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visual Recognition and Recommendation System for Cultural Tourism Attractions Based on Deep Learning 1st

Visual Recognition and Recommendation System for Cultural To...

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1st International conference on Pattern analysis and Machine Intelligence, ICPAMI 2024

作者： Li, Si Yang, Xiaoqi Guangzhou College of Commerce Guangzhou China

ISBN: (纸本)9789819633487

This article designs and constructs a deep prediction model that combines deep neural networks and Transformer encoders. Build a prediction model using deep neural networks and Transformer encoders. Due to the powerful feature extraction ability of deep neural networks and their ability to fuse heterogeneous data from multiple sources, they are used to process basic information about users and scenic spots. The Transformer encoder is used to process user comment data, modeling long-distance dependencies between words in sentences through position encoding and self attention at the encoding end, thereby fully mining the comprehensive features of comment data and obtaining more potential user interest features. The features of the last three networks are input into the factorization machine to further learn their potential interaction relationships, thereby improving the prediction accuracy of the model and alleviating the problem of data sparsity. Finally, the effectiveness and accuracy of this recommendation model were verified through experimental comparison with traditional recommendation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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DocVisor: A Multi-purpose Web-Based Interactive visualizer for Document Image Analytics 16th

DocVisor: A Multi-purpose Web-Based Interactive Visualizer f...

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16th IAPR International conference on Document analysis and Recognition (ICDAR)

作者： Belagavi, Khadiravana Tadimeti, Pranav Sarvadevabhatla, Ravi Kiran Int Inst Informat Technol Ctr Visual Informat Technol Hyderabad 500032 India

ISBN: (纸本)9783030861599;9783030861582

The performance for many document-based problems (OCR, Document Layout Segmentation, etc.) is typically studied in terms of a single aggregate performance measure (Intersection-Over-Union, Character Error Rate, etc.). While useful, the aggregation is a trade-off between instance-level analysis of predictions which may shed better light on a particular approach's biases and performance characteristics. To enable a systematic understanding of instance-level predictions, we introduce DocVisor - a web-based multi-purpose visualization tool for analyzing the data and predictions related to various document image understanding problems. DocVisor provides support for visualizing data sorted using custom-specified performance metrics and display styles. It also supports the visualization of intermediate outputs (e.g., attention maps, coarse predictions) of the processing pipelines. This paper describes the appealing features of DocVisor and showcases its multi-purpose nature and general utility. We illustrate DocVisor's functionality for four popular document understanding tasks - document region layout segmentation, tabular data detection, weakly-supervised document region segmentation and optical character recognition. DocVisor is available as a documented public repository for use by the community.

关键词： Forecasting

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ConceptExplorer: visual analysis of Concept Drifts in Multi-source Time-series data 15

ConceptExplorer: Visual Analysis of Concept Drifts in Multi-...

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15th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Wang, Xumeng Chen, Wei Xia, Jiazhi Chen, Zexian Xu, Dongshi Wu, Xiangyang Xu, Mingliang Schreck, Tobias Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China China Jiliang Univ Coll Informat Engn Hangzhou Peoples R China Hangzhou Dianzi Univ Inst Graph & Image Hangzhou Peoples R China Zhengzhou Univ Zhengzhou Peoples R China Graz Univ Technol Graz Austria

ISBN: (纸本)9781728180090

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

关键词： Temporal data data analysis reasoning problem solving and decision making machine learning techniques

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MIVA: Multimodal Interactions for Facilitating visual analysis with Multiple Coordinated Views 24

MIVA: Multimodal Interactions for Facilitating Visual Analys...

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24th International conference Information visualisation (IV)

作者： Chowdhury, Imran Moeid, Abdul Hoque, Enamul Kabir, Muhammad Ashad Hossain, Md Sabir Islam, Mohammad Mainul Chittagong Univ Engn & Technol Chittagong Bangladesh York Univ Toronto ON Canada Charles Sturt Univ Bathurst NSW Australia Verizon Media Sunnyvale CA USA

ISBN: (纸本)9781728191348

Typically, people perform visual data analysis using mouse and touch interactions. While such interactions are often easy to use, they can be inadequate for users to express complex information and may require many steps to complete a task. Recently natural language interaction has emerged as a promising technique for supporting exploration with visualization, as the user can express a complex analytical question more easily. In this paper, we investigate how to synergistically combine language and mouse-based direct manipulations so that weakness of one modality can be complemented by the other. To this end, we have developed a novel system, named Multimodal Interactions System for visual analysis (MIVA), that allows user to provide input using both natural language (e.g., through speech) and direct manipulation (e.g., through mouse or touch) and presents the answer accordingly. To answer the current question in the context of past interactions, the system incorporates previous utterances and direct manipulations made by the user within a finite-state model. We tested the applicability of MIVA on several dashboards including a COVID-19 dashboard that visualizes coronavirus cases around the globe. Our demonstration provides initial indication that the MIVA system enhances the flow of visual analysis by enabling fluid, iterative exploration and refinement of data in a dashboard with multiple-coordinated views.

关键词： Multimodal interaction multiple-coordinated views natural language interface direct manipulation

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Combining visual analytics and machine learning for reverse engineering in assembly quality control

Combining visual analytics and machine learning for reverse ...

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2021 conference on visualization and data analysis, VDA 2021

作者： Ruediger, Patrick Claus, Felix Hamann, Bernd Hagen, Hans Leitte, Heike Technische Universität Kaiserslautern Germany

The authors introduce an integrative approach for the analysis of the high-dimensional parameter space relevant for decision-making in the context of quality control. Typically, a large number of parameters influence the quality of a manufactured part in an assembly process, and our approach supports the visual exploration and comprehension of the correlations among various parameters and their effects on part quality. We combine visualization and machine learning methods to help a user with the identification of important parameter value settings having certain effects on a part. The goal to understand the influence of parameter values on part quality is treated from a reverse engineering perspective, driven by the goal to determine what values cause what effects on part quality. The high-dimensional parameter value domain generally cannot be visualized directly, and the authors employ dimension reduction techniques to address this problem. Their prototype system makes possible the identification of regions in a high-dimensional parameter value space that lead to desirable (or non-desirable) parameter value settings for quality assurance. They demonstrate the validity and effectiveness of our methods and prototype by applying them to a sheet metal deformation example. © 2020 Society for Imaging Science and Technology.

关键词： Quality control

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Out of the Box: Embodied Navigation in the Real World 19th

Out of the Box: Embodied Navigation in the Real World

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19th International conference on Computer analysis of Images and Patterns (CAIP)

作者： Bigazzi, Roberto Landi, Federico Cornia, Marcella Cascianelli, Silvia Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9783030891275;9783030891282

The research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of indoor and photorealistic environments. These two factors have opened the doors to a new generation of intelligent agents capable of achieving nearly perfect PointGoal Navigation. However, such architectures are commonly trained with millions, if not billions, of frames and tested in simulation. Together with great enthusiasm, these results yield a question: how many researchers will effectively benefit from these advances? In this work, we detail how to transfer the knowledge acquired in simulation into the real world. To that end, we describe the architectural discrepancies that damage the Sim2Real adaptation ability of models trained on the Habitat simulator and propose a novel solution tailored towards the deployment in real-world scenarios. We then deploy our models on a LoCoBot, a Low-Cost Robot equipped with a single Intel RealSense camera. Different from previous work, our testing scene is unavailable to the agent in simulation. The environment is also inaccessible to the agent beforehand, so it cannot count on scene-specific semantic priors. In this way, we reproduce a setting in which a research group (potentially from other fields) needs to employ the agent visual navigation capabilities as-a-Service. Our experiments indicate that it is possible to achieve satisfying results when deploying the obtained model in the real world. Our code and models are available at https://***/aimagelab/LoCoNav.

关键词： Embodied AI Sim2Real visual navigation

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Prospect evaluation based on integrated petroleum system analysis: Block E case study, south-eastern edge of Precaspian basin Kazakhstan

Prospect evaluation based on integrated petroleum system ana...

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SPE Annual Caspian Technical conference 2021, CTC 2021

作者： Zhanserkeyeva, Ainura Kassenov, Akzhan Satbayev University 5A OIL

ISBN: (纸本)9781613998366

Positive geological and geochemical prerequisites have been identified for the purpose of increasing hydrocarbon resource potential in the under-explored study area. A methodology has been developed for assessing the hydrocarbon potential and prospecting for new promising oil and gas accumulation zones using the technology of basin modeling, provided there is a lack of initial data. A high hydrocarbon source rock generative potential and the degree of thermal maturity of the Lower Permian, Mid Carboniferous and Upper Devonian strata of the south-eastern part of the Precaspian depression have been revealed. Seismostratigraphic and geodynamic analysis was carried out and the main stages of the geodynamic evolution of the study area were reconstructed based on combination of all available geological and geophysical information, recent exploration drilling results and unpublished subsurface studies. The results of thermotectonic modelling confirm the possibility of vertical migration of hydrocarbons generated in Paleozoic sediments. A revision of the previously performed interpretation of 3D seismic data has been carried out;and for the first time, intrasalt sedimentary packets of presumably Upper Permian age have been identified as independent objects, which can be potential hydrocarbon traps. For the Lower Permian deposits, type iiI kerogen predominates, which may be associated with an increase in collisional processes in the Late Paleozoic time and an active input of plant organic matter. For Mid Carboniferous sediments, mixed type ii / iiI kerogen or type ii kerogen prevails. analysis of the evolution of thermal maturity indicates the unevenness of the entry of potential oil and gas source strata into the main zone of oil generation. For kerogen type iiI of the Lower Permian source rocks, the peak of oil generation falls on the Late Cretaceous. For predominantly carbonate and terrigenous-carbonate Middle Carboniferous source rocks the peak of generation falls on the

关键词： Kerogen

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exploration of Aural & visual Media About Birds Informs Lessons for Citizen Science Design 20

Exploration of Aural & Visual Media About Birds Informs Less...

引用

ACM Designing Interactive Systems conference (DIS)

作者： Oliver, Jessica L. Brereton, Margot Turkay, Selen Watson, David M. Roe, Paul Queensland Univ Technol Brisbane Qld Australia Charles Sturt Univ Albury NSW Australia

ISBN: (纸本)9781450369749

Acoustic sensing has been hailed as a game-changer for detecting furtive wildlife, but uptake has been constrained by the laborious process of reviewing resultant torrents of audio data. To inform the design of interactive interfaces for reviewing audio recordings, we explored how people interact with aural and visual media about birds. We observed how twelve participants with different levels of interest in birds engaged with vocalization recordings, visualizations of bird calls, photographs, and range maps of three species. By conducting thematic analysis, we identified a variety of Challenges of exploration and Benefits of a Media Assortment. We contribute lessons for designing to Bridge Knowledge & Context and to Facilitate Long-term Engagement with audio in ways that are fun, accessible, and informative. We provide explicit guidance for designers to diversify how citizen scientists interact with nature through audio as they move from engagement to conservation action.

关键词： Audio interactions aural birds citizen science multisensory nature connection visual wildlife

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CLASS SPECIFIC INTERPRETABILITY IN CNN USING CAUSAL analysis

CLASS SPECIFIC INTERPRETABILITY IN CNN USING CAUSAL ANALYSIS

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IEEE International conference on Image Processing (ICIP)

作者： Yadu, Ankit Suhas, P. K. Sinha, Neelam Samsung R&D India Bangalore Karnataka India Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9781665441155

A singular problem that mars the wide applicability of machine learning (ML) models is the lack of generalizability and interpretability. The ML community is increasingly working on bridging this gap. Prominent among them are methods that study causal significance of features, with techniques such as Average Causal Effect (ACE). In this paper, our objective is to utilize the causal analysis framework to measure the significance level of the features in binary classification task. Towards this, we propose a novel ACE-based metric called "Absolute area under ACE (A-ACE)" which computes the area of the absolute value of the ACE across different permissible levels of intervention. The performance of the proposed metric is illustrated on (i) ILSVRC (Imagenet) dataset and (ii) MNIST data set (similar to 42000 images) by considering pair-wise binary classification problem. Encouraging results have been observed on these two datasets. The computed metric values are found to be higher - peak performance of 10x higher than other for ILSVRC dataset and 50% higher than others for MNIST dataset - at precisely those locations that human intuition would mark as distinguishing regions. The method helps to capture the quantifiable metric which represents the distinction between the classes learnt by the model. This metric aids in visual explanation of the model's prediction and thus, makes the model more trustworthy.

关键词： Causal Inference Interpretability CNN Explainability Machine Learning

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Innovative Deep Learning Techniques for Solving Complex Computer Vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： T. Jayachitra S. Sathya Ramesh P S Jothikumar R Gouri M. S Mohammed Waheeduddin Hussain Department of Electrical Electronics and Communication Engineering Sharda University Greater Noida India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tiruttani India Department of Smart Computing Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Shadan College of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Baselios Mathews - II College of Engineering Sasthamcotta Kollam India Department of Information Technology Shadan College of Engineering and Technology Peerancheru Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications.

关键词： Deep learning Computer vision visualization Accuracy Network topology Neural networks data augmentation Topology Pattern recognition Object recognition

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