Semantic Document Layout Analysis of Handwritten Manuscripts

A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

Clustering analysis algorithm for security supervising data based on semantic description in coal mines

In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.

The Semantic Analysis of the Negative Marker Meiyou

"Meiyou"is an important negative marker in Chinese. This thesis explores the semantic features of"meiyou"in two basic negative structures, such as"meiyou + NP"and"meiyou + VP"and the concurrence of"meiyou"with aspect markers,such as" zhe","le","guo". This thesis insists that"meiyou + NP"and"meiyou + VP"have the same semantic structure,namely, the negative marker"meiyou"negates discrete events in both structures.

Latent semantic analysis for query interfaces of deep web sites

To further enhance the efficiencies of search engines,achieving capabilities of searching,indexing and locating the information in the deep web,latent semantic analysis is a simple and effective way.Through the latent semantic analysis of the attributes in the query interfaces and the unique entrances of the deep web sites,the hidden semantic structure information can be retrieved and dimension reduction can be achieved to a certain extent.Using this semantic structure information,the contents in the site can be inferred and the similarity measures among sites in deep web can be revised.Experimental results show that latent semantic analysis revises and improves the semantic understanding of the query form in the deep web,which overcomes the shortcomings of the keyword-based methods.This approach can be used to effectively search the most similar site for any given site and to obtain a site list which conforms to the restrictions one specifies.

Visualization Analysis of Multi-Domain Access Control Policy Integration Based on Tree-Maps and Semantic Substrates

The complexity of multi-domain access control policy integration makes it difficult to understand and manage the policy conflict information. The policy information visualization technology can express the logical relation of the complex information intuitively which can effectively improve the management ability of the multi-domain policy integration. Based on the role-based access control model, this paper proposed two policy analyzing methods on the separated domain statistical information of multi-domain policy integration conflicts and the policy element levels of inter-domain and element mapping of cross-domain respectively. In addition, the corresponding visualization tool is developed. We use the tree-maps algorithm to statistically analyze quantity and type of the policy integration conflicts. On that basis, the semantic substrates algorithm is applied to concretely analyze the policy element levels of inter-domain and role and permission mapping of cross-domain. Experimental result shows tree-maps and semantic substrates can effectively analyze the conflicts of multi-domain policy integration and have a good application value.

Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

In recent years,multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas,especially for automatic image annotation,whose purpose is to provide an efficient and effective searching environment for users to query their images more easily. In this paper,a semi-supervised learning based probabilistic latent semantic analysis( PLSA) model for automatic image annotation is presenred. Since it's often hard to obtain or create labeled images in large quantities while unlabeled ones are easier to collect,a transductive support vector machine( TSVM) is exploited to enhance the quality of the training image data. Then,different image features with different magnitudes will result in different performance for automatic image annotation. To this end,a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible. Finally,a PLSA model with asymmetric modalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores. Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PLSA for the task of automatic image annotation.

Social network search based on semantic analysis and learning

Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.

AN HMM BASED ANALYSIS FRAMEWORK FOR SEMANTIC VIDEO EVENTS

Semantic video analysis plays an important role in the field of machine intelligence and pattern recognition. In this paper, based on the Hidden Markov Model (HMM), a semantic recognition framework on compressed videos is proposed to analyze the video events according to six low-level features. After the detailed analysis of video events, the pattern of global motion and five features in foreground—the principal parts of videos, are employed as the observations of the Hidden Markov Model to classify events in videos. The applications of the proposed framework in some video event detections demonstrate the promising success of the proposed framework on semantic video analysis.

Novel Epistemic and Predictive Heuristic for Semantic and Dynamic Social Networks Analysis

Using Kripke semantics, we have identified and reduced an epistemic incompleteness in the metaphor commonly employed in Social Networks Analysis (SNA), which basically compares information flows with current flows in advanced centrality measures. Our theoretical approach defines a new paradigm for the semantic and dynamic analysis of social networks including shared content. Based on our theoretical findings, we define a semantic and predictive model of dynamic SNA for Enterprises Social Networks (ESN), and experiment it on a real dataset.

A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

Social media platforms provide new value for markets and research companies.This article explores the use of social media data to enhance customer value propositions.The case study involves a company that develops wearable Internet of Things(IoT)devices and services for stress management.Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users’stress management practices.The aim is to analyze the tweets about stress management practices and to identify the context from the tweets.Thereafter,we map the tweets on pleasure and arousal to elicit customer insights.We analyzed a case study of a marketing strategy on the Twitter platform.Participants in the marketing campaign shared photos and texts about their stress management practices.Machine learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign participants.The computational semantic analysis of the tweets was compared to the text analysis of the tweets.The content analysis of only tweet images resulted in 96%accuracy in detecting tweet context,while that of the textual content of tweets yielded an accuracy of 91%.Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

Exploiting multi-context analysis in semantic image classification

As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.

Metaphor Analysis Method Based on Latent Semantic Analysis

Current research on metaphor analysis is generally knowledge-based and corpus-based,which calls for methods of automatic feature extraction and weight calculation.Combining natural language processing(NLP),latent semantic analysis(LSA),and Pearson correlation coefficient,this paper proposes a metaphor analysis method for extracting the content words from both literal and metaphorical corpus,calculating correlation degree,and analyzing their relationships.The value of the proposed method was demonstrated through a case study by using a corpus with keyword“飞翔(fly)”.When compared with the method of Pearson correlation coefficient,the experiment shows that the LSA can produce better results with greater significance in correlation degree.It is also found that the number of common words that appeared in both literal and metaphorical word bags decreased with the correlation degree.The case study also revealed that there are more nouns appear in literal corpus,and more adjectives and adverbs appear in metaphorical corpus.The method proposed will benefit NLP researchers to develop the required step-by-step calculation tools for accurate quantitative analysis.

Basic Color Terms “Black” and “White” Cognitive Semantic Analysis

Chinese Color Words two words have a higher degree turn and turn-grade class degree,they are "white"(white) and "black"(black),these two sets of words are generally located on human color perception of the system the top three,we believe that the typical basic color terms most likely to turn and turn-grade class,but different history,culture and other aspects of cognition,cross-grammatical category they are different order.Based on this,in English and Chinese Basic Color Terms "Black" and "White" Cognitive Semantic Analysis of the research topic,this in-depth study of this aspect of the study hope to be beneficial to help.

A Review on Semantic Waves and Its Application in Discourse Analysis

Since the past 10 years,the theory of semantic waves has further progressed.This theory is deeply rooted in the theory of knowledge structures,legitimation code theory,and systemic functional linguistics.In addition,the theory can also be applied in discourse analysis,language learning,language teaching,and many other fields.

A Comparative Analysis between Walt Disney and DreamWorks Based on the Theory of Semantic Roles of Argument Nominals

Anchored on Yule’s categories of semantic roles,the present study examined the language of cartoon scripts with Chinese characters in Walt Disney’s Mulan 1 and 2 and DreamWorks’s Kung Fu Panda 1 and 2.Specifically it described the:(1)semantic features of the scripts in terms of semantic roles;and(2)similarities and differences in the language of the scripts semantically.Data analyzed were limited to 800 sentences which were randomly selected from the scripts of Mulan 1 and 2 and Kung Fu Panda 1 and 2.More specifically,200 lines per film were analyzed by taxonomizing the utterances in terms of identifying the semantic roles of argument nominals in each utterance.Results revealed the roles of agent and experiencer in the subject positions are dominant in contrast with the frequency of occurrences of theme,goal,location and source.In conclusion,the language of animated film is relatively simpler,literal and direct to suit the level of the target audience who are generally children.Finally,this research suggests that more linguistic levels should be conducted to explore the language features on cartoon movies in the future.

Stylistic Analysis of Whitman'S Poem O Captain!My Captain!

The poem O Captain! My Captain! is a good poem written by the great American poet, Walt Whitman. It is a eulogy written on the death of Abraham Lincoln. In this poem the poet expresses his sadness at the death of the great leader and expresses his great love of his beloved leader. This paper is to have a stylistic analysis of the poem by observing its phonological, lexical, semantic and syntactic features.

The Formal Semantics of an UML Activity Diagram

Due to lack of strictly defined formal semantics, an UML activity diagram is unsuitable for the tasks of formal analysis, verification and assertion on the system it describes. In this paper, Petri net is used to define the formal semantics of an UML activity diagram containing object flow states, laying a foundation for the precise description and analysis of a workflow system.

语义引文的实践进展研究——以Semantic Scholar和Scite为例

随着计算机技术的进步,语义引文逐渐从依赖手工分析的理论走向自动化处理的实践,并显现出改变学术发现与评价方式的可能。Semantic Scholar和Scite两个平台在生产环境中建立了相关实践,并应用于学术发现增强、评价指标拓展等场景。它们在语义引文建立过程中呈现出通用的技术步骤,但是在核心环节分别采用了监督分类方法、神经多任务分类框架、深度学习方法等不同路线。推动语义引文的发展和应用,需要关注这些实践和相关研究进展在语义引文方法的选择、类别数量的控制、交叉研究的加强、规模化数据样本集的建设、学术开放水平的提升、现阶段语义引文的应用推广等方面的启示。