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Machine Learning-Based Multi-Modal Information Perception for Soft Robotic Hands

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Tsinghua Science and technology 2020年第2期25卷 255-269页

作者： Haiming Huang Junhao Lin Linyuan Wu Bin Fang Zhenkun Wen Fuchun Sun College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518060China Department of Computer Science and Technology Tsinghua UniversityTsinghua UniversityBeijing 100084China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China

This paper focuses on multi-modal Information Perception(IP)for Soft Robotic Hands(SRHs)using Machine Learning(ML)algorithms.A flexible Optical Fiber-based Curvature Sensor(OFCS)is fabricated,consisting of a Light-Emitting Diode(LED),photosensitive detector,and optical *** the roughened optical fiber generates lower light intensity,which reflecting the curvature of the soft *** with the curvature and pressure information,multi-modal IP is performed to improve the recognition *** of gesture,object shape,size,and weight are implemented with multiple ML approaches,including the Supervised Learning Algorithms(SLAs)of K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Logistic Regression(LR),and the unSupervised Learning Algorithm(un-SLA)of K-Means Clustering(KMC).Moreover,Optical Sensor Information(OSI),Pressure Sensor Information(PSI),and Double-Sensor Information(DSI)are adopted to compare the recognition *** experiment results demonstrate that the proposed sensors and recognition approaches are feasible and *** recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all ***,DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.

关键词： multi-modal sensors optical fiber gesture recognition object recognition Soft Robotic Hands(SRHs) Machine Learning(ML)

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

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computers in Biology and Medicine 2024年 174卷 108484-108484页

作者： Ma, Xinqian Li, Zhen Du, Zhenya Xu, Yan Chen, Yifan Zhuo, Linlin Fu, Xiangzheng Liu, Ruijun School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325027 China Guangzhou Xinhua University Guangzhou510520 China School of Computer Science of Information Technology Qiannan Normal University for Nationalities Guizhou Duyun558000 China Institute of Computational Science and Technology Guangzhou University Guangzhou510000 China College of Computer and Information Engineering Central South University of Forestry and Technology Hunan Changsha410004 China College of Computer Science and Electronic Engineering Hunan University Changsha410012 China School of Software Beihang University Beijing China

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at:

关键词： Genes

Information geometry of bosonic Gaussian thermal states

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arXiv 2024年

作者： Huang, Zixin Wilde, Mark M. School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States

Bosonic Gaussian thermal states form a fundamental class of states in quantum information science. This paper explores the information geometry of these states, focusing on characterizing the distance between two nearby states and the geometry induced by a parameterization in terms of their mean vectors and Hamiltonian matrices. In particular, for the family of bosonic Gaussian thermal states, we derive expressions for their Fisher–Bures and Kubo–Mori information matrices with respect to their mean vectors and Hamiltonian matrices. An important application of our formulas consists of fundamental limits on how well one can estimate these parameters. We additionally establish formulas for the derivatives and the symmetric logarithmic derivatives of bosonic Gaussian thermal states. The former could have applications in gradient descent algorithms for quantum machine learning when using bosonic Gaussian thermal states as an ansatz, and the latter in formulating optimal strategies for single parameter estimation of bosonic Gaussian thermal states. Finally, the expressions for the aforementioned information matrices could have additional applications in natural gradient descent algorithms when using bosonic Gaussian thermal states as an ansatz. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

Identification Algorithm Framework and Structural model on Input Pattern of Metamorphic Relations 9

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Identification Algorithm Framework and Structural model on I...

9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Yan, Shiyu Yang, Xiaohua Lu, Zhongjian Li, Meng Gong, Helin Liu, Jie Computer School University of South China Hunan Hengyang China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China Cnnc Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Metamorphic Relation (MR) plays a crucial role in Metamorphic Testing (MT), which can be regarded as the implication relation from the input pattern to the output pattern. Based on MR's properties, the identification of Metamorphic Relations (MRs) can be from its input pattern and output pattern, respectively. However, the finding of the effective input pattern is very first and important. Especially for complicated mathematical function includes more than two input variables, we systematically analyze the structural model of input pattern in MR, propose the concept of the class of basic input pattern, as well as prove that any one input pattern can be expressed as a combination of basic input patterns in scientific computing programs. Moreover, the structural model of input pattern in MR is established based on previous proof. Finally, we put forward an identification algorithm framework and analysis its complexity. The core contribution of our method is to decompose the input pattern identification problem in multi-dimensional space into a single one-dimensional. For this reason, the examples show that the complexity of the algorithm is reduced significantly. © 2022 IEEE.

关键词： Computational complexity

Safe Route Recommendation based on Crime Risk Prediction with Urban and Crime Data

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Safe Route Recommendation based on Crime Risk Prediction wit...

IEEE International Conference on Big Data Computing Service and Applications (BigDataService)

作者： Daye Kim Juwon Baek Jihu Yang Hyun Roh Heewon Jeong Bryanna Ruiz Minji Lee Eric T. Matson Software Convergence Kyung Hee University Yongin Republic of Korea Computer Science and Electrical Engineering Handong Global University Pohang Republic of Korea Computer Engineering Chungbuk National University Cheongju Republic of Korea Computer and Information Technology Purdue University West Lafayette IN USA Technology Purdue University West Lafayette IN USA

Public safety is an escalating concern in the United States, given the rising crime rates. However, existing research paper on recommending safe routes often overlooks crucial factors essential for assessing road risk. In contrast to prior studies, this study introduces an innovative approach that recommends safe routes based on road risk prediction. By employing Gaussian Kernel Density Estimation, the study estimates the densities of crime and urban data for each coordinate in Chicago while considering the severity of crimes. These estimated densities are then utilized to predict future crime density using a machine learning model. The predicted crime density is assigned to each node and edge of the Chicago graph map, serving as an indicator of road risk. Four types of weighted graphs are generated by combining road risk and road length in different ratios, which are subsequently applied in the Dijkstra shortest path algorithm. The proposed application of this study is expected to decrease the likelihood of crime incidents, thereby fostering a safer environment. Furthermore, the results analyzed in this paper can serve as valuable reference material for community safety initiatives.

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Facial Data Minimization: Shallow Model as Your Privacy Filter

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arXiv 2023年

作者： Pu, Yuwen Chen, Jiahao Pan, Jiayu Yan, Diqun Zhang, Xuhong Ji, Shouling College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang310027 China College of Software Technology Zhejiang University NingBo Zhejiang315048 China Faculty of Electrical Engineering and Computer Science Ningbo University NingBo Zhejiang315211 China

Face recognition service has been used in many fields and brings much convenience to people. However, once a user's facial data is transmitted to a service provider, the user will lose control of his/her private data. In recent years, there are various security and privacy issues due to the leakage of facial data. Although many privacy-preserving methods have been proposed, they usually fail when they are not accessible to adversaries' strategies or the complete face recognition model. Hence, in this paper, by fully considering two cases of uploading facial images and facial features, which are very typical in practical face recognition service systems, we proposed a data privacy minimization transformation (PMT) method. This method can process the original facial data based on the shallow model of the authorized face recognition model to obtain the obfuscated data. The obfuscated data cannot only maintain satisfactory performance on authorized models and restrict the performance on other unauthorized models but also prevent original privacy data from leaking by AI methods and human visual theft. Additionally, since a service provider may execute preprocessing operations on the received data, we also propose an enhanced perturbation method to improve the robustness of PMT. Besides, to authorize one facial image to multiple service models simultaneously, a multiple-restriction mechanism is proposed to improve the scalability of PMT. Finally, we conduct extensive experiments and evaluate the effectiveness of the proposed PMT in defending against face reconstruction, data abuse, and face attribute estimation attacks. These experimental results demonstrate that PMT performs well in preventing facial data abuse and privacy leakage while maintaining high face recognition accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

Research on the two-dimensional polynomial fitting method of piezoresistive differential pressure transducer

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Research on the two-dimensional polynomial fitting method of...

IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

作者： Yang Yang Yufeng Jin Guangyi Shi Yuan Wang School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University Shenzhen China School of Software and Microelectronics Peking University Beijing China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350359831

ISBN: (纸本)9798350359848

This paper lies in the comprehensive examination of the data obtained from the piezoresistive differential pressure transducer. It reveals that employing a two-dimensional polynomial fitting method can achieve an impressive accuracy of 0.028%FS across the entire full scale. In-depth scrutiny of the various sub-terms of the fitting method, encompassing planar and higher-order nonlinear terms, was conducted. Notably, we observed that the accuracy of the fitting method attains its pinnacle at the quadratic term concerning output voltage and ambient temperature. However, beyond this point, irregularities in the fitting error become apparent. Furthermore, our research extends successfully implement of a compact 64-channel pressure scanning device. This achievement was made possible through the integration of multiple pressure sensors using advanced technology, coupled with a multi-channel gas path module.

关键词： Temperature sensors Pressure sensors Accuracy Transducers Temperature Fitting Voltage

The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

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arXiv 2023年

作者： Wang, Zihan Song, Siyang Luo, Cheng Zhou, Yuzhi Wu, Shiling Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China School of Computing and Mathematical Sciences University of Leicester United Kingdom

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence;(ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation;and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://***/wzh125/ABAW-5 .MSC Codes 68T40 Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning