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Support vector machine with discriminative low-rank embedding

CAAI Transactions on Intelligence Technology 2024年第5期9卷 1249-1262页

作者： Guangfei Liang Zhihui Lai Heng Kong Computer Vision Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Breast and Thyroid Surgery BaoAn Central Hospital of ShenzhenShenzhenChina

Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.

关键词： iterative methods machine leaning support vector machunes

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Spatio-temporal Attention Graph Convolutions for Skeleton-based Action Recognition 23rd

Spatio-temporal Attention Graph Convolutions for Skeleton-b...

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22nd Scandinavian Conference on Image Analysis, SCIA 2023

作者： Le, Cuong Liu, Xin Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden

ISBN: (纸本)9783031314346

In skeleton-based action recognition, graph convolutional networks (GCN) have been applied to extract features based on the dynamic of the human body and the method has achieved excellent results recently. However, GCN-based techniques only focus on the spatial correlations between human joints and often overlook the temporal relationships. In an action sequence, the consecutive frames in a neighborhood contain similar poses and using only temporal convolutions for extracting local features limits the flow of useful information into the calculations. In many cases, the discriminative features can present in long-range time steps and it is important to also consider them in the calculations to create stronger representations. We propose an attentional graph convolutional network, which adapts self-attention mechanisms to respectively model the correlations between human joints and between every time steps for skeleton-based action recognition. On two common datasets, the NTU-RGB+D60 and the NTU-RGB+D120, the proposed method achieved competitive classification results compared to state-of-the-art methods. The project’s GitHub page: STA-GCN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Multimodal Price Prediction

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Annals of Data Science 2023年第3期10卷 619-635页

作者： Zehtab-Salmasi, Aidin Feizi-Derakhshi, Ali-Reza Nikzad-Khasmakhi, Narjes Asgari-Chenaghlu, Meysam Nabipour, Saeideh Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department Computer and Electrical Engineering University of Mohaghegh Ardabili Ardabil Iran

Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Price prediction Multimodal learning Convolutional neural network Inception Classification Prediction Multimodal Price Deep learning Data science Mobile phone

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An analysis of parameters affecting ampacity in aircraft bipolar MVDC power cables via coupled electrical,thermal,and computational fluid dynamic modelling

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High Voltage 2024年第6期9卷 1208-1220页

作者： Arian Azizi Mona Ghassemi Zero Emission Realization of Optimized Energy Systems(ZEROES)LaboratoryDepartment of Electrical and Computer EngineeringThe University of Texas at DallasRichardsonTexasUSA

The next generation of aircraft,including more electric aircraft and all-electric aircraft(AEA),requires electric power systems with high power density and low system mass *** the voltage of the system to the range of a few kV,medium voltage(MV),is a reasonable approach to achieving high-power-density and low-system-mass EPSs for aircraft *** voltages,however,pose many challenges for aviation MV power cables such as arcs and arc tracking,partial discharges(PDs),and thermal *** this regard,thermal management is more challenging since heat transfer by convection is greatly reduced at wide-body aircraft's cruising altitudes due to the reduced air *** this paper,a finite element method(FEM)model is devel-oped in COMSOL Multiphysics for an aircraft bipolar MVDC(5 kV)power *** the model,the maximum permissible cable current at a low pressure of 18.8 kPa(at an altitude of 12.2 km from sea level,the usual cruising altitude for wide-body aircraft)is ***,an analytical model is developed based on analytical and proven empirical correlations governing conductive,radiative,and convective heat transfers at the steady state to estimate the ampacity of the bipolar cable system at reduced *** was shown that the proposed analytical model can be used for atmospheric pressure and systems with a larger number of poles,expanding its range of *** results of the FEM and analytical models correlate at wide ranges of parameters such as ambient temperature,duct size,distance between the positive and negative pole cables,and the overall diameter of the *** influence of horizontal and vertical arrangement of poles is included in the analytical *** results of this study can be used to design bipolar MVDC power cable systems for the envisaged wide-body AEA.

关键词： bipolar power thermal

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Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

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computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb Khalid Javeed Musaed Alhussein Imad Rida Syed Irtaza Haider Anubha Parashar Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of Computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature ***,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and ***,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer *** proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： computer vision deep learning gait recognition sign language recognition machine learning

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Secure speech coding using chaotic shift keying for encryption combined with error recovery

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Multimedia Tools and Applications 2025年第7期84卷 3893-3927页

作者： Boumaraf, Messaouda Merazka, Fatiha LISIC Laboratory Telecommunications Department Electronic and Computer Engineering Faculty USTHB University Algiers Algeria LISIC Laboratory Telecommunications Department Electrical Engineering Faculty USTHB University Algiers Algeria

This paper presents a speech encryption algorithm utilizing chaotic shift keying alongside error recovery techniques for the G.722.2 codec. The algorithm employs chaotic data interleaving on speech inter-frames to replace continuous packet loss with isolated losses, thus restoring missing data packets. Codec parameters undergo encryption through the combination of multiple chaotic maps applied to intra sub-frames of speech, facilitating confusion and diffusion processes. Enhanced security is achieved through the utilization of multiple chaotic maps, including chaotic shift keying generators applied to codec sub-frame parameters. Specifically, the chaotic shift keying mechanism is employed using four chaotic maps: a Henon map for xn and yn, a logistic map for zn, a tent map for wn, and a cubic map for un. The keys derived from these maps are utilized for chaotic data interleaving on both inter-frames and intra sub-frames of the speech file to shuffle frame parameters (confusion) and scramble sub-frame parameters (diffusion). Additionally, the proposed system enhances speech quality by mitigating degradation caused by packet loss through a waveform substitution-based recovery method, which replaces lost packets with previously received copies. A series of experiments, including waveform representation, spectrogram analysis, Enhanced Modified Bark Spectral Distortion (EMBSD) test, WideBand Perceptual Evaluation of Speech Quality (WB-PESQ) test, MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) score, Mean Opinion Score (MOS) score, correlation test, entropy test, Number of Samples Change Rate (NSCR) test, Unified Average Modified Intensity (UACI) test, key space analysis, and sensitivity test, are conducted to evaluate the security and speech quality of the proposed system. The results validate the system’s security and confirm its ability to maintain good audio quality even under packet loss rates ranging from 5% to 20%. © The Author(s), under exclusive licen

关键词： Cryptography

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Large circuit models: opportunities and challenges

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Science China(Information Sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Committed-programming reductions: formalizations,implications and relations

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Science China(Information Sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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Part-aware Surface Slice and Polarization Implicit Function with Regular Discretization for 3D Human Surface Reconstruction

Part-aware Surface Slice and Polarization Implicit Function ...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Yang, Yehui Deng, Yong Li, Baoxing Zhao, Xu Shanghai Jiao Tong University Department of Automation Laboratory of Computer Vision Shanghai200240 China

ISBN: (纸本)9798350372601

Detailed 3D human surface reconstruction and editing relies on reasonable and elaborate representations. Currently, representation for 3D human surface can be broadly categorized into mesh-based and function-based approaches. Mesh-based representation offers intuitive visualization of intricate structure but is constrained by resolution limitations. Function-based representation surpasses resolution constraints to achieve higher precision but is spatial redundant for fitting the whole space. To address these challenges, we introduce the Part-aware surface Slice Polarization Implicit Function (PaSP-IF) for 3D human surfaces, aiming at capturing details and reducing redundancy. By establishing polar coordinate systems for slice plane of each body part, we represent the human surface using polar angle and radial distance. Additionally, as for visualization, discretization applied on PaSP-IF can be real-ized by selecting appropriate slice and polar angle resolutions to form the discretized radius matrix. Pixel-level operations can be easily applied on the radius matrix to edit the human surface. Experiments on the THuman2.0 datasets, along with applications in surface editing and clothing transferring, have demonstrated the effectiveness of the proposed representation. © 2024 IEEE.

关键词： Mesh generation

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Co-clustering method for cold start issue in collaborative filtering movie recommender system

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Multimedia Tools and Applications 2024年 1-25页

作者： AbbasiRad, Ensieh Keyvanpour, Mohammad Reza Tohidi, Nasim Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Computer Engineering Faculty K.N.Toosi University of Technology Tehran Iran Computer Science & amp Engineering University of Michigan Ann ArborMI United States

Recommender systems play an essential role in decision-making in the information age by reducing information overload via retrieving the most relevant information in various applications. They also present great opportunities and challenges for business, government, education, and other fields. The cold start problem is a significant issue in these systems. If recommender systems fail to provide satisfactory personalized recommendations for new users, the user’s trust can easily be lost. Hence, in this paper, using co-clustering and utilizing user demographic information and the behavioral history of users, a solution to this critical issue for recommending movies is introduced. In the proposed method, in addition to dealing with the problem of relative cold start, the problem of absolute cold start is also addressed. The proposed method was evaluated via two RMSE and MAE criteria, which accordingly has achieved 0.85 and 0.49 on the Movielens dataset and 1.05 and 0.6 on the EachMovie dataset, respectively, according to the number of comments that Cold Start users have registered. Moreover, it achieved 0.9 and 0.55 on the Movielens dataset and 1.42 and 0.89 on the EachMovie dataset, respectively, according to the number of registered comments for the cold start items. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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