristic algorithms Urban areas Layout Transportation data visualization

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Conceptual State analysis

Conceptual State Analysis

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Complex Adaptive Systems conference on Big data, IoT, and AI for a Smarter Future

作者： Mordecai, Yaniv Crawley, Edward F. MIT 77 Massachusetts Ave Cambridge MA 02138 USA

Conceptual State analysis (CSA) is the process of defining, exploring, and reasoning about state attributes and state spaces in complex system architectures. Model-Based State analysis enhances CSA with formal modeling and analysis methods. We propose a process for generating the state vector of a given system architecture, based on a graph representation of the architecture's model in Object-Process Methodology. A robust graph data structure that represents the model is queried to produce the architecture's state space. The process facilitates analysis, reasoning, and model revision based on improved understanding of state vectors and state spaces. State attribute refinements within the model allow for model validity, viability, and reusability as the architecture evolves, with multiple agents, attributes, and attribute state values. The CSA approach advocates and facilitates careful, dynamic, and interactive state space exploration in lieu of exhaustive upfront enumeration of state space permutations. The results can be fed into other analysis, simulation, or visualization tools. We demonstrate CSA on driver assistance technology. (c) 2021 The Authors. Published by Elsevier B.v. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems conference, June 2021.

关键词： Conceptual State analysis Model Based State analysis Model Based Systems Architecting Graph data Structures

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Assessing the viability of visual vibrometry for use in structural engineering 16th

Assessing the viability of visual vibrometry for use in stru...

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16th East Asian-Pacific conference on Structural Engineering and Construction, 2019

作者： Slinger, v. Lao, D. Nguyen, v.T. Singh, S. Gattas, J. School of Civil Engineering University of Queensland Brisbane Australia School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia

ISBN: (纸本)9789811580789

visual vibrometry is a computer vision methodology that allows a user to perform modal analysis on an object using typical video cameras, for example a standard smartphone. There is significant potential in using visual vibrometry for structural monitoring and building modal analysis;it significantly reduces cost and set-up time as compared to traditional contact and non-contact based modal analysis techniques, and allows for the measurement of non-conventional structures which might be difficult to otherwise instrument. This paper describes an initial benchmarking of the effectiveness of the visual vibrometry technique in structural modal analysis, with testing conducted on a simply supported beam and a complex semi-rigid frame structure using both visual vibrometry and classical contact-based methods. Results indicated that visual vibrometry was effectively able to detect natural frequencies under 20 Hz for both structures, with reasonable correlation seen between visual and contact-based measurements, as well as analytical and numerical solutions. The poor detection of higher frequency modes was attributed to the receptance frequency response, generated when the collected visual displacement data is transformed to the frequency domain. Doing so highlights low frequency responses but suppresses intermediate and high frequency responses. Use of high-speed cameras may improve high-frequency detection, but further work is needed to determine practical limitations. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Modal analysis

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