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Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume school of computer science and engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of computer science&engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging

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Quantitative Biology 2022年第2期10卷 188-207页

作者： Aishwarza Panday Muhammad Ashad Kabir Nihad Karim Chowdhury Department of computer science&engineering Stamford UniversityDhaka 1217Bangladesh school of Computing and Mathematics Charles Sturt UniversityNSW 2795Australia Department of computer science&engineering University of ChittagongChittagong 4349Bangladesh

Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria.Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19.Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.

关键词： COVID-19 machine learning deep learning detection classification diagnosing X-ray CT scan

BSTFNet:An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

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computers, Materials & Continua 2024年第3期78卷 3929-3951页

作者： Hong Huang Xingxing Zhang Ye Lu Ze Li Shaohua Zhou school of computer science and engineering Sichuan University of Science&EngineeringYibin644002China school of Mathematics and Statistics Sichuan University of Science&EngineeringYibin644002China

While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.

关键词： Encrypted malicious traffic classification bidirectional encoder representations from transformers text convolutional neural network bidirectional gated recurrent unit

Steady-State Visual Evoked Potential (SSVEP) in a New Paradigm Containing Dynamic Fixation Points

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China Communications 2022年第2期19卷 15-30页

作者： Yuang Li Yong Ge Xuefei Zhong Xiong Zhang school of Electronic science&engineering Southeast UniversityNanjing 210096China

Steady-state visual evoked potential(SSVEP)has become a powerful tool for Brain computer Interface(BCI)because of its high signal-tonoise ratio,high information transmission rate,and minimal user training.At present,the edge information of each region cannot be identified in spatial coding based on SSVEP-BCI technology,and the user experience is poor.To solve this problem,this paper designed a new paradigm to explore the relationship between the fixation point position of continuous sliding and the correlation coefficient ratio in the dualfrequency case.Firstly,the standard sinusoidal signal was employed to simulate the Electroencephalogram(EEG)signal,which verified the reliability of characterizing the amplitude variation of test signal by correlation coefficient.Then,the relationship between the amplitude response of SSVEP and the distance between the fixation point and the stimulus in the horizontal direction was tested by Canonical Correlation Analysis(CCA)and Filter bank CCA(FBCCA).Finally,the experimental data were offline analyzed under the condition of continuous sliding of the fixation point.It is feasible and reasonable to detect the amplitude change of frequency component in SSVEP by utilizing the spatial coding method in this paper to improve the extraction accuracy of spatial information.

关键词： SSVEP spatial-coding dual-stimulation correlation coefficient ratio

A multichannel human-swarm robot interaction system in augmented reality

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Virtual Reality & Intelligent Hardware 2020年第6期2卷 518-533页

作者： Mingxuan CHEN Ping ZHANG Zebo WU Xiaodan CHEN South China University of Technology School of Computer Science&EngineeringGuangzhou 510006China

Background A large number of robots have put forward the new requirements for human robot interaction.One of the problems in human-swarm robot interaction is how to naturally achieve an efficient and accurate interaction between humans and swarm robot systems.To address this,this paper proposes a new type of human-swarm natural interaction system.Methods Through the cooperation between three-dimensional(3D)gesture interaction channel and natural language instruction channel,a natural and efficient interaction between a human and swarm robots is achieved.Results First,A 3D lasso technology realizes a batch-picking interaction of swarm robots through oriented bounding boxes.Second,control instruction labels for swarm-oriented robots are defined.The instruction label is integrated with the 3D gesture and natural language through instruction label filling.Finally,the understanding of natural language instructions is realized through a text classifier based on the maximum entropy model.A head-mounted augmented reality display device is used as a visual feedback channel.Conclusions The experiments on selecting robots verify the feasibility and availability of the system.

关键词： Human-swarm interaction Augmented reality Multichannel integration

A lock-free approach to parallelizing personalized PageRank computations on GPU

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Frontiers of computer science 2023年第1期17卷 219-220页

作者： Zhigang WANG Ning WANG Jie NIE Zhiqiang WEI Yu GU Ge YU Faculty of Information science&engineering Ocean University of ChinaQingdao 266100China school of computer science and engineering Northeastern UniversityShenyang 110819China

1 Introduction Personalized PageRank(PPR)is a classic topology-based proximity measure and it is most widely computed by Forward Push.That is,given a starting vertex in graph,it iteratively computes the importance score of any vertex in with respect to,and then broadcasts the new score as messages to’s neighboring vertices.The process converges until all scores hold stable.Recently,Graphical Processing Units(GPU)with massive threads has been extensively used to parallelize such compute-intensive process.It yields performance improvement but also involves two atomic locks for correctness.Such locks are practically inefficient and become a new performance bottleneck.This paper proposes a separation technique to partially eliminate atomic protections,termed as Lightweight Forward Push.A Forward Pull solution is further devised to support lock-free PPR computations but also causes useless reads.For best performance,a new Hybrid Framework is then designed to adaptively balance locking costs and reading costs.

关键词： PageRank GPU Push

A simplified tether model for molecular motor transporting cargo

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Chinese Physics B 2010年第2期19卷 106-112页

作者：李防震蒋立春 School of Computer Science&Engineering Shandong Economic University

Molecular motors are proteins or protein complexes which function as transporting engines in biological cells. This paper models the tether between motor and its cargo as a symmetric linear potential. Different from Elston and Peskin＇s work for which performance of the system was discussed only in some limiting cases, this study produces analytic solutions of the problem for general cases by simplifying the transport system into two physical states, which makes it possible to discuss the dynamics of the motor--cargo system in detail. It turns out that the tether strength between motor and cargo should be greater than a threshold or the motor will fail to transport the cargo, which was not discussed by former researchers yet. Value of the threshold depends on the diffusion coefficients of cargo and motor and also on the strength of the Brownian ratchets dragging the system. The threshold approaches a finite constant when the strength of the ratchet tends to infinity.

关键词： molecular motor Brownian ratchet symmetric linear potential motor--cargo system

Feature-preserving color pencil drawings from photographs

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Computational Visual Media 2023年第4期9卷 807-825页

作者： Dong Wang Guiqing Li Chengying Gao Shengwu Fu Yun Liang College of Mathematics and Informatics South China Agricultural UniversityGuangzhouChina School of Computer Science&Engineering South China University of TechnologyGuangzhouChina school of Data and computer science Sun Yat-sen UniversityGuangzhouChina

Color pencil drawing is well-loved due to its rich expressiveness.This paper proposes an approach for generating feature-preserving color pencil drawings from photographs.To mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction mapping.The lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph features.Color saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious tone.Finally,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are consistent.Comprehensive experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

关键词： non-photorealistic rendering pencil drawings image editing feature preservation

Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder

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computers, Materials & Continua 2024年第2期78卷 2269-2286页

作者： S.Abinaya K.Uttej Kumar School of Computer Science&Engineering Vellore Institute of TechnologyChennaiTamilnadu600127India

A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.

关键词： Recommender systems multicriteria rating collaborative filtering sparsity issue scalability issue stacked-autoencoder Kernel Fuzzy C-Means Clustering