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arXiv 2024年

作者： Xia, Shuyin Shi, Bolun Wang, Yifan Xie, Jiang Wang, Guoyin Gao, Xinbo Chongqing Key Laboratory of Computational Intelligence The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education The Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in human brain, resulting in those methods’ bad performance in efficiency, generalization ability and robustness. To address this problem, we propose a new clustering algorithm called granular-ball clustering (GBCT) via granular-ball computing. Firstly, GBCT generates a smaller number of granular-balls to represent the original data, and forms clusters according to the relationship between granular-balls, instead of the traditional point relationship. At the same time, its coarse-grained characteristics are not susceptible to noise, and the algorithm is efficient and robust;besides, as granular-balls can fit various complex data, GBCT performs much better in non-spherical data sets than other traditional clustering methods. The completely new coarse granularity representation method of GBCT and cluster formation mode can also used to improve other traditional methods. All codes can be available from the following link. Copyright © 2024, The Authors. All rights reserved.

关键词： Granular computing

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Granular-ball Representation Learning for Deep CNN on Learning with Label Noise

arXiv

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arXiv 2024年

作者： Dai, Dawei Zhu, Hao Xia, Shuyin Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

In actual scenarios, whether manually or automatically annotated, label noise is inevitably generated in the training data, which can affect the effectiveness of deep CNN models. The popular solutions require data cleaning or designing additional optimizations to punish the data with mislabeled data, thereby enhancing the robustness of models. However, these methods come at the cost of weakening or even losing some data during the training process. As we know, content is the inherent attribute of an image that does not change with changes in annotations. In this study, we propose a general granular-ball computing (GBC) module that can be embedded into a CNN model, where the classifier finally predicts the label of granular-ball (gb) samples instead of each individual samples. Specifically, considering the classification task: (1) in forward process, we split the input samples as gb samples at feature-level, each of which can correspond to multiple samples with varying numbers and share one single label;(2) during the backpropagation process, we modify the gradient allocation strategy of the GBC module to enable it to propagate normally;and (3) we develop an experience replay policy to ensure the stability of the training process. Experiments demonstrate that the proposed method can improve the robustness of CNN models with no additional data or optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Image annotation

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QoS Prediction based on the Low-rank Autoregressive Tensor Completion

QoS Prediction based on the Low-rank Autoregressive Tensor C...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Xia, Hong Dong, Qingyi Chen, Yanping Zheng, Jiahao Gao, Cong Wang, ZhongMin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

With the rapid development of network services and edge computing, Quality of Service (QoS) has become an important indicator to validate performances of a network. Recommend high-quality services to users based on QoS values. However, the high sparsity of QoS data is because users usually call certain services only at a given time. Missing QoS data is very common in various service recommendation systems. Therefore, it is essential to predict QoS data to accurately recommend high-quality services to users. For the QoS data prediction problem, we build a third-order data tensor 'User-Service-Time' for the time series characteristics of QoS data. And introduce time-series variation as a regularization term into third-order tensor data prediction. We propose a QoS prediction framework using Low-Rank Autoregressive Tensor Completion (LATC). In particular, constructing a third-order tensor data model can better capture the global consistency of the data structure. Time regularization is introduced to take into account the local correlation of the data. Finally, in order to solve the constrained optimization problem, we use the general Alternating Direction Method of Multipliers (ADMM) to minimize the iterative optimization of variables and autoregressive parameters to obtain the final prediction result. Meanwhile, we conduct extensive research experiments on the real dataset WS-Dream. Experiments show that the QoS data prediction accuracy of our proposed QoS prediction method is higher than that of existing prediction methods under different degrees of data density. © 2022 IEEE.

关键词： Forecasting

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Entity disambiguation method based on Graph Attention Networks 14

Entity disambiguation method based on Graph Attention Networ...

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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Qiang, Chengyu Li, Xiaogo Ma, Xianyan Han, Wei Yao, Yi School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an71012 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi 'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665499781

Entity disambiguation based on entity-link is a technique which constructs the mappings between entity reference items appearing in the short text and target entity in knowledge base respectively. In this paper we propose an entity disambiguation method based on Graph Attention Networks for semi-structured knowledge base data. First, a global Knowledge Graph is constructed from the semi-structured knowledge base, and the entity reference items are embedded by Bert pre-trained model meanwhile. Next, Graph Attention Networks which leverages masked self-attention layers is applyed on candidate entity nodes of global Knowledge Graph to fetch a vector of node level. Furtherly, we compute similarity scores rank between the entity reference items and the candidate entity to complete the task of entity disambiguation. The experimental results on CCKS2019 dataset achieve state-of-the-art. © 2022 IEEE

关键词： Natural language processing systems

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Learning discriminative foreground-and-background features for few-shot segmentation

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Multimedia Tools and Applications 2024年第18期83卷 55999-56019页

作者： Jiang, Cong Zhou, Yange Liu, Zhaoshuo Feng, Chaolu Li, Wei Yang, Jinzhu School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China College of Science Northeastern University Liaoning Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang110819 China

Few-shot Semantic Segmentation (FSS) endeavors to segment novel categories in a query image by referring to a support set comprising only a few annotated examples. Presently, many existing FSS methodologies primarily embrace the prototype learning paradigm and concentrate on optimizing the matching mechanism. However, these approaches tend to overlook the discrimination between the features of foreground background. Consequently, the segmentation results are often imprecise when it comes to capturing intricate structures, such as boundaries and small objects. In this study, we introduce the Discriminative Foreground-and-Background feature learning Network (DFBNet) to enhance the distinguishability of bilateral features. DFBNet comprises three major modules: a multi-level self-matching module (MSM), a feature separation module (FSM), and a semantic alignment module (SAM). The MSM generates prior masks separately for the foreground and background, employing a self-matching strategy across different feature levels. These prior masks are subsequently used as scaling factors within the FSM, where the features of the query’s foreground and background are independently scaled up and then concatenated along the channel dimension. Furthermore, we incorporate a two-layer Transformer encoder-based semantic alignment module (SAM) in DFBNet to refine the features, thereby creating a greater distinction between the foreground and background features. The performance of DFBNet is evaluated on the PASCAL-5i and COCO-20i benchmarks, demonstrating its superiority over existing solutions and establishing new state-of-the-art results in the field of few-shot semantic segmentation. The codes will be released if this paper is accepted. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Semantic Segmentation

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A Low-Rank Matching Attention Based Cross-Modal Feature Fusion Method for Conversational Emotion Recognition

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IEEE Transactions on Affective computing 2024年第02期16卷 1177-1189页

作者： Shou, Yuntao Liu, Huan Cao, Xiangyong Meng, Deyu Dong, Bo Xi'An Jiaotong School of Computer Science and Technology Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi'an710049 China Xi'An Jiaotong School of Computer Science and Technology Shaanxi Xi'an710049 China BigKEJoint Innovation Center SHMEEA Shanghai China Xi'An Jiaotong University School of Mathematics and Statistics Xi'an China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. © 2024 IEEE.

关键词： Semantics

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Prior Semantic-Embedding Representation Learning for On-the-Fly

SSRN

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SSRN 2024年

作者： Liu, Yingge Dai, Dawei Zhou, Kenan Tan, Xiufang Wu, Yiqiao Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

In the context of fine-grained sketch-based image retrieval (FG-SBIR) framework, sketch drawing is a time-consuming and skill-intensive process that greatly restricts its practical application. Our study aims to enhance the early-stage retrieval efficiency by utilizing partial sketches with a minimum number of strokes in the on-the-fly FG-SBIR framework. Nevertheless, partial sketches with few strokes usually lack global information and consist only of sparse local details. This lack of detail results in ambiguous semantic content and indistinct differences among images, which consequently impairs early-stage retrieval. To acquire an efficient representation for these sketches, we propose a multi-granularity feature representation model that utilizes prior knowledge embedding to deal with the sketch drawing process. In the first stage,we design a triplet network to learn the joint a coarse representation embedding space that is common between the complete sketch and the image. In the second stage, We enhanced the semantic representation of sketches by utilizing prior knowledge, which aids in supplementing missing fine-grained features. This was achieved by formalizing the prior knowledge and integrating it into the feature vector obtained in the initial stage, thereby acquiring the final representation of a subset of sketches. Experiments conducted on two public datasets indicate that our method outperforms the state-of-the-art baseline during the early retrieval phase. In practical applications, our approach has also achieved good results. © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

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Computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

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User Information Enhanced Knowledge Graph Convolutional Networks for Recommender Systems 14

User Information Enhanced Knowledge Graph Convolutional Netw...

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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Hao, Junyu Li, Xiaoge School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an71012 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665499781

To deeply excavate the information contained in user data and better alleviate cold start problem, we propose KGCN++, a user information enhanced knowledge graph convolutional networks model for recommender system, which simulates nerves' influence diffusion of information of user and items in a unified framework. The model combines user information and user-item interaction information into a user knowledge graph, which advances KGCN by injecting high-order user potential information reflected in user graph and high-order influence of items reflected in item knowledge graph. In addition, we have designed the influence factors of different items on users, which are used to characterize the weight of items on the user's information dissemination, so as to distinguish users with different information. Through extensive experiments on real-world datasets, we demonstrate that KGCN++ achieves substantial gains in a variety of ecenarios, including movie and book recommendation, better performance compared to the other methods. © 2022 IEEE

关键词： Recommender systems

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