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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Aaron M. Roth Samantha Reig Umang Bhatt Jonathan Shulgach Tamara Amin Afsaneh Doryab Fei Fang Manuela Veloso Robotics Institute Pittsburgh PA USA Human-Computer Interaction Institute Pittsburgh PA USA College of Engineering Pittsburgh PA USA Institute for Software Research Pittsburgh PA USA Machine Learning Department Carnegie Mellon University Pittsburgh PA USA

As robots are increasingly endowed with social and communicative capabilities, they will interact with humans in more settings, both collaborative and competitive. We explore human-robot relationships in the context of a competitive Stackelberg Security Game. We vary humanoid robot expressive language (in the form of “encouraging” or “discouraging” verbal commentary) and measure the impact on participants' rationality, strategy prioritization, mood, and perceptions of the robot. We learn that a robot opponent that makes discouraging comments causes a human to play a game less rationally and to perceive the robot more negatively. We also contribute a simple open source Natural Language Processing framework for generating expressive sentences, which was used to generate the speech of our autonomous social robot.

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An Iterative unsupervised Person Search Algorithm on Natural Scene Images

An Iterative unsupervised Person Search Algorithm on Natural...

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Chinese Automation Congress (CAC)

作者： Sisi Cao Yuehu Liu School of Software Engineering Xi’an Jiaotong University Xi’an China Key Lab of Digital Technology and Intelligent System of Shaanxi Province Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China

Person search is a challenging task due to the different requirements of annotations between person detection and Re-identification. In general, person search methods use the supervised person Re-identification methods, where abundant identity labels of the bounding boxes are essential. However, most person images are unlabeled in the real-world scenario and it is unpractical to annotate the abundant fine-grained labels for unlabeled images. Obviously, the existing supervised methods are not appropriate with the real-world scenario. Therefore, we propose an unsupervised learning method for person search in this paper, which contacts two parts: one is unsupervised person detection and the other is unsupervised person Re-identification. The experimental results on two well-known datasets, CUHK-SYSU and PRW, indicate that proposed method achieves competitive performance than the state-of-art unsupervised methods. Note that proposed method has greater practical significance even though it does not get the results as good as the general supervised methods.

关键词： Training Clustering algorithms Noise measurement Task analysis Search methods Feature extraction Unsupervised learning

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Traffic Sensory Data Classification by Quantifying Scenario Complexity

Traffic Sensory Data Classification by Quantifying Scenario ...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Wang, Jiajie Zhang, Chi Liu, Yuehu Zhang, Qilin School of Software Engineering Xi'An Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an China HERE Technologies ChicagoIL60606 United States

ISBN: (纸本)9781538644522

For unmanned ground vehicle (UGV) off-line testing and performance evaluation, massive amount of traffic scenario data is often required. The annotations in current off-line traffic sensory dataset typically include I) types of roadways II) scene types III) specific characteristics that are generally considered challenging for cognitive algorithms. While such annotations are helpful in manual selection of data, they are insufficient for comprehensive and quantitate measurement of per-roadway-segment scenario complexity. To resolve such limitations, we propose a traffic sensory data classification paradigm based on quantifying the scenario complexity for each roadway segment, where such quantification is jointly based on road semantic complexity and traffic element complexity. The road semantic complexity is a proposed measurement of the complexity incurred by the static elements such as curvy roads, intersections, merges and splits, which is predicted with a Support Vector Regression (SVR). The traffic element complexity is a measurement of complexity due to dynamic traffic elements, such as nearby vehicles and pedestrians. Experimental results and a case study verify the efficacy of the proposed method. © 2018 IEEE.

关键词： Semantics

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Improving the multi-class classification of Alzheimer's disease with machine learning-based techniques: An EEG-fNIRS hybridization study

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2021年第S7期17卷 e057565-e057565页

作者： Thi Kieu Khanh Ho Minhee Kim YoungHoon Jeon Eunchan Na Zahid Ullah Byeong C Kim Kun Ho Lee Jong-In Song Jae Gwan Kim Jeonghwan Gwak Department of Software Korea National University of Transportation Republic of South Korea Department of Biomedical Science and Engineering Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea Department of Neurology Chonnam National University Medical School Republic of South KoreaGwangju South Korea Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center Chosun University Republic of South KoreaGwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Republic of South KoreaDaegu South Korea Department of Biomedical Science Chosun University Republic of South KoreaGwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Republic of South Korea Department of AI Robotics Engineering Korea National University of Transportation Republic of South Korea Department of Biomedical Engineering Korea National University of Transportation Republic of South Korea

BACKGROUND: As an incurable neurodegenerative disorder, Alzheimer's Disease (AD) is being projected to become one of the most expensive diseases for understanding the pathophysiological mechanism. Exploring alterations in the AD brain network is therefore of central importance for early and accurate diagnosis of cognitive deficits levels, yet diagnostic uncertainties exist merely confirmed by postmortem brain examination. The hybrid approach of two modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) reveals an ideal technique-derived diagnosis due to the inexpensive and noninvasive experiments coupled with the reliability and versatility that make it highly desirable for AD multi-class classification tasks. METHODS: In this study, the hybrid EEG-fNIRS was employed in constructing the classification machine learning (ML)-based models to categorize four subject groups, including healthy controls (HC) and three AD patient classes. First, a concurrent EEG-fNIRS setup was utilized to record data from 41 subjects during the Oddball - a cognitive ability test and 1-back - a memory ability test. Second, while fNIRS features were computed and extracted, the event-related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands were extracted. Third, a conventional neural network and a hybrid deep Convolutional Neural Network (CNN) - Long Short-Term Memory Network (LSTM) was built for the multi-class classification using the fNIRS and EEG features, respectively. To boost the classification accuracy, the output of different models was combined by majority voting methods. RESULTS: The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (71.01% ± 0.004) by combing their complementary properties, compared to using EEG (66.35% ± 0.016) or fNIRS (67.15% ± 0.017) alone. In addition, fNIRS and ERSP feature patterns displayed a significant difference between subject

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DeepADNet: A CNN-LSTM model for the multi-class classification of Alzheimer's disease using multichannel EEG

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2021年第S7期17卷 e057573-e057573页

作者： Thi Kieu Khanh Ho YoungHoon Jeon Eunchan Na Zahid Ullah Byeong C Kim Kun Ho Lee Jong-In Song Jeonghwan Gwak Department of Software Korea National University of Transportation Republic of South Korea School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Republic of South KoreaGwangju South Korea Department of Neurology Chonnam National University Medical School Republic of South KoreaGwangju South Korea Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center Chosun University Republic of South KoreaGwangju South Korea Aging Neuroscience Research Group Korea Brain Research Institute Republic of South KoreaDaegu South Korea Department of Biomedical Science Chosun University Republic of South KoreaGwangju South Korea Department of IT & Energy Convergence (BK21 FOUR) Korea National University of Transportation Republic of South Korea Department of AI Robotics Engineering Korea National University of Transportation Republic of South Korea Department of Biomedical Engineering Korea National University of Transportation Republic of South Korea

BACKGROUND: Early and accurate diagnosis of Alzheimer's disease (AD) - an incurable and progressive brain disorder, along with a sharp upward trend in the incidence rate, is significantly important for patients to take prevention and appropriate treatments. However, the pathophysiological mechanism at the levels of AD severity is still poorly understood in spite of the worldwide financial and research efforts. Using an inexpensive and noninvasive modality, such as Electroencephalography (EEG) coupled with Deep Learning (DL)-derived diagnostic tool to retain the accuracy and versatility, therefore, has gained much attention in AD multi-class classification. METHODS: The present study proposes DL model, named DeepADNet, for the AD multi-class classification using multichannel EEG. First, we extracted the event-related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands relative to a specific experimental event. Second, the hybrid deep Convolutional Neural Network (CNN) - Long Short-Term Memory Network (LSTM) was built to exploit the wide range of ERSP patterns on a time-frequency domain and to generate discriminant features to classify healthy controls (HC) with two AD subject groups. To tackle the imbalance problem and improve the classification accuracy, we also applied different over-sampling techniques RESULTS: The EEG data were collected from the Oddball - a cognitive ability test with 63 subjects (23 HC, 17 Pre-symptomatic AD (aAD) and 23 Prodromal AD (pAD) subjects) and the N-back - a memory ability test with 36 subjects (13 HC, 11 aAD and 12 pAD subjects). We demonstrated that the ERSP patterns displayed significant differences among three subject groups during the two experimental stages. Classification results also reveal that a CNN-LSTM model could overcome the existing methods utilizing the hand-engineered features which demand on prior knowledge of AD analysis and achieved the highest

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A Guide to Convolutional Neural Networks for Computer Vision

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2018年

作者： Salman Khan Hossein Rahmani Syed Afaq Ali Shah Mohammed Bennamoun Gerard Medioni Sven Dickinson

ISBN: (数字)9781681730226

Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

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Precise Dental Staging Method through Panoramic Radiographs Based on Deep Learning

Precise Dental Staging Method through Panoramic Radiographs ...

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Chinese Automation Congress (CAC)

作者： Hongcheng Han Shaoyi Du Dong Zhang Hong Long Yucheng Guo Institute of Artificial Intelligence and Robotics College of Artificial Intelligence Xi’an Jiaotong University Xi’an P.R. China School of Software Engineering Xi’an Jiaotong University Xi’an P.R. China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an P.R. China Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research College of Stomatology Xi’an Jiaotong University Xi’an P.R. China Stomatological Hospital of Xi'an Jiaotong University Xi’an P.R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, we use a deep learning method to realize automatic dental staging, which is an image classification task. Sample imbalance is found to influence the staging accuracy of deep learning method. Aiming at the problem, an optimized method for dental staging is proposed. Firstly, based on the thought of coarse-to-fine, the dental staging task is converted into a binary-classification and two multi-classification tasks to reduce the difficulty of each classification task. Secondly, data augmentation is used in multi-classification to balance the samples in different categories. Besides, the Easy-Ensemble method is used to reduce the influence of sample imbalance in the binary-classification task. According to the experiment results, the dental staging effect of the proposed method is improved in accuracy, recall, and accuracy compared with the unoptimized method, which verifies the availability of the proposed method.

关键词： Dentistry Teeth Training Task analysis Image recognition Medical diagnostic imaging Deep learning

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Deep Supervised Hashing With Anchor Graph

Deep Supervised Hashing With Anchor Graph

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International Conference on Computer Vision (ICCV)

作者： Yudong Chen Zhihui Lai Yujuan Ding Kaiyi Lin Waikeung Wong College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong China School of Software and Microelectronics Peking University Beijing China Institute of Textiles and Clothing. The Hong Kong Polytechnic University

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Recently, a series of deep supervised hashing methods were proposed for binary code learning. However, due to the high computation cost and the limited hardware's memory, these methods will first select a subset from the training set, and then form a mini-batch data to update the network in each iteration. Therefore, the remaining labeled data cannot be fully utilized and the model cannot directly obtain the binary codes of the entire training set for retrieval. To address these problems, this paper proposes an interesting regularized deep model to seamlessly integrate the advantages of deep hashing and efficient binary code learning by using the anchor graph. As such, the deep features and label matrix can be jointly used to optimize the binary codes, and the network can obtain more discriminative feedback from the linear combinations of the learned bits. Moreover, we also reveal the algorithm mechanism and its computation essence. Experiments on three large-scale datasets indicate that the proposed method achieves better retrieval performance with less training time compared to previous deep hashing methods.

关键词： Binary codes Training Data models Neural networks Linear programming Computational modeling Training data

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Toward Human-in-the-Loop Prohibited Item Detection in X-ray Baggage Images

Toward Human-in-the-Loop Prohibited Item Detection in X-ray ...

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Chinese Automation Congress (CAC)

作者： Sisi Cao Yuehu Liu Wenwen Song Zhichao Cui Xiaojun Lv Jingwei Wan School of Software Engineering Xi’an Jiaotong University Xi’an China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China China Academy of Railway Science Corporation Limited Institute of Computing Techonologie Beijing China Department of Criminal Science and Technology Railway Police College Zhengzhou China

X-ray baggage security screening is a demanding task for aviation and rail transit security; automatic prohibited item detection in X-ray baggage images can help reduce the work of inspectors. However, as many items are placed too close to each other in the baggages, it is difficult to fully trust the detection results of intelligent prohibited item detection algorithms. In this paper, a human-in-the-loop baggage inspection framework is proposed. The proposed framework utilizes the deep-learning-based algorithm for prohibited item detection to find suspicious items in X-ray baggage images, and select manual examination when the detection algorithm cannot determine whether the baggage is dangerous or safe. The advantages of proposed inspection process include: online to capture new sample images for training incrementally prohibited item detection model, and augmented prohibited item detection intelligence with human-computer collaboration. The preliminary experimental results show, human-in-the-loop process by combining cognitive capabilities of human inspector with the intelligent algorithms capabilities, can greatly improve the efficiency of in-baggage security screening.

关键词： X-ray imaging Inspection Training Proposals Feature extraction Detection algorithms Object detection

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Body Part Level Attention Model For Skeleton-Based Action Recognition

Body Part Level Attention Model For Skeleton-Based Action Re...

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Chinese Automation Congress (CAC)

作者： Han Zhang Yonghong Song Yuanlin Zhang School of Software Engineering Xi’an Jiaotong University No.28 Xianning West Road Xi’an Shannxi P.R.China Institute of Artifical Intelligence and Robotics Xi’an Jiaotong University No.28 Xianning West Road Xi’an Shannxi P.R.China

Skeleton-based action recognition has catched more eyes recently. Many approaches model the human skeleton sequences spatio-temporal representation by Recurrent Neural Networks, while RNNs-based approaches don't explicitly have the attention scheme. In this paper, a body part level attention method is proposed for determining which parts are discriminative. On the basis of human joints natural connection, human body is divided to disjoint parts which then are fed into corresponding modules to generate action scores. A body part level attention module which distributes different importance to different action scores is proposed to adaptively learn informative body parts. The final representative of the action video is produced in a weighted average way and it will be fed into DNNs for classification. Out method outperforms other attention model and we obtain the state-of-the-art performance compared to other methods on two popularly used benchmark NTU dataset and UT Kinect dataset.

关键词： Recurrent neural networks Skeleton Legged locomotion Task analysis Adaptation models Computer architecture Neurons

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