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Modeling a target-selection motion by leveraging an optimal feedback control mechanism

Science China(Information Sciences) 2018年第4期61卷 209-211页

作者： Jin HUANG Xiaolan PENG Feng TIAN Hongan WANG Guozhong DAI Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

Target selection has always been a popular research topic in the human-computer interaction(HCI)*** with continuous interactive spaces,such as video games,augmented reality(AR),and virtual reality(VR),are becom-

关键词： Modeling a target-selection motion by leveraging an optimal feedback control mechanism

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An Ensemble of VGG Networks for Video-Based Facial Expression Recognition

An Ensemble of VGG Networks for Video-Based Facial Expressio...

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Asian Conference on Affective Computing and Intelligent interaction (ACII Asia)

作者： Zirui Jiao Fengchun Qiao Naiming Yao Zhihao Li Hui Chen Hongan Wang Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538653128

This paper presents a fusion-based ensemble of VGG networks for the Multimodal Emotion Recognition Challenge 2017. Image fusion is used to aggregate consecutive frames from video sequences for the representation of temporal information. Then, an ensemble of four VGGFace models which have been fine-tuned on the MEC dataset, is utilized to extract facial expression features from the fused images. The VGGFace-BiLSTM and VGGFace-Bi-GRU arealso implemented for comparison. The accuracies of the fine-tuned VGGFace-ensemble, VGGFace-Bi-LSTM and VGGFace-Bi-GRU on validation data are 51.06%, 43.95% and 44.92% respectively, indicating the effectiveness of our method.

关键词： Feature extraction Training Hidden Markov models Face recognition Bidirectional control human computer interaction Software

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Developing a real-time inference approach for rule-based reasoning systems 13

Developing a real-time inference approach for rule-based rea...

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2013 Research in Adaptive and Convergent Systems, RACS 2013

作者： Qiao, Ying Leng, Chang Wang, Hongan Liu, Jian Beijing Key Lab. of Human-computer Interaction Institute of Software Chinese Academy of Sciences No.4 Zhong Guan Cun Nan Si St. Beijing China Shanxi Electric Power Research Institute YouYi East Rd. Xi'an Shan Xi China

ISBN: (纸本)9781450323482

Rule-based reasoning systems play importance roles for many real-time intelligent systems that need to take time-critical actions in response to the continuously arriving events. In this paper, we propose a novel inference approach, called RTINF to make the reasoning system meet its hard deadlines. A series of simulation studies are conducted to evaluate the performance of RTINF. © 2013 ACM.

关键词： Intelligent systems

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SketchGAN: Joint Sketch Completion and Recognition with Generative Adversarial Network

SketchGAN: Joint Sketch Completion and Recognition with Gene...

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Fang Liu Xiaoming Deng Yu-Kun Lai Yong-Jin Liu Cuixia Ma Hongan Wang State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Cardiff University Tsinghua University

ISBN: (纸本)9781728132945

Hand-drawn sketch recognition is a fundamental problem in computer vision, widely used in sketch-based image and video retrieval, editing, and reorganization. Previous methods often assume that a complete sketch is used as input;however, hand-drawn sketches in common application scenarios are often incomplete, which makes sketch recognition a challenging problem. In this paper, we propose SketchGAN, a new generative adversarial network (GAN) based approach that jointly completes and recognizes a sketch, boosting the performance of both tasks. Specifically, we use a cascade Encode-Decoder network to complete the input sketch in an iterative manner, and employ an auxiliary sketch recognition task to recognize the completed sketch. Experiments on the Sketchy database benchmark demonstrate that our joint learning approach achieves competitive sketch completion and recognition performance compared with the state-of-the-art methods. Further experiments using several sketch-based applications also validate the performance of our method.

关键词： Sketches images computer vision Recognition (Psychology) hand-drawn

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MOTION ESTIMATION OF MULTIPLE DEPTH CAMERAS USING SPHERES

MOTION ESTIMATION OF MULTIPLE DEPTH CAMERAS USING SPHERES

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IEEE International Conference on Image Processing

作者： Xiaoming Deng Jie Liu Feng Tian Liang Chang Hongan Wang Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences College of Information Science and Technology Beijing Normal University

ISBN: (纸本)9781479957521

Automatic motion estimation of multiple depth cameras has remained a challenging topic in computer vision due to its reliance on the image correspondence problem. In this paper, spherical objects are employed to estimate motion parameters between multiple depth cameras. We move a sphere several times in the common view of depth cameras. We fit the spherical point clouds to get the sphere centers in each depth camera system, and then introduce a factorization based approach to estimate motions between the depth cameras. Both simulated and real experiments show the robustness and effectiveness of our method.

关键词： Depth cameras Motion estimation Sphere object

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human-computer interactions for virtual reality

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Virtual Reality & Intelligent Hardware 2019年第3期1卷 I0001-I0002页

作者： Feng TIAN Beijing Key Laboratory of Human-Computer Interaction Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Chinese Academy of SciencesBeijing 100190China

human-computer interactions constitute an important subject for the development and popularization of information technologies,as they are not only an important frontier technology in computer science but also an important auxiliary technology in virtual reality(VR).In recent years,Chinese researchers have made significant advances in human-computer *** systematically display China's latest advances in human-computer interactions and thus provide an impetus for the development of VR and other related fields,we have solicited articles for this special issue from experts in this area to participate in the review *** following articles have been selected for publication in this special issue.

关键词： computer human frontier

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A dual-channel beamformer based on time-delay compensation estimator and shifted PCA for speech enhancement

A dual-channel beamformer based on time-delay compensation e...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Zhang Jie Hong Liu Open Lab on Human-Robot Interaction School of Electrical and Computer Engineering Peking University China Open Lab on Human-Robot Interaction School of Electrical and Computer Engineering Peking University Beijing China

Speech enhancement is an essential technique to process degraded audio in various applications. Beamforming to eliminate interferences based on sensor arrays is the most well-known method for this issue. However, traditional beamformers often face magnitude incoherence towards received signals due to directional weighting. Therefore, a novel dual-channel beamformer based on time-delay compensation (TDC) and shifted principal components analysis (PCA) is presented in this work. Firstly, our enhancement algorithm utilizes TDC estimator to preserve binaural cues, including interaural time-delay and intensity difference. Then the estimated cues are comprised to improve the shifted PCA, which can reduce noise by extracting primary components. Finally, the aforehand processed audio are input to a beamformer with post-filter to obtain enhanced speech. Experiments have demonstrated that the proposed algorithm could achieve some superiorities in speech intelligibility compared with the state-of-the-arts against real scenarios.

关键词： Speech Principal component analysis Speech enhancement Signal to noise ratio Array signal processing

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Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing 35

Auto-PIP: Real-time Identification of Critical Performance I...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Xiong, Xiao Li, Mengyao Sun, Yongqian Zhao, Yongxin Chen, Xia Deng, Bowen Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area"(SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research. © 2024 IEEE.

关键词： Software testing

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Multivariate Time Series Anomaly Detection based on Pre-trained Models with Dual-Attention Mechanism 35

Multivariate Time Series Anomaly Detection based on Pre-trai...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Sun, Yongqian Guo, Yang Liang, Minghan Wen, Xidao Kuang, Junhua Zhang, Shenglin Li, Hongbo Xia, Kaixu Pei, Dan Nankai University China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China BizSeer Haihe Laboratory of Information Technology Application Innovation China Tencent Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

In major tech companies, monitoring server performance data with anomaly detection algorithms is crucial for assessing operational status. Existing models often require separate training or fine-tuning for each server due to generalization limitations, leading to increased storage, memory, and training costs. As the number of servers grows, this approach becomes impractical. To address this, we propose using pretrained language models for time series anomaly detection, leveraging their strong generalization capabilities. Specifically, we employ two pre-trained GPT-2 models as backbones and implement a two-stage fine-tuning strategy to retain learned knowledge while adapting to specific business data characteristics. Our experiments on multiple anomaly detection datasets demonstrate that our method achieves the best average F1-Score, outperforming the leading baseline by 7%. © 2024 IEEE.

关键词： Time series

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Lightweight Method for Plant Disease Identification Using Deep Learning

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Intelligent Automation & Soft Computing 2023年第7期37卷 525-544页

作者： Jianbo Lu Ruxin Shi Jin Tong Wenqi Cheng Xiaoya Ma Xiaobin Liu Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal UniversityNanning530001China School of Computer and Information Engineering Nanning Normal UniversityNanning530001China School of Logistics Management and Engineering Nanning Normal UniversityNanning530001China Hangzhou Hikvision Digital Technology Co.Ltd. Hangzhou310052China

In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal devices with limited computational *** this study,a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is *** the proposed model,the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions;the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features;and the ReLU activation function is replaced with the ReLU6 activation function to prevent the gen-eration of large *** are conducted on the public dataset *** results show that the proposed model achieves an accuracy of 99.43%,which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 *** to lightweight network models,such as MobileNetV2,MobileNetV3,EfficientNet,and EfficientNetV2,and classical convolutional neural network models,such as ResNet34,ResNet50,and ResNet101,the proposed model has fewer parameters and higher recognition accuracy,which provides guidance for deploying crop pest identification methods on resource-constrained devices,including mobile terminals.

关键词： Plant disease identification mixed depthwise convolution lightweight ShuffleNetV2 attention mechanism

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