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检索条件"任意字段=2023 IEEE Visualization in Data Science, VDS 2023"

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Design of Deep Learning Models for the Identification of Harmful Attack Activities in the Industrial Internet of Things (IIOT)

Design of Deep Learning Models for the Identification of Har...

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ieee UP Section Conference on Electrical Computer and Electronics (UPCON)

作者： Rajesh M Rajiv Vincent Sakshi Kathuria Bhavana Jamalpur Thirupathi Durgam Tarun Jaiswal School of Computer Science and Engineering Vellore Institute of Technology Chennai Computer Science and Engineering K. R. Mangalam University University Gurugram Haryana School of Computer Science and Artificial Intelligence SR University Warangal Telangana ECE St. Martins Engineering College JNTUH University (Hyderabad) Department. of Computer Application National Institute of Technology Raipur C.G.

Traditional industrial processes benefit greatly from the Industrial Internet of Things (IIoT), which increases efficiency as well as productivity. However, as IIoT becomes more connected, critical infrastructure is exposed to a variety of cyber risks. In order to accurately identify dangerous attack behaviors within IIoT systems, the present research focuses on the development and execution of deep learning models, combining recurrent neural networks (RNNs) including artificial neural networks (ANNs). The approach uses Python-based visualization techniques, model creation, and data pretreatment. By proactively identifying and mitigating cyber risks, our research seeks to strengthen IIoT security and ensure the resilience and honesty of industrial operations in a world that is becoming more linked.

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Classification-Based Dynamic Network for Efficient Super-Resolution

Classification-Based Dynamic Network for Efficient Super-Res...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qi Wang Weiwei Fang Meng Wang Yusong Cheng Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China

Deep neural networks (DNNs) based approaches have achieved superior performance in single image super-resolution (SR). To obtain better visual quality, DNNs for SR are generally designed with massive computation overhead. To accelerate network inference under resource constraints, we propose a classification-based dynamic network for efficient super-resolution (CDNSR), which combines the classification and SR networks in a unified framework. Specifically, CDNSR decomposes a large image into a number of image-patches, and uses a classification network to categorize them into different classes based on the restoration difficulty. Each class of image-patches will be handled by the SR network that corresponds to the difficulty of this class. In particular, we design a new loss to trade off between the computational overhead and the reconstruction quality. Besides, we apply contrastive learning based knowledge distillation to guarantee the performance of SR networks and the quality of reconstructed images. Extensive experiments show that CDNSR significantly outperforms the other SR networks and backbones on image quality and computational overhead.

关键词： Knowledge engineering Image quality visualization Superresolution Neural networks Dynamic scheduling Image restoration

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Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation

Active Learning on Neural Networks through Interactive Gener...

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ieee Integrated STEM Education Conference (ISEC)

作者： Dong H. Jeong Jin-Hee Cho Feng Chen Audun Jøsang Soo-Yeon Ji Department of Computer Science and Information Technology University of the District of Columbia Washington D.C. USA Department of Computer Science Virginia Tech. Falls Church USA Department of Computer Science University of Texas at Dallas Richardson USA Department of Informatics University of Oslo Oslo Norway Department of Computer Science Bowie State University Bowie USA

Artificial neural networks (ANNs) have been broadly utilized to analyze various data and solve different domain problems. However, neural networks (NNs) have been considered a black box operation for years because their underlying computation and meaning are hidden. Due to this nature, users often face difficulties in interpreting the underlying mechanism of the NNs and the benefits of using them. In this paper, to improve users’ karning and understanding of NNs, an interactive learning system is designed to create digit patterns and recognize the m in real time. To help users clearly understand the visual differences of digit patterns (i.e., $0\sim 9$) and their results with an NN, integrating visualization is considered to present all digit patterns in a two-dimensional display space with supporting multiple user interactions. An evaluation with multiple datasets is conducted to determine its usability for active learning In addition, informal user testing is managed during a summer workshop by asking the workshop participants to use the system.

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Research on Variable Speed Fault Diagnosis Method for Bearings Based on Domain Adaption

Research on Variable Speed Fault Diagnosis Method for Bearin...

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ieee International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Jia Ma Guanqiu Zhao Xinbei Zhang College of Economics and Management North China University of Electric Power Beijing China College of Science Minzu University of China Beijing China College of Mathematical Sciences University of Jinan Jinan China

For the practical application of bearings have been variable speed and the problem of severe shortage of training samples, the process of using multiple samples to train will generate a lot of computational burden, so this paper proposes a domain adaptive bearing variable speed fault diagnosis method based on the domain. The steps of this diagnostic method are studied as follows: first, Construct the dataset by time-frequency transformation of the bearing public data. Then, Obtain the network frame with high robustness by noise immunity test. Next, Migrate the model for 1730rpm trained google net to 1797rpm data. Then, (target domain) adopt data for model transfer and extract the source and target domain features of the fully connected layer and perform t-SNE visualization. Finally, construct a feature alignment model by a lattice method of LLE to shorten the distance between source and target domains. In addition, simply train the fully connected network using the source domain feature data and test the target domain data to complete the migration process. Validated by the public dataset, the article improved the correct rate from 78.57% to 91.67% by migrating the domain adaptive method extraction, thus verifying the effectiveness of the proposed method.

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Fall Determinants in Older Adults

Fall Determinants in Older Adults

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AI IoT Congress (AIIoT), World

作者： Joyeeta Banerjee Jinzhu Gao Jonathan M. Saxe Lewis E. Jacobson Courtney Jensen School of Engineering and Computer Science University of the Pacific Stockton US Trauma Center St. Vincent Hospital Indianapolis Indiana US Health Education and Sports Medicine University of the Pacific Stockton US

Older adults commonly require special care and falling is a major reason for hospital admission. Identifying the cause of falls would help enable precautionary measures to be taken, which may reduce both injury and medical expenses. This study focuses on understanding underlying medical conditions or medicines associated with an increased risk of falls. Two datasets, one with trauma patient details and the other with fall-related admitted patient details, were analyzed to determine whether any relationship exists between falls and any underlying medical conditions and associated medications. data visualization techniques and machine learning algorithms (e.g., SVC, Logistic Regression, and Naïve Bayes) have been used to study the cause of falls and whether they can be anticipated prior to their occurrence.

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A Multimodal Image Registration System for Histology Images

A Multimodal Image Registration System for Histology Images

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Annual ieee Symposium on Computer-Based Medical Systems

作者： Rodrigo Escobar Díaz Guerrero Yubraj Gupta Thomas Bocklitz José Luis Oliveira DETI and IEETA University of Aveiro and BMD Software Portugal Faculty of Mathematics Physics & Computer Science Institute of Computer Science Bayreuth Germany IEETA DETI and LASI University of Aveiro Portugal

Histology image registration involves aligning microscopy images for various purposes, including creating 3D reconstructions from 2D, combining data from slices with different stain samples, and from multimodal registration. However, this process poses several challenges, including high resolution, non-linear elastic deformation, occlusions, missing sections, non-rigid deformation, contrast differences, and differences in appearance and local structure. Multimodal image registration is particularly challenging because different modalities may have different characteristics and require specific optimization algorithms. To address this issue, it is important to develop software that allows users to test different image registration algorithms and combine annotations made on them, in order to leverage the benefits of multiple modalities. To address this challenge, we developed a cloud-based Multimodal Image Registration system that enables developers and researchers to visually test the outcomes of various image registration algorithms. The system includes a project manager, an algorithm manager, and an image visualization system. The system was developed using the framework Django, JavaScript, and multiple libraries that facilitate the management and annotation of very high-resolution images. To demonstrate the effectiveness and flexibility of our system, we tested it using two different algorithms, SIFT and ORB, on nonlinear multimodal and brightfield images using the Hematoxylin and Eosin staining methods. The results show the system's ability to handle challenging image registration tasks while providing visualization tools to improve user experience.

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T-FinKB: A Platform of Temporal Financial Knowledge Base Construction

T-FinKB: A Platform of Temporal Financial Knowledge Base Con...

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International Conference on data Engineering

作者： Xinyi Zhu Liping Wang Hao Xin Xiaohan Wang Zhifeng Jia Jiyao Wang Chunming Ma Yuxiang Zengt The Hong Kong University of Science and Technology (GZ) Guangzhou China The Hong Kong University of Science and Technology Hong Kong SAR China The Chinese University of Hong Kong Hong Kong SAR China

In recent years, domain-specific knowledge bases (KBs) have attracted more attention in academics and industry because of their expertise and in-depth representation in a specific domain. However, when constructing a domain-specific KB, one needs to address not only the challenges in constructing a general KB, but also the difficulties raised by the nature of domain-specific raw data. Considering the usability of financial Knowledge Bases (KBs) in many downstream applications, such as financial risk analysis and fraud detection, we aim to build a Temporal Financial KB. However, the complex, time-varying financial knowledge and the volatile financial market evolution make construction quite challenging. Thus, we propose a Platform for Temporal Financial KB Construction, T-FinKB Platform 1 . This platform consists of three fundamental modules, i.e., evolved knowledge extraction module, temporal record linkage and conflicts resolution module, and dynamic knowledge update module designed for financial knowledge. Generated temporal financial KBs (T-FinKBs) from T-FinKB Platform can be updated automatically. T-FinKB has been successfully applied to a downstream application, stock trend prediction with backtesting evaluation. In addition, T-FinKB Platform provides several APIs for visualization, queries and analytics.

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Pedestrian-centric Augmented Reality visualization of Real-time Autonomous Vehicle Dynamics

Pedestrian-centric Augmented Reality Visualization of Real-t...

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ieee International Conference on Cloud Networking (CLOUDNET)

作者： Yiwei Cheng Jin Nakazato Ehsan Javanmardi Chia-Ming Chang Manabu Tsukada Graduate School of Information Science and Technology University of Tokyo Tokyo

Connected Autonomous Vehicles (CAVs) produce a variety of information within their systems. With the advancement of communication and V2X (Vehicle-to-Everything) communication technology, there is a growing challenge to effectively convey this information to pedestrians and enhance their sense of safety when encountering such vehicles. Efforts to communicate this information to pedestrians have been made through various means, with Augmented Reality (AR) emerging as a notable approach. However, previous studies have yet to integrate a functional AR application with a real-world autonomous driving system. In response to this gap, we proposed an architecture for an AR application that visualizes real-time data from an active CAV and subsequently developed the system. Furthermore, we conducted field experiments using this developed system and conducted user surveys during exhibitions to gather insights into the public’s perception of the system. Our results showed that the system can effectively transmit information from the CAV, and when provided with additional information, people tend to feel safer regarding the vehicle.

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An Efficient IoT-based Mobile Application for Continuous Monitoring of Patients

An Efficient IoT-based Mobile Application for Continuous Mon...

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data science and Engineering in Healthcare, Medicine and Biology, ieee EMBS Special Topic Conference on

作者： Shereen Afifi Andrew Emad Radwa Taha Mirza Baig Assistant Professor in Computer science and Engineering German University in Cairo Egypt Senior student in Computer science and Engineering German University in Cairo Egypt Master Candidate in Computer science and Engineering German University in Cairo Egypt Senior Solutions Specialist in Orion Health Auckland New Zealand

Internet of Things (IoT) is gaining much popularity nowadays, especially in the field of healthcare, where different wearables, sensors and data analysis-based systems are connected together. This research work proposes an IoT-based system/application dedicated for continuous monitoring of the patient’s health parameters and vital signs. The designed application can be used by both the patient and the doctor as the doctor can track the patient’s parameters remotely in real-time to facilitate the diagnosis and to early detect patient’s health deterioration. It also enables patients to spend more time in the comfort of their own homes and communicate with their care facilities, aiming to improve the patient’s independent living and support activities of daily life. The biosensors in the smart watch or the mobile phone are utilized to measure the health parameters of the patient, which are then used by the devloped application for further processing, analysis and efficient visualization using charts and diagrams. A notification is sent to the monitoring doctor to facilitate rapid response once any of the patient’s vital signs exceeds the normal range. The developed application could be integrated within an existing hospital system or could be made available to individuals/patients via smartphones for providing support at *** Relevance: Doctors are always in need to have exact health information for their patients to receive care and therapy in a timely manner. The task of ongoing monitoring of patients’ vital signs is carried out by nurses in the interim, but the availability of a skilled and sizable team of nurses is a major concern. Thus, we propose an efficient continuous monitoring system that automates and facilitates the process of remote and continuous monitoring of patients, targeting early detection of patient deterioration while supporting independent living.

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Multi-modal Sensor Fusion for Learning Rich Models for Interacting Soft Robots

Multi-modal Sensor Fusion for Learning Rich Models for Inter...

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ieee International Conference on Soft Robotics (RoboSoft)

作者： Thomas George Thuruthel Fumiya Iida Department of Engineering The Bio-Inspired Robotics Lab University of Cambridge UK Department of Computer Science University College London London UK

Soft robots are typically approximated as low-dimensional systems, especially when learning-based methods are used. This leads to models that are limited in their capability to predict the large number of deformation modes and interactions that a soft robot can have. In this work, we present a deep-learning methodology to learn high-dimensional visual models of a soft robot combining multimodal sensorimotor information. The models are learned in an end-to-end fashion, thereby requiring no intermediate sensor processing or grounding of data. The capabilities and advantages of such a modelling approach are shown on a soft anthropomorphic finger with embedded soft sensors. We also show that how such an approach can be extended to develop higher level cognitive functions like identification of the self and the external environment and acquiring object manipulation skills. This work is a step towards the integration of soft robotics and developmental robotics architectures to create the next generation of intelligent soft robots.

关键词： visualization Shape control Soft sensors Tactile sensors Soft robotics Predictive models Robot sensing systems

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