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Discrimination-Aware Domain Adversarial Neural Network

Journal of Computer Science & Technology 2020年第2期35卷 259-267页

作者： Yun-Yun Wang Jian-Min Gu Chao Wang Song-Can Chen Hui Xue College of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210046China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 210023China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing 210023China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention *** DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are *** a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not *** this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain ***2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple *** results show that DA2NN can achieve better classification performance compared with the DANN methods.

关键词： adversarial learning inter-class separation deep neural network discrimination-aware domain adaptation

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Research on intelligent Scheduling Method of Multi-Machine Cooperative Operation Based on Nsga-Iii and Improved Ant Colony Algorithm

SSRN

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SSRN 2022年

作者： Zhang, Man Li, Shichao Cao, Ruyue Wang, Ning Zhang, Zhenqian Ji, Yuhan Li, Han Wang, Hao Key Lab of Smart Agriculture System Integration Ministry of Education China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China National Research Center of Intelligent Equipment for Agriculture Beijing100097 China

Aiming at the problems of over-reliance on traditional manual harvest operation experience and lack of effective collaborative operation scheduling method in agricultural machinery cluster operation in agricultural harvest scenario. A multi-objective combined optimization intelligent scheduling model of harvesters and grain transport vehicles was established to save agricultural machinery resources, and improve the overall efficiency of harvesters and transport vehicles, to meet the constraints of plot location, task number, operation time window, and path planning. The model was solved by an intelligent search algorithm, and an intelligent scheduling method of command multi-machine cooperative operation based on NSGA-III and an improved ant colony algorithm was proposed. It can be seen from the simulation test that the scheduling model established, and the solution algorithm designed in this research can obtain many groups of good optimization results. Moreover, it can make the unproductive waiting time of harvester 0, and minimize the transfer distance and number of grain transporters. This method can be combined closely with the practical application of unmanned farms, which lays a theoretical foundation for improving the efficiency of multi-machine cooperative operations. © 2022, The Authors. All rights reserved.

关键词： Scheduling

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Siamese Generative Adversarial Predicting Network for Extremely Sparse data in Recommendation System

Siamese Generative Adversarial Predicting Network for Extrem...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Qingxian Wang Renjian Zhang Kangkang Ma Bo Chen Jiufang Chen Xiaoyu Shi School of Computer Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Intelligent Terminal Key Laboratory of SiChuan Province Yibin China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

In the recommendation system, user-item preferences are described by a High-Dimensional and Sparse (HiDS) matrix. Collaborative Filtering (CF)-based models have been widely adopted to solve unknown entries estimation. However, a CF-based model does not take the data distribution characteristic of an HiDS matrix into account, thereby its representation ability limited. To address this issue, this paper proposes to build a Siamese Generative Adversarial Predicting Network (SGAPN) to estimate the unknown entries in an HiDS matrix. Firstly, we build a model to learn the data distribution characteristic of an HiDS matrix. Secondly, based on the learned data distribution and observed entries, we build a model to estimate the unknown entries. Compared with the CF-based model, our model takes the data distribution of an HiDS matrix into account to address the unknown entries estimation. Experimental results over four HiDS matrices generated by industrial applications demonstrate that compared with several state-of-the-art models, the proposed model achieves competitive prediction accuracy.

关键词： Training Collaborative filtering Estimation Predictive models data models Sparse matrices

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Correcting the Bias: Mitigating Multimodal Inconsistency Contrastive Learning for Multimodal Fake News Detection

Correcting the Bias: Mitigating Multimodal Inconsistency Con...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhi Zeng Mingmin Wu Guodong Li Xiang Li Zhongqiang Huang Ying Sha College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Urumqi China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Wuhan China

Multimodal fake news detection has become a topical research of fake news detection. Existing models have made great efforts in capturing and fusing multimodal semantics of news for classification. However, they overlooked mitigating inconsistency between different modalities, which may result in learning biased statistical information. Therefore, we propose a mitigating multimodal inconsistency contrastive learning framework (MMICF), which mitigates inconsistency in multi-modal relations for fake news detection. Inspired by various forms of artificial fake news, we summarize two patterns of multimodal inconsistency: local and global inconsistency. To mitigate local inconsistency in multimodal relations, we use a causal-relation reasoning module by causally removing the direct effects of the textual and visual entities. Considering the influence of global inconsistency in multimodal semantics, our contrastive learning framework mitigates the semantic deviation of contrastive text-image objectives, which are constrained into a unified semantic space by a modal unified module. Thus, our MMICF can jointly mitigate local and global inconsistency for further maximally exploiting multimodal consistent semantics for fake news detection. The extensive experimental results show that the MMICF can improve the performance of multimodal fake news detection and provide a novel paradigm for mitigating multimodal inconsistency contrastive learning.

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A Q-values Sharing Framework for Multi-agent Reinforcement Learning under Budget Constraint

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ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS 2021年第2期15卷 1–28页

作者： Zhu, Changxi Leung, Ho-Fung Hu, Shuyue Cai, Yi South China Univ Technol Sch Software Engn Guangzhou Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Natl Univ Singapore Dept Comp Sci AlLab 3Comp 113 ComputComp 113 Comp Dr Singapore 117417 Singapore South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Peoples R China

In a teacher-student framework, a more experienced agent (teacher) helps accelerate the learning of another agent (student) by suggesting actions to take in certain states. In cooperative multi-agent reinforcement learning (MARL), where agents must cooperate with one another, a student could fail to cooperate effectively with others even by following a teacher's suggested actions, as the policies of all agents can change before convergence. When the number of times that agents communicate with one another is limited (i.e., there are budget constraints), an advising strategy that uses actions as advice could be less effective. We propose a partaker-sharer advising framework (PSAF) for cooperative MARL agents learning with budget constraints. In PSAF, each Q-learner can decide when to ask for and share its Q-values. We perform experiments in three typical multi-agent learning problems. The evaluation results indicate that the proposed PSAF approach outperforms existing advising methods under both constrained and unconstrained budgets. Moreover, we analyse the influence of advising actions and sharing Q-values on agent learning.

关键词： Multi-agent reinforcement learning cooperative learning Q-learner knowledge sharing

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A reservation system with an efficient matching-and-scheduling mechanism for private parking-sharing programs 25

A reservation system with an efficient matching-and-scheduli...

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25th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2021

作者： Yan, Pengyu Cai, Xiaoqiang Chu, Feng Ni, Debing School of Management and Economics University of Electronic Science and Technology of China Chengdu China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Guangdong China Shenzhen Research Institute of Big Data Guangdong China Laboratoire IBISC Univ Evry Universitie Paris-Saclay Evry91025 France

ISBN: (纸本)9781713835196

We design a smart parking-sharing reservation system composed of three modules: Pre-processing, matching, and pricing. With the announced supply and demand information, the pre-processing module identifies a set of feasible parking slots for each driver concerning the compatibility of slot supply for the driver's demand. The matching module generates a system-wide optimal matching solution and further schedules the driver's parking time by using the flexibility of driver travel plans. The pricing module adopts a novel VCG-based pricing to determine the parking fees of drivers under an incomplete information setting in which each driver (player) cannot obtain the knowledge of the other competitors (players) who will compete against her. We find that the designed reservation system can not only elicit drivers' truthful demand reports but also achieve the optimal performance of the system. In this short paper we briefly describe our system and the main solution approaches we use. © WMSCI *** right reserved.

关键词： Costs

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Weakly-supervised multi-granularity map learning for vision-and-language navigation 22

Weakly-supervised multi-granularity map learning for vision-...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Peihao Chen Dongyu Ji Kunyang Lin Runhao Zeng Thomas H. Li Mingkui Tan Chuang Gan South China University of Technology and Information Technology R&D Innovation Center of Peking University South China University of Technology South China University of Technology and Pazhou Laboratory Shenzhen University Information Technology R&D Innovation Center of Peking University South China University of Technology and Key Laboratory of Big Data and Intelligent Robot Ministry of Education MIT-IBM Watson AI Lab and UMass Amherst

ISBN: (纸本)9781713871088

We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. The instructions often contain descriptions of objects in the environment. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects. However, enabling a robot to build a map that well represents the environment is extremely challenging as the environment often involves diverse objects with various attributes. In this paper, we propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively. Moreover, we propose a weakly-supervised auxiliary task, which requires the agent to localize instruction-relevant objects on the map. Through this task, the agent not only learns to localize the instruction-relevant objects for navigation but also is encouraged to learn a better map representation that reveals object information. We then feed the learned map and instruction to a waypoint predictor to determine the next navigation goal. Experimental results show our method outperforms the state-of-the-art by 4.0% and 4.6% w.r.t. success rate both in seen and unseen environments, respectively on VLN-CE dataset.

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Flow Characteristic of Scroll Compressor with Combined Profile Scroll 7th

Flow Characteristic of Scroll Compressor with Combined Profi...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Liu, Zhenghui Chen, Zhongli Qian, Lijuan Lin, Jian Liu, Yongqing College of Mechanical and Electrical Engineering China Jiliang University Zhejiang Hangzhou310018 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Zhejiang Santian Automotive Air Conditioning Compressor Co. Ltd. Zhejiang Lishui323000 China

ISBN: (纸本)9789811672125

Scroll compressor is widely used in machines for gases compression, and scroll profile is the basic of a scroll compressor. The combined profile of scroll has advantages of several single scroll profiles. For improving the performance of a scroll compressor, it is necessary to investigate characteristics of the internal flow of combined profile of scroll compressors. In this paper, the combined profile is composed of the base circle involute-variable radius and base circle involute. A two-dimensional transient flow model of the combined profile of scroll refrigeration compressor operating with ammonia is established. The pressure, temperature, and velocity distribution of the working chamber are collected using the computational fluid dynamic method. Based on comparison between the combined profile and the circular involute profile, it is found that the combined profile is more favorable to bear the pressure, decrease the leakage, and decrease the fluctuation of mass flow rate than the circular involute profile. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Computational fluid dynamics

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Long term results in children underwent nuss procedure preschool with pectus excavatum: Real-world study with propensity matching

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Heliyon 2024年第20期10卷 e39477页

作者： Wang, Quan Kong, Xiangpan Ren, Chunian Pan, Zhengxia Wu, Chun Li, Hongbo Li, Yonggang Wang, Gang Dai, Jiangtao Department of Cardiothoracic Surgery Children's Hospital of Chongqing Medical University Ministry of Education Key Laboratory of Child Development and Disorders International Science and Technology Cooperation Base of Child Development and Critical Disorders National Clinical Research Center for Child Health and Disorders Chongqing Key Laboratory of Pediatrics Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering Chongqing Higher Institution Engineering Research Center of Children's Medical Big Data Intelligent Application Chongqing China Department of Urology Children's Hospital of Chongqing Medical University China

Background: It is controversial when surgery should be performed for pectus excavatum (PE). The study retrospectively compared the long-term prognosis in PE children undergoing the Nuss procedure at the age of 4–6 versus those performing surgery at the age of 12–14. Methods: 178 patients were included in the retrospective real-world research. Clinical Characteristics were collected at baseline. The endpoint of the study was set as the last follow-up at the age of 18 in outpatient services. Satisfaction and complication rate were set as the primary outcome and were collected at the endpoint. Hospital stay post-surgery and analgesia dosage were regard as the secondary outcome. To reduce potential bias between two different age groups, propensity score matching (PSM) analysis was analysized. Results: The oral analgesic dosage of children in the 4–6 years old age group is significantly lower than that of the 12–14 years old group (0.70/kg, 0.30–1.50/kg versus 1.50/kg, 0.90–2.30/kg, P < 0.001), and children in the younger age group are discharged earlier. There was no difference in overall satisfaction (89.1 %, versus 88.20 %, P = 0.99) and recurrence rate (7.2 %, versus 6.8 %, P = 0.99) between the two groups of when they reach the age of 18. Conclusions: Performing Nuss procedure at the age of 4–6 does not affect the overall long-term satisfaction of the child or increase the long-term recurrence rate. Surgical intervention in children with pectus excavatum preschool can provide a better postoperative experience in hospital compared with intervention in adolescent. © 2024 The Authors

关键词： Long-term results Nuss procedure Pectus excavatum Preschool

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective Computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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