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IEEE International Conference on Communications Workshops, ICC

作者： Yi– Jing Liu Gang Feng Yao Sun Xiaoqian Li Jianhong Zhou Shuang Qin National Key Laboratory of Science and Technology on Communications Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China James Watt School of Engineering University of Glasgow School of Computer and Software Engineering Xihua University

Federated learning (FL) has recently become one of the hottest focuses in network edge intelligence. In the FL framework, user equipments (UEs) train local machine learning (ML) models and transmit the trained models to an aggregator where a global model is formed and then sent back to UEs, such that FL can enable collaborative model training. In large-scale and dynamic edge networks, both local model training and transmission may not be always successful due to constrained power and computing resources at mobile devices, wireless channel impairments, bandwidth limitations, etc., which directly degrades FL performance in terms of model accuracy and/or training time. On the other hand, we need to quantify the benefits and cost of deploying edge intelligence when we plan to improve network performance by using artificial intelligence (AI) techniques which definitely incur certain cost. Therefore, it is imperative to deeply understand the relationship between the required multiple-dimensional resources and FL performance to facilitate FL enabled edge intelligence. In this paper, we construct an analytical model for investigating the relationship between the accuracy of ML model and consumed network resources in FL enabled edge networks. Based on the analytical model, we can explicitly quantify the trained model accuracy given spatial-temporal domain distribution, available user computing and communication resources. Numerical results validate the effectiveness of our theoretical modeling and analysis. Our analytical model in this paper provides some useful guidelines for appropriately promoting FL enabled edge network intelligence.

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A fast humanoid robot arm for boxing based on servo motors

A fast humanoid robot arm for boxing based on servo motors

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High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Jianchu Lin Zhouzhi Gu Ali Mokhtarzadeh Amir Xiaobin Chen Kahadka Ashim Kun Shi Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an China Gandaki College of Engineering and Science Pokhara Nepal Beijing Liaowang Shenzhou Technology LCO Beijing China

ISBN: (纸本)9781665413282

The sport of boxing has been gradually accepted by the public as a kind of fitness excise. A humanoid robot arm is necessary for this sport since it can reduce the tedious training work of coaching and increase the fun of this exercise. A 4-degree of freedom (DOF) robotic arm was designed by analyzing the basic gestures of boxing, and the servo motor was employed to support the arm’s fast movement. The results show that the robotic arm can fulfill the basic gestures of jab, hook, and swing correctly and the arm joints with servo motor are faster than with stepper motor.

关键词： Training Humanoid robots Arms Manipulators Servomotors Sports

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Hierarchical Crowdsourcing for Data Labeling with Heterogeneous Crowd

Hierarchical Crowdsourcing for Data Labeling with Heterogene...

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International Conference on Data engineering

作者： Haodi Zhang Wenxi Huang Zhenhan Su Junyang Chen Di Jiang Lixin Fan Chen Zhang Defu Lian Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Department of Computing Hong Kong Polytechnic University WeBank Institute of Financial Technology Shenzhen University Webank Co. Ltd. School of Data Science University of Science and Technology of China

With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model *** address the above problem, we propose a hierarchical crowdsourcing framework 1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs.

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A Machine Learning Approach for Predicting Air Quality Index in Smart Cities

A Machine Learning Approach for Predicting Air Quality Index...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Swamynathan N. Sneha S. P. Ramesh R. Niranjana D. David Neels Ponkumar R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering School of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

One essential natural resource, the air quality index has been deteriorated by economic activities. Many studies have been conducted about the forecasting of periods of unsafe atmospheric conditions; however, the majority of these studies are limited in their capacity to include seasonal and other factors due to insufficient longitudinal data. India noise pollution has compiled a 6-year dataset that has been used to build several prediction models. There are encouraging outcomes when AQI levels are predicted using machine learning methodologies such as support vector machines (SVMs) artificial neural networks (ANNs), and Naive Bayes (NB). However, in several tests involving datasets from three different locations, NB, ANN, and SVM performed well to maximize prediction performance. When it comes to mean absolute error (MAE), R-squared (R2), and Root-mean-square deviation (RMSE), the combined performance of stacks is unsurpassed. In the related work section, summarize the limitations of existing models, such as inadequate data preprocessing, low spatialtemporal resolution, limited feature selection, or overfitting in complex models. Highlight gaps in scalability, real-time adaptability, and generalization across regions, emphasizing the need for robust, efficient models tailored to air quality prediction.

关键词： Support vector machines Adaptation models Atmospheric modeling Biological system modeling Computational modeling Artificial neural networks Predictive models Air quality Data models Indexes

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Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingzhe Dai Xiaoshuang Shi Xiaofeng Zhu Tingrui Pan Kang Li School of Software Engineering University of Science and Technology of China Hefei China West China Biomedical Big Data Center West China Hospital of Sichuan University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024.

关键词： Training Analytical models Solid modeling Three-dimensional displays Annotations Magnetic resonance imaging Feature extraction Vectors Optimization Biomedical imaging

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Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection

arXiv

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arXiv 2024年

作者： Gan, Yuhang Xuan, Wenjie Luo, Zhiming Fang, Lei Wang, Zengmao Liu, Juhua Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University Wuhan China Land Satellite Remote Sensing Application Center MNR Beijing China Information Technology Co. Ltd. Ningbo China

When given two similar images, humans identify their differences by comparing the appearance (e.g., color, texture) with the help of semantics (e.g., objects, relations). However, mainstream change detection models adopt a supervised training paradigm, where the annotated binary change map is the main constraint. Thus, these methods primarily emphasize the difference-aware features between bi-temporal images and neglect the semantic understanding of the changed landscapes, which undermines the accuracy in the presence of noise and illumination variations. To this end, this paper explores incorporating semantic priors to improve the ability to detect changes. Firstly, we propose a Semantic-Aware Change Detection network, namely SA-CDNet, which transfers the common knowledge of the visual foundation models (i.e., FastSAM) to change detection. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features. Secondly, we design a single-temporal semantic pre-training strategy to enhance the semantic understanding of landscapes, which brings further increments. Specifically, we construct pseudo-change detection data from public single-temporal remote sensing segmentation datasets for large-scale pre-training, where an extra branch is also introduced for the proxy semantic segmentation task. Experimental results on five challenging benchmarks demonstrate the superiority of our method over the existing stateof- the-art methods. The code is available at SA-CD. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

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Local Phase Correction Method Based on Multi-Frequency Phase Heterodyne 26

Local Phase Correction Method Based on Multi-Frequency Phase...

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26th International Conference on Automation and Computing, ICAC 2021

作者： Tang, Jianfeng Feng, Xuan Xu, Yuanping Cao, Yanlong Li, Tukun Qiao, Wenbo Xu, Zhijie Chengdu University of Information Technology School of Software Engineering Chengdu China Sichuan Institute of Computer Sciences Chengdu China Zhejiang University School of Mechanical Engineering Hangzhou China University of Huddersfield School of Computing and Engineering West Yorkshire HuddersfieldHD1 3DH United Kingdom

ISBN: (纸本)9781860435577

The multi-frequency phase heterodyne method is the most common method applied in the unwrap phase, but it failed to reduce the noise error in the image and the sinusoidal error from the projector. In order to address these issues, this study proposes a local phase correction method. This study starts to determine whether a phase map needs to be corrected by calculating the deviation between the predicted and actual phase according to the slope of the projected standard fringe pattern. Then the phase is corrected by different intensities at different sections. Finally, the method compensates the phase error of the detail part with the minimum error phase correction. Experimental results showed that the local phase correction method is the optimal phase correction method, which reduced the phase error by 86.5%. Finally, the phase map of the metal workpiece surface was calculated by the various methods. And the phase map with the highest quality was obtained by using the local phase correction method. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Electromagnetic wave attenuation

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TV-3DG: Mastering Text-to-3D Customized Generation with Visual Prompt

arXiv

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arXiv 2024年

作者： Yang, Jiahui Di, Donglin Ma, Baorui Yang, Xun Ma, Yongjia Sun, Wenzhang Chen, Wei Cui, Jianxun Xue, Zhou Wang, Meng Liu, Yebin Harbin Institute of Technology Heilongjiang Harbin150001 China Space AI Li Auto Beijing101399 China School of Software Tsinghua University Beijing100084 China School of Information Science and Technology University of Science and Technology of China Anhui Hefei230026 China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China Department of Automation Tsinghua University Beijing100084 China

In recent years, advancements in generative models have significantly expanded the capabilities of text-to-3D generation. Many approaches rely on Score Distillation Sampling (SDS) technology. However, SDS struggles to accommodate multi-condition inputs, such as text and visual prompts, in customized generation tasks. To explore the core reasons, we decompose SDS into a difference term and a classifier-free guidance term. Our analysis identifies the core issue as arising from the difference term and the random noise addition during the optimization process, both contributing to deviations from the target mode during distillation. To address this, we propose a novel algorithm, Classifier Score Matching (CSM), which removes the difference term in SDS and uses a deterministic noise addition process to reduce noise during optimization, effectively overcoming the low-quality limitations of SDS in our customized generation framework. Based on CSM, we integrate visual prompt information with an attention fusion mechanism and sampling guidance techniques, forming the Visual Prompt CSM (VPCSM) algorithm. Furthermore, we introduce a Semantic-Geometry Calibration (SGC) module to enhance quality through improved textual information integration. We present our approach as TV-3DG, with extensive experiments demonstrating its capability to achieve stable, high-quality, customized 3D generation. Project page: https://***/TV-3DG © 2024, CC BY.

关键词： Semantics

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Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

arXiv

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arXiv 2023年

作者： Liang, Xinnian Zhou, Zefan Huang, Hui Wu, Shuangzhi Xiao, Tong Yang, Muyun Li, Zhoujun Bian, Chao State Key Lab of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Northeastern University Shenyang China Faculty of Computing Harbin Institute of Technology Harbin China Lark Platform Engineering-AI Beijing China

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and completely ignore word information. Although Whole Word Masking can alleviate this, the semantics in words is still not well represented. In this paper, we revisit the segmentation granularity of Chinese PLMs. We propose a mixed-granularity Chinese BERT (MigBERT) by considering both characters and words. To achieve this, we design objective functions for learning both character and word-level representations. We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT. Experimental results show that MigBERT achieves new SOTA performance on all these tasks. Further analysis demonstrates that words are semantically richer than characters. More interestingly, we show that MigBERT also works with Japanese. Our code has been released here 1 and you can download our model here 2 © 2023, CC BY.

关键词： Semantics

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A Novel Method for Reducing Risk with Optimized Anomaly Classifier 7

A Novel Method for Reducing Risk with Optimized Anomaly Clas...

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7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Kavitha, C. Jayakarthick, R. Nidhya, M.S. Madurai Kamaraj University College Dept of Computer Science Tamil Nadu Madurai India Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Dept.of. Computer Science Tamil Nadu Chennai India PeriyarManiammai Institute of Science Technology Dept of Software Engineering TamilNadu Thanjavur India

ISBN: (纸本)9781665405201

Deciding on an uncertain event may lead to risk. Uncertainty occurs due to the lack of knowledge of a particular event or a situation. The only way to avoid this is to analyze the Risk. The risk analyzed properly will reduce the impacts to a great extent. Here the risk management dealt with the anomaly detection mechanism, which is carried out by clustering the data to find the outlier as the *** proposed method employs the PCA mechanism for dimensionality reduction which is further clustered with K means algorithm and classified with decision tree algorithm. © 2021 IEEE.

关键词： Uncertainty analysis

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