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2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)最新文献

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Examination and Categorization of Keywords Associated with Online Review Sorting 与在线评论排序相关的关键词的检查和分类
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00016
Weiping Xie
{"title":"Examination and Categorization of Keywords Associated with Online Review Sorting","authors":"Weiping Xie","doi":"10.1109/CONF-SPML54095.2021.00016","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00016","url":null,"abstract":"In modern days, product sold online are reviewed online. For example, large online platforms like Amazon allows all users to leave reviews composed of a rating and a comment to any product, whether the purchased it or not. Reviews that are immediately visible influence the purchase decisions of potential customers, therefore the platforms need to sort the reviews so that the first few reflect the product’s quality accurately. However, current algorithms are still not yet mature enough for the task. In order to determine the credibility and reliability of online product reviews, we summarize the factors typically considered in review sorting studies into four categories: product-specific factors, general attitude factors, reviewer-specific factors, and time factors. We then examine the contents and implications of each category. Future designers of algorithms can use the factors to their advantages.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129300443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Intra Fast Mode Decision Algorithm and Hardware Design for AVS2 AVS2快速模式内决策算法及硬件设计
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00073
Hao Xie, Kaiyang Liu, Yang Zhao, Xiao Zheng, Xianguo Qing
{"title":"Intra Fast Mode Decision Algorithm and Hardware Design for AVS2","authors":"Hao Xie, Kaiyang Liu, Yang Zhao, Xiao Zheng, Xianguo Qing","doi":"10.1109/CONF-SPML54095.2021.00073","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00073","url":null,"abstract":"AVS2 is a kind of video coding standard proposed by China, it adopts 33 intra prediction modes to improve coding performance, while the computational complexity has increased dramatically. In order to reduce the complexity of AVS2 intra mode decision and make the AVS2 hardware encoder meet real-time requirements, this paper proposes the AVS2 intra fast mode decision algorithm and hardware design. The experimental results show that the intra mode decision algorithm and hardware design proposed in this paper can meet the throughput requirement of 1920x1080@60fps at a clock frequency of 300MHz. By using Xilinx FPGA XC7K325T 900 on the Vivado HLS platform for synthesis, only 10% of the LUT, 5% of FF, 5% of BRAM, and 6% of DSP in FPGA resources are consumed to meet the design requirements.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125696131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Design of Intelligent Garbage Classification Bin Based on LD3320 基于LD3320的智能垃圾桶设计
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00010
Chen Xiong
{"title":"Design of Intelligent Garbage Classification Bin Based on LD3320","authors":"Chen Xiong","doi":"10.1109/CONF-SPML54095.2021.00010","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00010","url":null,"abstract":"In this paper, based on LD3320 non-specific person speech recognition chip, Arduino UNO R3 MCU, LX-225 serial bus intelligent steering gear, an intelligent trash can is designed, which realizes the functions of voice input of garbage name, intelligent retrieval of garbage type, intelligent opening and closing of garbage can cover, etc.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125437652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
An Overview of Deep Learning Based Small Sample Medical Imaging Classification 基于深度学习的小样本医学影像分类综述
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00060
Kai Wang
{"title":"An Overview of Deep Learning Based Small Sample Medical Imaging Classification","authors":"Kai Wang","doi":"10.1109/CONF-SPML54095.2021.00060","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00060","url":null,"abstract":"Deep Learning (DL) has been proven to be a promising technique for image analysis tasks such as image classification and object recognition. Compared with other fields, the accuracy of DL tasks in medical imaging depends heavily on the dataset volume. However, DL has been suffering from the problem of small sample datasets caused by a variety of ethical and financial reasons in medical imaging. Data augmentation and transfer learning are the two most commonly used approaches to enhance the practicability of the DL algorithms in medical imaging. This article discusses the data augmentation methods including image manipulation and generative adversarial networks. Feature-extracting and fine-tuning methods of transfer learning are also discussed. Finally, the paper mentions the real-life applications of many architectures, advantages and disadvantages, and future works.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126077456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Automatic Brightness Control for Face Analysis in Near-Infrared Spectrum 近红外光谱人脸分析的自动亮度控制
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00064
J. Vugrin, S. Lončarić
{"title":"Automatic Brightness Control for Face Analysis in Near-Infrared Spectrum","authors":"J. Vugrin, S. Lončarić","doi":"10.1109/CONF-SPML54095.2021.00064","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00064","url":null,"abstract":"Face analysis is a broad and well-established research area whose main focus is put on face detection, segmentation, recognition and facial features extraction. A crucial prerequisite to face analysis algorithms properly work is to have an input image of high quality with similar properties in different conditions. For this reason, near-infrared images are used due to being more robust to change in lighting conditions and time of day than the visible light spectrum images. Automatic brightness control is used to properly adjust scene brightness to extract useful information. A novel algorithm implementation for the automatic brightness control is proposed based on a split range feedback controller with a camera occlusion detection included. The proposed algorithm is accurate, fast and suitable for real-time embedded system implementation.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131593607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
FPGA-Based Deep Convolutional Neural Network Optimization Method 基于fpga的深度卷积神经网络优化方法
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00030
Lilan Wen
{"title":"FPGA-Based Deep Convolutional Neural Network Optimization Method","authors":"Lilan Wen","doi":"10.1109/CONF-SPML54095.2021.00030","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00030","url":null,"abstract":"With the increasing demand for computing speed and real-time data processing in various fields, deep learning and convolutional neural networks are more and more widely used in the field of computer vision. FPGA-based deep convolutional neural networks (CNN) have been proposed and developed rapidly due to its high parallel processing ability, portability, and low power consumption. To further improve the network efficiency, this paper studies the software acceleration tool Vivado HLS provided by Xilinx, the quantification and pruning of convolution neural network model, which can effectively optimize the network model and accelerate the reasoning process.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Coarse-to-Fine Loss Based On Viterbi Algorithm for Weakly Supervised Action Segmentation 弱监督动作分割中基于Viterbi算法的粗到细损失
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00009
Longshuai Sheng, Ce Li, Yihan Tian
{"title":"Coarse-to-Fine Loss Based On Viterbi Algorithm for Weakly Supervised Action Segmentation","authors":"Longshuai Sheng, Ce Li, Yihan Tian","doi":"10.1109/CONF-SPML54095.2021.00009","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00009","url":null,"abstract":"Weakly supervised action segmentation has been extensively studied to get the category and start time of actions that occur in videos, but it remains an unsolved issue because of lacking great annotation data in video analysis. To handle this issue, weakly supervised action segmentation only uses the action annotation on the whole sequence in a long video instead of specific labeling of each frame, which greatly reduces the difficulty of obtaining video datasets. However, the task remains challenging for the complex temporal length partition of actions in the videos. In this paper, we make use of the Viterbi algorithm to generate an initial action segmentation as the baseline and then design a new coarse-to-fine loss function to refine the length partition and learn the scores of valid and invalid segmentation routes respectively. The new coarse-to-fine loss is learned in the pipeline to reduce the weight of invalid segmentation routes and obtain the best video segmentation. Comparing with the state-of-the-art (SOTA) methods, the experiments on the breakfast and 50 salads datasets show that our fine partition model and coarse-to-fine loss function can be used to obtain higher frame accuracy and significantly reduce the time spent for action segmentation.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133607386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Design of UAV Flight Control Law Based on PID Control 基于PID控制的无人机飞行控制律设计
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00028
L. Liu
{"title":"Design of UAV Flight Control Law Based on PID Control","authors":"L. Liu","doi":"10.1109/CONF-SPML54095.2021.00028","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00028","url":null,"abstract":"In recent years, UAVs have received more and more attention from countries all over the world. The diverse functions and unique advantages have caused the world to set off an upsurge in UAV development. Taking UAV as the research object, this thesis mainly studies the design of the flight control law of the UAV flight control system based on PID control, so as to facilitate the flight simulation of UAV. In response to this problem, the longitudinal flight control law and the lateral flight control law are designed to maintain and control the movement of the UAV’s altitude, pitching angle, roll angle and course angle.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131453126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bayesian Optimization: Model Comparison With Different Benchmark Functions 贝叶斯优化:不同基准函数下的模型比较
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00071
Ning Qin, Xinyu Zhou, Jiaqi Wang, Chujie Shen
{"title":"Bayesian Optimization: Model Comparison With Different Benchmark Functions","authors":"Ning Qin, Xinyu Zhou, Jiaqi Wang, Chujie Shen","doi":"10.1109/CONF-SPML54095.2021.00071","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00071","url":null,"abstract":"Bayesian optimization(BO) is a global optimization problem. It is an important approach in machine learning, hyperparameter tuning and other fields such as drug discovery. BO consists of two main parts which are probabilistic model for the objective function and acquisition function. This paper mainly focused on assessing the strengths and weaknesses of two different probabilistic models which are Gaussian Process (GP) and Random Forests (RF). This paper illustrated several results, which indicated the performance of each probabilistic model and helped us find the optimal model corresponding to each benchmark function. RF will be preferred if the function is smooth. GP will be preferred if the function has many local minima. Moreover, implementability of other probabilistic models were discussed in this paper.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132820491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Manifold Guided Graph Neural Networks for Skeleton-based Action Recognition in Human Computer Interaction Videos 基于骨架的人机交互视频动作识别的流形导图神经网络
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00053
Xin Li, Ce Li, Xianlong Wei, Feng Yang
{"title":"Manifold Guided Graph Neural Networks for Skeleton-based Action Recognition in Human Computer Interaction Videos","authors":"Xin Li, Ce Li, Xianlong Wei, Feng Yang","doi":"10.1109/CONF-SPML54095.2021.00053","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00053","url":null,"abstract":"As the key application in video analysis for human computer interaction (HCI), the problem of skeleton-based action recognition has been solved by some researchers with graph neural networks, but it remains an unsolved issue on complex variations of spatiotemporal dependence across skeleton joints flow. A newly dynamic spatio-temporal graph structure learning method, manifold guided graph neural networks (MGNN), was proposed to solve this problem. In MGNN, a novel manifold guided graph updating mechanism is built based on the baseline graph neural network to further describe the spatio-temporal dependence. With the manifold guided multi-scale skeleton graph, the proposed MGNN is further trained with two streams of joint and bone to improve the efficiency, which forms a single network seamlessly and enables it be trained in a same umbrella. Comparing with the existing methods, MGNN has been proved that it yields better performance on challenging datasets: NTU RGB+D 60 and Kinetics 400.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115853720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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