Automatic Satisfaction Analysis in Call Centers Considering Global Features of Emotion and Duration

Analysis of customers＇ satisfaction provides a guarantee to improve the service quality in call centers.In this paper,a novel satisfaction recognition framework is introduced to analyze the customers＇ satisfaction.In natural conversations,the interaction between a customer and its agent take place more than once.One of the difficulties insatisfaction analysis at call centers is that not all conversation turns exhibit customer satisfaction or dissatisfaction. To solve this problem,an intelligent system is proposed that utilizes acoustic features to recognize customers＇ emotion and utilizes the global features of emotion and duration to analyze the satisfaction. Experiments on real-call data show that the proposed system offers a significantly higher accuracy in analyzing the satisfaction than the baseline system. The average F value is improved to 0. 701 from 0. 664.

Tire Defect Detection Using Local and Global Features

In this paper, we present a tire defect detection algorithm based on sparse representation. The dictionary learned from reference images can efficiently represent the test image. As the representation coefficients of normal images have a specific distribution, the local feature can be estimate by comparing representation coefficient distribution. Meanwhile, a coding length is used to measure the global features of representation coefficients. The tire defect is located by both these local and global features. Experimental results demonstrate that the proposed method can accurately detect and locate the tire defects.

English Text Named Entity Recognition Method by Fusing Local and Global Features

Because of the ambiguity and dynamic nature of natural language,the research of named entity recognition is very challenging.As an international language,English plays an important role in the fields of science and technology,finance and business.Therefore,the early named entity recognition technology is mainly based on English,which is often used to identify the names of people,places and organizations in the text.International conferences in the field of natural language processing,such as CoNLL,MUC,and ACE,have identified named entity recognition as a specific evaluation task,and the relevant research uses evaluation corpus from English-language media organizations such as the Wall Street Journal,the New York Times,and Wikipedia.The research of named entity recognition on relevant data has achieved good results.Aiming at the sparse distribution of entities in text,a model combining local and global features is proposed.The model takes a single English character as input,and uses the local feature layer composed of local attention and convolution to process the text pieceby way of sliding window to construct the corresponding local features.In addition,the self-attention mechanism is used to generate the global features of the text to improve the recognition effect of the model on long sentences.Experiments on three data sets,Resume,MSRA and Weibo,show that the proposed method can effectively improve the model’s recognition of English named entities.

Chinese New Word Identification:A Latent Discriminative Model with Global Features

Chinese new words are particularly problematic in Chinese natural language processing. With the fast development of Internet and information explosion, it is impossible to get a complete system lexicon for applications in Chinese natural language processing, as new words out of dictionaries are always being created. The procedure of new words identification and POS tagging are usually separated and the features of lexical information cannot be fully used. A latent discriminative model, which combines the strengths of Latent Dynamic Conditional Random Field （LDCRF） and semi-CRF, is proposed to detect new words together with their POS synchronously regardless of the types of new words from Chinese text without being pre-segmented. Unlike semi-CRF, in proposed latent discriminative model, LDCRF is applied to generate candidate entities, which accelerates the training speed and decreases the computational cost. The complexity of proposed hidden semi-CRF could be further adjusted by tuning the number of hidden variables and the number of candidate entities from the Nbest outputs of LDCRF model. A new-word-generating framework is proposed for model training and testing, under which the definitions and distributions of new words conform to the ones in real text. The global feature called ＂Global Fragment Features＂ for new word identification is adopted. We tested our model on the corpus from SIGHAN-6. Experimental results show that the proposed method is capable of detecting even low frequency new words together with their POS tags with satisfactory results. The proposed model performs competitively with the state-of-the-art models.

Remote Sensing Image Retrieval Based on 3D-Local Ternary Pattern(LTP)Features and Non-subsampled Shearlet Transform(NSST)Domain Statistical Features

With the increasing popularity of high-resolution remote sensing images,the remote sensing image retrieval(RSIR)has always been a topic of major issue.A combined,global non-subsampled shearlet transform(NSST)-domain statistical features(NSSTds)and local three dimensional local ternary pattern(3D-LTP)features,is proposed for high-resolution remote sensing images.We model the NSST image coefficients of detail subbands using 2-state laplacian mixture(LM)distribution and its three parameters are estimated using Expectation-Maximization(EM)algorithm.We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband,and concatenate all of them with the 2-state LM parameters to describe the global features of the image.The various properties of NSST such as multiscale,localization and flexible directional sensitivity make it a suitable choice to provide an effective approximation of an image.In order to extract the dense local features,a new 3D-LTP is proposed where dimension reduction is performed via selection of‘uniform’patterns.The 3D-LTP is calculated from spatial RGB planes of the input image.The proposed inter-channel 3D-LTP not only exploits the local texture information but the color information is captured too.Finally,a fused feature representation(NSSTds-3DLTP)is proposed using new global(NSSTds)and local(3D-LTP)features to enhance the discriminativeness of features.The retrieval performance of proposed NSSTds-3DLTP features are tested on three challenging remote sensing image datasets such as WHU-RS19,Aerial Image Dataset(AID)and PatternNet in terms of mean average precision(MAP),average normalized modified retrieval rank(ANMRR)and precision-recall(P-R)graph.The experimental results are encouraging and the NSSTds-3DLTP features leads to superior retrieval performance compared to many well known existing descriptors such as Gabor RGB,Granulometry,local binary pattern(LBP),Fisher vector(FV),vector of locally aggregated descriptors(VLAD)and median robust extended local binary pattern(MRELBP).For WHU-RS19 dataset,in terms of{MAP,ANMRR},the NSSTds-3DLTP improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{41.93%,20.87%},{92.30%,32.68%},{86.14%,31.97%},{18.18%,15.22%},{8.96%,19.60%}and{15.60%,13.26%},respectively.For AID,in terms of{MAP,ANMRR},the NSSTds-3DLTP improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{152.60%,22.06%},{226.65%,25.08%},{185.03%,23.33%},{80.06%,12.16%},{50.58%,10.49%}and{62.34%,3.24%},respectively.For PatternNet,the NSSTds-3DLTP respectively improves upon Gabor RGB,Granulometry,LBP,FV,VLAD and MRELBP descriptors by{32.79%,10.34%},{141.30%,24.72%},{17.47%,10.34%},{83.20%,19.07%},{21.56%,3.60%},and{19.30%,0.48%}in terms of{MAP,ANMRR}.The moderate dimensionality of simple NSSTds-3DLTP allows the system to run in real-time.

An Aridity Trend in China and Its Abrupt Feature in Association with the Global Warming

A distinct aridity trend in China in last 100 years is presented by applying a linear fitting to both the climate records and the hydrological records, which is supported by evidence of environmental changes and seems to be associated with a global warming trend during this period.The Mann Kendall Rank statistic test reveals a very interesting feature that the climate of China entered into a dry regime abruptly in about 1920's, which synchronized with the rapid warming of the global temperature at almost the same time.According to an analysis of the meridional profile of observed global zonal mean precipitation anomalies during the peak period of global wanning (1930-1940), the drought occurred in whole middle latitude zone (25°N-55°N) of the Northern Hemisphere, where the most part of China is located in. Although this pattern is in good agreement with the latitude distribution of the difference of zonal mean rates of precipitation between 4 × CO2 and 1 × CO2 simulated by climate model (Manabe and Wetherald, 1983), more studies are required to understand the linkage between the aridity trend in China and the greenhouse effect.The EOF analysis of the Northern Hemisphere sea level pressure for the season of June to August shows an abrupt change of the time coefficient of its first eigenvector from positive to negative in mid-1920's, indicating an enhancement of the subtropical high over Southeast Asia and the western Pacific after that time. This is an atmospheric circulation pattern that is favorable to the development of dry climate in China.

A Two-Phase Paradigm for Joint Entity-Relation Extraction

An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task.However,these models sample a large number of negative entities and negative relations during the model training,which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance.In order to address the above issues,we propose a two-phase paradigm for the span-based joint entity and relation extraction,which involves classifying the entities and relations in the first phase,and predicting the types of these entities and relations in the second phase.The two-phase paradigm enables our model to significantly reduce the data distribution gap,including the gap between negative entities and other entities,aswell as the gap between negative relations and other relations.In addition,we make the first attempt at combining entity type and entity distance as global features,which has proven effective,especially for the relation extraction.Experimental results on several datasets demonstrate that the span-based joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-ofthe-art span-based models for the joint extraction task,establishing a new standard benchmark.Qualitative and quantitative analyses further validate the effectiveness the proposed paradigm and the global features.

Global innovators to feature in Intertextile Shanghai's Functional Lab

Heading into the second half of the year,the global apparel fabrics and accessories industry’s attention has begun to focus on the 2016 Autumn Edition of Intertextile Shanghai Apparel Fabrics which will be held from 11–13 October.Over 5,000 exhibitors from more than 25 countries and regions will take part and showcase an all-encompassing range of products across 260,000 sqm.of exhibition space at the Nation Exhibition and Convention Center(Shanghai).To

Developing global image feature analysis models to predict cancer risk and prognosis

In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

Aquatic Medicine Knowledge Graph Completion Based on Hybrid Convolution

Aquatic medicine knowledge graph is an effective means to realize intelligent aquaculture.Graph completion technology is key to improving the quality of knowledge graph construction.However,the difficulty of semantic discrimination among similar entities and inconspicuous semantic features result in low accuracy when completing aquatic medicine knowledge graph with complex relationships.In this study,an aquatic medicine knowledge graph completion method(TransH+HConvAM)is proposed.Firstly,TransH is applied to split the vector plane between entities and relations,ameliorating the poor completion effect caused by low semantic resolution of entities.Then,hybrid convolution is introduced to obtain the global interaction of triples based on the complete interaction between head/tail entities and relations,which improves the semantic features of triples and enhances the completion effect of complex relationships in the graph.Experiments are conducted to verify the performance of the proposed method.The MR,MRR and Hit@10 of the TransH+HConvAM are found to be 674,0.339,and 0.361,respectively.This study shows that the model effectively overcomes the poor completion effect of complex relationships and improves the construction quality of the aquatic medicine knowledge graph,providing technical support for intelligent aquaculture.

Feature fusing in face recognition

With the aim of extracting the features of face images in face recognition, a new method of face recognition by fusing global features and local features is presented. The global features are extracted using principal component analysis （PCA）. Active appearance model （AAM） locates 58 facial fiducial points, from which 17 points are characterized as local features using the Gabor wavelet transform （GWT）. Normalized global match degree （local match degree） can be obtained by global features （local features） of the probe image and each gallery image. After the fusion of normalized global match degree and normalized local match degree, the recognition result is the class that included the gallery image corresponding to the largest fused match degree. The method is evaluated by the recognition rates over two face image databases （AR and SJTU-IPPR）. The experimental results show that the method outperforms PCA and elastic bunch graph matching （EBGM）. Moreover, it is effective and robust to expression, illumination and pose variation in some degree.

Summed volume region selection based three-dimensional automatic target recognition for airborne LIDAR

Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information,which can improve its effectiveness of automatic target recognition in the complex environment.Compared with 2D information,3D information performs better in separating objects and background.However,an aircraft platform can have a negative influence on LIDAR obtained data because of various flight attitudes,flight heights and atmospheric disturbances.A structure of global feature based 3D automatic target recognition method for airborne LIDAR is proposed,which is composed of offline phase and online phase.The performance of four global feature descriptors is compared.Considering the summed volume region(SVR) discrepancy in real objects,SVR selection is added into the pre-processing operations to eliminate mismatching clusters compared with the interested target.Highly reliable simulated data are obtained under various sensor’s altitudes,detection distances and atmospheric disturbances.The final experiments results show that the added step increases the recognition rate by above 2.4% and decreases the execution time by about 33%.

Video summarization via global feature difference optimization

Video summarization aims at selecting valuable clips for browsing videos with high efficiency.Previous approaches typically focus on aggregating temporal features while ignoring the potential role of visual representations in summarizing videos.In this paper,we present a global difference-aware network(GDANet)that exploits the feature difference across frame and video as guidance to enhance visual features.Initially,a difference optimization module(DOM)is devised to enhance the discriminability of visual features,bringing gains in accurately aggregating temporal cues.Subsequently,a dual-scale attention module(DSAM)is introduced to capture informative contextual information.Eventually,we design an adaptive feature fusion module(AFFM)to make the network adaptively learn context representations and perform feature fusion effectively.We have conducted experiments on benchmark datasets,and the empirical results demonstrate the effectiveness of the proposed framework.

Cycle GAN-MF:A Cycle-consistent Generative Adversarial Network Based on Multifeature Fusion for Pedestrian Re-recognition

In pedestrian re-recognition,the traditional pedestrian re-recognition method will be affected by the changes of background,veil,clothing and so on,which will make the recognition effect decline.In order to reduce the impact of background,veil,clothing and other changes on the recognition effect,this paper proposes a pedestrian re-recognition method based on the cycle-consistent generative adversarial network and multifeature fusion.By comparing the measured distance between two pedestrians,pedestrian re-recognition is accomplished.Firstly,this paper uses Cycle GAN to transform and expand the data set,so as to reduce the influence of pedestrian posture changes as much as possible.The method consists of two branches:global feature extraction and local feature extraction.Then the global feature and local feature are fused.The fused features are used for comparison measurement learning,and the similarity scores are calculated to sort the samples.A large number of experimental results on large data sets CUHK03 and VIPER show that this new method reduces the influence of background,veil,clothing and other changes on the recognition effect.

Object tracking method based on joint global and local feature descriptor of 3D LIDAR point cloud

To fully describe the structure information of the point cloud when the LIDAR-object distance is long,a joint global and local feature(JGLF)descriptor is constructed.Compared with five typical descriptors,the object recognition rate of JGLF is higher when the LIDAR-object distances change.Under the situation that airborne LIDAR is getting close to the object,the particle filtering(PF)algorithm is used as the tracking frame.Particle weight is updated by comparing the difference between JGLFs to track the object.It is verified that the proposed algorithm performs 13.95%more accurately and stably than the basic PF algorithm.

3D face recognition:A comprehensive survey in 2022

In the past ten years,research on face recognition has shifted to using 3D facial surfaces,as 3D geometric information provides more discriminative features.This comprehensive survey reviews 3D face recognition techniques developed in the past decade,both conventional methods and deep learning methods.These methods are evaluated with detailed descriptions of selected representative works.Their advantages and disadvantages are summarized in terms of accuracy,complexity,and robustness to facial variations(expression,pose,occlusion,etc.).A review of 3D face databases is also provided,and a discussion of future research challenges and directions of the topic.

Form Properties of Moving Targets Bias Smooth Pursuit Target Selection in Monkeys

During natural viewing,we often recognize multiple objects,detect their motion,and select one object as the target to track.It remains to be determined how such behavior is guided by the integration of visual form and motion perception.To address this,we studied how monkeys made a choice to track moving targets with different forms by smooth pursuit eye movements in a two-target task.We found that pursuit responses were biased toward the motion direction of a target with a hole.By computing the relative weighting,we found that the target with a hole exhibited a larger weight for vector computation.The global hole feature dominated other form properties.This dominance failed to account for changes in pursuit responses to a target with different forms moving singly.These findings suggest that the integration of visual form and motion perception can reshape the competition in sensorimotor networks to guide behavioral selection.

Bioimage-based protein subcellular location prediction： a comprehensive review

Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location pat- terns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic char- acteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systemati- cally reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and clas- sified them into four categories including growing of bioim- age databases, description of subcellular location distribution patterns, classification methods, and applications of the pre- diction systems. Besides, we also discussed some potential directions in this field.

Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern

To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.

Impacts of climate change on mangrove ecosystems:a region by region overview

Inter-related and spatially variable climate change factors including sea level rise,increased storminess,altered precipitation regime and increasing temperature are impacting mangroves at re-gional scales.This review highlights extreme regional variation in climate change threats and impacts,and how these factors impact the structure of mangrove communities,their biodiversity and geo-morphological setting.All these factors interplay to determine spatially variable resiliency to climate change impacts,and because mangroves are varied in type and geographical location,these systems are good models for understanding such interactions at different scales.Sea level rise is likely to in-fluence mangroves in all regions although local impacts are likely to be more varied.Changes in the frequency and intensity of storminess are likely to have a greater impact on N and Central America,Asia,Australia,and East Africa than West Africa and S.America.This review also highlights the nu-merous geographical knowledge gaps of climate change impacts,with some regions particularly understudied(e.g.,Africa and the Middle East).While there has been a recent drive to address these knowledge gaps especially in South America and Asia,further research is required to allow research-ers to tease apart the processes that influence both vulnerability and resilience to climate change.A more globally representative view of mangroves would allow us to better understand the importance of mangrove type and landscape setting in determining system resiliency to future climate change.