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
Action recognition is an important topic in computer vision. Recently, deep learning technologies have been successfully used in lots of applications including video data for sloving recognition problems. However, mos...Action recognition is an important topic in computer vision. Recently, deep learning technologies have been successfully used in lots of applications including video data for sloving recognition problems. However, most existing deep learning based recognition frameworks are not optimized for action in the surveillance videos. In this paper, we propose a novel method to deal with the recognition of different types of actions in outdoor surveillance videos. The proposed method first introduces motion compensation to improve the detection of human target. Then, it uses three different types of deep models with single and sequenced images as inputs for the recognition of different types of actions. Finally, predictions from different models are fused with a linear model. Experimental results show that the proposed method works well on the real surveillance videos.展开更多
The interpenetrating polymer networks (IPN) thin film with the –C=O group in one network and the terminal –N=C=O group in another network on an aluminum substrate to reinforce the adherence between IPN and aluminum ...The interpenetrating polymer networks (IPN) thin film with the –C=O group in one network and the terminal –N=C=O group in another network on an aluminum substrate to reinforce the adherence between IPN and aluminum through interfacial reactions, were obtained by dip-pulling the pretreated aluminum substrate into the viscous-controlled IPN precursors and by the following thinning treatment to the IPN film to a suitable thickness. The interfacial actions and the adhesion strengths of the IPN on the pretreated aluminum substrate were investigated by the X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and strain-stress(?-?) measurements. The XPS and FTIR detection results indicated that the elements’ contents of N, O, and Al varied from the depths of IPN. The in-terfacial reaction occurred between the –N=C=O group of IPN and the AlO(OH) of pretreated aluminum. The in-creased force constant for –C=O double bond and the lower frequency shift of –C=O stretching vibration absorption peak both verified the formation of hydrogen bond between the –OH group in AlO(OH) and the –C=O group in IPN. The adherence detections indicated that the larger amount of –N=C=O group in the IPN, the higher shear strengths between the IPN thin film and the aluminum substrate.展开更多
In this paper, we propose a novel game-theoretical solution to the multi-path routing problem in wireless ad hoc networks comprising selfish nodes with hidden information and actions. By incorporating a suitable traff...In this paper, we propose a novel game-theoretical solution to the multi-path routing problem in wireless ad hoc networks comprising selfish nodes with hidden information and actions. By incorporating a suitable traffic allocation policy, the proposed mechanism results in Nash equilibria where each node honestly reveals its true cost, and forwarding subgame perfect equilibrium in which each node does provide forwarding service with its declared service reliability. Based on the generalised second price auction, this mechanism effectively alleviates the over-payment of the well-known VCG mechanism. The effectiveness of this mechanism will be shown through simulations.展开更多
Action recognition is important for understanding the human behaviors in the video,and the video representation is the basis for action recognition.This paper provides a new video representation based on convolution n...Action recognition is important for understanding the human behaviors in the video,and the video representation is the basis for action recognition.This paper provides a new video representation based on convolution neural networks（CNN）.For capturing human motion information in one CNN,we take both the optical flow maps and gray images as input,and combine multiple convolutional features by max pooling across frames.In another CNN,we input single color frame to capture context information.Finally,we take the top full connected layer vectors as video representation and train the classifiers by linear support vector machine.The experimental results show that the representation which integrates the optical flow maps and gray images obtains more discriminative properties than those which depend on only one element.On the most challenging data sets HMDB51 and UCF101,this video representation obtains competitive performance.展开更多
目的运用中药网络药理学方法结合分子对接技术,初步探究甘草泻心汤治疗化疗后恶心呕吐(chemotherapy-induced nausea and vomiting,CINV)的作用机制。方法从中药系统药理学数据库和分析平台获取甘草泻心汤的活性化合物及其作用靶点;使用...目的运用中药网络药理学方法结合分子对接技术,初步探究甘草泻心汤治疗化疗后恶心呕吐(chemotherapy-induced nausea and vomiting,CINV)的作用机制。方法从中药系统药理学数据库和分析平台获取甘草泻心汤的活性化合物及其作用靶点;使用Perl软件通过UniProt数据库匹配靶点对应的标准基因符号;从GeneCards、OMIM、PharmGKB、Therapeutic Target Database和DrugBank 5个数据库中检索CINV相关疾病靶点基因;利用R软件筛选甘草泻心汤靶点与CINV靶点的交集基因;通过Cytoscape 3.10.3软件绘制“药物-成分-靶点-疾病”交互网络图;基于STRING平台构建蛋白质-蛋白质相互作用网络;对关键活性成分与核心靶点进行分子对接,验证其结合活性;采用R 4.2.2软件进行基因本体(gene ontology,GO)功能富集分析和京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)通路富集分析。结果基于口服生物利用度(oral bioavailability,OB)和类药性(drug-likeness,DL)阈值筛选,获得甘草泻心汤118种去重活性成分,甘草泻心汤与CINV存在31个共同作用靶点。GO富集分析共获得1831条显著条目,包括生物过程1621条、细胞组分79条和分子功能131条;KEGG通路分析筛选出128条相关通路。甘草泻心汤的主要活性成分为槲皮素、β-谷甾醇、汉黄芩素、山柰酚、荷叶碱;核心靶点为蛋白激酶B、白细胞介素-1β、细胞间黏附分子-1、肿瘤蛋白p53、表皮生长因子受体、前列腺素内过氧化物合酶2、B细胞淋巴瘤-2、雌激素受体α、连环蛋白β1。分子对接表明,活性成分(如槲皮素)与核心靶点前列腺素内过氧化物合酶2结合稳定,作用机制涉及外源性刺激响应、胶质细胞增殖、细胞凋亡等生物过程,并通过调控磷脂酰肌醇3激酶-蛋白激酶B、缺氧诱导因子-1、Janus激酶-信号转导子和转录激活子等信号通路发挥抗CINV作用。结论甘草泻心汤通过槲皮素、β-谷甾醇、汉黄芩素等活性成分,作用于蛋白激酶B、前列腺素内过氧化物合酶2等多靶点,调控炎症反应、细胞凋亡及相关信号通路,从而发挥治疗化疗后恶心呕吐的作用。展开更多
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Action recognition is an important topic in computer vision. Recently, deep learning technologies have been successfully used in lots of applications including video data for sloving recognition problems. However, most existing deep learning based recognition frameworks are not optimized for action in the surveillance videos. In this paper, we propose a novel method to deal with the recognition of different types of actions in outdoor surveillance videos. The proposed method first introduces motion compensation to improve the detection of human target. Then, it uses three different types of deep models with single and sequenced images as inputs for the recognition of different types of actions. Finally, predictions from different models are fused with a linear model. Experimental results show that the proposed method works well on the real surveillance videos.
文摘The interpenetrating polymer networks (IPN) thin film with the –C=O group in one network and the terminal –N=C=O group in another network on an aluminum substrate to reinforce the adherence between IPN and aluminum through interfacial reactions, were obtained by dip-pulling the pretreated aluminum substrate into the viscous-controlled IPN precursors and by the following thinning treatment to the IPN film to a suitable thickness. The interfacial actions and the adhesion strengths of the IPN on the pretreated aluminum substrate were investigated by the X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and strain-stress(?-?) measurements. The XPS and FTIR detection results indicated that the elements’ contents of N, O, and Al varied from the depths of IPN. The in-terfacial reaction occurred between the –N=C=O group of IPN and the AlO(OH) of pretreated aluminum. The in-creased force constant for –C=O double bond and the lower frequency shift of –C=O stretching vibration absorption peak both verified the formation of hydrogen bond between the –OH group in AlO(OH) and the –C=O group in IPN. The adherence detections indicated that the larger amount of –N=C=O group in the IPN, the higher shear strengths between the IPN thin film and the aluminum substrate.
文摘In this paper, we propose a novel game-theoretical solution to the multi-path routing problem in wireless ad hoc networks comprising selfish nodes with hidden information and actions. By incorporating a suitable traffic allocation policy, the proposed mechanism results in Nash equilibria where each node honestly reveals its true cost, and forwarding subgame perfect equilibrium in which each node does provide forwarding service with its declared service reliability. Based on the generalised second price auction, this mechanism effectively alleviates the over-payment of the well-known VCG mechanism. The effectiveness of this mechanism will be shown through simulations.
基金Supported by the National High Technology Research and Development Program of China(863 Program,2015AA016306)National Nature Science Foundation of China(61231015)+2 种基金Internet of Things Development Funding Project of Ministry of Industry in 2013(25)Technology Research Program of Ministry of Public Security(2016JSYJA12)the Nature Science Foundation of Hubei Province(2014CFB712)
文摘Action recognition is important for understanding the human behaviors in the video,and the video representation is the basis for action recognition.This paper provides a new video representation based on convolution neural networks（CNN）.For capturing human motion information in one CNN,we take both the optical flow maps and gray images as input,and combine multiple convolutional features by max pooling across frames.In another CNN,we input single color frame to capture context information.Finally,we take the top full connected layer vectors as video representation and train the classifiers by linear support vector machine.The experimental results show that the representation which integrates the optical flow maps and gray images obtains more discriminative properties than those which depend on only one element.On the most challenging data sets HMDB51 and UCF101,this video representation obtains competitive performance.
