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IEEE International Conference on Robotic Computing (IRC)

作者： Hwawon Lee Dohyun Chung Seongmin Kim Jiwon Lim Yoonha Bahng Suhyun Park Anthony H. Smith School of Software Soongsil University Seoul Korea Industrial Security Chung-Ang University Seoul Korea Computer Science Kangwon National University Chuncheon Korea School of Software Kwangwoon University Seoul Korea School of Computer Science & Engineering Chung-Ang University Seoul Korea Computer Engineering Paichai University Seoul Korea Computer and Information Technology Purdue University West Lafayette United States

ISBN: (纸本)9781665472616

The inefficiency of fire evacuation has been the issue since the present evacuation method is unsuitable for complex buildings. In order to improve the evacuation system, this paper aims at three main components. First, Kalman Filter and deep learning models were utilized to estimate the user’s location accurately. Second, Q-learning based evacuation algorithm was designed to deal with various fire situations. Lastly, AR and a 2D map offer effective navigation systems. The proposed system offers the safest path based on accurate location with a user-friendly visual supplement.

关键词： Deep learning Location awareness Visualization Q-learning Machine learning algorithms Navigation Real-time systems

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Timeliness-Aware Multiple Rumor Sources Estimation in Dynamic Online Social Networks

SSRN

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SSRN 2024年

作者： Huang, Da-Wen Wu, Wenjie Bi, Jichao Li, Junli Gan, Chenquan Zhou, Wei College of Computer Science Sichuan Normal University Chengdu China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating the damage caused by them. However, most studies are not suitable for identifying rumor sources in dynamic OSNs with community structures. Moreover, these studies tend to neglect the impact of timeliness on the spread of rumors, potentially leading to an overestimation of rumors' propagation ability. To overcome these limitations, this paper proposes a multiple rumor sources estimation framework for OSNs. First, a community-based dynamic network model is introduced to depict the temporal nature and community feature of OSNs. Second, a meticulously designed microcosmic SIR model, incorporating the timeliness of rumor topics, is developed to unravel the complex dynamics of rumor propagation. Then, a computationally efficient multiple rumor sources estimation algorithm is proposed. This algorithm utilizes the infection information collected by sensors and applies the maximum likelihood estimation (MLE) method to identify the rumor sources. Finally, experimental results on real-world and synthetic temporal networks demonstrate the effectiveness of the proposed rumor source estimation algorithm. © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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Shooting for the Stars: How a Stem Initiative Has Evolved to Address Gender Challenges in Work and Education

SSRN

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SSRN 2023年

作者： Travers, Marie Bromell, Jenna Crowley, Katie Patten, James Vincent O’Connor, Mairéad Richardson, Ita Lero – The Science Foundation Ireland Research Centre for Software University of Limerick Limerick Ireland Johnson & Johnson Vision Care Ireland UC Limerick Ireland Lero – The Science Foundation Ireland Research Centre for Software Dept of Computer Science & Information Systems University of Limerick Limerick Ireland School of Information Systems & Technology Management UNSW Business School University of New South Wales Sydney Sydney Australia

This paper discusses science technology Engineering and Mathematics (STEM) interventions and presents our experience with a specific intervention implemented in Ireland, the University of Limerick-Lero/Johnson & Johnson WiSTEM2D (Women in STEM, Manufacturing and Design) programme. The programme has evolved based on the learnings identified in areas such as participant group dynamics, invited speaker engagement and presentation delivery, and teamwork-centred activities. Such learnings have applications across a range of STEM fields, including software engineering. We believe that initiatives, such as WiSTEM2D, which support and promote gender equality in STEM2D fields, and specifically computer science (CS) education and industry, are of key importance. Our review of the programme has identified three primary lessons: (1) individuals did not benefit as strongly as those working in groups (2) online events allow for more speakers and greater transfer of knowledge and (3) working in teams provides the students with the opportunity to socialise with other women in a work environment. For the last 3 years, the programme has seen the number of applications steadily increase. The value of the lessons learned from the WiSTEM2D programme are discussed in terms of the economic, ethical, and legal areas. © 2023, The Authors. All rights reserved.

关键词： Students

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Bayesian Decision-based Bandwidth Resource Control Optimization Algorithm for Integer-Pixel Motion Estimation in AVS3

Bayesian Decision-based Bandwidth Resource Control Optimizat...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Chen Li Guoqing Xiang Yukun Zhang Yan Cui Peng Zhang Wei Yan School of Software and Microelectronics Peking University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China School of Computer Science Peking University Beijing China Forecasting and Strategic Target Management Research Center Zhejiang Economic Information Center Hangzhou China PengCheng Laboratory Shenzhen China

Compared to the previous AVS2 standards, AVS3 enhances its coding flexibility through the utilization of partition mechanisms including Quad-tree, Binary-Tree, and Extended quad-tree, which permit adaptable Coding Unit(CU) partitioning. The increased flexibility in block partitioning imposes considerable bandwidth resource costs for the Integer-Pixel Motion Estimation (IME) process. However, some CUs show little benefit from IME in coding performance, skipping the search of these CUs will yield substantial bandwidth resource savings. In this paper, we propose a bandwidth resource control strategy based on the Bayesian decision to mitigate and regulate the bandwidth resource consumption of AVS3. By adjusting the motion estimation scheme for coding blocks as the Largest Coding Units (LCU), the bandwidth resource is minimized to a defined level with less performance loss. This proposed method can adjust bandwidth resource from 100% to 20%. Experimental results show the effectiveness of our proposed approach.

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General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment

arXiv

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arXiv 2023年

作者： Xie, Liang Yang, Yibo Wang, Wenxiao Lin, Binbin Cai, Deng He, Xiaofei Liang, Ronghua Colleague of Computer Science and Technology Zhejiang University of Technology Hangzhou China State Key Lab of CAD & CG Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China JD Explore Academy Beijing China Fabu Inc. Hangzhou China

Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformation. There are many recent SOTA works dedicated to rotation-invariant learning for 3D point clouds. However, current rotation-invariant methods lack generalizability on the point clouds in the open scenes due to the reliance on the global distribution, i.e., the global scene and backgrounds. Considering that the output activation is a function of the pattern and its orientation, we need to eliminate the effect of the orientation. In this paper, inspired by the idea that the network weights can be considered a set of points distributed in the same 3D space as the input points, we propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) via aligning the features with the principal axes of the network weights. Our WFA algorithm provides a general solution for the point clouds of all scenes. WFA ensures the model achieves the target that the response activity is a necessary and sufficient condition of the pattern matching degree. Practically, we perform experiments on the point clouds of both single objects and open large-range scenes. The results suggest that our method almost bridges the gap between rotation invariance learning and normal methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Pattern matching

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Application of Deep Learning Algorithm for Prediction of Flood Severity

Application of Deep Learning Algorithm for Prediction of Flo...

引用

Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： C Jamunadevi N Naveen M Manimaran M Janani Department of Computer Technology PG Kongu Engineering College Erode Department of IT Excel Engineering College Namakkal School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India M.Sc (Software Systems) Kongu Engineering College Erode

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Natural disasters like floods have caused immense harm to lives and resources, underscoring the need for accurate prediction methods to mitigate their impact. Meteorological flood indices such as the standardized precipitation index help identify flood occurrences and severity levels, enabling proactive measures. While past research primarily used statistical methods like Least Square-Support Vector Machine (LS-SVM), these often lack accuracy for long-term forecasts. Advancements in machine learning, particularly artificial neural networks and deep neural networks, offer more accurate predictions over extended periods. Long Short-Term Memory (LSTM) models and Convolutional Neural Networks (CNN) are used to predict flood indices, handling real-time nonlinear data effectively. Benchmarking trials have shown that an LSTM and CNN configuration produced the best predictions, with an accuracy, prediction, MSE, and RMSE of 97.93,0.89, 0.01, and 0.10 respectively.

关键词： Accuracy Weather forecasting Predictive models Surfaces Data models Vectors Floods

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Domain Adaptive Semantic Segmentation via Image Translation and Representation Alignment

Domain Adaptive Semantic Segmentation via Image Translation ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jingxuan Kang Bin Zang Weipeng Cao School of Computer Science University of Liverpool Liverpool UK School of Computer Science and Technology Beijing Institute of Technology Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Domain Adaptation for semantic segmentation is of vital significance since it enables effective knowledge transfer from a labeled source domain (i.e., synthetic data) to an unlabeled target domain (i.e., real images), where no effort is devoted to annotating target samples. Prior domain adaptation methods are mainly based on image-to-image translation model to minimize differences in image conditions between source and target domain. However, there is no guarantee that feature representations from different classes in the target domain can be well separated, resulting in poor discriminative representation. In this paper, we propose a unified learning pipeline, called Image Translation and Representation Alignment (ITRA), for domain adaptation of segmentation. Specifically, it firstly aligns an image in the source domain with a reference image in the target domain using image style transfer technique (e.g., CycleGAN) and then a novel pixel-centroid triplet loss is designed to explicitly minimize the intra-class feature variance as well as maximize the inter-class feature margin. When the style transfer is finished by the former step, the latter one is easy to learn and further decreases the domain shift. Extensive experiments demonstrate that the proposed pipeline facilitates both image translation and representation alignment and significantly outperforms previous methods in both GTA5 → Cityscapes and SYNTHIA → Cityscapes scenarios.

关键词： Image segmentation Adaptation models Semantics Pipelines Predictive models Linear programming Robustness

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Joint Depth Estimation and Mixture of Rain Removal From a Single Image

arXiv

引用

arXiv 2023年

作者： Wang, Yongzhen Yan, Xuefeng Niu, Yanbiao Gong, Lina Guo, Yanwen Wei, Mingqiang School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China the National Key Lab for Novel Software Technology Nanjing University Nanjing210000 China the Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen518038 China

—Rainy weather significantly deteriorates the visibility of scene objects, particularly when images are captured through outdoor camera lenses or windshields. Through careful observation of numerous rainy photos, we have found that the images are generally affected by various rainwater artifacts such as raindrops, rain streaks, and rainy haze, which impact the image quality from both near and far distances, resulting in a complex and intertwined process of image degradation. However, current deraining techniques are limited in their ability to address only one or two types of rainwater, which poses a challenge in removing the mixture of rain (MOR). In this study, we propose an effective image deraining paradigm for Mixture of rain REmoval, called DEMore-Net, which takes full account of the MOR effect. Going beyond the existing deraining wisdom, DEMore-Net is a joint learning paradigm that integrates depth estimation and MOR removal tasks to achieve superior rain removal. The depth information can offer additional meaningful guidance information based on distance, thus better helping DEMore-Net remove different types of rainwater. Moreover, this study explores normalization approaches in image deraining tasks and introduces a new Hybrid Normalization Block (HNB) to enhance the deraining performance of DEMore-Net. Extensive experiments conducted on synthetic datasets and real-world MOR photos fully validate the superiority of the proposed DEMore-Net. Code is available at https://***/yz-wang/DEMore-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Rain

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China Stevens Institute of Technology Department of Systems and Enterprises United States Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Viet Nam King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Biosystems and Machines Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Dhahran Saudi Arabia Uppsala University Department of Game Design Faculty of Arts Sweden Tiangong University School of Software Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. © 2025 IEEE.

关键词： Generative adversarial networks

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