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arXiv 2024年

作者： Sadashivaiah, Vijay Yan, Pingkun Hendler, James A. Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Deep learning models have revolutionized medical imaging and diagnostics, yet their opaque nature poses challenges for clinical adoption and trust. Amongst approaches to improve model interpretability, concept-based explanations aim to provide concise and human-understandable explanations of any arbitrary classifier. However, such methods usually require a large amount of manually collected data with concept annotation, which is often scarce in the medical domain. In this paper, we propose Conceptual Counterfactual Explanations for Chest X-ray (CoCoX), which leverages the joint embedding space of an existing vision-language model (VLM) to explain black-box classifier outcomes without the need for annotated datasets. Specifically, we utilize textual concepts derived from chest radiography reports and a pre-trained chest radiography-based VLM to explain three common cardiothoracic pathologies. We demonstrate that the explanations generated by our method are semantically meaningful and faithful to underlying pathologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Optimization and Integration Strategies for Hybrid Renewable Energy Systems in the Brazilian Power Grid: A Systematic Review

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IEEE Access 2025年 13卷 84170-84187页

作者： Filho, Dalton F. Guedes Miranda, Rodrigo L. Correia, Lucas A. Fernandes, Lucas E. S. Medrado, Ricardo C. Filho, Marcio C. Filho, José B. M. Marinho, Manoel H. N. Sperandio Nascimento, Erick G. SENAI CIMATEC University Center BA Salvador41650-010 Brazil Eletrobras Company RJ Rio de Janeiro20.091-005 Brazil Polytechnic School of Pernambuco PE Recife50100-010 Brazil Faculty of Engineering and Physical Sciences Surrey Institute for People-Centred AI University of Surrey School of Computer Science and Electronic Engineering United Kingdom

The global transition to renewable energy requires efficient Hybrid Renewable Energy Systems (HRES) to mitigate the intermittency of solar and wind power. This paper presents a review of HRES design and optimization methodologies, emphasizing their integration into the national power grid. We analyze optimization techniques, including metaheuristic algorithms and simulation tools such as HOMER, in addition to regulatory and operational challenges. A systematic literature review was conducted using Scopus, applying bibliometric analysis to identify trends in HRES research. Our findings highlight critical gaps in optimization strategies, energy storage integration, and economic feasibility analysis. This study provides insights for researchers and policymakers to enhance the reliability and efficiency of HRES in large-scale grid applications. © 2013 IEEE.

关键词： Electric energy storage

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A Hybrid Model and Learning-Based Force Estimation Framework for Surgical Robots

A Hybrid Model and Learning-Based Force Estimation Framework...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Hao Yang Haoying Zhou Gregory S. Fischer Jie Ying Wu Department of Computer Science Vanderbilt University TN USA Department of Robotics Engineering Worcester Polytechnic Institute MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Haptic feedback to the surgeon during robotic surgery would enable safer and more immersive surgeries but estimating tissue interaction forces at the tips of robotically controlled surgical instruments has proven challenging. Few existing surgical robots can measure interaction forces directly and the additional sensor may limit the life of instruments. We present a hybrid model and learning-based framework for force estimation for the Patient Side Manipulators (PSM) of a da Vinci Research Kit (dVRK). The model-based component identifies the dynamic parameters of the robot and estimates free-space joint torque, while the learning-based component compensates for environmental factors, such as the additional torque caused by trocar interaction between the PSM instrument and the patient’s body wall. We evaluate our method in an abdominal phantom and achieve an error in force estimation of under 10% normalized root-mean-squared error. We show that by using a model-based method to perform dynamics identification, we reduce reliance on the training data covering the entire workspace. Although originally developed for the dVRK, the proposed method is a generalizable framework for other compliant surgical robots. The code is available at https://***/vu-maple-lab/dvrk_force_estimation.

关键词： Learning systems Torque Medical robotics Instruments Force Dynamics Estimation Surgery Robot sensing systems Trajectory optimization

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HTD: A Fast Human-centered Text-locating Method for Auxiliary Reading 4

HTD: A Fast Human-centered Text-locating Method for Auxiliar...

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4th International Conference on Robotics and computer Vision, ICRCV 2022

作者： Yu, Zhenghong Wang, Hao Yang, Hang Zhou, Huabing Guangdong Polytechnic of Science and Technology School of Robotics Zhuhai China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology College of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

As computer vision and human-computer interaction technologies are getting mature, vision-based auxiliary text reading has become the mainstream method to optimize learning and reading experience. To overcome the drawbacks of the common multi-step detection way and further improve the text reading experience, we propose a new human-centered fast text-locating method, named HTD. It transforms the relatively time-consuming serial procedure to a hybrid object detection problem, utilizing the hand-text hybrid object detection model to instantly locate the text of interest to readers. Considering the particularity of this task, we choose to perform the weighted NMS to infer final predicted bounding box. To verify the effectiveness of the proposed method, we tested the performance on the homemade hand-text hybrid object detection dataset HTDD. The experiment indicated that our proposed method is more efficiency and can produce satisfying results, which could meet the needs of human-computer interactive auxiliary reading in text reading scenarios. © 2022 IEEE.

关键词： Object detection

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Fall Detection Scheme based on Deep Learning Model for High-Quality Life 3

Fall Detection Scheme based on Deep Learning Model for High-...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Jayakarthik, R. Srinivasan, Aravindan Goswami, Sohan Shivaranjini Mahaveerakannan, R. Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Dept of Comp Science India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Technique Polytechnic Institute West Bengal 712102 India Viswam Degree College Department of Computer Science Madanapalle India Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Elderly individuals worldwide have a significant risk of injury or death due to falls. An aged person's mobility, independence, and quality of life can be significantly impacted if he or she falls and does not receive timely assistance. This paper proposes the creation of a low-power network, smart devices, and cloud computing-based fall detection system for senior individuals in indoor spaces as part of this study. 10 volunteers volunteered to have their fall and non-fall movement data collected using a wireless telemetry surface electromyography capture system. Generative Adversarial Network (GAN) models use the effective signal segments as input. Notifications are automatically sent to caregivers of the elderly if a fall is detected, and an alert is triggered. As the final step, the system offers cloud-based services. There is a service that allows healthcare professionals to obtain the fall data for further examination from a medical standpoint. In experiments, fall detection has been found to be highly accurate, precise, and recallable. © 2022 IEEE.

关键词： Big data

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The Immense Impact of Reverse Edges on Large Hierarchical Networks

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engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Anisotropy of two-dimensional ReS_(2)and advances in its device application

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Rare Metals 2021年第12期40卷 3357-3374页

作者： Ya-Di Cao Ying-Hui Sun Su-Fei Shi Rong-Ming Wang Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Key Laboratory for Magneto-Photoelectrical Composite and Interface ScienceSchool of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing100083China Department of Chemical and Biological Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic InstituteTroyNY 12180USA

Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted growing interest regarding their potential applications in next-generation electronic and optoelectronic *** to their atomic thickness and tunable bandgap,they exhibit unique mechanical,electrical,and optical *** a specific member of the TMDC family,rhenium disulfide(ReS_(2))has stimulated intensive interest due to its anisotropic crystal structure,weak inter-layer coupling,and anisotropic electrical and optical *** this review,we summarize the distinct crystal structure and intrinsic anisotropic properties of ReS_(2),followed by an introduction to its synthesis *** current applications of ReS_(2)and its heterojunctions are presented based on its anisotropic *** review not only provides a timely summary of the current applications of ReS_(2)and its heterojunctions,but also inspires new approaches to develop other innovative devices based on 2D materials with a low lattice symmetry.

关键词： Transition metal dichalcogenides(TMDCs) Rhenium disulfide Anisotropic property Heterojunction Functional device

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An Efficient ODR for computer Night Vision Using Multi-Scale Retinex Network-Aided Enhanced Images

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Charles Prabu V P. Pandiaraja V Sathiyamoorthi P. Durgadevi Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Engineering Government Polytechnic College Dharmapuri Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)" model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)" model. The You Only Look Once Version 7 (YoloV7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%.

关键词： Night vision YOLO Deep learning Tracking Computational modeling Image edge detection Surveillance Streaming media Real-time systems Human activity recognition

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A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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GDEMO-HAT: Knowledge-guided Medical Text Classification Using Heterogeneous Graph-based Dependency Modeling with Hierarchical Attention 3

GDEMO-HAT: Knowledge-guided Medical Text Classification Usin...

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2023 IEEE 3rd International Conference on Data science and computer Application, ICDSCA 2023

作者： Zhou, Yunfan Chen, Hao Li, Ying Sun, Xuxue Zeng, Hao Qin, Aihong Ma, Tongqing Shen, Shanshan Zhejiang University Polytechnic Institute Hangzhou China Zhejiang University College of Computer Science Hangzhou China University of Zhejiang College of Media Engineering Communication Hangzhou China Zhejiang University Zhejiang Hospital Hangzhou China

ISBN: (纸本)9798350341546

The medical texts contain rich information about the health status of patients, including personal demographic details, examination items, diagnostic narratives, and corresponding outcomes. There is often an underlying dependency between a patient and multiple medical texts. Such dependency has a substantial influence on the accuracy and reliability of the patient's comprehensive diagnostic outcomes, thus necessitating the modeling of dependency. Most of the existing studies on medical text classification focused only on textual content and often overlooked the inherent dependency between the patient and medical texts, leading to the loss of relevant contextual information. To address the above problems, we propose an approach for medical text classification using heterogeneous Graph-based DEpendency MOdeling with Hierarchical ATtention (GDEMO-HAT), which accounts for patient-notes dependency and retains the contextual information. We model the dependencies between the patients and their medical texts via heterogeneous graphs. To facilitate medical text mining, we incorporate domain knowledge into the construction of a Patient-Notes-Physician (PNP) graph. Based on the constructed PNP graph, we introduce the hierarchical attention mechanism for classification that assigns different levels of importance to nodes based on distinct semantic contexts and thereby effectively captures the intricate interdependencies among these nodes. The representations of dependencies are integrated at the semantic level, thereby enabling a more comprehensive understanding of textual data. To demonstrate the effectiveness of the proposed approach, we conduct a classification task of the diagnosis of cardiovascular and cerebrovascular diseases based on medical text. The classification accuracy rate achieves 94%, which is significantly higher than the performances of the other compared models, such as Bert. © 2023 IEEE.

关键词： dependency modeling heterogeneous graph hierarchical attention mechanism medical text classification physician knowledge

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