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

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LegisLatio: A visualization Tool for Legislative Roll-call vote data 22

LegisLatio: A visualization Tool for Legislative Roll-call V...

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15th International Symposium on visual Information Communication and Interaction, vINCI2022

作者： Méndez, Gonzalo Gabriel Moreno, Oscar Mendoza, Patricio Escuela Superior Politécnica Del Litoral Guayaquil Ecuador

ISBN: (纸本)9781450398077

Appropriate communication and understanding of political data are key to achieve a healthy dialogue between civil society and political institutions. This is particularly important for data generated by bodies that most directly represent citizens, such as congresses, senates, parliaments, and other forms of legislatures. visualization tools have the potential to support the exploration and understanding of such data and make it more accessible and appealing to the general public. With this vision, we present LegisLatio, an interactive visualization tool that enables open-ended analysis of legislative roll-call vote data from multi-party electoral systems. We describe the design of the tool and illustrate its effectiveness with data generated by the Ecuadorian legislature. We also discuss the findings of a qualitative observation that hints at LegisLatio's potential to promote citizen engagement and participation. © 2022 ACM.

关键词： data visualization

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A Bayesian-Optimized Convolutional Neural Network to Decode Reach-to-Grasp from Macaque Dorsomedial visual Stream 8th

A Bayesian-Optimized Convolutional Neural Network to Decode ...

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8th Annual International conference on Machine Learning, Optimization and data science (LOD)

作者： Borra, Davide Filippini, Matteo Ursino, Mauro Fattori, Patrizia Magosso, Elisa Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon Cesena Campus Cesena Italy Univ Bologna Dept Biomed & Neuromotor Sci DIBINEM Bologna Italy Univ Bologna Alma Mater Res Inst Human Ctr Artificial Intellig Bologna Italy

ISBN: (纸本)9783031258909;9783031258916

Neural decoding is crucial to translate the neural activity for Brain-Computer Interfaces (BCIs) and provides information on how external variables (e.g., movement) are represented and encoded in the neural system. Convolutional neural networks (CNNs) are emerging as neural decoders for their high predictive power and are largely applied with electroencephalographic signals;these algorithms, by automatically learning the more relevant class-discriminative features, improve decoding performance over classic decoders based on handcrafted features. However, applications of CNNs for single-neuron decoding are still scarce and require further validation. In this study, a CNN architecture was designed via Bayesian optimization and was applied to decode different grip types from the activity of single neurons of the posterior parietal cortex of macaque (area v6A). The Bayesian-optimized CNN significantly outperformed a naive Bayes classifier, commonly used for neural decoding, and proved to be robust to a reduction of the number of cells and of training trials. Adopting a sliding window decoding approach with a high time resolution (5 ms), the CNN was able to capture grip-discriminant features early after cuing the animal, i.e., when the animal was only attending the object to grasp, further supporting that grip-related neural signatures are strongly encoded in v6A already duringmovement preparation. The proposed approach may have practical implications in invasive BCIs to realize accurate and robust decoders, and may be used together with explanation techniques to design a general tool for neural decoding and analysis, boosting our comprehension of neural encoding.

关键词： Neural decoding Convolutional neural networks Dorsomedial visual stream v6A Bayesian optimization

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Evaluation and analysis of forest carbon sequestration and oxygen release value under cloud computing framework 3

Evaluation and analysis of forest carbon sequestration and o...

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3rd International conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Han, Yao Gao, Shan Liu, Chunyan Harbin Cambridge University Harbin150069 China

Forestry carbon sink refers to the net income generated by forests, trees and woodlands in the process of absorbing atmospheric carbon dioxide and fixing renewable forest resources. With the rapid development of Chinese economy and accelerating industrialization and urbanization, global climate change has become the focus of attention of all countries. Forest has a strong carbon sink function. The study of forest carbon sequestration and oxygen release value assessment is of great significance for the protection, development and utilization of forest resources and the mitigation of climate change. In this paper, the distributed storage model based on cloud computing architecture was adopted to evaluate the value of forest carbon sequestration and oxygen release. This model can be used to quickly calculate the value of forest carbon sequestration and oxygen release in a certain region, provide data support for forestry carbon sink trading, and provide method reference and scientific basis for forestry carbon sink project accounting. © 2023 The Authors. Published by Elsevier B.v.

关键词： Cloud computing architecture

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Machine Learning Algorithms to visualize the Weld Quality and Evaluation of Health Issues during the Welding

Machine Learning Algorithms to Visualize the Weld Quality an...

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2023 International conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Prasad, R.v.v.S.v. Ambadas, Parkhe Ravindra Panda, Somesubhra Ansari, Mohammed Saleh Al Deepthi, K. Swarnandhra College of Engineering & Technology Department of Information Technology Narsapur India Pravara Rural Engineering Collage Mechanical Engineering Loni India Global Institute of Science & Technology Department of Electrical Engineering Purba Medinipur India University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Avanthi Institute of Engineering and Technology Avev Mechanical Engineering Kotabhogapuram India

ISBN: (纸本)9798350317060

This research explores the potential of machine learning methods to use in the welding sector. Welding operations encounter several challenges that might use machine learning approaches. Welding processes have been found to benefit greatly from the use of machine learning algorithms, which increase both productivity and quality. Industrial robots powered by artificial intelligence may assist in addressing many difficult problems in the manufacturing industry. Many welding procedures depend on human knowledge to choose optimal settings, making them less efficient and more prone to human mistakes. To lessen this reliance, neural networks educate robots and autonomous systems to provide consistent weld quality and increased efficiency. Weld quality is mainly determined by visual examination. As a result, machine learning is also used to provide a visual representation of the welding process. In two dimensions, 12 machine learning pipelines are built: settings for feature engineering and ML techniques. Four feature settings and three machine learning methods were considered. Using data from two active industrial production lines containing 25 welding machines, a machine-learning pipeline was tested. These approaches may also be used to analyze the components using regression analysis to determine which factors relate to certain health concerns. © 2023 IEEE.

关键词： Welding machines

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Object Detection Using YOLO-v8

Object Detection Using YOLO-V8

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International conference on Computing and Networking Technology (ICCNT)

作者： B Karthika M Dharssinee v Reshma R venkatesan R Sujarani SRC/SASTRA Deemed To Be University Kumbakonam India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The growing need for automated object detection and recognition in visual data has become crucial in various domains, including surveillance, autonomous vehicles, and image analysis. Traditional methods often struggle with speed and accuracy, making real-time applications challenging. The main objective of this experiment is to make the computers identify and detect objects in the visual data. The algorithm is used to detect the object with classification and localization based on visual inputs. This project uses YOLO(You Only Look Once) algorithm to detect the objects. YOLO v8 is known for its speedy and accurate object detection. This idea is used to identify multiple objects in images, videos, and live webcams. This algorithm adapts to various scenarios such as image analysis to dynamic object tracking.

关键词： YOLO visualization Accuracy Webcams Heuristic algorithms Surveillance Classification algorithms Object recognition vehicle dynamics videos

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MARvEL: Multidimensional Abstraction and Reasoning through visual Evaluation and Learning 38

MARVEL: Multidimensional Abstraction and Reasoning through V...

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jiang, Yifan Zhang, Jiarui Sun, Kexuan Sourati, Zhivar Ahrabian, Kian Ma, Kaixin Ilievski, Filip Pujara, Jay Information Sciences Institute University of Southern California United States Tencent AI Lab BellevueWA United States Department of Computer Science Vrije Universiteit Amsterdam Netherlands

While multi-modal large language models (MLLMs) have shown significant progress across popular visual reasoning benchmarks, whether they possess abstract visual reasoning abilities remains an open question. Similar to the Sudoku puzzles, abstract visual reasoning (AvR) problems require finding high-level patterns (e.g., repetition constraints on numbers) that control the input shapes (e.g., digits) in a specific task configuration (e.g., matrix). However, existing AvR benchmarks only consider a limited set of patterns (addition, conjunction), input shapes (rectangle, square), and task configurations (3 × 3 matrices). And they fail to capture all abstract reasoning patterns in human cognition necessary for addressing real-world tasks, such as geometric properties and object boundary understanding in real-world navigation. To evaluate MLLMs' AvR abilities systematically, we introduce MARvEL founded on the core knowledge system in human cognition, a multidimensional AvR benchmark with 770 puzzles composed of six core knowledge patterns, geometric and abstract shapes, and five different task configurations. To inspect whether the model performance is grounded in perception or reasoning, MARvEL complements the standard AvR question with perception questions in a hierarchical evaluation framework. We conduct comprehensive experiments on MARvEL with ten representative MLLMs in zero-shot and few-shot settings. Our experiments reveal that all MLLMs show near-random performance on MARvEL, with significant performance gaps (40%) compared to humans across all patterns and task configurations. Further analysis of perception questions reveals that MLLMs struggle to comprehend the visual features (near-random performance). Although closed-source MLLMs, such as GPT-4v, show a promising understanding of reasoning patterns (on par with humans) after adding textual descriptions, this advantage is hindered by their weak perception abilities. We release our entire code and dataset at ht

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Clustering Running Titles to Understand the Printing of Early Modern Books 18th

Clustering Running Titles to Understand the Printing of Earl...

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18th International conference on Document analysis and Recognition (ICDAR)

作者： vogler, Nikolai Goyal, Kartik Lemley, Samuel, v Schuldt, D. J. Warren, Christopher N. GSell, Max Berg-Kirkpatrick, Taylor Univ Calif San Diego La Jolla CA 92093 USA Georgia Inst Technol Atlanta GA 30332 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Simmons Univ Boston MA 02115 USA

ISBN: (纸本)9783031705427;9783031705434

We propose a novel computational approach to automatically analyze the physical process behind printing of early modern letterpress books via clustering the running titles found at the top of their pages. Specifically, we design and compare custom neural and feature-based kernels for computing pairwise visual similarity of a scanned document's running titles and cluster the titles in order to track any deviations from the expected pattern of a book's printing. Unlike body text which must be reset for every page, the running titles are one of the static type elements in a skeleton forme i.e. the frame used to print each side of a sheet of paper, and were often re-used during a book's printing. To evaluate the effectiveness of our approach, we manually annotate the running title clusters on about 1600 pages across 8 early modern books of varying size and formats. Our method can detect potential deviation from the expected patterns of such skeleton formes, which helps bibliographers understand the phenomena associated with a text's transmission, such as censorship. We also validate our results against a manual bibliographic analysis of a counterfeit early edition of Thomas Hobbes' Leviathan (1651) [27]. (Code and data available at https://***/nvog/clustering-running-titles)

关键词： Historical document analysis Document provenance Document image processing Document clustering

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6th Iberian Robotics conference, Robot 2023

6th Iberian Robotics Conference, Robot 2023

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6th Iberian Robotics conference, Robot 2023

ISBN: (纸本)9783031586750

The proceedings contain 90 papers. The special focus in this conference is on Iberian Robotics. The topics include: Map Merge and Accurate Localization in Multi-robot Systems in Real Environments;category Theory for Autonomous Robots: The Marathon 2 Use Case;Development of a Low-Cost 3D Mapping Technology with 2D LIDAR for Path Planning Based on the A* Algorithm;synPhoRest - A Procedural Generation Tool of Synthetic Photorealistic Forest datasets;Evaluation of Point Cloud data Augmentation for 3D-LiDAR Object Detection in Autonomous Driving;Green Deep Learning: Comparative Study of Road Object Detectors Between Jetson Boards and PC;Exploring Domain Adaptation with Depth-Based 3D Object Detection in CARLA Simulator;improving Energy Performance of Camera Lidar Fusion by Intermittent Human Detection for Social Navigation;visual Tracking of Synthetic Space Platforms in Low Orbit Using International Space Station video Stream and Rendered Earth Model;driver Activity Recognition by Fusing Multi-object and Key Points Detection;cross-view Generalisation in Action Recognition: Feature Design for Transitioning from Exocentric To Egocentric views;assessing Infotaxis Sensitivity to Model Quality Through Evolutionary Computation;A Feasibility Study of a Leader-Follower Multi-robot Formation for TDLAS Assisted Methane Detection in Open Spaces;studying Exploitation and exploration Trade-Off of Cognitive Odour-Guided Search;Performance analysis of ORB-SLAM in Foggy Environments;socially Reactive Navigation Models for Mobile Robots in Dynamic Environments;human Motion Trajectory Prediction Using the Social Force Model for Real-Time and Low Computational Cost Applications;DeepRL-Based Robot Local Motion Planning in Unknown Dynamic Indoor Environments;parallel Curves Path Planning Based on Tangent Segments to Concentric Circles;fuzzy Logic Based Decision-Making for Urban Platooning on Urban Roundabout Scenarios;fast Tunnel Traversal for Ground vehicles by Bearing Estimation with Neura

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Unveiling Crime Patterns and Empowering Investigative Insights Through Machine Learning and NLP 2

Unveiling Crime Patterns and Empowering Investigative Insigh...

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2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Ragam, Prashanth Mallesh, H.v. Suma, L. Pavithra Yasaswini, L. Kumar, M. varun varsha, N. Siri Jyothi, Sankati School of Computer Science & Engineering VIT-AP UNIVERSITY AP Vijayawada522237 India AP Vijayawada522237 India

ISBN: (纸本)9798350366570

The well-being of society and public safety are severely threatened by crime. Policymakers, law enforcement agencies, and community stakeholders must comprehend the distribution of crimes, their patterns, and the variables that drive criminal activity. Stakeholders may identify regional patterns in crime by visualising state-level crime data, which facilitates the allocation of resources to high-risk locations and supports targeted actions. Furthermore, content recommendation systems provide relevant information about crimes, enabling stakeholders to design successful crime prevention plans and back community engagement programmes with the knowledge and tools they need. These actual criminal scenarios may be effectively addressed with the help of the models used in this study. With the use of regression models, law enforcement organisations and legislators may forecast future trends and effectively deploy resources by doing predictive analysis of crime rates based on previous data. Additionally, the powerful natural language processing techniques used by the content recommendation system make it easier for stakeholders to receive pertinent information about crimes. The technology improves accessibility to actionable ideas and best practices in crime prevention and intervention by using machine learning algorithms to evaluate user searches and article content. In general, the incorporation of these models provides stakeholders with data-driven decision-making abilities, enabling them to successfully tackle criminal activity and protect communities. © 2024 IEEE.

关键词： Crime

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CueCAn: Cue-driven Contextual Attention for Identifying Missing Traffic Signs on Unconstrained Roads

CueCAn: Cue-driven Contextual Attention for Identifying Miss...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Gupta, varun Subramanian, Anbumani Jawahar, C., v Saluja, Rohit IIT Hyderabad Ctr Visual Informat Technol CVIT Lab Hyderabad India IIT Mandi Mandi India

ISBN: (纸本)9798350323658

Unconstrained Asian roads often involve poor infrastructure, affecting overall road safety. Missing traffic signs are a regular part of such roads. Missing or non-existing object detection has been studied for locating missing curbs and estimating reasonable regions for pedestrians on road scene images. Such methods involve analyzing task-specific single object cues. In this paper, we present the first and most challenging video dataset for missing objects, with multiple types of traffic signs for which the cues are visible without the signs in the scenes. We refer to it as the Missing Traffic Signs video dataset (MTSvD). MTSvD is challenging compared to the previous works in two aspects i) The traffic signs are generally not present in the vicinity of their cues, ii) The traffic signs' cues are diverse and unique. Also, MTSvD is the first publicly available missing object dataset. To train the models for identifying missing signs, we complement our dataset with 10K traffic sign tracks, with 40% of the traffic signs having cues visible in the scenes. For identifying missing signs, we propose the Cue-driven Contextual Attention units (CueCAn), which we incorporate in our model's encoder. We first train the encoder to classify the presence of traffic sign cues and then train the entire segmentation model end-to-end to localize missing traffic signs. Quantitative and qualitative analysis shows that CueCAn significantly improves the performance of base models.

关键词： Object detection

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