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Research Square 2021年

作者： Zhang, Ping Sheng, Jiyao Gao, Wanfu Hu, Juncheng Li, Yonghao College of Computer Science and Technology JiLin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Chemistry Jilin University Changchun China

Multi-label feature selection attracts considerable attention from multi-label learning. Information-theory based multi-label feature selection methods intend to select the most informative features and reduce the uncertain amount of information of labels. Previous methods regard the uncertain amount of information of labels as constant. In fact, as the classification information of the label set is captured by features, the remaining uncertainty of each label is changing dynamically. In this paper, we categorize labels into two groups: one contains the labels with few remaining uncertainty, which means that most of classification information with respect to the labels has been obtained by the already-selected features;another group contains the labels with extensive remaining uncertainty, which means that the classification information of these labels is neglected by already-selected features. Feature selection aims to select the new features with highly relevant to the labels in the second group. Existing methods do not distinguish the difference between two label groups and ignore the dynamic change amount of information of labels. To this end, a Relevancy Ratio is designed to clarify the dynamic change amount of information of each label under the condition of the already-selected features. Afterwards, a Weighted Feature Relevancy is defined to evaluate the candidate features. Finally, a new multi-label Feature Selection method based on Weighted Feature Relevancy (WFRFS) is proposed. The experiments obtain encouraging results of WFRFS in comparison to six multi-label feature selection methods on thirteen real-world data sets. © 2021, CC BY.

关键词： Information theory

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Ontology Revision based on Pre-trained Language Models

arXiv

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arXiv 2023年

作者： Ji, Qiu Zhang, Xiaoping Ye, Yuxin Qi, Guilin Li, Jiaye Li, Site Ren, Jianjie Lu, Songtao School of Modern Posts Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National Data Center ofTraditional Chinese MedicineChina Academy of ChineseMedical Sciences Beijing100700 China College of Computer Science and Technology Jilin University Changchun130012 China School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Mathematics Southeast University Nanjing China Chien-Shiung Wu College Southeast University Nanjing China

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers. To deal with this problem, various ontology revision approaches have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. In addition, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years. Therefore, in this paper, we study how to apply pre-trained models to revise ontologies. We first define four scoring functions to rank axioms based on a pre-trained model by considering various information from an ontology. Based on the functions, an ontology revision algorithm is then proposed to deal with unsatisfiable concepts at once. To improve efficiency, an adapted revision algorithm is designed to deal with unsatisfiable concepts group by group. We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, our algorithms could achieve promising performance. The adapted revision algorithm could improve the efficiency largely, and at most about 90% of the time could be saved for some ontology pairs. Some of our scoring functions like reliableOnt cos could help a revision algorithm obtain better results in many cases, especially for those challenging ontology pairs like OM8. We also provide discussion about the overall experimental results and guidelin

关键词： Ontology

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IE-GAN: An Improved Evolutionary Generative Adversarial Network Using a New Fitness Function and a Generic Crossover Operator

arXiv

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arXiv 2021年

作者： Li, Junjie Li, Jingyao Zhou, Wenbo Lü, Shuai The Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Ministry of Education China College of Computer Science and Technology Jilin University China The School of Information Science and Technology Northeast Normal University China

The training of generative adversarial networks (GANs) is usually vulnerable to mode collapse and vanishing gradients. The evolutionary generative adversarial network (E-GAN) attempts to alleviate these issues by optimizing the learning strategy with multiple loss functions. It uses a learning-based evolutionary framework, which develops new mutation operators specifically for general deep neural networks. However, the evaluation mechanism in the fitness function of E-GAN cannot truly reflect the adaptability of individuals to their environment, leading to an inaccurate assessment of the diversity of individuals. Moreover, the evolution step of E-GAN only contains mutation operators without considering the crossover operator jointly, isolating the superior characteristics among individuals. To address these issues, we propose an improved E-GAN framework called IE-GAN, which introduces a new fitness function and a generic crossover operator. In particular, the proposed fitness function, from an objective perspective, can model the evolutionary process of individuals more accurately. The crossover operator, which has been commonly adopted in evolutionary algorithms, can enable offspring to imitate the superior gene expression of their parents through knowledge distillation. Experiments on various datasets demonstrate the effectiveness of our proposed IE-GAN in terms of the quality of the generated samples and time efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Traffic Jam Prediction Based on Analysis of Residents Spatial Activities

Traffic Jam Prediction Based on Analysis of Residents Spatia...

引用

Computer Information and Big Data Applications (CIBDA), International Conference on

作者： Zhijin Lv Hao Fu Wei Tang Xiaoxu Chen College of Software Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun

ISBN: (数字)9781728198378

ISBN: (纸本)9781728198385

The prediction of urban traffic congestion has always been one of the important contents in the research of intelligent transportation systems. The difficulty in predicting urban traffic congestion is that urban traffic operation is essentially a collection of spatial activity planning for residents under certain conditions. The huge group of residents themselves has great complexity and uncertainty. Traditional neural networks mostly focus on road characteristic data and road condition data, and lack of in-depth exploration of the fundamental factors of traffic congestion for residents' travel. So we propose a traffic congestion prediction model based on the analysis of residents' spatial activities. Starting from the residents' activities, the simulation of urban traffic operation conditions can more realistically reflect the traffic congestion situation of the city at specific times and roads, and quickly generate model results. The experimental results show that the model analyzed by residents' spatial activities runs fast and has high prediction accuracy. The results are in line with the actual situation and have strong practical value.

关键词： Data models Traffic congestion Big Data applications Logistics

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Sample efficient imitation learning via reward function trained in advance

arXiv

引用

arXiv 2021年

作者： Zhang, LiHua Liu, Quan School of Computer Science and Technology Soochow University Soochow China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Jilin China

Imitation learning (IL) is a framework that learns to imitate expert behavior from demonstrations. Recently, IL shows promising results on high dimensional and control tasks. However, IL typically suffers from sample inefficiency in terms of environment interaction, which severely limits their application to simulated domains. In industrial applications, learner usually have a high interaction cost, the more interactions with environment, the more damage it causes to the environment and the learner itself. In this article, we make an effort to improve sample efficiency by introducing a novel scheme of inverse reinforcement learning. Our method, which we call Model Reward Function Based Imitation Learning (MRFIL), uses an ensemble dynamic model as a reward function, what is trained with expert demonstrations. The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by providing a positive reward upon encountering states in line with the expert demonstration distribution. In addition, we demonstrate the convergence guarantee for new objective function. Experimental results show that our algorithm reaches the competitive performance and significantly reducing the environment interactions compared to IL methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Demonstrations

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Understanding the Runtime Overheads of Deep Learning Inference on Edge Devices

Understanding the Runtime Overheads of Deep Learning Inferen...

引用

IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Xiu Ma Guangli Li Lei Liu Huaxiao Liu Xiaobing Feng College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

With the growing ubiquity of the Internet of Things, in-the-edge inference of deep neural network models has been a major driver for promoting the widespread use of intelligent applications. As model inference characteristics are crucial for optimizing and deploying deep neural networks on hardware platforms, many studies focus on analyzing the performance of neural networks such as latency, accuracy, throughput, and energy consumption. However, few existing works have ever discussed the runtime overheads hidden in neural network inference, despite the overheads are non-negligible for edge applications. The lack of in-depth analysis of the overheads hinders the understanding of how hardware designs and model structures impact on-device inference performance. In this paper, we characterize the runtime overheads of deep learning inference on representative edge devices by leveraging state-of-the-art neural network models, performing a systematical analysis from the perspective of end-to-end performance, hardware platforms, memory bandwidth, and neural network model structures. Based on experimental results, the crucial insights are offered to facilitate the design and configure of resource-efficient networks and pick appropriate models on the specific platform, which provides a comprehensive view of runtime overheads of in-the- edge neural network inference for architects and developers.

关键词： Deep learning Performance evaluation Analytical models Runtime Roads Neural networks Throughput

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Incomplete graph learning: A comprehensive survey

引用

Neural networks : the official journal of the International Neural Network Society 2025年 190卷 107682页

作者： Riting Xia Huibo Liu Anchen Li Xueyan Liu Yan Zhang Chunxu Zhang Bo Yang College of Computer Science Inner Mongolia University Hohhot 010021 China. Electronic address: xiart19@***. College of Computer Science Inner Mongolia University Hohhot 010021 China. Electronic address: liuhuibo@***. School of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. Electronic address: liac@***. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. Electronic address: xueyanliu@***. College of Computer Science Inner Mongolia University Hohhot 010021 China. Electronic address: yanz19@***. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. Electronic address: zhangchunxu@***. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin 130012 China. Electronic address: ybo@***.

Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields..

关键词： Attribute-incomplete graphs Attribute-missing graphs Graph learning Incomplete graph learning Incomplete graphs Robustness

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Graph-Based Terminal Ranking for Sparsification of Interference Coordination Parameters in Ultra-Dense Networks 10th

Graph-Based Terminal Ranking for Sparsification of Interfere...

引用

10th EAI International Conference on Mobile Networks and Management, MONAMI 2020

作者： Gou, Junxiang Wang, Lusheng Lin, Hai Peng, Min Ministry of Education School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230601 China Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan University Wuhan430072 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China

ISBN: (纸本)9783030640019

In the future mobile communication system, inter-cell interference becomes a serious problem due to the intensive deployment of cells and terminals. Traditional interference coordination schemes take long time for optimization in ultra-dense networks. Meanwhile, due to the increase of factors affecting communication and in order to better meet the communication needs of each terminal, an interference coordination scheme needs to fully consider multiple characteristic parameters of the terminal, which will further increase the scheme’s computational time. Therefore, we should compress all the data through sparsification of parameters before optimization. There are many terminal parameters, and the essence of sparsification of parameters is to rank terminals. In this paper, a graph-based terminal ranking scheme is designed. First, each terminal can be represented by its multiple parameters. Then, all terminals are used as the vertexes in the graph to form a complete weighted graph, and edge weights represent the degree of dissimilarity between terminals. A ranking of terminals is obtained by finding a minimum Hamiltonian path in the graph. Finally, the ranking of all parameter sequences is obtained according to terminals ranking, which makes the sparsity of all parameter sequences better. Simulation results show that the proposed scheme can accomplish sparsification of parameter sequences effectively, especially when the number of sequences increases. In addition, compared with the optimal coordination of traditional scheme, this scheme improves the fairness of the system while ensuring high system capacity, and dramatically reduces the computational time of interference coordination. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2020.

关键词： Approximation algorithms

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Few-shot learning based histopathological image classification of colorectal cancer

引用

Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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Document-level Relation Extraction with Relation Correlations

arXiv

引用

arXiv 2022年

作者： Han, Ridong Peng, Tao Wang, Benyou Liu, Lu Wan, Xiang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into DocRE task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular DocRE datasets are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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