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STATE:Learning structure and texture representations for novel view synthesis

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Computational Visual Media 2023年第4期9卷 767-786页

作者： Xinyi Jing Qiao Feng Yu-Kun Lai Jinsong Zhang Yuanqiang Yu Kun Li College of Intelligence and computing Tianjin UniversityTianjin 300350China school of Computer Science and informatics Cardiff UniversityCardiff CF244AGUK

Novel viewpoint image synthesis is very challenging,especially from sparse views,due to large changes in viewpoint and occlusion.Existing image-based methods fail to generate reasonable results for invisible regions,while geometry-based methods have difficulties in synthesizing detailed textures.In this paper,we propose STATE,an end-to-end deep neural network,for sparse view synthesis by learning structure and texture representations.Structure is encoded as a hybrid feature field to predict reasonable structures for invisible regions while maintaining original structures for visible regions,and texture is encoded as a deformed feature map to preserve detailed textures.We propose a hierarchical fusion scheme with intra-branch and inter-branch aggregation,in which spatio-view attention allows multi-view fusion at the feature level to adaptively select important information by regressing pixel-wise or voxel-wise confidence maps.By decoding the aggregated features,STATE is able to generate realistic images with reasonable structures and detailed textures.Experimental results demonstrate that our method achieves qualitatively and quantitatively better results than state-of-the-art methods.Our method also enables texture and structure editing applications benefiting from implicit disentanglement of structure and texture.Our code is available at http://***/faculty/likun/projects/STATE.

关键词： novel view synthesis sparse views spatioview attention structure representation texture representation

A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of computing Harbin Institute of Technology Department of Medical informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data school of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： Ontology Semantic representation Semantic similarity Protein-protein interaction

Identification of the clustering structure in microbiome data by density clustering on the Manhattan distance

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Science China(Information Sciences) 2016年第7期59卷 44-50页

作者： Xingpeng JIANG Xiaohua HU Tingting HE school of Computer Science Central China Normal University College of computing & informatics Drexel University

Clustering technology is a method for grouping data points into clusters containing a group of similar data points. In a real dataset such as microbiome data, the data points are presented as profiles or a probability distribution. These data points form the periphery of a cluster, making it difficult to identify the real clustering structure. In this study, we used density clustering on several distance measures to overcome this difficulty. Experiments using a real dataset indicated that the Manhattan distance is an appropriate distance measure for clustering analysis of microbiome data.

关键词： microbiome information distance data visualization density clustering microbial community

An Efficient Broadcast Encryption Supporting Designation and Revocation Mechanisms

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Chinese Journal of Electronics 2019年第3期28卷 445-456页

作者： ZHU Yan YU Ruyun CHEN E HUANG Dijiang school of Computer and Communication Engineering University of Science and Technology Beijing school of computing informatics and Decision System Engineering Arizona State University

In this paper our objective is to explore approaches of secure group-oriented communication with designation and revocation mechanisms simultaneously.We present a new scheme of Revocation-based broadcast encryption(RBBE) which is designed on Dan Boneh et al.’s scheme with the designation mechanism proposed in 2005. We combine two above-mentioned schemes into a new cryptosystem, called Dual-mode broadcast encryption(DMBE). Based on these work, we reach the following conclusions. We use the DMBE scheme as an example to show that it is feasible to construct a broadcast encryption scheme that supports designation and revocation mechanisms simultaneously. The cryptosystem with dual modes is more efficient than that with single mode over computational costs, and the performance is improved to at most O(「N/2」), where N is the total number of users in the system. We prove completely that both the RBBE scheme and the DMBE scheme are semantically secure against chosen plaintext attack with full collusion under the decisional bilinear Diffie-Hellman exponent assumption.

关键词： Cryptography Broadcast encryption Provable security Revocation Designation Dual modes

Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discovery

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World Journal of Clinical Oncology 2018年第5期9卷 98-109页

作者： Jeya Balaji Balasubramanian Vanathi Gopalakrishnan Intelligent Systems Program School of Computing and Information University of Pittsburgh Department of Biomedical informatics School of Medicine University of Pittsburgh

AIM To develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODS Bayesian rule learning(BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks(BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRL_p. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRL_p to other stateof-the-art classifiers commonly used in biomedicine.RESULTS We evaluated the degree of incorporation of prior knowledge into BRL_p, with simulated data by measuring the Graph Edit Distance between the true datagenerating model and the model learned by BRL_p. We specified the true model using informative structurepriors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRL_p caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve(AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor(EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRL_p model

关键词： Supervised machine learning Rule-based models Bayesian methods Background knowledge Informative priors Biomarker discovery

Unifying logic rules and machine learning for entity enhancing

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Science China(Information Sciences) 2020年第7期63卷 142-160页

作者： Wenfei FAN Ping LU Chao TIAN school of informatics University of Edinburgh Shenzhen Institute of computing Sciences Shenzhen University Beijing Advanced Innovation Center for Big Data and Brain computing Beihang University Alibaba Group

This paper proposes a notion of entity enhancing, which unifies entity resolution and conflict resolution, to identify tuples that refer to the same real-world entity and at the same time, correct semantic inconsistencies. We propose to unify rule-based and machine learning(ML) methods for entity enhancing, by embedding ML classifiers as predicates in logic rules. We model entity enhancing by extending the chase. We show that the chase warrants correctness justification and the Church-Rosser property. Moreover, we settle fundamental problems associated with entity enhancing, including the enhancing, consistency, satisfiability,and implication problems, ranging from NP-complete and coNP-complete to Π~p-complete. Taken together,these provide a new theoretical framework for unifying entity resolution and conflict resolution.

关键词： logic rules machine learning entity enhancing entity resolution conflict resolution

Retrieval and validation of long-term aerosol optical depth from AVHRR data over China

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International Journal of Digital Earth 2022年第1期15卷 1817-1832页

作者： Chunlin Jin Yong Xue Xingxing Jiang Shuhui Wu Yuxin Sun school of Environment and Spatial informatics China University of Mining and TechnologyXuzhouPeople’s Republic of China school of computing and Engineering College of Science and EngineeringUniversity of DerbyDerbyUK

Advanced Very High Resolution Radiometer(AVHRR)onboard National Oceanic and Atmospheric Administration(NOAA)satellites can provide over 40 years of global remote sensing observations,which can be used to retrieve long-term aerosol optical depth(AOD).This is of great significance to the study of global climate change.In this paper,we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations.With assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days,the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function(BRDF shape)and obtained AOD and surface property by optimal estimation(OE)method.The algorithm has been applied to NOAA-7,9,11,14,16,18,and 19 satellites and AVHRR-retrieved AOD with 5×10 km over China(15°–60°N,70°–140°E)has been obtained from 1982 to 2016.Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork(AERONET)(in and around China)and China Aerosol Remote Sensing Network(CARSNET)AOD show good consistency with 62.62%points within the uncertainty ofΔτ=±(0.05+0.25τ)and root-mean-square error(RMSE)of 0.26.Further comparison of the monthly mean AOD of multiple AOD datasets in the‘Beijing’,‘Dalanzadgad’,‘NCU_Taiwan’and‘Kanpur’stations shows that the results of the algorithm are stable.The yearly averaged AOD data also has similar agreements with MERRA-2(The Modern-Era Retrospective analysis for Research and Applications,Version 2)and AVHRRDB data(AVHRR‘Deep Blue’aerosol data set).The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01%and 67.29%compared with MERRA-2 AOD and AVHRRDB AOD respectively.

关键词： Long-term AOD AVHRR joint retrieval BRDF shape

Model architecture-oriented combat system effectiveness simulation based on MDE

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Journal of Systems Engineering and Electronics 2017年第5期28卷 900-922页

作者： Yonglin Lei Ning Zhu Jian Yao Hessam Sarjoughian Weiping Wang school of Information Systems and Management National University of Defense Technology school of computing Informatics and Decision Systems Engineering Arizona State University

Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.

关键词： combat system effectiveness simulation (CSES) model architecture model-driven engineering (MDE) simulation modeling platform (SMP) domain specific modeling (DSM) weapon effectiveness simulation system (WESS)

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Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

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Computer Systems Science & Engineering 2022年第4期41卷 305-320页

作者： Raniyah Wazirali Rami Ahmed College of computing and informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia The school of Information Technology Sebha UniversitySebha71Libya

The global pandemic of novel coronavirus that started in 2019 has ser-iously affected daily lives and placed everyone in a panic condition.Widespread coronavirus led to the adoption of social distancing and people avoiding unneces-sary physical contact with each other.The present situation advocates the require-ment of a contactless biometric system that could be used in future authentication systems which makesﬁngerprint-based person identiﬁcation ineffective.Periocu-lar biometric is the solution because it does not require physical contact and is able to identify people wearing face masks.However,the periocular biometric region is a small area,and extraction of the required feature is the point of con-cern.This paper has proposed adopted multiple features and emphasis on the periocular region.In the proposed approach,combination of local binary pattern(LBP),color histogram and features in frequency domain have been used with deep learning algorithms for classiﬁcation.Hence,we extract three types of fea-tures for the classiﬁcation of periocular regions for biometric.The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel.In order to extract the frequency domain fea-tures,the wavelet transformation is obtained.By learning from these features,a convolutional neural network(CNN)becomes able to discriminate the features and can provide better recognition results.The proposed approach achieved the highest accuracy rates with the lowest false person identiﬁcation.

关键词： Person identiﬁcation convolutional neural network local binary pattern periocular region Covid-19