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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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EMNLP 2022 - 2022 conference on Empirical Methods in Natural Language Processing, Proceedings of the Demonstrations Session

EMNLP 2022 - 2022 Conference on Empirical Methods in Natural...

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2022 conference on Empirical Methods in Natural Language Processing, EMNLP 2022

ISBN: (纸本)9781959429418

The proceedings contain 42 papers. The topics discussed include: CogKTR: a knowledge-enhanced text representation toolkit for natural language understanding;LM-Debugger: an interactive tool for inspection and intervention in transformer-based language models;EasyNLP: a comprehensive and easy-to-use toolkit for natural language processing;an explainable toolbox for evaluating pre-trained vision-language models;ELEvANT: a fully automatic fine-grained entity linking evaluation and analysis tool;a pipeline for generating, annotating and employing synthetic data for real world question answering;DeepKE: a deep learning based knowledge extraction toolkit for knowledge base population;evaluate & evaluation on the hub: better best practices for data and model measurements;and KeywordScape: visual document exploration using contextualized keyword embeddings.

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Sentinel-1 and ALOS data for lineament extraction: a comparative study 13

Sentinel-1 and ALOS data for lineament extraction: a compara...

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conference on Earth Resources and Environmental Remote Sensing/GIS Applications XIII Part of SPIE Remote Sensing conference

作者： Cardoso-Fernandes, Joana Teodoro, Ana Claudia Lima, Alexandre Menuge, Julian Bronner, Marco Steiner, Ralf Univ Porto Dept Geosci Environm & Spatial Planning Fac Sci Rua Campo Alegre P-4169007 Porto Portugal ICT Inst Earth Sci Porto Pole Portugal Rua Campo Alegre P-4169007 Porto Portugal Univ Coll Dublin Sch Earth Sci & iCRAG Dublin D04 V1W8 Ireland Geol Survey Norway Trondheim Norway Geo Unterweissacher GmbH Hochfilzen Austria

ISBN: (纸本)9781510655409;9781510655393

GREENPEG is a H2020 project developing exploration toolsets for critical materials in pegmatites. Lineament mapping is a powerful tool to identify geological structures at different scales, which is commonly applied in mineral exploration. To extract lineament information, different types of data can be employed, namely: (i) optical remote sensing data, (ii) radar remote sensing data, and (iii) digital elevation models (DEM5). All these data have been used by GREENPEG, where image processing of Sentinel-1 and ALOS (DEM) datasets were evaluated to identify regional-scale tectonic structures, such as faults, that may have controlled pegmatite melt emplacement. Automated geomatics methods to extract lineament information and perform lineament mapping were employed and the mean direction of the extracted lineaments was evaluated through rose diagrams. In general, the results of the radar image processing were affected by noise in the coastal areas and by the topography of the region. ALOS data presented some advantages, due to less influence from topography and human structures (saving time in the visual inspection of the results). Moreover, even though higher thresholds for the minimum length to consider as a lineament were applied to Sentinel-1 images, ALOS-derived lineaments were longer. The main trend of the extracted lineaments is in agreement between the two datasets, although specific trends are clearer in the ALOS-derived lineaments because the shaded reliefs produced already filtered the azimuth of lineaments to be extracted according to light incidence angle. For all three project demonstration sites (Leinster, Ireland;Wolfsberg, Austria;and Tysfjord, Norway) it was possible to identify lineaments that may relate to structures that controlled pegmatite emplacement. This study demonstrates the usefulness of different satellite data in structural mapping and mineral exploration.

关键词： Structural geology mineral exploration satellite geomatics pegmatite critical raw materials GREENPEG

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A Comparative analysis of Traffic analysis Frameworks in Software-Defined Networking

A Comparative Analysis of Traffic Analysis Frameworks in Sof...

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2025 International conference on visual Analytics and data visualization, ICvADv 2025

作者： Kumar, Gorli Uday Prabu, U. Leelakrishna, Yadlapalli Siddu, Pagadam Geetha, v. Velagapudi Ramakrishna Siddhartha School of Engineering Siddhartha Academy of Higher Education Department of Cse Vijayawada India Puducherry Technological University Department of Information Technology Puducherry India

ISBN: (纸本)9798331521394

Software-defined networking (SDN) has emerged as a transformative paradigm in modern networking by decoupling the control plane from the data plane, enabling centralized control and programmability. This architectural shift addresses the increasing complexity and dynamic requirements of networks by offering enhanced flexibility, scalability, and efficiency. A crucial aspect of SDN is the analysis of network traffic, which plays a pivotal role in performance optimization, network management, and troubleshooting. Recent frameworks have leveraged metrics such as round-trip time, throughput, bandwidth, and packet count to provide insights into network behavior and efficiency. Among these, the RYU controller stands out for its extensibility and flexibility in supporting traffic analysis tasks. A comprehensive review of the various techniques used in SDN traffic analysis highlights their strengths and limitations in optimizing SDN network performance and management. © 2025 IEEE.

关键词： Network Traffic analysis Network Traffic Frameworks RYU controller Software-defined Networking

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Synthesis of Formal LLM Models and Deep Learning Algorithms for Integrative analysis of Multimodal Educational data

Synthesis of Formal LLM Models and Deep Learning Algorithms ...

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International conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： E. I. Markin v. v. Zuparova A. I. Martyshkin Penza state technological University PSTU Penza Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

This paper presents a research aimed at developing a methodology for integrative analysis of multimodal educational data by synthesizing formal models of large language models (LLMs) and deep learning algorithms for visual information processing, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs). The main goal of the work is to develop a comprehensive system capable of analyzing both textual educational content and visual data (charts, graphs, illustrations), which provides a deeper understanding of the educational material and allows for the automation of task validation processes in Learning Management Systems (LMS). The methodology relies on the application of mechanisms of semiotic analysis, logical-semantic data representation and modern methods of multimodal embedding integration with dynamic regulation through attention mechanisms. An experimental study conducted on a specialized dataset confirms the improvement of key metrics (Accuracy, F1-Score, Fréchet Inception Distance, Cosine Similarity) compared to traditional approaches. Prospects for further research include expansion of modalities, optimization of computational processes and integration of the developed system into real educational platforms.

关键词： Deep learning Measurement visualization Learning management systems Attention mechanisms Automation Large language models data models Classification algorithms Optimization

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Preserving Tamil Brahmi Letters on Ancient Inscriptions: A Novel Preprocessing Technique for Diverse Applications 1

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8th International conference on data Management, Analytics and Innovation, ICDMAI 2024

作者： Poornimathi, K. Muralibhaskaran, v. Priya, L. Rajalakshmi Engineering College Chennai India

ISBN: (数字)9789819732456

ISBN: (纸本)9789819732449

Inscriptions play a crucial role in preserving historical, cultural, and linguistic information. The identification and analysis of patterns in Tamil letters found in inscriptions provide valuable insights into the evolution of the Tamil language and its script. However, manual analysis of these inscriptions is time-consuming and prone to errors. In recent years, deep learning techniques have shown promising results in pattern recognition tasks, motivating the exploration of various strategies to identify the patterns of Tamil letters on inscriptions. This paper focuses on leveraging deep learning algorithms for the automated identification of Tamil letter patterns in inscriptions. Firstly, a dataset of digitized Tamil inscriptions is collected, consisting of high-resolution images representing a wide range of letter variations. Preprocessing techniques are employed to enhance the quality and clarity of the images, removing noise and artifacts. various deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are then trained using the preprocessed image dataset. CNNs excel in extracting spatial features from images, enabling the recognition of letter shapes and contours. RNNs, on the other hand, capture temporal dependencies within sequences of letters, aiding in deciphering the structure and connectivity of the inscriptions. To improve the performance of the models, data augmentation techniques are employed to increase the dataset size and enhance its diversity. However, preprocessing plays a major role in sharpening the features present in the image. Hence, this paper addresses the preprocessing techniques such as Image Blur, Binarization, and Edge Detection with respect to inscription. Preprocessing techniques were identified and tested with the inscription image. Based on the results and response time, it is suggested that the Median filter with canny edge detection is working well for inscription images. After pre

关键词： Median filters

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What Are the Trends in Eco-Design Research When We Talk about Digital Technology? - A Five Year Analytical Dive into Research data

What Are the Trends in Eco-Design Research When We Talk abou...

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Zhuang, Nan Ma, Yanni Zhai, Shuo Zhao, Xin Weng, Shitong Ma, Zhenyuan Shi, Shang Ying, Fangtian School of Software Technology Zhejiang University China College of Computer Science and Technology Zhejiang University China Polytechnic Institute Zhejiang University China

ISBN: (纸本)9781643685069

The convergence of digital technology with eco-design offers an integrative approach, merging environmental sustainability with technological innovation in design. In the realm of eco-design, there is a growing emphasis on the application of digital technology. However, the focal areas and trends of eco-design studies utilizing digital technology remain underexplored. This leads to a deficiency in our deep understanding and prediction of the evolution and transformation of this domain. To comprehend the progression in this domain, we curated and scrutinized a dataset of 1,359 papers related to digital technology and eco-design presented at the CHI conference over the past five years. Through keywords analysis from the past half-decade, we pinpointed prevailing research areas in eco-design utilizing digital technology, such as virtual reality, sustainability, health, and human-computer interaction. We also gathered essential information about the papers for descriptive statistical analysis, including the count, authors' nationalities and gender distribution, and the scale of the authorship teams. Coupled by the visual insights from keyword clouds, we discerned trends in eco-design research in the backdrop of digital technology. Our findings underscore that the eco-design domain, when integrated with digital technology, persistently captivates academic interest, with comparatively smaller teams often embarking on projects in this research sphere. Predominantly, researchers from the United States and male participants lead these endeavors. Moreover, there's an escalating emphasis on people-centered design, striving to engineer solutions that harmonize environmental interactions with health promotions. This study's outcomes furnish insights for a more profound exploration in the realm of digital technology and eco-design, offering recommendations to further propel sustainable progress and innovative design. © 2024 The Authors.

关键词： Ecodesign

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Understanding Plant Leaf Diseases: A Review, analysis, exploration of Techniques and Technologies

Understanding Plant Leaf Diseases: A Review, Analysis, Explo...

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2025 International conference on visual Analytics and data visualization, ICvADv 2025

作者： Nagalakshmi, Thirumalasetty Sambasivudu, M. Sai, Thota Siva Ratna Dept. of Cse Telangana India

ISBN: (纸本)9798331521394

Modern agriculture relies on plant leaf disease detection to reduce crop losses and guarantee food security. This study takes a look at the three main types of methods used to identify plant leaf diseases: conventional methods, techniques based on Machine Learning (ML), and methodology based on Deep Learning (DL). Although they provide essential insights, the scalability and precision of traditional methods-which depend on chemical analysis, image processing, and human inspection-are severely lacking. ML techniques, which make use of algorithms such as Random Forests (RF) and Support vector Machines (SvM), automate feature-based categorization, which improves detection. Automated disease identification with high accuracy has recently been made possible by DL, which uses sophisticated methods like semantic segmentation, object recognition, and transfer learning in conjunction with architectures like Convolutional Neural Networks (CNNs). This study examines all of these approaches in detail, comparing and contrasting their advantages, disadvantages, and possible uses. Emerging developments, such as eXplainable Artificial Intelligence (XAI) and Internet of Things (IoT)-based systems, are also highlighted in the discussion, with an emphasis on how they will influence smart and sustainable agriculture in the future. © 2025 IEEE.

关键词： Semantic Segmentation

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Face-Sensitive Image-to-Emotional-Text Cross-modal Translation for Multimodal Aspect-based Sentiment analysis

Face-Sensitive Image-to-Emotional-Text Cross-modal Translati...

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2022 conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Yang, Hao Zhao, Yanyan Qin, Bing Harbin Institute of Technology China

Aspect-level multimodal sentiment analysis, which aims to identify the sentiment of the target aspect from multimodal data, recently has attracted extensive attention in the community of multimedia and natural language processing. Despite the recent success in textual aspect-based sentiment analysis, existing models mainly focused on utilizing the object-level semantic information in the image but ignore explicitly using the visual emotional cues, especially the facial emotions. How to distill visual emotional cues and align them with the textual content remains a key challenge to solve the problem. In this work, we introduce a face-sensitive image-to-emotional-text translation (FITE) method, which focuses on capturing visual sentiment cues through facial expressions and selectively matching and fusing with the target aspect in textual modality. To the best of our knowledge, we are the first that explicitly utilize the emotional information from images in the multimodal aspect-based sentiment analysis task. Experiment results show that our method achieves state-of-the-art results on the Twitter-2015 and Twitter-2017 datasets. The improvement demonstrates the superiority of our model in capturing aspect-level sentiment in multimodal data with facial expressions. © 2022 Association for Computational Linguistics.

关键词： Sentiment analysis

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data science - time series analysis of oil & gas production in mexican fields 3

Data science - time series analysis of oil & gas production ...

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3rd International conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Sanchez Morales, Maria de los Angeles Soler Anguiano, Francisca Irene Univ Nacl Autonoma Mexico A Univ 3000Ciudad Univ Mexico City 04510 DF Mexico

Nowadays, large industries of financial, technological, manufacturing, energetic, and service sectors have accomplished the incorporation of data Science in their operations, processes, and work structures, obtaining significant improvements in their productivity and service potentials. Whereby the oil and gas industry should not be foreign to this science that aids the decision-making processes using the acquired information by extracting massive, structuring data, combining statistics, mathematics, and informatics. Since the variations in petroleum and gas offer and demand are strongly related to the changes in prices, the use of data Science intends to administer and reduce the risks provoked by the processes and decisions related to every step of the industry's chain value. Oil and gas exploration, extraction, development, and production generate considerable amounts of data created by different means, lacking order and precision. Hence, data analysis provides formality to the experiments in this area, improving productivity alternatives and creating innovation opportunities. The present paper objective is to comprehend the production fluctuation trends to understand the possible behaviour of future productions in three Mexican fields with the highest petroleum and gas production, Maloob, Zaap, and Ayatzi for petroleum and Ku, Akal, and Maloob for gas, through the application of a Time Series analysis to each one of them. (C) 2022 The Authors. Published by Elsevier B.v.

关键词： Oil & Gas Production Time Series analysis Seasonal and Non-Seasonal Methods ARIMA and SARIMA Methods

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A Study on the Implementation of Dynamic Pricing Mechanisms for Sustainable Energy Management Using AI-Driven Demand Prediction 8th

A Study on the Implementation of Dynamic Pricing Mechanisms ...

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8th International conference on Energy and Environmental Science, ICEES 2024

作者： Mehta, v.G.P. Kohda, M. Shoji, S. Iwasaki, T. Nishimoto, M. TAS Design Group Tokyo Japan i GRID LAB Tokyo Japan Japan Society of Civil Engineers Tokyo Japan Japan Society of Artificial Intelligence Tokyo Japan

ISBN: (纸本)9783031639005

The pressing need for sustainable energy management drives the exploration of innovative strategies to optimize energy consumption patterns and alleviate strain on the grid. Investigating the effectiveness of dynamic pricing mechanisms, underpinned by AI-driven demand prediction models, we aim to incentivize consumers to adjust energy usage, thereby promoting energy efficiency and grid stability. In Japan, challenges in energy self-sufficiency and fluctuating demand necessitate inventive solutions, leading us to focus on integrating AI, Big data, and IoT technologies to enhance smart clean energy management and demand prediction. This research introduces a comprehensive Intelligent Energy data Management Platform (IDMP) called R.E.A.L., seamlessly integrating with Amazon Web Services (AWS) to utilize Amazon Timestream for time-series data and Amazon SageMaker for AI. At the core of our research lies the optimization of energy consumption through AI-driven demand prediction, aligning with principles of the circular economy. By accurately predicting energy demand and supply, our approach enhances energy efficiency and resource utilization, contributing to sustainability. Through historical data analysis and machine learning algorithms, waste is minimized, and reliance on fossil fuels reduced, thus advancing principles of the circular economy. Overall, this research revolutionizes sustainable energy management by improving operational efficiency, balancing supply and demand, and optimizing resource consumption. The study demonstrates the stability and versatility of the R.E.A.L. platform, offering valuable insights for stakeholders in the energy sector. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Circular economy

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