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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Tang, Xiaobin Zhang, Jing Chen, Bo Yang, Yang Chen, Hong Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Information School Renmin University of China China Zhejiang University China

ISBN: (纸本)9780999241165

knowledge graph alignment aims to link equivalent entities across different knowledge graphs. To utilize both the graph structures and the side information such as name, description and attributes, most of the works propagate the side information especially names through linked entities by graph neural networks. However, due to the heterogeneity of different knowledge graphs, the alignment accuracy will be suffered from aggregating different neighbors. This work presents an interaction model to only leverage the side information. Instead of aggregating neighbors, we compute the interactions between neighbors which can capture fine-grained matches of neighbors. Similarly, the interactions of attributes are also modeled. Experimental results show that our model significantly outperforms the best state-of-the-art methods by 1.9-9.7% in terms of HitRatio@1 on the dataset DBP15K. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： knowledge graph

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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Fast-Speed Image Recognition System on Retail Commodity Image

Fast-Speed Image Recognition System on Retail Commodity Imag...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Meiju Hu Xiaojing Zhong School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education

ISBN: (纸本)9781665444194

In recent years, image recognition technology has been widely used in commodity recognition in smart containers and retail shelves. However, as the number of images received by smart terminals is increasing, more and more commodity categories need to be recognized. The image recognition technology adopted by the industry now requires a large number of images, and the modeling time is very long, which cannot meet the requirements for fast and accurate recognition. In this paper, we design and implement a fast-speed image recognition system that can cover all categories of retail commodities to solve the problem. We use DenseNet to strengthen the feature extraction of commodities collected by cameras. At the same time, we utilize a large number of commodity images accumulated in the production system as the primary database to achieve the effect of identifying all categories of commodity. The proposed system can quickly model new commodity images and new packaging of commodities through matching with the database. The experimental results show that the system can overcome the limitation of the number of images and shorten the modeling time. Furthermore, the accuracy rate is not affected in a complex environment.

关键词： Industries Training Production systems Image recognition Costs databases Packaging

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Rule-enhanced Noisy knowledge Graph Embedding via Low-quality Error Detection

Rule-enhanced Noisy Knowledge Graph Embedding via Low-qualit...

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IEEE International Conference on big knowledge (ICBK)

作者： Yan Hong Chenyang Bu Tingting Jiang Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

knowledge graphs (KGs) have been widely applied in many fields such as recommendation systems and knowledge reasoning. Embedding KGs into a continuous vector space has quickly gained significant attention. However, most traditional KG embedding models assume that all the facts in the existing KGs are completely correct, ignoring that KG construction usually involves automatic mechanisms. These automatic construction processes inevitably generate a lot of noises and conflicts, including low-quality errors (e.g., entity type errors). Moreover, these low-quality noises could greatly influence the quality of rule extraction, which may reduce the efficiency of Rule-Guided Embedding model (RUGE). To address this problem, an efficient method to eliminate those entity type errors in triples is proposed and applied to RUGE. Experimental results demonstrate that the filtering of low-quality noises can greatly improve the accuracy of knowledge representation learning as well as the quality of rules, further illustrating the effectiveness of our method.

关键词： Noise measurement Semantics knowledge representation data mining Predictive models data models

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Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora. Most previous debiasing techniques used Counterfactual data Augmentation (CDA) to balance the training corpus. However, CDA slightly modifies the original corpus, limiting the representation distance between different demographic groups to a narrow range. As a result, the debiasing model easily fits the differences between counterfactual pairs, which affects its debiasing performance with limited text resources. In this paper, we propose an adversarial training-inspired two-stage debiasing model using Contrastive learning with Continuous Prompt Augmentation (named CCPA) to mitigate social biases in PLMs’ encoding. In the first stage, we propose a data augmentation method based on continuous prompt tuning to push farther the representation distance between sample pairs along different demographic groups. In the second stage, we utilize contrastive learning to pull closer the representation distance between the augmented sample pairs and then fine-tune PLMs’ parameters to get debiased encoding. Our approach guides the model to achieve stronger debiasing performance by adding difficulty to the training process. Extensive experiments show that CCPA outperforms baselines in terms of debiasing performance. Meanwhile, experimental results on the GLUE benchmark show that CCPA retains the language modeling capability of PLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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An Abnormal data Analysis and Processing Method for Genealogy Graph databases

An Abnormal Data Analysis and Processing Method for Genealog...

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IEEE International Conference on big knowledge (ICBK)

作者： Jianxuan Shao Guliu Liu Shengwei Ji Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Large amounts of data are generated continually in the real world. The objects in the data and the relationships between them have become increasingly complex. Graph is a powerful tool for representing these data and the complex relationships between them. To effectively describe the entities and connections in these data, the concept of a knowledge graph was proposed, and knowledge graph has become one of the bases for storing graph data, and the importance of it is self-evident. Genealogy data is a kind of graph data where nodes are used to represent the person in the genealogy data, and edges are used to represent the relationship between the person. Moreover, in a genealogy graph database, the "critical" relationship that is unique for an entity is defined. In contrast, the abnormal data derive from the existence of multiple "critical" relationships between entities. In a genealogy graph, abnormal data will cause the wrong relationships and redundant entities. To avoid such abnormal data in genealogy graph database, the abnormal data are categorized into four different types, and the corresponding processing methods are proposed for each type of abnormal data, respectively. The experiment results demonstrate that the processing method can effectively solve these abnormal data.

关键词： databases Biology Task analysis Anomaly detection data models knowledge engineering big data

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An integrated classification model for massive short texts with few words 19

An integrated classification model for massive short texts w...

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2019 International Conference on Robotics Systems and Vehicle Technology, RSVT 2019

作者： Tang, Xuetao Zhu, Yi Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China

ISBN: (纸本)9781450362429

The excellent performance of short texts classification has emerged in the past few years. However, massive short texts with few words like invoice data are different with traditional short texts like tweets in its no contextual and less semantic information, which hinders the application of conventional classification algorithms. To address these problems, we propose an integrated classification model for massive short texts with few words. More specifically, the word embedding model is introduced to train the word vectors of massive short texts with few words to form the feature space, and then the vector representation of each instance in texts is trained based on sentence embedding. With this integrated model, higher level representations are learned from massive short texts with few words. It can boost the performance of the base subsequent classifiers such as K-Nearest Neighbor. Extensive experiments conducted on dataset including 16 million real data demonstrate the superior classification performance of our proposed model compared with all competing state-of-the-art models. © 2019 Association for Computing Machinery.

关键词： Embeddings

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Towards Feature Distribution Alignment and Diversity Enhancement for data-Free Quantization

Towards Feature Distribution Alignment and Diversity Enhance...

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IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Richang Hong Haijun Zhang Jicong Fan Shuicheng Yan School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Singapore

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.

关键词： Degradation Training Quantization (signal) Semantics Neural networks Memory management data models

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Microphysical Characteristics of the 2020 Record-Breaking Meiyu Rainfall in Anhui, China

SSRN

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SSRN 2024年

作者： Yang, Qiqi Zhang, Shuliang Chen, Yiheng Jin, Yuhan Fang, Hongyuan Nanjing Normal University China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou China Key Laboratory of VGE Ministry of Education Nanjing Normal University Nanjing China Suzhou Big Data Research Institute Suzhou China School of Water Conservancy and Transportation Zhengzhou University Zhengzhou China Department of Civil Engineering University of Bristol Bristol United Kingdom

The 2020 Meiyu season in Anhui, China, brought unprecedented rainfall, causing severe socio-economic and ecological disruptions. This study investigates the microphysical characteristics of raindrops during the 2020 record-breaking Meiyu event using minute-level data from six disdrometer stations. Light rain events lasted longer but contributed little to total rainfall, while brief, intense rain events delivered most of the precipitation. Throughout the entire event, the mean drop diameter (Dm) increased from 1.18 mm to 1.9 mm, and mean drop concentration (log10 Nw) rose from 2.92 to 4.08 with increasing rain rate. Novel μ–Λ and Z−R relationships tailored for this extreme event uncovered significant regional variability. The Z−R relationships for total, stratiform, and convective rainfall types revealed higher coefficients than those found in other Meiyu studies, indicating denser raindrop concentrations and larger drop sizes. Cutting-edge dual-polarization radar rainfall prediction models were developed, with relationships between Zdr, Zh, Kdp, and rain rate (R) showing exceptional accuracy, as evidenced by correlation coefficients nearing 1.0 and low RMSE and NMAE values. Furthermore, various KE–R relationships were established for estimating rainfall kinetic energy (KE), with Power Law relationships providing the most accurate representation for KEtime–R and Logarithmic relationships being most effective for KEmm–R, particularly for total and stratiform rainfall. These findings enhance understanding of rainfall microphysics in extreme Meiyu events, offering critical insights for improving flood management and soil erosion control in eastern China. © 2024, The Authors. All rights reserved.

关键词： Rain

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