While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization ...While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.展开更多
Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automa...Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world.The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR.This paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of research.We explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural networks.Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign recognition.However,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of SLR.These challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic understanding.Additionally,developing lightweight network architectures for mobile applications is crucial for practical implementation.By addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community.展开更多
Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japane...Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.展开更多
Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign La...Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.展开更多
The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema...The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema helps to create a unique digital signature that deputizes a collective of people representing different groups of signers and may also include personal signers.The advantage of the proposed collective signature is that it can be built based on most of the well-known difficult problems such as the factor analysis,the discrete logarithm and finding modulo roots of large prime numbers and the current digital signature standards of the United States and Russian Federation.In this paper,we use the discrete logarithmic problem on prime finite fields,which has been implemented in the GOST R34.10-1994 digital signature standard,to build the proposed collective signature protocols.These protocols help to create collective signatures:Guaranteed internal integrity and fixed size,independent of the number of members involved in forming the signature.The signature built in this study,consisting of 3 components(U,R,S),stores the information of all relevant signers in the U components,thus tracking the signer and against the“disclaim of liability”of the signer later is possible.The idea of hiding the signer’s public key is also applied in the proposed protocols.This makes it easy for the signing group representative to specify which members are authorized to participate in the signature creation process.展开更多
This paper is concerned with the following fourth-order three-point boundary value problem , where , we discuss the existence of positive solutions to the above problem by applying to the fixed point theory in cones a...This paper is concerned with the following fourth-order three-point boundary value problem , where , we discuss the existence of positive solutions to the above problem by applying to the fixed point theory in cones and iterative technique.展开更多
Sign language fills the communication gap for people with hearing and speaking ailments.It includes both visual modalities,manual gestures consisting of movements of hands,and non-manual gestures incorporating body mo...Sign language fills the communication gap for people with hearing and speaking ailments.It includes both visual modalities,manual gestures consisting of movements of hands,and non-manual gestures incorporating body movements including head,facial expressions,eyes,shoulder shrugging,etc.Previously both gestures have been detected;identifying separately may have better accuracy,butmuch communicational information is lost.Aproper sign language mechanism is needed to detect manual and non-manual gestures to convey the appropriate detailed message to others.Our novel proposed system contributes as Sign LanguageAction Transformer Network(SLATN),localizing hand,body,and facial gestures in video sequences.Here we are expending a Transformer-style structural design as a“base network”to extract features from a spatiotemporal domain.Themodel impulsively learns to track individual persons and their action context inmultiple frames.Furthermore,a“head network”emphasizes hand movement and facial expression simultaneously,which is often crucial to understanding sign language,using its attention mechanism for creating tight bounding boxes around classified gestures.The model’s work is later compared with the traditional identification methods of activity recognition.It not only works faster but achieves better accuracy as well.Themodel achieves overall 82.66%testing accuracy with a very considerable performance of computation with 94.13 Giga-Floating Point Operations per Second(G-FLOPS).Another contribution is a newly created dataset of Pakistan Sign Language forManual and Non-Manual(PkSLMNM)gestures.展开更多
This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the trans...This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models.The image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning technique.The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of ATSL.It offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute community.This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.展开更多
The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine ...The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)devices.Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated problems.This study aims to use You Only Look Once version 7(YOLOv7),Convolutional Block Attention Module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the YOLOv7.Using a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high accuracy.The model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle systems.The study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam optimizer.This high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition.展开更多
Sign language is used as a communication medium in the field of trade,defence,and in deaf-mute communities worldwide.Over the last few decades,research in the domain of translation of sign language has grown and becom...Sign language is used as a communication medium in the field of trade,defence,and in deaf-mute communities worldwide.Over the last few decades,research in the domain of translation of sign language has grown and become more challenging.This necessitates the development of a Sign Language Translation System(SLTS)to provide effective communication in different research domains.In this paper,novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm(Hybrid-AO)for image segmentation is proposed for the translation of alphabet-level Indian Sign Language(ISLTS)with a 5-layer Convolution Neural Network(CNN).The focus of this paper is to analyze various image segmentation(Canny Edge Detection,Simple Thresholding,and Hybrid-AO),pooling approaches(Max,Average,and Global Average Pooling),and activation functions(ReLU,Leaky ReLU,and ELU).5-layer CNN with Max pooling,Leaky ReLU activation function,and Hybrid-AO(5MXLR-HAO)have outperformed other frameworks.An open-access dataset of ISL alphabets with approx.31 K images of 26 classes have been used to train and test the model.The proposed framework has been developed for translating alphabet-level Indian Sign Language into text.The proposed framework attains 98.95%training accuracy,98.05%validation accuracy,and 0.0721 training loss and 0.1021 validation loss and the perfor-mance of the proposed system outperforms other existing systems.展开更多
Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task.One of the main functions of sign language is to communicate with each o...Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task.One of the main functions of sign language is to communicate with each other through hand gestures.Recognition of hand gestures has become an important challenge for the recognition of sign language.There are many existing models that can produce a good accuracy,but if the model test with rotated or translated images,they may face some difficulties to make good performance accuracy.To resolve these challenges of hand gesture recognition,we proposed a Rotation,Translation and Scale-invariant sign word recognition system using a convolu-tional neural network(CNN).We have followed three steps in our work:rotated,translated and scaled(RTS)version dataset generation,gesture segmentation,and sign word classification.Firstly,we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation,Translation and Scale of the ori-ginal images to create the RTS version dataset.Then we have applied the gesture segmentation technique.The segmentation consists of three levels,i)Otsu Thresholding with YCbCr,ii)Morphological analysis:dilation through opening morphology and iii)Watershed algorithm.Finally,our designed CNN model has been trained to classify the hand gesture as well as the sign word.Our model has been evaluated using the twenty sign word dataset,five sign word dataset and the RTS version of these datasets.We achieved 99.30%accuracy from the twenty sign word dataset evaluation,99.10%accuracy from the RTS version of the twenty sign word evolution,100%accuracy from thefive sign word dataset evaluation,and 98.00%accuracy from the RTS versionfive sign word dataset evolution.Furthermore,the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition.展开更多
The subway is the primary travel tool for urban residents in China. Due to the complex structure of the subway and high personnel density in rush hours, subway evacuation capacity is critical. The subway evacuation mo...The subway is the primary travel tool for urban residents in China. Due to the complex structure of the subway and high personnel density in rush hours, subway evacuation capacity is critical. The subway evacuation model is explored in this work by combining the improved social force model with the view radius using the Vicsek model. The pedestrians are divided into two categories based on different force models. The first category is sensitive pedestrians who have normal responses to emergency signs. The second category is insensitive pedestrians. By simulating different proportions of the insensitive pedestrians, we find that the escape time is directly proportional to the number of insensitive pedestrians and inversely proportional to the view radius. However, when the view radius is large enough, the escape time does not change significantly, and the evacuation of people in a small view radius environment tends to be integrated. With the improvement of view radius conditions, the escape time changes more obviously with the proportion of insensitive pedestrians. A new emergency sign layout is proposed, and the simulations show that the proposed layout can effectively reduce the escape time in a small view radius environment. However, the evacuation effect of the new escape sign layout on the large view radius environment is not apparent. In this case, the exit setting emerges as an additional factor affecting the escape time.展开更多
With advancements in computing powers and the overall quality of images captured on everyday cameras,a much wider range of possibilities has opened in various scenarios.This fact has several implications for deaf and ...With advancements in computing powers and the overall quality of images captured on everyday cameras,a much wider range of possibilities has opened in various scenarios.This fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much easier.More than ever before,there is a plethora of info about sign language usage in the real world.Sign languages,and by extension the datasets available,are of two forms,isolated sign language and continuous sign language.The main difference between the two types is that in isolated sign language,the hand signs cover individual letters of the alphabet.In continuous sign language,entire words’hand signs are used.This paper will explore a novel deep learning architecture that will use recently published large pre-trained image models to quickly and accurately recognize the alphabets in the American Sign Language(ASL).The study will focus on isolated sign language to demonstrate that it is possible to achieve a high level of classification accuracy on the data,thereby showing that interpreters can be implemented in the real world.The newly proposed Mobile-NetV2 architecture serves as the backbone of this study.It is designed to run on end devices like mobile phones and infer signals(what does it infer)from images in a relatively short amount of time.With the proposed architecture in this paper,the classification accuracy of 98.77%in the Indian Sign Language(ISL)and American Sign Language(ASL)is achieved,outperforming the existing state-of-the-art systems.展开更多
Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtempora...Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video series.The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between nodes.The graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration,and short-term motion correlation modeling is completed via a temporal convolutional network.We adopted BLSTM to learn the long-termdependence and connectionist temporal classification to align the word-level sequences.The proposed method achieves competitive results regarding word error rates(1.59%)on the Chinese Sign Language dataset and the mean Jaccard Index(65.78%)on the ChaLearn LAP Continuous Gesture Dataset.展开更多
Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes resea...Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.展开更多
Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enha...Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enhanced outcomes.But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks.This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)model.The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures.The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)model.For gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language gestures.Lastly,the DHO algorithm is utilized for parameter optimization of the MLP model.The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects.The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.展开更多
In the field of traffic sign recognition,traffic signs usually occupy very small areas in the input image.Most object detection algorithms directly reduce the original image to a specific size for the input model duri...In the field of traffic sign recognition,traffic signs usually occupy very small areas in the input image.Most object detection algorithms directly reduce the original image to a specific size for the input model during the detection process,which leads to the loss of small object information.Addi-tionally,classification tasks are more sensitive to information loss than local-ization tasks.This paper proposes a novel traffic sign recognition approach,in which a lightweight pre-locator network and a refined classification network are incorporated.The pre-locator network locates the sub-regions of the traffic signs from the original image,and the refined classification network performs the refinement recognition task in the sub-regions.Moreover,an innovative module(named SPP-ST)is proposed,which combines the Spatial Pyramid Pool module(SPP)and the Swin-Transformer module as a new feature extractor to learn the special spatial information of traffic sign effec-tively.Experimental results show that the proposed method is superior to the state-of-the-art methods(82.1 mAP achieved on 218 categories in the TT100k dataset,an improvement of 19.7 percentage points compared to the previous method).Moreover,both the result analysis and the output visualizations further demonstrate the effectiveness of our proposed method.The source code and datasets of this work are available at https://github.com/DijiesitelaQ/TSOD.展开更多
Rapid advancement of intelligent transportation systems(ITS)and autonomous driving(AD)have shown the importance of accurate and efficient detection of traffic signs.However,certain drawbacks,such as balancing accuracy...Rapid advancement of intelligent transportation systems(ITS)and autonomous driving(AD)have shown the importance of accurate and efficient detection of traffic signs.However,certain drawbacks,such as balancing accuracy and real-time performance,hinder the deployment of traffic sign detection algorithms in ITS and AD domains.In this study,a novel traffic sign detection algorithm was proposed based on the bidirectional Res2Net architecture to achieve an improved balance between accuracy and speed.An enhanced backbone network module,called C2Net,which uses an upgraded bidirectional Res2Net,was introduced to mitigate information loss in the feature extraction process and to achieve information complementarity.Furthermore,a squeeze-and-excitation attention mechanism was incorporated within the channel attention of the architecture to perform channel-level feature correction on the input feature map,which effectively retains valuable features while removing non-essential features.A series of ablation experiments were conducted to validate the efficacy of the proposed methodology.The performance was evaluated using two distinct datasets:the Tsinghua-Tencent 100K and the CSUST Chinese traffic sign detection benchmark 2021.On the TT100K dataset,the method achieves precision,recall,and Map0.5 scores of 83.3%,79.3%,and 84.2%,respectively.Similarly,on the CCTSDB 2021 dataset,the method achieves precision,recall,and Map0.5 scores of 91.49%,73.79%,and 81.03%,respectively.Experimental results revealed that the proposed method had superior performance compared to conventional models,which includes the faster region-based convolutional neural network,single shot multibox detector,and you only look once version 5.展开更多
Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and...Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,etc.As a segment of the clever transportation structure,the acknowledgment of traffic signs is basic for the driving assistance system,traffic sign upkeep,self-administering driving,and various spaces.There are different assessments turns out achieved for traffic sign acknowledgment in the world.However,most of the works are only for explicit arrangements of traffic signs,for example,beyond what many would consider a possible sign.Traffic sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of traffic sign pictures.Two critical issues exist during the time spent identification(ID)and affirmation of traffic signals.Road signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road signs.Also concealing information from traffic scene pictures is affected by moving light achieved by environment conditions,time(day-night),and shadowing.Traffic sign revelation and affirmation structure has two guideline sorts out:The essential stage incorpo-rates the traffic sign limitation and the resulting stage portrays the perceived traffic signs into a particular class.展开更多
To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the future.The ultimate go...To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the future.The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image.To accomplish this goal,the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for com-prehensive traffic sign classification and Mask Region-based Convolutional Neural Networks(R-CNN)implementation for identifying and extracting signs in panoramic images.Data augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded,and considerably minimize the rates of discovering the extra boxes.The proposed model is tested on both the testing dataset and the actual images and gets 94.5%of the correct signs recognition rate,the classification rate of those signs discovered was 99.41%and the rate of false signs was only around 0.11.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62103375 and 62006106)the Zhejiang Provincial Philosophy and Social Science Planning Project(Grant No.22NDJC009Z)+1 种基金the Education Ministry Humanities and Social Science Foundation of China(Grant Nos.19YJCZH056 and 21YJC630120)the Natural Science Foundation of Zhejiang Province of China(Grant Nos.LY23F030003 and LQ21F020005).
文摘While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social networks.This paper addresses this gap by focusing on source localization in signed network models.Leveraging the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer selection.Additionally,by using the reverse propagation algorithm we present a method for information source localization in signed networks.Extensive experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization becomes.Interestingly,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among adversaries.In addition,the source located at the periphery of the network is not easy to identify.Furthermore,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.
基金supported from the National Philosophy and Social Sciences Foundation(Grant No.20BTQ065).
文摘Sign language,a visual-gestural language used by the deaf and hard-of-hearing community,plays a crucial role in facilitating communication and promoting inclusivity.Sign language recognition(SLR),the process of automatically recognizing and interpreting sign language gestures,has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world.The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR.This paper presents a comprehensive and up-to-date analysis of the advancements,challenges,and opportunities in deep learning-based sign language recognition,focusing on the past five years of research.We explore various aspects of SLR,including sign data acquisition technologies,sign language datasets,evaluation methods,and different types of neural networks.Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN)have shown promising results in fingerspelling and isolated sign recognition.However,the continuous nature of sign language poses challenges,leading to the exploration of advanced neural network models such as the Transformer model for continuous sign language recognition(CSLR).Despite significant advancements,several challenges remain in the field of SLR.These challenges include expanding sign language datasets,achieving user independence in recognition systems,exploring different input modalities,effectively fusing features,modeling co-articulation,and improving semantic and syntactic understanding.Additionally,developing lightweight network architectures for mobile applications is crucial for practical implementation.By addressing these challenges,we can further advance the field of deep learning for sign language recognition and improve communication for the hearing-impaired community.
基金supported by the Competitive Research Fund of the University of Aizu,Japan.
文摘Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
基金supported by National Social Science Foundation Annual Project“Research on Evaluation and Improvement Paths of Integrated Development of Disabled Persons”(Grant No.20BRK029)the National Language Commission’s“14th Five-Year Plan”Scientific Research Plan 2023 Project“Domain Digital Language Service Resource Construction and Key Technology Research”(YB145-72)the National Philosophy and Social Sciences Foundation(Grant No.20BTQ065).
文摘Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.
基金supported by Duy Tan University,Da Nang,Vietnam.
文摘The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema helps to create a unique digital signature that deputizes a collective of people representing different groups of signers and may also include personal signers.The advantage of the proposed collective signature is that it can be built based on most of the well-known difficult problems such as the factor analysis,the discrete logarithm and finding modulo roots of large prime numbers and the current digital signature standards of the United States and Russian Federation.In this paper,we use the discrete logarithmic problem on prime finite fields,which has been implemented in the GOST R34.10-1994 digital signature standard,to build the proposed collective signature protocols.These protocols help to create collective signatures:Guaranteed internal integrity and fixed size,independent of the number of members involved in forming the signature.The signature built in this study,consisting of 3 components(U,R,S),stores the information of all relevant signers in the U components,thus tracking the signer and against the“disclaim of liability”of the signer later is possible.The idea of hiding the signer’s public key is also applied in the proposed protocols.This makes it easy for the signing group representative to specify which members are authorized to participate in the signature creation process.
文摘This paper is concerned with the following fourth-order three-point boundary value problem , where , we discuss the existence of positive solutions to the above problem by applying to the fixed point theory in cones and iterative technique.
文摘Sign language fills the communication gap for people with hearing and speaking ailments.It includes both visual modalities,manual gestures consisting of movements of hands,and non-manual gestures incorporating body movements including head,facial expressions,eyes,shoulder shrugging,etc.Previously both gestures have been detected;identifying separately may have better accuracy,butmuch communicational information is lost.Aproper sign language mechanism is needed to detect manual and non-manual gestures to convey the appropriate detailed message to others.Our novel proposed system contributes as Sign LanguageAction Transformer Network(SLATN),localizing hand,body,and facial gestures in video sequences.Here we are expending a Transformer-style structural design as a“base network”to extract features from a spatiotemporal domain.Themodel impulsively learns to track individual persons and their action context inmultiple frames.Furthermore,a“head network”emphasizes hand movement and facial expression simultaneously,which is often crucial to understanding sign language,using its attention mechanism for creating tight bounding boxes around classified gestures.The model’s work is later compared with the traditional identification methods of activity recognition.It not only works faster but achieves better accuracy as well.Themodel achieves overall 82.66%testing accuracy with a very considerable performance of computation with 94.13 Giga-Floating Point Operations per Second(G-FLOPS).Another contribution is a newly created dataset of Pakistan Sign Language forManual and Non-Manual(PkSLMNM)gestures.
文摘This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models.The image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning technique.The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of ATSL.It offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute community.This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.
文摘The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)devices.Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated problems.This study aims to use You Only Look Once version 7(YOLOv7),Convolutional Block Attention Module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the YOLOv7.Using a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high accuracy.The model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle systems.The study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam optimizer.This high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition.
文摘Sign language is used as a communication medium in the field of trade,defence,and in deaf-mute communities worldwide.Over the last few decades,research in the domain of translation of sign language has grown and become more challenging.This necessitates the development of a Sign Language Translation System(SLTS)to provide effective communication in different research domains.In this paper,novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm(Hybrid-AO)for image segmentation is proposed for the translation of alphabet-level Indian Sign Language(ISLTS)with a 5-layer Convolution Neural Network(CNN).The focus of this paper is to analyze various image segmentation(Canny Edge Detection,Simple Thresholding,and Hybrid-AO),pooling approaches(Max,Average,and Global Average Pooling),and activation functions(ReLU,Leaky ReLU,and ELU).5-layer CNN with Max pooling,Leaky ReLU activation function,and Hybrid-AO(5MXLR-HAO)have outperformed other frameworks.An open-access dataset of ISL alphabets with approx.31 K images of 26 classes have been used to train and test the model.The proposed framework has been developed for translating alphabet-level Indian Sign Language into text.The proposed framework attains 98.95%training accuracy,98.05%validation accuracy,and 0.0721 training loss and 0.1021 validation loss and the perfor-mance of the proposed system outperforms other existing systems.
基金This work was supported by the Competitive Research Fund of The University of Aizu,Japan.
文摘Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task.One of the main functions of sign language is to communicate with each other through hand gestures.Recognition of hand gestures has become an important challenge for the recognition of sign language.There are many existing models that can produce a good accuracy,but if the model test with rotated or translated images,they may face some difficulties to make good performance accuracy.To resolve these challenges of hand gesture recognition,we proposed a Rotation,Translation and Scale-invariant sign word recognition system using a convolu-tional neural network(CNN).We have followed three steps in our work:rotated,translated and scaled(RTS)version dataset generation,gesture segmentation,and sign word classification.Firstly,we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation,Translation and Scale of the ori-ginal images to create the RTS version dataset.Then we have applied the gesture segmentation technique.The segmentation consists of three levels,i)Otsu Thresholding with YCbCr,ii)Morphological analysis:dilation through opening morphology and iii)Watershed algorithm.Finally,our designed CNN model has been trained to classify the hand gesture as well as the sign word.Our model has been evaluated using the twenty sign word dataset,five sign word dataset and the RTS version of these datasets.We achieved 99.30%accuracy from the twenty sign word dataset evaluation,99.10%accuracy from the RTS version of the twenty sign word evolution,100%accuracy from thefive sign word dataset evaluation,and 98.00%accuracy from the RTS versionfive sign word dataset evolution.Furthermore,the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51874183 and 51874182)the National Key Research and Development Program of China (Grant No. 2018YFC0809300)。
文摘The subway is the primary travel tool for urban residents in China. Due to the complex structure of the subway and high personnel density in rush hours, subway evacuation capacity is critical. The subway evacuation model is explored in this work by combining the improved social force model with the view radius using the Vicsek model. The pedestrians are divided into two categories based on different force models. The first category is sensitive pedestrians who have normal responses to emergency signs. The second category is insensitive pedestrians. By simulating different proportions of the insensitive pedestrians, we find that the escape time is directly proportional to the number of insensitive pedestrians and inversely proportional to the view radius. However, when the view radius is large enough, the escape time does not change significantly, and the evacuation of people in a small view radius environment tends to be integrated. With the improvement of view radius conditions, the escape time changes more obviously with the proportion of insensitive pedestrians. A new emergency sign layout is proposed, and the simulations show that the proposed layout can effectively reduce the escape time in a small view radius environment. However, the evacuation effect of the new escape sign layout on the large view radius environment is not apparent. In this case, the exit setting emerges as an additional factor affecting the escape time.
文摘With advancements in computing powers and the overall quality of images captured on everyday cameras,a much wider range of possibilities has opened in various scenarios.This fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much easier.More than ever before,there is a plethora of info about sign language usage in the real world.Sign languages,and by extension the datasets available,are of two forms,isolated sign language and continuous sign language.The main difference between the two types is that in isolated sign language,the hand signs cover individual letters of the alphabet.In continuous sign language,entire words’hand signs are used.This paper will explore a novel deep learning architecture that will use recently published large pre-trained image models to quickly and accurately recognize the alphabets in the American Sign Language(ASL).The study will focus on isolated sign language to demonstrate that it is possible to achieve a high level of classification accuracy on the data,thereby showing that interpreters can be implemented in the real world.The newly proposed Mobile-NetV2 architecture serves as the backbone of this study.It is designed to run on end devices like mobile phones and infer signals(what does it infer)from images in a relatively short amount of time.With the proposed architecture in this paper,the classification accuracy of 98.77%in the Indian Sign Language(ISL)and American Sign Language(ASL)is achieved,outperforming the existing state-of-the-art systems.
基金supported by the Key Research&Development Plan Project of Shandong Province,China(No.2017GGX10127).
文摘Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video series.The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between nodes.The graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration,and short-term motion correlation modeling is completed via a temporal convolutional network.We adopted BLSTM to learn the long-termdependence and connectionist temporal classification to align the word-level sequences.The proposed method achieves competitive results regarding word error rates(1.59%)on the Chinese Sign Language dataset and the mean Jaccard Index(65.78%)on the ChaLearn LAP Continuous Gesture Dataset.
基金supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493in part by the 2022 Yeungnam University Research Grant.
文摘Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura Universitysupporting this work by Grant Code:22UQU4310373DSR54.
文摘Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enhanced outcomes.But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks.This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)model.The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures.The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)model.For gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language gestures.Lastly,the DHO algorithm is utilized for parameter optimization of the MLP model.The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects.The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.
基金supported by the Natural Science Foundation of Sichuan,China (No.2022NSFSC0571)the Sichuan Science and Technology Program (No.2018JY0273,No.2019YJ0532)+1 种基金supported by funding of V.C.&V.R.Key Lab of Sichuan Province (No.SCVCVR2020.05VS)supported by the China Scholarship Council (No.201908510026).
文摘In the field of traffic sign recognition,traffic signs usually occupy very small areas in the input image.Most object detection algorithms directly reduce the original image to a specific size for the input model during the detection process,which leads to the loss of small object information.Addi-tionally,classification tasks are more sensitive to information loss than local-ization tasks.This paper proposes a novel traffic sign recognition approach,in which a lightweight pre-locator network and a refined classification network are incorporated.The pre-locator network locates the sub-regions of the traffic signs from the original image,and the refined classification network performs the refinement recognition task in the sub-regions.Moreover,an innovative module(named SPP-ST)is proposed,which combines the Spatial Pyramid Pool module(SPP)and the Swin-Transformer module as a new feature extractor to learn the special spatial information of traffic sign effec-tively.Experimental results show that the proposed method is superior to the state-of-the-art methods(82.1 mAP achieved on 218 categories in the TT100k dataset,an improvement of 19.7 percentage points compared to the previous method).Moreover,both the result analysis and the output visualizations further demonstrate the effectiveness of our proposed method.The source code and datasets of this work are available at https://github.com/DijiesitelaQ/TSOD.
基金funded by the National Key R&D Program of China,Grant Number 2017YFB0802803Beijing Natural Science Foundation,Grant Number 4202002.
文摘Rapid advancement of intelligent transportation systems(ITS)and autonomous driving(AD)have shown the importance of accurate and efficient detection of traffic signs.However,certain drawbacks,such as balancing accuracy and real-time performance,hinder the deployment of traffic sign detection algorithms in ITS and AD domains.In this study,a novel traffic sign detection algorithm was proposed based on the bidirectional Res2Net architecture to achieve an improved balance between accuracy and speed.An enhanced backbone network module,called C2Net,which uses an upgraded bidirectional Res2Net,was introduced to mitigate information loss in the feature extraction process and to achieve information complementarity.Furthermore,a squeeze-and-excitation attention mechanism was incorporated within the channel attention of the architecture to perform channel-level feature correction on the input feature map,which effectively retains valuable features while removing non-essential features.A series of ablation experiments were conducted to validate the efficacy of the proposed methodology.The performance was evaluated using two distinct datasets:the Tsinghua-Tencent 100K and the CSUST Chinese traffic sign detection benchmark 2021.On the TT100K dataset,the method achieves precision,recall,and Map0.5 scores of 83.3%,79.3%,and 84.2%,respectively.Similarly,on the CCTSDB 2021 dataset,the method achieves precision,recall,and Map0.5 scores of 91.49%,73.79%,and 81.03%,respectively.Experimental results revealed that the proposed method had superior performance compared to conventional models,which includes the faster region-based convolutional neural network,single shot multibox detector,and you only look once version 5.
文摘Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,etc.As a segment of the clever transportation structure,the acknowledgment of traffic signs is basic for the driving assistance system,traffic sign upkeep,self-administering driving,and various spaces.There are different assessments turns out achieved for traffic sign acknowledgment in the world.However,most of the works are only for explicit arrangements of traffic signs,for example,beyond what many would consider a possible sign.Traffic sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of traffic sign pictures.Two critical issues exist during the time spent identification(ID)and affirmation of traffic signals.Road signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road signs.Also concealing information from traffic scene pictures is affected by moving light achieved by environment conditions,time(day-night),and shadowing.Traffic sign revelation and affirmation structure has two guideline sorts out:The essential stage incorpo-rates the traffic sign limitation and the resulting stage portrays the perceived traffic signs into a particular class.
文摘To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent,the traffic signs detection system has become one of the necessary topics in recent years and in the future.The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image.To accomplish this goal,the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for com-prehensive traffic sign classification and Mask Region-based Convolutional Neural Networks(R-CNN)implementation for identifying and extracting signs in panoramic images.Data augmentation and normalization of the images are also applied to assist in classifying better even if old traffic signs are degraded,and considerably minimize the rates of discovering the extra boxes.The proposed model is tested on both the testing dataset and the actual images and gets 94.5%of the correct signs recognition rate,the classification rate of those signs discovered was 99.41%and the rate of false signs was only around 0.11.
