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16th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2021

作者： Ao, Jiaxin Ye, Jun Ji, Yuxuan Yang, Zhenyu College of Information Engineering Nanchang Institute of Technology Nanchang330000 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang330000 China

ISBN: (纸本)9789811912559

Feature selection methods that combine swarm intelligence optimization algorithms with rough sets have been widely used. Normally, this type of method can quickly search for feature subsets, effectively reducing the computational workload;However, in some decision tables of this type of method, there were also problems such as the searched feature subset containing redundant features, which led to a decrease in accuracy. Aiming at such problems in the current feature selection algorithms based on rough sets and fruit fly optimization, improvements have been made from two aspects. Firstly, a method for measuring the importance of discernibility matrix attributes was proposed and used as a heuristic factor to constructed a new fitness function;Secondly, guided by the fitness function, a fruit fly optimized feature selection method was designed, which can avoid redundant attributes in the feature subset. Examples and experimental results on the UCI datasets show that the new feature selection method can effectively search for the smallest feature subset and improve the stability. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Fruits

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End States Guided Matching Network for Retrieval-based Multi-turn Conversation 3

End States Guided Matching Network for Retrieval-based Multi...

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2021年第三届先进计算机技术、信息科学与通信国际会议(CTISC2021)

作者： Weixin Tan Dandan Song Yujin Gao Lab of High Volume language Information Processing & Cloud Computing Beijing Lab of Intelligent Information TechnologyBeijing Institute of Technology

ISBN: (纸本)9781665418683

Multi-turn conversation response selection aims to choose the best response from multiple candidates based on matching it with the dialogue context. Mostly, a response full of context-related information tends to be a proper ***, in some cases, a brief response like "ok" could be the more appropriate one. We find that it is a semantically ended conversation that a brief response usually comes after,so there is no need to provide any context-related information after that. Thus, in addition to match the response with context,it is also critical to recognize the state of whether a dialogue has ended, and learn how to get necessary information from context of different end states separately. To achieve this, we propose an end states guided matching network to determine and incorporate the end states by jointly consider the length of response and the local similarity between the response and last few utterances. In addition, we adopt multiple descriptive sequence representations for a more reliable matching *** results demonstrate that our model outperforms the state-of-the-art methods in multiple datasets.

关键词： information science Computational modeling Computer network reliability Reliability Data mining

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Integrating Multi-scale Contextualized information for Byte-based Neural Machine Translation

arXiv

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arXiv 2024年

作者： Huang, Langlin Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Subword tokenization is a common method for vocabulary building in Neural Machine Translation (NMT) models. However, increasingly complex tasks have revealed its disadvantages. First, a vocabulary cannot be modified once it is learned, making it hard to adapt to new words. Second, in multilingual translation, the imbalance in data volumes across different languages spreads to the vocabulary, exacerbating translations involving low-resource languages. While byte-based tokenization addresses these issues, byte-based models struggle with the low information density inherent in UTF-8 byte sequences. Previous works enhance token semantics through local contextualization but fail to select an appropriate contextualizing scope based on the input. Consequently, we propose the Multi-Scale Contextualization (MSC) method, which learns contextualized information of varying scales across different hidden state dimensions. It then leverages the attention module to dynamically integrate the multi-scale contextualized information. Experiments show that MSC significantly outperforms subword-based and other byte-based methods in both multilingual and out-of-domain scenarios. We have uploaded the code to github1 © 2024, CC BY-NC-ND.

关键词： Neural machine translation

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Adaptive Multi-modal Data Fusion Method for Dermatosis Diagnosis

Adaptive Multi-modal Data Fusion Method for Dermatosis Diagn...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Yaning Yang, Xiaodong Chen, Yiqiang Chen, Shubai Zeng, Bixiao Cai, Hong Chinese Academy of Sciences The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing China University of Jinan College of Information Science and Engineering Shandong China Shandong Academy of Intelligent Computing Technology Shandong China Air Force Medical Center of the PLA Department of Dermatology Beijing China

ISBN: (纸本)9798350337488

Meta-data, e.g., gender, medical history, etc., is of vital importance for dermatosis diagnosis clinically, while most existing AI-based methods ignore this information and only employ dermoscopy images. Due to the modality disequilibrium of image and meta-data, there would be unequal optimization during the modal fusion, resulting in the weak meta-data modality being suppressed and losing their contributions to the classification. To address this issue, we propose an Adaptive Multi-modal Fusion (AMF) method for dermatosis diagnosis, which reweights the fusion factor of image and meta-data by evaluating their optimization discrepancy through the Euclidean norm. What's more, the Co-Attention mechanism is employed to align the two modalities implicitly. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on multi-modal dermatosis public datasets including ISIC 2019, PDA-UFES-20, and HAM10000. The results demonstrate that the proposed method can achieve better performance in comparison to the state-of-the-art methods. © 2023 IEEE.

关键词： Deep Learning Dermatosis Diagnosis Multi-modal Fusion Transformer

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Two Physics-Based Schwarz Preconditioners for Three-Temperature Radiation Diffusion Equations in High Dimensions

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Communications in Computational Physics 2022年第8期32卷 829-849页

作者： Xiaoqiang Yue Jianmeng He Xiaowen Xu Shi Shu Libo Wang National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105P.R.China Laboratory of Computational Physics Institute of Applied Physics and ComputationalMathematicsBeijing 100094P.R.China College of Information Science and Technology JinanUniversityGuangzhou 510632P.R.China

We concentrate on the parallel,fully coupled and fully implicit solution of the sequence of 3-by-3 block-structured linear systems arising from the symmetrypreserving finite volume element discretization of the unsteady three-temperature radiation diffusion equations in high *** this article,motivated by[***,***,***,SIAM *** ***.33(2012)653–680]and[***,***,***,***.442(2021)110513],we aim to develop the additive and multiplicative Schwarz preconditioners subdividing the physical quantities rather than the underlying domain,and consider their sequential and parallel implementations using a simplified explicit decoupling factor approximation and algebraic multigrid subsolves to address such linear ***,computational efficiencies and parallel scalabilities of the proposed approaches are numerically tested in a number of representative real-world capsule implosion benchmarks.

关键词： Radiation diffusion equations Schwarz methods algebraic multigrid parallel and distributed computing

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 11

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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11th International Conference on computing and Pattern Recognition, ICCPR 2022

作者： Sun, Pingping Qiang, Baohua Liu, Zhiguang Yang, Xianyi Xi, Guangyong Liu, Weigang Chen, Ruidong Zhang, Shihao Guilin University of Electronic Technology China The 54th Research Institute of Cetc Hebei Key Laboratory of Intelligent Information Perception and Processing China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods. © 2022 ACM.

关键词： Alignment

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A Tolerance Classes Partition-Based Re-Definition of the Rough Approximations for Incomplete information System

A Tolerance Classes Partition-Based Re-Definition of the Rou...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2021

作者： Wang, Lei Liu, Bin Cai, Xiangxiang Wang, Chong Jiangxi Provincial Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang330099 China College of Information Engineering Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9789811669620

The information with missing attribute value is known as the incomplete information system. In an incomplete information system, there exist tolerance relations satisfying reflexivity and symmetry among objects in the universe of discourse and the tolerance classes for each object can be derived from the tolerance relation directly. Generally speaking, the tolerance classes of all objects can form some coverages of the universe. The research of this paper is based on the tolerance classes of each object in the incomplete information system. Firstly, a method is proposed in which a partition of the universe is constructed by using tolerance classes of objects. Then, the upper and lower approximations of concept, i.e., a subset of universe, is re-defined on the basis of acquisition of the partition of the universe using proposed method and this new definition can improve the approximation accuracy of concept effectively compared to the traditional definition. Finally, an illustrated example is exhibited for demonstrating the method to obtain a partition to universe by tolerance class as well as the calculation of the rough approximations of concept based on the partition of universe by tolerance class. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： information use

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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

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Frontiers of information technology & Electronic Engineering 2023年第11期24卷 1541-1556页

作者： Shihmin WANG Binqi ZHAO Zhengfeng ZHANG Junping ZHANG Jian PU Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China

As one of the most fundamental topics in reinforcement learning(RL),sample efficiency is essential to the deployment of deep RL *** most existing exploration methods that sample an action from different types of posterior distributions,we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal hierarchy of the ***,we first employ clustering methods in the policy sampling process to generate an action candidate *** we introduce a clustering buffer for modeling the internal hierarchy,which consists of on-policy data,off-policy data,and expert data to evaluate actions from the clusters in the action candidate set in the exploration *** this way,our approach is able to take advantage of the supervision information in the expert demonstration *** on six different continuous locomotion environments demonstrate superior reinforcement learning performance and faster convergence of selective *** particular,on the LGSVL task,our method can reduce the number of convergence steps by 46.7%and the convergence time by 28.5%.Furthermore,our code is open-source for *** code is available at https://***/Shihwin/SelectiveSampling.

关键词： Reinforcement learning Sample eficiency Sampling process Clustering methods Autonomous driving

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Software Defect Prediction Based on SMOTE-Tomek and XGBoost 16th

Software Defect Prediction Based on SMOTE-Tomek and XGBoost

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16th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2021

作者： Yang, Haotian Li, Min School of Information Engineering Nanchang Institute of Technology Jiangxi330099 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Jiangxi330099 China

ISBN: (纸本)9789811912528

The use of machine learning techniques to predict software defects has received extensive attention over the years. In practice, there are far fewer defective software samples than non-defective software samples, resulting in a high imbalance of sample categories. The problem of imbalanced data classification is a hotspot and difficulty in machine learning and data mining. In order to effectively solve the problem of software defect prediction, we introduce a classification model that combines SMOTE-Tomek sampling and ensemble learning algorithm XGBoost. Our initial experimental results on ten NASA software defect datasets indicate a clear superiority of the proposed techniques when compared to other hybrid model regarding various assessment metrics, such Accuracy, F-Measure, and AUC. © 2022, Springer Nature Singapore Pte Ltd.

关键词： NASA

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A Low-complexity Decision Variables Classification Brainstorm Optimization Algorithm

A Low-complexity Decision Variables Classification Brainstor...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Li, Qiyue Qiu, Lipeng Zhou, Aoran Wu, Yali Wang, Ziren Department of Information and Control Engineering Xi'An University of Technolog Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd. Xi'an710016 China

In the complex optimization scenarios of real life, we often need to not only weigh the conflicts between multi-objectives but also face the challenges brought by large-scale decision variables. When the scale of decision variables increases sharply, the search space will expand exponentially, which makes it difficult for the algorithm to traverse the whole search space under limited resources, so that the number of local optimal solutions increases sharply. The so-called 'dimension disaster' appears. To effectively deal with this problem, this paper proposes a large-scale multi-objective brainstorm optimization algorithm (LMaOBSO) based on low complexity decision variable classification method and improved penalty boundary intersection strategy. The algorithm uses a low-complexity tree sorting variable classification method to divide the decision variables into converges and diversity variables quickly. At the same time, the convergent variables are optimized by the improved penalty boundary crossover strategy to solve the problems existing in large-scale optimization problems. © Published under licence by IOP Publishing Ltd.

关键词： Disasters

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