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16th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Wang, Zhongmin Jia, Jingwen Gao, Cong Chen, Yanping Xia, Hong Xi'an University of Posts and Telecommunications Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an China Xi'an University of Posts and Telecommunications School of Computer Science Technology Xi'an China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China

ISBN: (纸本)9781665405539

Time series is the main form of sensor data. Time series prediction is conducive to reduce the energy consumption of sensor nodes and increase the service life. As time cost and computation cost of the existing hybrid prediction methods for time series are not good, this paper proposes a hybrid prediction method based on MODWT-EMD for time series. Firstly, the original time series is decomposed into multiple frequency components by Maximal Overlapping Discrete Wavelet Transform (MODWT). Secondly, the multiple frequency components are decomposed successively by Empirical Mode Decomposition (EMD) to obtain IMF components and residuals. IMFs and residuals contain the feature information of time series. Then, the importance of the extracted feature information is scored and sorted by random forest. We select feature information with a higher score. Finally, the selected feature information is input into Bi-GRU for training. We predicted the original time series by the trained Bi-GRU and obtained the prediction results. The experimental results show that the method proposed in this paper can predict the time series effectively. © 2021 IEEE.

关键词： Empirical mode decomposition

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Technical Function-effect Based Patent Multi-to-One Negation Game Model 25

Technical Function-effect Based Patent Multi-to-One Negation...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Du, Lu Liu, Weidong Xiao, Kaiyang Gao, Shixuan Han, Yong College of Computer Science Inner Mongolia University Hohhot China Institute of Scientific and Technical Information of China Beijing China Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China Inner Mongolia Intellectual Property Service Center Hohhot China

ISBN: (纸本)9781665405270

As the number of patent applications increases yearly, the negation relation between patents has become intertwined, which makes it difficult for constructing negation relation in patent examination manually. Therefore, how to automatically construct negation relation between patents has become the hot research. Constructing negation relation is the process of comparing technical features between patents, which depends on the technical features in the patent claims and the technical function-effect. Although there are many researches on patent one-to-one negation, few researches on patent multi-to-one negation. The research on negation relation of multi-to-one patents, has the following challenges: (1) the definition of patent features is ambiguous in comparison of negation relation;(2) the role of patent function-effect is often ignored in judgement of negation relation. To solve the above issues, we propose a technical function-effect based patent multi-to-one negation game model. In the model, we decompose patent technical features into technical utility and technical function-effect. Patent technical utility is mainly realized by using the valence theory in linguistics. After obtaining technical utility, we choose to have the same technical effect, express the technical features of different technical functions, and obtain the patent technical function-effect. Finally, we combine the technical utility and function-effect to construct a technical utility/function-effect game tree to judge the patent negation relation. According to the two-tailed test in the A/B-test, our model outperforms the baseline model in the evaluation measurement. This model can subsequently be used to calculate the portfolio value and the strategic value of the patents. © 2022 IEEE.

关键词： Patents and inventions

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Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder 1

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21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Qi, Fumin Jing, Xiao-Yuan Zhu, Xiaoke Jia, Xiaodong Cheng, Li Dong, Yichuan Fang, Ziseng Ma, Fei Feng, Shengzhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Wuhan University Wuhan China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China National Supercomputing Center in Shenzhen Guangzhou Guangdong China School of Computer Science Qufu Normal University Rizhao China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In Software Effort Estimation (SEE) practice, the data drought problem has been plaguing researchers and practitioners. Leveraging heterogeneous SEE data collected by other companies is a feasible solution to relieve the data drought problem. However, how to make full use of the heterogeneous effort data to conduct SEE, which is called as Heterogeneous Software Effort Estimation (HSEE), has not been well studied. In this paper, we propose a HSEE model, called Dynamic Heterogeneous Software Effort Estimation (i.e., DHSEE), which leverages the adversarial auto-encoder and convolutional neural network techniques. Meanwhile, we have investigated the scenario of conducting HSEE with dynamically increasing effort data. Experiments on ten public datasets indicate that our approach can significantly outperform the state-of-the-art HSEE method and other competing methods on both static and dynamic SEE scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Drought

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Low-resource generation of multi-hop reasoning questions 58

Low-resource generation of multi-hop reasoning questions

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Yu, Jianxing Liu, Wei Qiu, Shuang Su, Qinliang Wang, Kai Quan, Xiaojun Yin, Jian School of Data and Computer Science Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9781952148255

This paper focuses on generating multi-hop reasoning questions from the raw text in a low resource circumstance. Such questions have to be syntactically valid and need to logically correlate with the answers by deducing over multiple relations on several sentences in the text. Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text. Since the labeled data is limited and insufficient for training, we propose to learn the model with the help of a large scale of unlabeled data that is much easier to obtain. Such data contains rich expressive forms of the questions with structural patterns on syntax and semantics. These patterns can be estimated by the neural hidden semi-Markov model using latent variables. With latent patterns as a prior, we can regularize the generation model and produce the optimal results. Experimental results on the HotpotQA data set demonstrate the effectiveness of our model. Moreover, we apply the generated results to the task of machine reading comprehension and achieve significant performance improvements. © 2020 Association for Computational Linguistics

关键词： Semantics

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STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medical Information

STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medi...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Wentong Xu Chao Fu Jing Yu Caifeng Si Zuoyong Li Chaochao Li Haoyi Fan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Ultrasound First Affiliated Hospital of Zhengzhou University Zhengzhou Henan China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Anomaly detection has been widely explored by training an out-of-distribution detector with only normal data. Medical image analysis, as a common tool, faces the challenge of how to further enhance the detection performance for thyroid cancer, especially with the application of various deep learning methods in the thyroid-related field. In this paper, we validate the effectiveness of sonographic feature descriptions (textual information) for thyroid cancer diagnosis. Building on this, we propose a simple multimodal self-supervised framework that leverages both textual and image modalities for thyroid cancer anomaly detection. Results on a private dataset demonstrate the effectiveness and superiority of our proposed method compared to advanced methods.

关键词： Deep learning Training Image analysis Thyroid cancer Signal processing Feature extraction Informatics Anomaly detection Medical diagnostic imaging Thyroid

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A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning 6th

A Class Imbalance Monitoring Model for Fetal Heart Contracti...

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6th International Conference on data Mining and big data, DMBD 2021

作者： Qin, Chen Liu, Shaopeng Lin, Shengxiang Li, Guangzhe Hong, Jiaming School of Medical Information Engineering Guangzhou University of Chinese Medicine Guangzhou China Department of Computer Science Guangdong Polytechnic Normal University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9789811675010

Aiming at the imbalance and cost-sensitive problem of sample categories in actual fetal monitoring, as well as actual needs, we proposed a category imbalance fetal contraction monitoring model based on GBDT (Gradient Boosting Decision Tree) combined learning. Subsets with balanced category were generated by random under-sampling and applied to train several GBDT base classifiers using the method of feature selection. We integrated the base classifiers by the simple average method and calculated the final prediction probability. In this study, AUC and cost-sensitive error rate were used as evaluation indicators to compare with the commonly used single learning models such as Decision Tree, Logistic Regression and combined learning models like Random Forest to verify the effectiveness of the model. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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An Adaptive Framework of Multimodal Emotion Recognition Based on Collaborative Discriminative Learning

An Adaptive Framework of Multimodal Emotion Recognition Base...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yadi Wang Xiaoding Guo Yibo Zhang Yiyuan Ren Wendi Huang Zunyan Liu Yuming Feng Xiangguang Dai Wei Zhang Hangjun Che School of Computer and Information Engineering Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

Multimodal emotion recognition has great application scenarios in the field of human-computer interaction, and the main task lies in how to capture and handle the consistency and complementarity of multimodal signals. However, in numerous scenarios, multimodal fusion is significantly challenged by variances in the strengths and weaknesses of different modality signals in carrying emotional information. This paper aims to learn the essential information of strong modalities and inter-modality correlations thus achieving better emotion recognition. We propose an adaptive multimodal emotion recognition framework based on collaborative discriminative learning (AMCDL), a framework for emotion recognition that engages in collaborative learning and adjusts to the information-processing capabilities of various emotion recognition models. AMCDL calculates the inter-modality correlation using Canonical Correlation analysis (CCA) and uses it as an influencing factor to boost the multiplicative combination method to create a class loss function. When trained on several modalities, the class loss function can integrate complementary information to enable strong modal expression, enhancing emotion perception. On the benchmark dataset CMU-MOSI, AMCDL significantly improves classification accuracy when compared to the state-of-the-art models. The experiments showed the flexibility and generality of AMCDL by applying several emotion recognition models to it and producing better classification results than the original model.

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Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledge

arXiv

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arXiv 2023年

作者： Zhang, Yufeng Wang, Meng-Xiang Yu, Jianxing School of Artificial Intelligence Sun Yat-sen University Zhuhai519082 China Guangdong Key Laboratory of Big Data Analysis and Processing 510006 China China National Institute of Standardization 100088 China Pazhou Lab Guangzhou510330 China

This paper proposes a new task in the field of Answering Subjective Induction Question on Products (SUBJPQA). The answer to this kind of question is non-unique, but can be interpreted from many perspectives. For example, the answer to ‘whether the phone is heavy’ has a variety of different viewpoints. A satisfied answer should be able to summarize these subjective opinions from multiple sources and provide objective knowledge, such as the weight of a phone. That is quite different from the traditional QA task, in which the answer to a factoid question is unique and can be found from a single data source. To address this new task, we propose a three-steps method. We first retrieve all answer-related clues from multiple knowledge sources on facts and opinions. The implicit commonsense facts are also collected to supplement the necessary but missing contexts. We then capture their relevance with the questions by interactive attention. Next, we design a reinforcement-based summarizer to aggregate all these knowledgeable clues. Based on a template-controlled decoder, we can output a comprehensive and multi-perspective answer. Due to the lack of a relevant evaluated benchmark set for the new task, we construct a large-scale dataset, named SupQA, consisting of 48,352 samples across 15 product domains. Evaluation results show the effectiveness of our approach. © 2023, CC BY.

关键词： Reinforcement learning

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GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning 25

GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation f...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and data Mining V.1

作者： Zhe-Rui Yang Jindong Han Chang-Dong Wang Hao Liu CSE Sun Yat-sen University Guangzhou China AI Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China EMIA Hong Kong University of Science and Technology Hong Kong China and AI Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China CSE Sun Yat-sen University Guangzhou China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China AI Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China and CSE Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9798400712456

Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in handling a range of graph analytical tasks across various domains, such as e-commerce and social networks. Despite their versatility, GNNs face significant challenges in transferability, limiting their utility in real-world applications. Existing research in GNN transfer learning overlooks discrepancies in distribution among various graph datasets, facing challenges when transferring across different distributions. How to effectively adopt a well-trained GNN to new graphs with varying feature and structural distributions remains an under-explored problem. Taking inspiration from the success of Low-Rank Adaptation (LoRA) in adapting large language models to various domains, we propose GraphLoRA, an effective and parameter-efficient method for transferring well-trained GNNs to diverse graph domains. Specifically, we first propose a Structure-aware Maximum Mean Discrepancy (SMMD) to align divergent node feature distributions across source and target graphs. Moreover, we introduce low-rank adaptation by injecting a small trainable GNN alongside the pre-trained one, effectively bridging structural distribution gaps while mitigating the catastrophic forgetting. Additionally, a structure-aware regularization objective is proposed to enhance the adaptability of the pre-trained GNN to target graph with scarce supervision labels. Extensive experiments on eight real-world datasets demonstrate the effectiveness of GraphLoRA against fourteen baselines by tuning only 20% of parameters, even across disparate graph domains. The code is available at https://***/AllminerLab/GraphLoRA.

关键词： graph neural network

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data processing Technology for Network Abnormal Traffic Detection

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Procedia Computer Science 2024年 243卷 610-618页

作者： Kun Wang Yu Fu Xueyuan Duan Jianqiao Xu Taotao Liu Department of Information Security Naval University of Engineering Wuhan 430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang 464000 China College of Computer and Information Technology Xinyang Normal University Xinyang 464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang 464000 China

At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. Therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization (PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. The results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. This method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection.

关键词： Network Anomaly Traffic Detection data processing Technology particle swarm optimization

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