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IEEE International Conference on Web Services (ICWS)

作者： Puwei Wang Zhouxing Sun Rui Li Jinchuan Chen Ping Gong Xiaoyong Du School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering (Ministry of Education) Renmin University of China School of Mathematics Renmin University of China College of Computer and Cyber Security Fujian Normal University

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently.

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Personalized Recommendation Based on Entity Attributes and Graph Features 12

Personalized Recommendation Based on Entity Attributes and G...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Zhu, Yi Dong, Bingbing Sha, Zhiqing Yangzhou University School of Information Engineering Yangzhou China Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Big Knowledge Science Hefei University of Technology Hefei China

ISBN: (纸本)9781665438582

With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods. © 2021 IEEE.

关键词： Websites

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A Zero-shot Learning Method with a Multi-Modal knowledge Graph

A Zero-shot Learning Method with a Multi-Modal Knowledge Gra...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuhong Zhang Haitao Shu Chenyang Bu Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Zero-shot learning aims to recognize unseen-classes using some seen-class samples as training set. It is challenging owing to that the feature representations of unseen-class samples are unavailable. Existing methods transfer the mapping from seen-classes to unseen-classes with the correlation as a bridge, in which, the semantic representations are used to discriminate the classes. However, the unavailability of visual representations for unseen-classes and the insufficient discrimination of semantic representations make the zero-shot learning challenging. Therefore, the visual representations are learned as complements to semantic representations to construct a multi-modal knowledge graph (KG), and a zero-shot learning method based on multi-modal KG is proposed in this paper. Specially, a semantic KG is introduced to capture the correlation of classes, and with the correlation, the visual feature representations of all classes are learned. Then, the discriminative visual representations and the semantic representations are used together to construct a multi-modal KG. With the multi-modal KG, the classifier for seen-classes is transferred to unseen classes. Extensive experimental results show the effectiveness of our method.

关键词： Learning systems Representation learning Training Bridges Visualization Correlation Semantics

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RESIDUAL ATTENTION BLOCK SEARCH FOR LIGHTWEIGHT IMAGE SUPER-RESOLUTION

RESIDUAL ATTENTION BLOCK SEARCH FOR LIGHTWEIGHT IMAGE SUPER-...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Liao, Wenrui Zhao, Zhong-Qiu Shen, Hao Tian, Weidong School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China

ISBN: (纸本)9781665438643

Recently, lightweight neural networks with different manual designs have presented a promising performance in single image super-resolution (SR). However, these designs rely on too much expert experience. To address this issue, we focus on searching a lightweight block for efficient and accurate image SR. Due to the frequent use of various residual blocks and attention mechanisms in SR methods, we propose the residual attention search block (RASB) which combines an operation search block (OSB) with an attention search block (ASB). The former is used to explore the suitable operation at the proper position, and the latter is applied to discover the optimal connection of various attention mechanisms. Moreover, we build the modified residual attention network (MRAN) with stacked found blocks and a refinement module. Extensive experiments demonstrate that our MRAN achieves a better trade-off against the state-of-the-art methods in terms of accuracy and model complexity. © 2021 IEEE

关键词： Economic and social effects

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Multi-lingual acquisition on multimodal pre-training for cross-modal retrieval 22

Multi-lingual acquisition on multimodal pre-training for cro...

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

作者： Liang Zhang Anwen Hu Qin Jin School of Information Renmin University of China School of Information Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering (MOE) Renmin University of China

ISBN: (纸本)9781713871088

Vision and diverse languages are important information sources in our living world. A model that understands multi-modalities and multi-languages can be applied to a wider range of real-life scenarios. To build such a multimodal and multilingual model, existing works try to ensemble vision-language data from multiple languages in pre-training. However, due to the large number of languages, these works often require huge computing resources and cannot be flexibly extended to new languages. In this work, we propose a Multi-Lingual Acquisition (MLA) framework that can easily empower a monolingual Vision-Language Pre-training (VLP) model with multilingual capability. Specifically, we design a lightweight language acquisition encoder based on state-of-the-art monolingual VLP models. We further propose a two-stage training strategy to optimize the language acquisition encoder, namely the Native Language Transfer stage and the Language Exposure stage. With much less multilingual training data and computing resources, our model achieves state-of-the-art performance on multilingual image-text and video-text retrieval benchmarks.

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xinzheng Li Yuhong Zhang Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Cross-network node classification aims to use a labeled source network to classify nodes in an unlabeled target network. Most of the existing cross-network node classification methods learn the network representations by capturing the node neighborhood and train the classifier on these representations. The performance is highly dependent on the high-quality neighborhood in the network. However, in applications, the degree of nodes generally follows a long-tail distribution, i.e., a significant proportion of nodes are tail nodes with sparse neighborhood. It poses a challenge to existing methods. To this end, a structure similarity graph for cross-network node classitication method (SCNC) is proposed in this paper. Firstly, the potential links between nodes are predicted with the structural similarity metric to construct structure similarity graph, which can enrich the neighborhood of tail nodes. Then, the embedding representations of the structural similarity graph are learned to capture more neighborhood information. Finally, the adversarial is used to learn the domain invariant representations to address cross-network divergence. Extensive experimental results show that our SCNC outperforms the state-of-the-art methods.

关键词： Network topology Tail Learning (artificial intelligence) Prediction algorithms Classification algorithms Noise measurement Task analysis

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Which Companies are Likely to Invest: knowledge-graph-based Recommendation for Investment Promotion

Which Companies are Likely to Invest: Knowledge-graph-based ...

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IEEE International Conference on data Mining (ICDM)

作者： Chenyang Bu Jiawei Zhang Xingchen Yu Le Wu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology China and Institute of Big Knowledge Science Hefei University of Technology Hefei China

Investment promotion refers to the process by which the government uses disposable resources to attract investors to the region for production and business activities. The existing basic mode of attracting investment is to collect information about enterprises and entrepreneurs through manual methods, determine the target enterprise from the list of enterprises, and then attract investment through visits, negotiations, and other methods. As contacting and visiting companies one by one requires huge amounts of manpower and time, the choice of target companies is critical for attracting investments. However, to the best of our knowledge, no study has conducted research from the perspective of knowledge-graph-based recommendation. In this study, we define the problem of target company recommendation in the process of investment promotion, and analyze the characteristics of the problem and the challenges it faces based on the background of the actual problem. Then, a two-tier model for solving this problem is provided from the perspective of knowledge graph reasoning. Aiming at the problem that the knowledge graph will frequently change, the model is designed based on the idea of combining the advantages of global and local link prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed model.

关键词： Economics knowledge acquisition Government Companies Production Manuals Predictive models

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Hierarchical Multi-Granular Decision Networks for Hierarchical Classification

SSRN

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SSRN 2024年

作者： Liu, Haoyang Hu, Xue gang Bai, Shengxing Lin, Yaojin Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Anhui Hefei230009 China School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Fujian Zhangzhou363000 China

Hierarchical classification learning is an effective means of solving large-scale classification problems, which models classification problems at different levels of granularity according to a hierarchical structure. In hierarchical classification tasks, classification results at different levels of granularity are mutually beneficial, where coarse-grained results can reduce the number of fine-grained candidate categories, and fine-grained results can provide a basis for coarse-grained classification. However, for the final decision, the contribution of the granularity levels in the different hierarchies is different and unknown. Feedback from different granularity levels should be flexibly integrated. In addition, errors in hierarchical classification are not equivalent, and some errors can have serious results for practical applications. In order to address the above challenges, we propose a new hierarchical network to integrate multi-granularity knowledge for decision making. On the one hand, the network computes the decision scores of instances at each granularity level and learns a set of weights to combine all scores. Based on the learnable weights, the model can effectively utilize the multi-granularity classification results to obtain the final decision. On the other hand, we design local and global classification losses to guide model optimization, which allows the model to gain a hierarchical perspective and the ability to understand the degree of hierarchical misclassification. As a result, the extent to which the method results in misclassification consequences is limited. Experimental results on five benchmark datasets show that our method arises as a state-of-the-art hierarchical classification method. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Scene-Adaptive Person Search via Bilateral Modulations

arXiv

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arXiv 2024年

作者： Jiang, Yimin Wang, Huibing Peng, Jinjia Fu, Xianping Wang, Yang School of Information Science and Technology Dalian Maritime University Dalian China School of Cyber Security and Computer Hebei University Baoding China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China

Person search aims to localize specific a target person from a gallery set of images with various scenes. As the scene of moving pedestrian changes, the captured person image inevitably bring in lots of background noise and foreground noise on the person feature, which are completely unrelated to the person identity, leading to severe performance degeneration. To address this issue, we present a Scene-Adaptive Person Search (SEAS) model by introducing bilateral modulations to simultaneously eliminate scene noise and maintain a consistent person representation to adapt to various scenes. In SEAS, a Background Modulation Network (BMN) is designed to encode the feature extracted from the detected bounding box into a multi-granularity embedding, which reduces the input of background noise from multiple levels with norm-aware. Additionally, to mitigate the effect of foreground noise on the person feature, SEAS introduces a Foreground Modulation Network (FMN) to compute the clutter reduction offset for the person embedding based on the feature map of the scene image. By bilateral modulations on both background and foreground within an end-to-end manner, SEAS obtains consistent feature representations without scene noise. SEAS can achieve state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU with 97.1% mAP and PRW with 60.5% mAP. The code is available at https://***/whbdmu/SEAS. © 2024, CC0.

关键词： Embeddings

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Multi-source Multi-label Feature Selection

Multi-source Multi-label Feature Selection

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiulan Yuan Xuegang Hu Peipei Li Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei City China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei City China

Feature selection for multi-source multi-label data has attracted much attention, because there are a lot of scenarios that produce multiple source data with multi-labels in the real-world applications, which aggravates the problems of dimensional disaster and the label skewness. However, most of existing multi-source feature selection methods miss the multi-label issue while existing multi-label feature selection methods can not select the optimal feature set in the multi-source environment. Motivated by this, we propose a novel feature selection method, called MSMLFS. To be specific, the Inf-FS algorithm is firstly introduced to handle multi-label feature selection for each data source, which considers the label weight in the feature selection. Secondly, the over-sampling mechanism and the inter-source feature fusion method are used to handle the label skewness of multi-label data and the feature selection in multiple sources respectively. Finally, extensive experiments conducted on synthetic and real-world multi-source multi-label data sets demonstrate that the proposed method outperforms several state-of-the-art multi-source or multi-label feature selection methods.

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