2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)最新文献_第8页

Highly Fluent Sign Language Synthesis Based on Variable Motion Frame Interpolation 基于可变运动帧插值的高度流畅的手语合成

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283193

Ni Zeng, Yiqiang Chen, Yang Gu, Dongdong Liu, Yunbing Xing

{"title":"Highly Fluent Sign Language Synthesis Based on Variable Motion Frame Interpolation","authors":"Ni Zeng, Yiqiang Chen, Yang Gu, Dongdong Liu, Yunbing Xing","doi":"10.1109/SMC42975.2020.9283193","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283193","url":null,"abstract":"Sign Language Synthesis (SLS) is a domain-specific problem where multiple sign language words are stitched to generate a whole sentence in video, which serves to facilitate communications between the hearing-impaired people and healthy population. This paper presents a Variable Motion Frame Interpolation (VMFI) method for highly fluent SLS in scattered videos. Existing approaches for SLS mainly focus on mechanical virtual human technology, lacking high flexibility and natural effect. Also, the representative solutions to interpolate frames usually assume that the motion object moves at a constant speed which is not suitable for predicting the complex hand motion in frames of scattered sign language videos. To address the above issues, the proposed VMFI adopts acceleration to predict more accurate interpolated frames based on an end-to-end convolutional neural network. The framework of VMFI consists of variable optical flow estimation network and high-quality frame synthesis network that can approximate and fuse the intermediate optical flow to generate interpolated frames for synthesis. Experimental results on our realistic collected Chinese sign language dataset demonstrate that the proposed VMFI model achieves efficiency by performing better in PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and MA (Motion Activity) and gets higher score in MOS (Mean Opinion Score) than other two representative methods.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"7 1","pages":"1772-1777"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84382184","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

Modeling Disease Progression via Weakly Supervised Temporal Multitask Matrix Completion 通过弱监督时间多任务矩阵完成建模疾病进展

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283150

Lingsheng Wang, L. Xu, P. Li, Siming Zha, Lei Chen

{"title":"Modeling Disease Progression via Weakly Supervised Temporal Multitask Matrix Completion","authors":"Lingsheng Wang, L. Xu, P. Li, Siming Zha, Lei Chen","doi":"10.1109/SMC42975.2020.9283150","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283150","url":null,"abstract":"Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. Understanding AD progression can empower the patients in taking proactive care. Mini Mental State Examination (MMSE) and AD Assessment Scale Cognitive subscale (ADAS-Cog) are two prevailing clinical measures designed to evaluate the AD progression. In this paper, we propose a weakly supervised Temporal Multitask Matrix Completion (TMMC) framework, which combines a novel transductive multitask feature selection scheme, to simultaneously predict AD progression measured by MMSE and ADAS-Cog, and identify related biomarkers trackable of AD progression. Specifically, by treating the prediction of cognitive scores at each time point as a regression task, we first formulate AD progression problem as a standard Multitask Matrix Completion (MMC) model. Secondly, considering the limited number of samples available in this study, we introduce a transductive feature selection scheme to jointly select the task-shared features for multiple time points and the task-specific features for different time points, and thus alleviate the over-fitting defect caused by Small-Sample-Size issue. Thirdly, aiming at the small change of cognitive scores between successive time points for a patient, we employ a temporal regularization scheme to capture the temporal smoothness of cognitive scores. Furthermore, we design an efficient optimization algorithm based on Alternative Minimization and Difference of Convex Programming techniques to solve the proposed TMMC framework. Finally, the extensive experiments performed on real-world Alzheimer’s disease dataset demonstrate the effectiveness of our TMMC framework.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"19 1","pages":"1141-1148"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84396549","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

Machine Learning Applied to Topological Mapping for Structure Recognition 机器学习在结构识别拓扑映射中的应用

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283475

Francisco B. de S. Rocha, Bruno Vicente Alves de Lima, R. Leal, Diego P. Rocha, Karoline de M. Farias, R. Rabêlo, A. M. Santana

引用次数: 0

Kinematics Analysis and Tracking Control of Novel Single Actuated Lizard Type Robot 新型单驱动蜥蜴式机器人运动学分析与跟踪控制

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283226

Shunsuke Nansai, Y. Ando, N. Kamamichi, H. Itoh

{"title":"Kinematics Analysis and Tracking Control of Novel Single Actuated Lizard Type Robot","authors":"Shunsuke Nansai, Y. Ando, N. Kamamichi, H. Itoh","doi":"10.1109/SMC42975.2020.9283226","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283226","url":null,"abstract":"The purpose of this paper is to propose a new type of a kinetic chained walking robot capable of walking with only a single actuator, and is to design its trajectory tracking control system. Legged robots are able to move across irregular terrains, however, have an issue on energy efficiency compared with other morphology. A bio-inspired approach often provides effective solutions, for example, a lizard is able to mainly walk by utilizing only twisting its waist. To mimic this characteristic by robotics, a robot consisting of four-bar linkage mechanism is proposed. This idea improves simplification of its locomotion analysis. In this paper, two important kinematics characteristics are analyzed in order to propose locomotion ability and effectiveness of the robot. In particular, a turning angle and a stride distance are analysed. After that, a trajectory tracking control system is designed based on the PID control low. Ideas for the control system design in this paper are both to decide an bias of an input angle function as a input of the system and to set a control period on half period of the input angle function. Finally, effectiveness of the designed control system is verified via numerical simulations. A straight line and a circle trajectory are adopted for the verification. As the results, it is shown that the designed trajectory tracking control system is capable of tracking two different trajectory. In addition, it is also shown that the designed trajectory tracking control system satisfies the kinematics analysis results from the side of view of the kinematic of the robot.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"5 1","pages":"315-320"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85194058","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

Variational Inference of Infinite Generalized Gaussian Mixture Models with Feature Selection 具有特征选择的无限广义高斯混合模型的变分推理

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283007

Srikanth Amudala, Samr Ali, N. Bouguila

引用次数: 1

Detection of Driver Workload Using Wrist-Worn Wearable Sensors: A Feasibility Study 利用腕戴式可穿戴传感器检测驾驶员工作负荷的可行性研究

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9282860

Ryuto Tanaka, T. Akiduki, Hirotaka Takahashi

引用次数: 1

Robust Point Set Registration Based on Semantic Information 基于语义信息的鲁棒点集配准

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9282862

Qinlong Wang, Yang Yang, Teng Wan, S. Du

引用次数: 0

Co-Analysis of Connectivity, Location, and Situation in Mission-Critical Hybrid Communication Networks 关键任务混合通信网络中连通性、位置和情况的联合分析

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283142

Yaniv Mordecai, Dan Zadok

{"title":"Co-Analysis of Connectivity, Location, and Situation in Mission-Critical Hybrid Communication Networks","authors":"Yaniv Mordecai, Dan Zadok","doi":"10.1109/SMC42975.2020.9283142","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283142","url":null,"abstract":"Mission-critical communication (MCC) enables and supports operations by providing reliable connectivity and interoperability, facilitating operational continuity and allows mission-performers to focus on mission goals and objectives. Any communication technology used in isolation may fail. For instance, land mobile radio (LMR) networks may provide poor coverage to tactical \"push-to-talk\" radio devices within stone buildings, while cellular devices may not satisfy strict performance criteria (e.g. setup and response time). An alternative approach would be dynamic orchestration of such hybrid communication networks, which also involve Bluetooth, cellular, and cloud-based networking technologies. We propose an integrated approach that accounts for situation, location, and connectivity considerations to enhance MCC network availability, and mission-performers’ connectivity and readiness, by harnessing communication, location, and situational awareness in networking technologies, applications, and users. This framework provides a holistic cyber-physical perspective on the problem. Our approach is useful in various real-life applications for operational connectivity of first responders, e.g. when breaking into a scene of an emergency, in which LMR coverage is expected to deteriorate significantly.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"19 1","pages":"706-712"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76845999","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

Learning Effective Value Function Factorization via Attentional Communication 通过注意沟通学习有效的价值函数分解

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283355

Bo Wu, Xiaoya Yang, Chuxiong Sun, Rui Wang, Xiaohui Hu, Yan Hu

{"title":"Learning Effective Value Function Factorization via Attentional Communication","authors":"Bo Wu, Xiaoya Yang, Chuxiong Sun, Rui Wang, Xiaohui Hu, Yan Hu","doi":"10.1109/SMC42975.2020.9283355","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283355","url":null,"abstract":"How to achieve efficient cooperation among agents in partially observed environments remains an overarching problem in multi-agent reinforcement learning (MARL). Value function factorization learning is a promising way as it can efficiently address multi-agent credit assignment problem. However, existing value function factorization methods have been focusing on learning fully decentralized value functions, which are not effective for some complex tasks. To address this limitation, we propose a framework which enhances value function factorization by allowing communication during execution. Communication introduces extra information to help agents understand the complex environment and learn sophisticated factorization. Furthermore, the proposed mechanism of communication differs from existing methods since we additionally design a descriptive key along with the message. By the descriptive key, agents can dynamically measure the importance of different messages and achieve attentional communication. We evaluate our framework on a challenging set of StarCraft II micromanagement tasks, and show that it significantly outperforms existing value function factorization methods.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"46 1","pages":"629-634"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80791674","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

Forward and Inverse Approaches to Model Calibration for Uncertain Data * 不确定数据模型校正的正反方法*

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283230

L. G. Crespo

{"title":"Forward and Inverse Approaches to Model Calibration for Uncertain Data *","authors":"L. G. Crespo","doi":"10.1109/SMC42975.2020.9283230","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283230","url":null,"abstract":"This article proposes a framework for calibrating parametric models according to data subject to uncertainty. Data uncertainty might be caused by a poor metrology system, measurement noise, model-form uncertainty or by the inability to directly measure the inputs and/or outputs of interest. The formulations developed, called Forward Maximum Likelihood (FML) and Inverse Maximum Likelihood (IML), are applicable to datasets with and without uncertainty. The FML approach performs the calibration in the space of the model’s output thereby requiring repeated model simulations. Conversely, the IML approach leverages an ensemble of solutions to an inverse problem in order to perform the calibration in the space of the model’s parameters. The potential loss of performance incurred by the IML approach is often justified by a sizable reduction in computational cost. In addition, we use chance-constrained optimization to eliminate the effects of outliers on the calibrated model. This practice yields a model that increases the likelihood of most of the data in exchange for a reduction in the likelihood of a few of the worst-performing data points. Metrics for evaluating the benefits and risks of outlier elimination are also presented.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"452 1","pages":"64-69"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85545718","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