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12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017

作者： Sun, Qiule Zhang, Jianxin Yang, Aoqi Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian Dalian China

ISBN: (纸本)9789811073885

Palmprint recognition has become popular and significant in many fields because of its high efficiency and accuracy in personal identification. In this paper, we present a scheme for palmprint features extraction based on deep convolutional neural network (CNN). The CNN, which naturally integrates low/mid/high-level feature, performs excellently in processing images, video and speech. We extract the palmprint features using the CNN-F architecture, and exactly evaluate the convolutional features from different layers in the network for both identification and verification tasks. The experimental results on public PolyU palmprint database illuminate that palmprint features from the CNN-F respectively achieve the optimal identification rate of 100% and verification accuracy of EER= 0.25%, which demonstrate the effectiveness and reliability of the proposed palmprint CNN features. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Palmprint recognition

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Term translation extraction from historical classics using modern Chinese explanation 17th

Term translation extraction from historical classics using m...

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17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Wu, Xiaoting Zhao, Hanyu Che, Chao Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116622 China

ISBN: (纸本)9783030017156

Extracting term translation pairs is of great help for Chinese historical classics translation since term translation is the most time-consuming and challenging part in the translation of historical classics. However, it is tough to recognize the terms directly from ancient Chinese due to the flexible syntactic of ancient Chinese and the word segmentation errors of ancient Chinese will lead to more errors in term translation extraction. Considering most of the terms in ancient Chinese are still reserved in modern Chinese and the terms in modern Chinese are more easily to be identified, we propose a term translation extracting method using multi-features based on character-based model to extract historical term translation pairs from modern Chinese-English corpora instead of ancient Chinese-English corpora. Specifically, we first employ character-based BiLSTM-CRF model to identify historical terms in modern Chinese without word segmentation, which avoids word segmentation error spreading to the term alignment. Then we extract English terms according to initial capitalization rules. At last, we align the English and Chinese terms based on co-occurrence frequency and transliteration feature. The experiment on Shiji demonstrates that the performance of the proposed method is far superior to the traditional method, which confirms the effectiveness of using modern Chinese as a substitute. © Springer Nature Switzerland AG 2018.

关键词： Extraction

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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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Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

arXiv

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arXiv 2021年

作者： Zheng, Yizhen Jin, Ming Pan, Shirui Li, Yuan-Fang Peng, Hao Li, Ming Li, Zhao Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University Australia SouthportQLD4222 Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua321004 China Alibaba Group Hangzhou311100 China

Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an unsupervised manner, they suffer from certain limitations, such as the heavy reliance on monotone contrastiveness and limited scalability. To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme. Specifically, this mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales: micro (i.e., node-level), meso (i.e., neighborhood-level), and macro (i.e., subgraph-level). Firstly, we generate two augmented views of the input graph via two different graph augmentations. Then, we establish three different contrastiveness on the above three scales progressively, from node, neighboring, to subgraph level, where we maximize the agreement between graph representations across scales. While we can extract valuable clues from a given graph on the micro and macro perspectives, the neighboring-level contrastiveness offers G-Zoom the capability of a customizable option based on our adjusted zooming scheme to manually choose an optimal viewpoint that lies between the micro and macro perspectives to better understand the graph data. Additionally, to make our model scalable to large graphs, we employ a parallel graph diffusion approach to decouple model training from the graph size. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms state-of-the-art methods consistently. © 2021, CC BY.

关键词： Graph neural networks

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Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

arXiv

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arXiv 2021年

作者： Li, Yue Zhou, Xuyang Colnaghi, Timoteo Wei, Ye Marek, Andreas Li, Hongxiang Bauer, Stefan Rampp, Markus Stephenson, Leigh T. Max-Planck Institut für Eisenforschung GmbH Max-Planck-Straße 1 Düsseldorf40237 Germany Max Planck Computing and Data Facility Gießenbachstraße 2 Garching85748 Germany State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing100083 China Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany

Nanoscale L12-type ordered structures are widely used in face-centred cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyse the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type d¢–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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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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Graphene quantum dots induced performance enhancement in memristors

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Nanoscale 2025年第23期17卷 2025 May 28页

作者： Jintao He Guangdong Zhou Bai Sun Lingpeng Yan Xiaochen Lang Yongzhen Yang Haotian Hao MOE Key Laboratory of Interface Science and Engineering in Advanced Materials College of Materials Science and Engineering Taiyuan University of Technology Taiyuan 030024 China. yangyongzhen@***. College of Artificial Intelligence Brain-Inspired Computing & Intelligent Control of Chongqing Key Lab Southwest University Chongqing 400715 China. Frontier Institute of Science and Technology (FIST) Xi'an Jiaotong University Xi'an Shaanxi 710049 China. College of Artificial Intelligence Institute of Biomedical Engineering Taiyuan University of Technology Taiyuan 030024 China. haohaotian@***.

With the rapid development of information technology, the demand for miniaturization, integration, and intelligence of electronic devices is growing rapidly. As a key device in the non-von Neumann architecture, memristors can perform computations while storing data, enhancing computational efficiency and reducing power consumption. Memristors have become pivotal in driving the advancement of artificial intelligence (AI) and Internet of Things technologies. Combining the electronic properties of graphene with the size effects of quantum dots, graphene quantum dot (GQD)-based memristors exhibit potential applications in constructing brain-inspired neuromorphic computing systems and achieving AI hardware acceleration, making them a focal point of research interest. This review provides an overview of the preparation, mechanism, and application of GQD-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQD-based memristors are presented from three perspectives: the metal conductive filament mechanism, the electron trapping and detrapping mechanism, and the oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQD-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interfaces. Finally, the challenges and future development prospects of GQD-based memristors are discussed.

关键词： Quantum computers

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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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Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

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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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