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Computing, Robotics and System sciences (ICRSS), International Conference on

作者： Shahid Karim Muhammad Ibrar Anas Bilal Akeel Qadir Irfana Bibi School of Information Engineering Xi'an Eurasia University Xi'an China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Lahore Punjab Pakistan

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou _ nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection.

关键词： YOLO Training Accuracy Sensitivity Service robots Neural networks Fires Clustering algorithms Smart homes Safety

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Road Surface Damage Detection Based on Knowledge Distillation

Road Surface Damage Detection Based on Knowledge Distillatio...

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Image Processing and computer Applications (ICIPCA), IEEE International Conference on

作者： Qiao Sun Yi Yang Jingjun Gu Zhi Yu Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China School of Electronic Engineering Naval University of Engineering Wuhan China Zhejiang Provincial Key Laboratory of Service Robot School of Software Technology Zhejiang University Ningbo China

As an important public infrastructure, road contributes to economic development and growth, brings significant social benefits. However, due to a variety of factors, the road surfaces will wear and deteriorate over time. When there is a slight crack in the pavement, most will deteriorate rapidly if not repaired promptly, which can greatly affect the life of the road. Therefore, developing a road surface damage detection algorithm is an urgent demand. We use the knowledge distillation technology to solve this problem. First, the similarity-preserving knowledge distillation was improved by adding a mask block to make the model transmit only the key feature information of the local region. Secondly, by making the boundary box of the teacher output and ground truth do non-maximum suppression, the image area of the distillation in the output layer is restricted, and the transmission of background information by the teacher is reduced. the performance of the student is improved by 1.2% in mAP@0.5 on average and 2.2% in mAP@0.5:0.95 on average compared with the state-of-the-art method.

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PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

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IEEE Transactions on Network science and engineering 2025年

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chandrasekhar Rao Katru Sathish Srinivasan Mohan Kumar T G Sunita Yadwad G. Satish Ramya Maranan Lead Software Engineer Indian Land SC USA Oracle Cloud Infrastructure Division Machine Learning & AI Development San Francisco Bay Area California USA Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.

关键词： Cloud computing Energy consumption Algebra Scalability Quality of service Transformers Real-time systems Resource management Time factors Service level agreements

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FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

arXiv

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arXiv 2023年

作者： Yi, Liping Yu, Han Shi, Zhuan Wang, Gang Liu, Xiaoguang Cui, Lizhen Li, Xiaoxiao College of Computer Science TMCC SysNet DISSec GTIISC Nankai University Tianjin China College of Computing and Data Science Nanyang Technological University Singapore Switzerland School of Software Shandong University Jinan China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system heterogeneity. Model-Heterogeneous Personalized FL (MHPFL) has emerged to address this challenge. Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs. To address these limitations, we propose the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks, which splits each client’s model into a heterogeneous (structure-different) feature extractor and a homogeneous (structure-same) classification header. It performs local-to-global knowledge transfer via semantic similarity-based header parameter aggregation. In addition, global-to-local knowledge transfer is achieved via an adaptive parameter stabilization strategy which fuses the seen-class parameters of historical local headers with that of the latest global header for each client. FedSSA does not rely on public datasets, while only requiring partial header parameter transmission to save costs. Theoretical analysis proves the convergence of FedSSA. Extensive experiments present that FedSSA achieves up to 3.62% higher accuracy, 15.54 times higher communication efficiency, and 15.52 times higher computational efficiency compared to 7 state-of-the-art MHPFL baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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A Three-Stage Cutting Simulation System Based on Mass-Spring Model

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computer Modeling in engineering & sciences 2021年第4期127卷 117-133页

作者： Xiaorui Zhang Jiali Duan Wei Sun Tong Xu Sunil Kumar Jha Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Faculty of Information Technology University of Information Technology and ManagementRzeszow35-225Poland

The cutting simulation of soft tissue is important in virtual *** includes three major challenges in computation:Soft tissue simulation,collision detection,and handling,as well as soft tissue *** order to address the earlier challenges,we propose a virtual cutting system based on the mass-spring *** this system,MSM is utilized to simulate the soft tissue *** stress is introduced to the model for simulating the shrinking effect of soft tissue in ***,a cylinder-based collision detection method is used to supervise the collision between surgical tools and soft ***,we simulate the cutting operation with a three-stage cutting method with swept volume,B´ezier curve,and an algorithm named shortest distance nodes matching *** order to verify the system performance,we carry out three validation experiments on the proposed system:Cutting accuracy experiment,collision detection validation,and practical cutting *** indicate that our system can well perform the shrinking effect of soft tissue in *** system has fast and accurate collision ***,the system can reconstruct smooth incisions vividly.

关键词： Mass-spring model collision algorithm virtual surgery soft tissue simulation

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Initno: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

Initno: Boosting Text-to-Image Diffusion Models via Initial ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiefan Guo Jinlin Liu Miaomiao Cui Jiankai Li Hongyu Yang Di Huang State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent strides in the development of diffusion models, ex-emplified by advancements such as Stable Diffusion, have underscored their remarkable prowess in generating visu-ally compelling images. However, the imperative of achieving a seamless alignment between the generated image and the provided prompt persists as a formidable challenge. This paper traces the root of these difficulties to invalid initial noise, and proposes a solution in the form of Initial Noise Optimization (INITNO), a paradigm that refines this noise. Considering text prompts, not all random noises are effective in synthesizing semantically-faithful images. We design the cross-attention response score and the selfattention conflict score to evaluate the initial noise, bifurcating the initial latent space into valid and invalid sectors. A strategically crafted noise optimization pipeline is developed to guide the initial noise towards valid regions. Our method, validated through rigorous experimentation, shows a commendable proficiency in generating images in strict accordance with text prompts. Our code is available at https://***/xiefan-guo/initno.

关键词： computer vision Navigation Instruments Noise Pipelines Text to image Aerospace electronics

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Analyzing Facial Temporal Patterns for Face Anti-Spoofing 2020

Analyzing Facial Temporal Patterns for Face Anti-Spoofing

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Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

作者： Jingtian Xia Siyang Song Yan Tang Linlin Shen College of Computer Science and Software Engineering Shenzhen University China School of Computer Science The University of Nottingham UK

ISBN: (纸本)9781450387835

Face anti-spoofing is crucial as face recognition systems are widely challenged by the print attack and replay attack. Since facial temporal patterns of these attacks and real face are naturally different, this paper proposes two temporal modelling approaches to face anti-spoofing tasks. Firstly, we propose to analyze the temporal patterns of mid-level facial attributes in spectral domain, aiming to find the unique frequency patterns of real face and each attack, respectively. Then, we propose to directly model dynamics from the given data, by employing the dynamic image algorithm to generate low-level spatiotemporal representations of videos. In particular, we extract deep features from both global and local face parts, i.e. eyes, nose and mouth, and then fuse them for face spoofing detection. Then, a Convolutional Neural Networks (CNN) - Long Short-Term Memory (LSTM) units (CNN-LSTM) architecture is introduced to learn the high-level spatiotemporal features from dynamic facial images. The proposed approaches were evaluated on two benchmark databases. The results suggest the effectiveness of the second approaches, i.e. as low as 1.85% Equal Error Rate (EER) on CASIA-FASD and 0.00% Average Classification Error Rate (ACER) on REPLAY-ATTACK have been achieved.

关键词： convolutional neural networks fourier transform dynamic image long short-term memory Face anti-spoofing

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EBFT: Efficient BFT Consensus through Shortcut Replies

EBFT: Efficient BFT Consensus through Shortcut Replies

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

作者： Qingsheng Lei Yongjun Xia Qitao Guo Rui Hao Xiaohai Dai Weiqi Dai State Grid Hubei Electric Power Company Limited Economic & Technology Research Institute Hubei Central China Technology Development of Electric Power Company Limited Software Institute Nanjing University School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology

As the core building block of blockchain technology, Byzantine Fault Tolerance (BFT) consensus has gathered renewed attention during the past decade. A large number of BFT consensus protocols have been proposed since its inception in the 1980s, the best-known of which is Practical Byzantine Fault Tolerance (PBFT). Generally speaking, PBFT consists of two sub-protocols: normal-case protocol and view-change protocol. However, it requires $7 \delta$ for a client to commit a request in the normal case, where $\delta$ represents the actual network delay. If a request happens to go through the view change process, it will take additional $7 \delta$ for a client to commit a request. To reduce the consensus latency, we propose a novel BFT consensus protocol named EBFT, which introduces the shortcut replies to the consensus design. With these shortcut replies, EBFT reduces the normal-case latency and view-change latency to $5 \delta$ and $2 \delta$, respectively. Extensive experiments are conducted to evaluate EBFT, whose results demonstrate its effectiveness.

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

MetaGCD: Learning to Continually Learn in Generalized Catego...

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International Conference on computer Vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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