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arXiv 2021年

作者： Liu, Xue Wei, Wei Feng, Xiangnan Cao, Xiaobo Sun, Dan Beijing System Design Institute of Electro-Mechanic Engineering Beijing100854 China School of Mathematical Sciences Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Ministry of Education 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen Guangdong518066 China

Most existing popular methods for learning graph embedding only consider fixed-order global structural features and lack structures hierarchical representation. To address this weakness, we propose a novel graph embedding algorithm named GraphCSC that realizes classification based on skeleton information using fixed-order structures learned in anonymous random walks manner, and component information using different size subgraphs. Two graphs are similar if their skeletons and components are both similar, thus in our model, we integrate both of them together into embeddings as graph homogeneity characterization. We demonstrate our model on different datasets in comparison with a comprehensive list of up-to-date state-of-the-art baselines, and experiments show that our work is superior in real-world graph classification tasks. © 2021, CC BY.

关键词： Musculoskeletal system

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Pulmonary nodule detection based on multi-branch 3D squeeze-and-excitation network 17

Pulmonary nodule detection based on multi-branch 3D squeeze-...

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17th IEEE International Conference on Dependable, Autonomic and Secure computing, IEEE 17th International Conference on Pervasive Intelligence and computing, IEEE 5th International Conference on Cloud and Big Data computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

作者： Zhang, Yuechao Zhang, Jianxin Che, Chao Lin, Kai Zhou, Dongsheng Zhang, Qiang Wei, Xiaopeng Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781728130255;9781728130248

Convolutional neural networks based regional proposal networks (RPN) have recently achieved breakthrough results in a variety of medical image detection tasks. Among them, 3D RPN network in conjunction with advanced ResNet gains the state-of-the-art performance. However, current 3D RPN network cannot fully consider different hierarchically spatial information and relationship between channels contained in networks themselves. To overcome this problem, this work proposes a novel multi-branch 3D Squeeze-and-Excitation (SE) network, i.e., Mb3DSENet, which embeds SE-Inception and SE-Residual-Inception blocks into existing 3D RPN network by appropriately assembling SE unit, Inception module and residual connection. Mb3DSENet not only inherits the merits of the basic 3D RPN network, but also enhances its spatial representation power and selectively highlights channel-wise feature responses. Experiment results on the public LUNA16 dataset demonstrate the effectiveness of the proposed network. © 2019 IEEE.

关键词： 3D RPN network Pulmonary nodule detection SE-inception block SE-residual-inception block

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design and Implementation of Ideological and Political education Evaluation System

Design and Implementation of Ideological and Political Educa...

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2018计算机科学与工程技术国际研讨会

作者： Zhenyuan Lv Changjun Zhou Chunye Zhou Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education School of Politics and Law Shangrao Normal University

With the development of informatization, in order to improve office efficiency and manage students more effectively, universities have produced many management systems, such as library management systems and educational management systems. These systems mostly manage student information and other information in an integrated manner. The refinement of each module, due to the large number of colleges and universities, graduates and enrolls a large number of students every year, and the archiving of students' ideological and political courses and students' ideological and political evaluation is quite complicated. Some college management systems may only provide ideological and political courses. Course information and student test scores did not specifically manage the student's ideological and political education assessment and assess the student's psychological activities based on the student's daily performance, nor did they provide detailed course scheduling information for specific ideological and political related courses. Therefore, colleges and universities should also implement detailed information management for students' ideological and political education methods, which can improve management efficiency and reduce unnecessary human resources. The management system is developed by the Java[1-3]development language through Myeclipise as a development tool. The mysql database is used as the background to realize the information management of the ideological and political education of college students. It can promote the detailed management of all aspects of students' information management. Service school as well as students. And the machine learning algorithm is added. Through the previous students' data, the classification model is trained by Logistic regression algorithm to evaluate the students' ideological and political good condition.

关键词： design and Implementation of Ideological and Political education Evaluation System

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Achieving handle-level random access in an encrypted DNA archival storage system via frequency dictionary mapping coding

Patterns

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Patterns 2025年

作者： Ben Cao Xue Li Bin Wang Tiantian He Yanfen Zheng Xiaokang Zhang Qiang Zhang School of Computer Science and Technology Dalian University of Technology Dalian 116024 China School of Computer Science and Technology China University of Petroleum Qingdao 266580 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China School of Computer Science and Engineering Nanyang Technological University Singapore 639798 Singapore Centre for Frontier AI Research Agency for Science Technology and Research (A∗STAR) Singapore 138632 Singapore

DNA storage provides a high storage density and durability, but current systems experience high latency and lack data security. This study proposed the use of frequency dictionary mapping coding (FDMC) to enable handle-level random access of DNA archival storage. In addition, a hybrid e-molecular encryption strategy and a multi-level error-correction algorithm were introduced to ensure data security and integrity. The simulation and wet experiment results demonstrated that FDMC achieved handle-level random access in a lossless encrypted DNA storage system, which balanced security and robustness. Even in extreme cases, in which there was a 10% loss of DNA sequences, this system still recovered 91.74% of the original data while ensuring a storage density above 1.80 bits per nucleotide. In summary, FDMC enhances the application potential of DNA as a storage medium and bridges the gap between DNA storage and traditional storage modes.

关键词： DNA storage random access frequency dictionary mapping coding data security

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A Novel Solution of Underdetermined Equations for DGTD based on Prior knowledge

A Novel Solution of Underdetermined Equations for DGTD based...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Qi Qi Xinyuan Cao Mingsheng Chen Zhixiang Huang Yi Liu Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei China School of Electronic Engineering Huainan Normal University Huainan China

ISBN: (数字)9781728157337

ISBN: (纸本)9781728157344

To accelerate solving transient electromagnetic problems by discontinuous Galerkin time domain (DGTD) method, a solution of underdetermined equations is established based on prior knowledge. In the proposed method, nodal basis function and a leapfrog scheme are used to Maxwell curl equations for spatial and time discretization. Electromagnetic fields in each element of computation domain are updated individually by applying conventional DGTD in initial time. When the electromagnetic wave covers the domain, the fields of all elements are updated as a whole. The underdetermined equations are established by randomly extracting rows from global mass matrix. Taking the results of a few previous time steps as the prior knowledge, a sparse transform is constructed. Then the underdetermined system of equations can be solved by recovery algorithms. Meanwhile,in order to maintain the advantage of low computational complexity, a restart mechanism is presented. The validity of the proposed method is verified by numerical experiments.

关键词： Microwave technology Millimeter wave technology Transforms Sparse matrices Electromagnetic fields Transient analysis Time-domain analysis

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False positive eliminating using frame association information 2019

False positive eliminating using frame association informati...

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3rd International Conference on Innovation in Artificial Intelligence, ICIAI 2019

作者： Zhu, Yuhu Wei, Xiaopeng Yi, Pengfei Zhang, Qiang Zhou, Dongsheng Yang, Deyun Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116024 China College of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Information Science and Technology Taishan University Tai'an Shandong Province271000 China

ISBN: (纸本)9781450361286

As a fundamental technology of human motion analysis, video based pose estimation has attracted more and more attention of researchers. While most mainstream methods treated videos as a collection of unrelated frames, random false positives caused by light and shadow changes will bring greater uncertainty to the results of human body estimation. To eliminate these false positives in multi-person 2D pose estimation, a method of multi-person 2D pose estimation using the time-space relationship of adjacent frames is proposed. At first, Frame Association Information(FAI) is defined to represent the relationship between person objects in adjacent frames, which contents similarity of position, size and poses. Then, a method of false positives eliminating is designed using FAIs between frames, which can identify and remove false positives in frames according to their similarities to human objects in a benchmark frame. The following experiments show the effectiveness of our method. © 2019 Association for computing Machinery.

关键词： Information use

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designing logic gates based on 3-way DNAzyme complex

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Analytical Methods 2019年第5期12卷 693-700页

作者： Donglin Liu Yuan Liu Bin Wang Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian China

In recent years, DNA has been regarded as a reliable raw material for building biological computers and biochips due to its nanoscale size, ultralow energy consumption, and high-performance computing potential. As the basis of building a biological computer, the research on the construction of nanoscale logic arithmetic and nanoscale biochemical logic circuits based on DNA molecules as carriers has attracted increasing attention. Although researchers use DNA strand replacement systems to achieve it this requires adjustment and careful design of the toehold, making sequence selection more difficult. To reduce dependence on the toehold, we propose a 3-way DNAzyme complex composed of three E6 DNAzymes assembled using the biological characteristics of E6 DNAzyme. This complex enriched the recognition vector of E6 DNAzyme, which can be used for multiple substrates, thus improving the reusability and efficiency of DNA molecules. At the same time, based on the 3-way DNAzyme complex and without the involvement of the toehold, we designed logic gates such as the OR gate, the AND gate, and the INHIBIT gate, and realized the construction of a new half subtractor and nanoscale biochemical logic circuit. These explorations and attempts extended the practicality of the 3-way DNAzyme complex. We believe that these logic elements will have a wide range of applications in DNA nanoscale programming, biological computing, and nanoscale medicine.

关键词：

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Exploring Multi-level Mutual Information for Drug-target Interaction Prediction

Exploring Multi-level Mutual Information for Drug-target Int...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiatao Chen Liang Zhang Ke Cheng Bo Jin Xinjiang Lu Chao Che Yiwei Liu Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China International Business College Dongbei University of Finance and Economics Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China Business Intelligence Lab Baidu Research

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Recent advances in graph representation learning provide new opportunities for computational drug-target interaction (DTI) prediction. Inspired by the emerging graph mutual information-based algorithms, we propose MMIDTI, a multi-level mutual information-aware DTI prediction framework based on a heterogeneous network (i.e., drug-protein, drug-drug and protein-protein interaction network; drug-disease, drug-side-effect, and protein-disease association network; drug-structure and protein-sequence similarity network). More specifically, MMIDTI leverages an encoder-decoder framework that can learn the type-aware and meta-path augmented node representations by following a contrastive learning paradigm. The encoder part is a Graph Convolutional Network (GCN) and the decoder is an inner product of the learned representations to recover the original heterogeneous network. Meanwhile, MMIDTI exploits two levels of mutual information: (1) maximizing local mutual information, to obtain node representations that capture the global information content of the entire heterogeneous graph. (2) maximizing the global mutual information, to constrain the node representation to have desired statistical characteristics. Experimental results show that our model can achieve better prediction performance than state-of-the-art methods.

关键词： Mutual information Proteins Drugs Predictive models Diffusion tensor imaging Heterogeneous networks Task analysis

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Knowledge-routed visual question reasoning: Challenges for deep representation embedding

arXiv

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arXiv 2020年

作者： Cao, Qingxing Li, Bailin Liang, Xiaodan Wang, Keze Lin, Liang School of Intelligent Systems Engineering Sun Yat-sen University China DarkMatter AI Research China Department of statistics University of California Los AngelesCA90095 United States School of Computer Science and Engineering Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Engineering Research Center for Advanced Computing Engineering Software of Ministry of Education China

Though beneficial for encouraging the Visual Question Answering (VQA) models to discover the underlying knowledge by exploiting the input-output correlation beyond image and text contexts, the existing knowledge VQA datasets are mostly annotated in a crowdsource way, e.g., collecting questions and external reasons from different users via the internet. In addition to the challenge of knowledge reasoning, how to deal with the annotator bias also remains unsolved, which often leads to superficial over-fitted correlations between questions and answers. To address this issue, we propose a novel dataset named Knowledge-Routed Visual Question Reasoning for VQA model evaluation. Considering that a desirable VQA model should correctly perceive the image context, understand the question, and incorporate its learned knowledge, our proposed dataset aims to cutoff the shortcut learning exploited by the current deep embedding models and push the research boundary of the knowledge-based visual question reasoning. Specifically, we generate the question-answer pair based on both the Visual Genome scene graph and an external knowledge base with controlled programs to disentangle the knowledge from other biases. The programs can select one or two triplets from the scene graph or knowledge base to push multi-step reasoning, avoid answer ambiguity, and balanced the answer distribution. In contrast to the existing VQA datasets, we further imply the following two major constraints on the programs to incorporate knowledge reasoning: i) multiple knowledge triplets can be related to the question, but only one knowledge relates to the image object. This can enforce the VQA model to correctly perceive the image instead of guessing the knowledge based on the given question solely;ii) all questions are based on different knowledge, but the candidate answers are the same for both the training and test sets. We make the testing knowledge unused during training to evaluate whether a model can unde

关键词： Knowledge based systems

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Detection of Deepfake videos using long distance attention

arXiv

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arXiv 2021年

作者： Lu, Wei Liu, Lingyi Luo, Junwei Zhao, Xianfeng Zhou, Yicong Huang, Jiwu School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100195 China School of Cyber Security University of Chinese Academy of Sciences Beijing100195 China Department of Computer and Information Science University of Macau 999078 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video contents and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this paper, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and inter-frame inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective respectively. The two components are designed using a novel long distance attention mechanism. The one component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves the state-of-the-art performance, and the proposed long distance attention method can effectively capture pivotal parts for face forgery. Copyright © 2021, The Authors. All rights reserved.

关键词： Time domain analysis

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