Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map(3D-ILM),an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman(RSA)is presented in this paper.In the first stage,a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence,Lyapunov exponent,and Shannon entropy.In the second stage,combined with the public key RSA algorithm,a new key acquisition mathematical model(MKA)is constructed to obtain the initial keys for the 3D-ILM.Consequently,the key stream can be produced depending on the plain image for a higher security.Moreover,a novel process model(NPM)for the input of the 3D-ILM is built,which is built to improve the distribution uniformity of the chaotic sequence.In the third stage,to encrypt the plain image,a pre-process by exclusive OR(XOR)operation with a random matrix is applied.Then,the pre-processed image is performed by a permutation for rows,a downward modulo function for adjacent pixels,a permutation for columns,a forward direction XOR addition-modulo diffusion,and a backward direction XOR addition-modulo diffusion to achieve the final cipher image.Moreover,experiments show that the the proposed algorithm has a better performance.Especially,the number of pixels change rate(NPCR)is close to ideal case 99.6094%,with the unified average changing intensity(UACI)close to 33.4634%,and the information entropy(IE)close to 8.

An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images.The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance.To address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model.Then,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model.Finally,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud model.We also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor scenes.Experimental results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic map.Finally,it constructs a globally consistent high-precision indoor scenes 3D semantic map.

Design and Realization of Block Level Augmented Reality Three-Dimensional Map

In response to the construction needs of “Real 3D China”, the system structure, functional framework, application direction and product form of block level augmented reality three-dimensional map is designed. Those provide references and ideas for the later large-scale production of augmented reality three-dimensional map. The augmented reality three-dimensional map is produced based on skyline software. Including the map browsing, measurement and analysis and so on, the basic function of three-dimensional map is realized. The special functional module including housing management, pipeline management and so on is developed combining the need of residential quarters development, that expands the application fields of augmented reality three-dimensional map. Those lay the groundwork for the application of augmented reality three-dimensional map. .

A modified method of discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces

This paper presents an automated method for discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces.Specifically,the method consists of five steps:(1)detection of trace feature points by normal tensor voting theory,(2)co ntraction of trace feature points,(3)connection of trace feature points,(4)linearization of trace segments,and(5)connection of trace segments.A sensitivity analysis was then conducted to identify the optimal parameters of the proposed method.Three field cases,a natural rock mass outcrop and two excavated rock tunnel surfaces,were analyzed using the proposed method to evaluate its validity and efficiency.The results show that the proposed method is more efficient and accurate than the traditional trace mapping method,and the efficiency enhancement is more robust as the number of feature points increases.

Three-dimensional computed tomography mapping of posterior malleolar fractures

BACKGROUND Posterior malleolar fractures have been reported to occur in<40%of ankle fractures.AIM To reveal the recurrent patterns and characteristics of posterior malleolar fractures by creating fracture maps of the posterior malleolar fractures through the use of computed tomography mapping.METHODS A consecutive series of posterior malleolar fractures was used to create threedimensional reconstruction images,which were oriented and superimposed to fit an ankle model template by both aligning specific biolandmarks and reducing reconstructed fracture fragments.Fracture lines were found and traced in order to generate an ankle fracture map.RESULTS This study involved 112 patients with a mean age of 49,comprising 32 pronationexternal rotation grade IV fractures and 80 supination-external rotation grade IV fractures according to the Lauge-Hansen classification system.Three-dimensional maps showed that the posterior ankle fracture fragments in the supinationexternal rotation grade IV group were relatively smaller than those in the pronation-external rotation grade IV group after posterior malleolus fracture.In addition,the distribution analyses on posterior malleolus fracture lines indicated that the supination-external rotation grade IV group tended to have higher linear density but more concentrated and orderly distribution fractures compared to the pronation-external rotation grade IV group.CONCLUSION Fracture maps revealed the fracture characteristics and recurrent patterns of posterior malleolar fractures,which might help to improve the understanding of ankle fracture as well as increase opportunities for follow-up research and aid clinical decision-making.

CSMCCVA:Framework of cross-modal semantic mapping based on cognitive computing of visual and auditory sensations

Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.

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.

The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data

With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.

A new projection method for biological semantic map generation

Low-dimensional representation is a convenient method of obtaining a synthetic view of complex datasets and has been used in various domains for a long time. When the representation is related to words in a document, this kind of representation is also called a semantic map. The two most popular methods are self-organizing maps and generative topographic mapping. The second approach is statistically well-founded but far less computationally efficient than the first. On the other hand, a drawback of self-organizing maps is that they do not project all points, but only map nodes. This paper presents a method of obtaining the projections for all data points complementary to the self-organizing map nodes. The idea is to project points so that their initial distances to some cluster centers are as conserved as possible. The method is tested on an oil flow dataset and then applied to a large protein sequence dataset described by keywords. It has been integrated into an interactive data browser for biological databases.

Building of cognizing semantic map in large-scale semi-unknown environment

The quick response code based artificial labels are applied to provide semantic concepts and relations of surroundings that permit the understanding of complexity and limitations of semantic recognition and scene only with robot's vision.By imitating spatial cognizing mechanism of human,the robot constantly received the information of artificial labels at cognitive-guide points in a wide range of structured environment to achieve the perception of the environment and robot navigation.The immune network algorithm was used to form the environmental awareness mechanism with "distributed representation".The color recognition and SIFT feature matching algorithm were fused to achieve the memory and cognition of scenario tag.Then the cognition-guide-action based cognizing semantic map was built.Along with the continuously abundant map,the robot did no longer need to rely on the artificial label,and it could plan path and navigate freely.Experimental results show that the artificial label designed in this work can improve the cognitive ability of the robot,navigate the robot in the case of semi-unknown environment,and build the cognizing semantic map favorably.

Balanced Functional Maps for Three-Dimensional Non-Rigid Shape Registration

Three-dimensional(3D)shape registration is a challenging problem,especially for shapes under non-rigid transformations.In this paper,a 3D non-rigid shape registration method is proposed,called balanced functional maps(BFM).The BFM algorithm generalizes the point-based correspondence to functions.By choosing the Laplace-Beltrami eigenfunctions as the function basis,the transformations between shapes can be represented by the functional map(FM)matrix.In addition,many constraints on shape registration,such as the feature descriptor,keypoint,and salient region correspondence,can be formulated linearly using the matrix.By bi-directionally searching for the nearest neighbors of points’indicator functions in the function space,the point-based correspondence can be derived from FMs.We conducted several experiments on the Topology and Orchestration Specification for Cloud Applications(TOSCA)dataset and the Shape Completion and Animation of People(SCAPE)dataset.Experimental results show that the proposed BFM algorithm is effective and has superior performance than the state-of-the-art methods on both datasets.

Application of Three-dimensional Laser Scanning Technology in the Teaching Practice of Surveying and Mapping of Ancient Buildings

Based on the study of the application of three-dimensional laser scanning technology in ancient building surveying and mapping,this paper briefly describes the working principle and flow of three-dimensional laser scanning technology.Based on the practical application,this paper puts forward the discussion of related problems and matters needing attention.This has a certain reference significance for the study of new technology in surveying and mapping of ancient buildings.

Ontology based approach of semantic information integration

In order to solve the problem of semantic heterogeneity in information integration, an ontology based semantic information integration （OSII） model and its logical framework are proposed. The OSII adopts the hybrid ontology approach and uses OWL （web ontology language） as the ontology language. It obtains unified views from multiple sources by building mappings between local ontologies and the global ontology. A tree- based multi-strategy ontology mapping algorithm is proposed. The algorithm is achieved by the following four steps： pre-processing, name mapping, subtree mapping and remedy mapping. The advantages of this algorithm are： mapping in the compatible datatype categories and using heuristic rules can improve mapping efficiency; both linguistic and structural similarity are used to improve the accuracy of the similarity calculation; an iterative remedy is adopted to obtain correct and complete mappings. A challenging example is used to illustrate the validity of the algorithm. The OSII is realized to effectively solve the problem of semantic heterogeneity in information integration and to implement interoperability of multiple information sources.

Semantic Web研究综述

近年来 ,SemanticWeb逐渐成为WWW领域的研究热点以及智能化网络服务和应用开发中的关键技术之一。归纳了Se manticWeb技术的研究背景和主要发展历史。在分析了典型的SemanticWeb概念后 ,给出了SemanticWeb的定义。通过讨论SemanticWeb构想的层次框架模型 ,指出了各个层次扮演的角色 ,并着重分析了SemanticWeb的重要研究领域 ,指出了它们在SemanticWeb构架中的核心作用。通过分析SemanticWeb的应用领域和相关开发工具以及面临的问题和挑战 ,指明了SemanticWeb研究和实践的方向。作为总结 ,给出了SemanticWeb领域下一步的研究趋势。

Science Letters:Dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modelling language (VRML)

This research studies the process of 3D reconstruction and dynamic concision based on 2D medical digital images using virtual reality modelling language (VRML) and JavaScript language, with a focus on how to realize the dynamic concision of 3D medical model with script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be built with such high quality that they are better than those obtained from the traditional methods. With the function of dynamic concision, the VRML browser can offer better windows for man-computer interaction in real-time environment than ever before. 3D reconstruction and dynamic concision with VRML can be used to meet the requirement for the medical observation of 3D reconstruction and have a promising prospect in the fields of medical imaging.

A New Method of Semantic Network Knowledge Representation Based on Extended Petri Net

Abstract： It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic network knowledge representation processing. A new extended Petri net is defined based on qualitative mapping, which strengths the expressive ability of the feature of thinking and the mode of action of brain. A model of semantic network knowledge representation based on new Petri net is given. Semantic network knowledge has a more efficient representation and reasoning mechanism. This model not only can reflect the characteristics of associative memory in semantic network knowledge representation, but also can use Petri net to express the criterion changes and its change law of recognition judgment, especially the cognitive operation of thinking based on extraction and integration of sensory characteristics to well express the thinking transition course from quantitative change to qualitative change of human cognition.

Ontology Based Resolution of Semantic Conflicts in Information Integration

Semantic conflict is the conflict caused by using different ways in heterogeneous systems to express the same entity in reality. This prevents information integration from accomplishing semantic coherence. Since ontology helps to solve semantic problems, this area has become a hot topic in information integration. In this paper, we introduce semantic conflict into information integration of heterogeneous applications. We discuss the origins and categories of the conflict, and present an ontology-based schema mapping approach to eliminate semantic conflicts. Key words ontology - CCSOL - semantic conflict - schema mapping CLC number TP 301 Biography: LU Han (1980-), male, Master candidate, research direction: ontology and information integration.

Smap:基于文献语义的学科知识图景可视化

随着文献爆炸式增长,学科领域不断交叉融合,科研规模扩大和知识体系复杂性日益提升,如何清晰地可视化学科知识图景,进而把握知识结构和研究态势,引起了科技情报人员的广泛关注。本研究基于文档表示学习和流形学习算法,提供了一种科学领域语义地图(semantic map,Smap)构建方法。首先以Doc2Vec捕获文献间的高维语义特征,然后利用UMAP(uniform manifold approximation and projection)对文献语义临近性进行非线性降维,最后以核密度估计根据文献分布异质性刻画领域知识结构。在实证分析阶段,本研究对文献规模覆盖了从千级到百万级的4个学科领域,进行了领域可视化、知识层级结构识别以及动态演化分析。进而,本研究借助引用关系、关键词以及数据集的分类体系,通过量化Smap地图上文献分布的局部纯粹性以及全局地图距离和研究差异的相关性,验证了所提方法的有效性。本研究通过与随机实验对比,进一步地量化了有效性的显著程度。本研究为当前科学领域可视化方法提供了有益补充,可为大规模科技文献数据驱动的科技情报服务提供分析工具。

Next Generation Semantic and Spatial Joint Perception——Neural Metric-Semantic Understanding

Efficient perception of the real world is a long-standing effort of computer vision.Mod⁃ern visual computing techniques have succeeded in attaching semantic labels to thousands of daily objects and reconstructing dense depth maps of complex scenes.However,simultaneous se⁃mantic and spatial joint perception,so-called dense 3D semantic mapping,estimating the 3D ge⁃ometry of a scene and attaching semantic labels to the geometry,remains a challenging problem that,if solved,would make structured vision understanding and editing more widely accessible.Concurrently,progress in computer vision and machine learning has motivated us to pursue the capability of understanding and digitally reconstructing the surrounding world.Neural metric-se⁃mantic understanding is a new and rapidly emerging field that combines differentiable machine learning techniques with physical knowledge from computer vision,e.g.,the integration of visualinertial simultaneous localization and mapping(SLAM),mesh reconstruction,and semantic un⁃derstanding.In this paper,we attempt to summarize the recent trends and applications of neural metric-semantic understanding.Starting with an overview of the underlying computer vision and machine learning concepts,we discuss critical aspects of such perception approaches.Specifical⁃ly,our emphasis is on fully leveraging the joint semantic and 3D information.Later on,many im⁃portant applications of the perception capability such as novel view synthesis and semantic aug⁃mented reality(AR)contents manipulation are also presented.Finally,we conclude with a dis⁃cussion of the technical implications of the technology under a 5G edge computing scenario.

Three-Dimensional World of Similes in English Fictional Writing

The paper offers a three-dimensional linguosemiotic study of similes, which implies integral analysis of their semantic, syntactic, and pragmatic aspects. Such an approach to the study of similes is quite new as they have been hitherto considered either from a literary viewpoint as one of the stylistic expressive means of language or in the philosophy of language in correlation with metaphor. The three-dimensional linguosemiotic methodology of research has enabled us： （1） to reveal the cognitive, psychological, and metaphorical essence of similes and work out the invariant conceptual model which remains unchanged throughout their structural-semantic variation in the text; （2） to single out pragmatic features of similes, the set of which defines their linguistic status as a language-in-use construct, i.e., textual phenomenon; （3） to study the denotational-cognitive aspect of similes pointing out the parameters according to which similes have been differentiated into semantic types and subtypes and （4） to generalize the syntactical aspect of similes and define the set of their structural modifications in the text conditioned both by the intralinguistic regularities and by pragmatic factors. Therefore, we have worked out an interdisciplinary theory of similes implying the synergy of the data of linguistic, literary, cognitive, and psychological studies