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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Zhang, Zhi Wuhan University of Science and Technology College of Computer Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection of Financial Fraudulent Activities with Machine Learning:A Case Study of Detecting Potential Tax and Invoice Fraud 7

Detection of Financial Fraudulent Activities with Machine Le...

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7th International Conference on Computer science and Artificial Intelligence, CSAI 2023

作者： Tian, Maohong Liang, Jian Zhang, Dequan Zhang, Xintong Wang, Zuo Li, Hualin ChongQing Institute of Engineering Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center ChongQing China Shu Yi Xin Credit Management Co LTD ChongQing China

ISBN: (纸本)9798400708688

Financial fraud is a widespread problem that can cause significant economic losses. Traditional fraud detection methods often rely on manual audits and rules-based systems, which can be time-consuming and error-prone. In recent years, machine learning methods have emerged as a promising approach to automating fraud detection by leveraging large-scale data analysis. This article explores the use of machine learning methods to detect financial fraud by using tax, invoice, and big data. We first introduce the challenges and opportunities of using these data sources for fraud detection, and then survey various machine learning techniques that have been applied to this problem. We also discuss the evaluation metrics and case studies of these methods, and highlight the potential benefits and limitations of using machine learning for fraud detection. Finally, we identify some future research directions and challenges in this area. This article aims to provide a comprehensive method of the state-of-the-art in using machine learning methods for financial fraud detection, and to inspire further research and development in this important field. © 2023 ACM.

关键词： big data

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Token2vec: A Joint Self-Supervised Pre-Training Framework Using Unpaired Speech and Text 48

Token2vec: A Joint Self-Supervised Pre-Training Framework Us...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yue, Xianghu Ao, Junyi Gao, Xiaoxue Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of HongKong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

Self-supervised pre-Training has been successful in both text and speech processing. Speech and text offer different but complementary information. The question is whether we are able to perform a speech-Text joint pre-Training on unpaired speech and text. In this paper, we take the idea of self-supervised pre-Training one step further and propose token2vec, a novel joint pre-Training framework for unpaired speech and text based on discrete representations of speech. Specifically, we introduce two modality-specific tokenizers for speech and text. Based on these tokenizers, we convert speech/text sequences into discrete speech/text token sequences consisting of similar language units, thus mitigating the domain mismatch problem and length mismatch problem, which are caused by the distinct characteristics between speech and text. Finally, we feed the discrete speech and text tokens into a modality-Agnostic Transformer encoder and pre-Train with token-level masking language modeling (tMLM). Experiments show that token2vec is significantly superior to various speech-only pre-Training baselines, with up to 17.7% relative WER reduction. Token2vec model is also validated on a non-ASR task, i.e., spoken intent classification, and shows good transferability. © 2023 IEEE.

关键词： Modeling languages

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Hierarchical Multiview Top-k Pooling with Deep-Q-Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2985-2996页

作者： Li, Zhi-Peng Su, Hai-Long Wu, Yong- Zhang, Qin-Hu Yuan, Chang-An Gribova, Valeriya Filaretov, Vladimir Fedorovich Huang, De-Shuang Eastern Institute of Technology Zhejiang Ningbo315201 China University of Science and Technology of China School of Life Sciences Anhui Hefei230026 China Tongji University Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Shanghai201804 China Guangxi Academy of Sciences Institute of Big Data and Intelligent Computing Research Center Nanning530007 China Far Eastern Branch of the Russian Academy of Sciences Institute of Automation and Control Processes Vladivostok690041 Russia

Graph neural networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on graph convolutional neural networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the hierarchical multiview top-k pooling (HMTPool) with deep-Q-networks, which scores nodes taking into account multiview information (considering graph structure and features) and does not rely on the empirical adaptive selection of the best k value. HMTPool is a two-stage process. It first uses a variant GCN and multilayer perceptron to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multiview information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a deep-Q-network-based top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUdatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach. © 2020 IEEE.

关键词： Convolution

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Unveiling the planar deformation mechanisms for improved formability in pre-twinned AZ31 Mg alloy sheet at warm temperature

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Journal of Magnesium and Alloys 2023年第12期11卷 4659-4678页

作者： Xiaohuan Pan Lifei Wang Pengbin Lu Hua Zhang Guangsheng Huang Liuwei Zheng Bin Xing Weili Cheng Hongxia Wang Wei Liang Kwang Seon Shin Shanxi Key Laboratory of Advanced Magnesium-Based Materials College of Materials Science and EngineeringTaiyuan University of TechnologyTaiyuan 030024China National Engineering Laboratory for Industrial Big-data Application Technology Chongqing Innovation Center of Industrial Big-Data Co.Ltd.Chongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute for Advanced Studies in Precision Materials Yantai UniversityYantai 264005China National Engineering Research Center for Magnesium Alloys College of Materials Science&EngineeringChongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National University1 Gwanak-roGwanak-guSeoul 08826South Korea

To investigate the role of pre-twins in Mg alloy sheets during warm planar deformation,the stretch forming is conducted at 200℃.Results suggest the formability of the pre-twinned AZ31 Mg alloy sheet is enhanced to 11.30 *** mechanisms for the improved formability and the deformation behaviors during the planar stretch forming are systematically investigated based on the planar stress *** Schmid factor for deformation mechanisms are calculated,the results reveal that planar stress states extremely affect the Schmid factor for{10-12}*** detwinning is activated and the prismatic slip is enhanced in the pre-twinned sheet,especially under the planar extension stress state in the outer ***,the thickness-direction strain is accommodated *** dynamic recrystallization(DRX)type is continuous DRX(CDRX)regardless of the planar stress ***,the CDRX degree is greater under the planar extension stress *** twin lattices deviate from the perfect{10-12}twinning relation due to the planar compression stress state and the *** basal texture is weakened when the planar stress state tends to change the texture components.

关键词： AZ31 Mg alloy sheet Planar stretch forming Planar stress states Continuous dynamic recrystallization {10-12}tensile twinning

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Two efficient beamforming methods for hybrid IRS-aided AF relay wireless networks

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science China(Information sciences) 2025年第4期68卷 363-379页

作者： Xuehui WANG Qingbo LI Wen ZHU Feng SHU Mengxing HUANG Fuhui ZHOU Riqing CHEN Cunhua PAN Yongpeng WU Jiangzhou WANG School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University School of Information and Communication Engineering Hainan University Collaborative Innovation Center of Information Technology Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Digital Fujian Institute of Big Data for Agriculture Fujian Agriculture and Forestry University National Mobile Communications Research Laboratory Southeast University Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent

Owing to its ability to mitigate the double-fading effect by amplifying the reflected signal, the active intelligent reflecting surface(IRS) has garnered significant attention. In this paper, an amplify-and-forward(AF) relay network assisted by a hybrid IRS consisting of both passive and active units is developed. A signal-to-noise ratio(SNR) maximization problem is formulated, where the AF relay beamforming matrix and the hybrid IRS reflecting coefficient matrices for two-time slots need to be optimized. To address the SNR maximization problem, this paper proposes both a high-performance(HP) method and a low-complexity(LC) method. The HP method is based on the semidefinite relaxation and fractional programming(SDR-FP)algorithm, with rank-1 solutions obtained through Gaussian randomization. For the LC method, the amplification coefficient of each active IRS element is assumed to be equal. The SNR maximization problem is then addressed using the whitening filter,generalized power iteration, and generalized Rayleigh-Ritz(WF-GPI-GRR) approach. Simulation results show that compared with the benchmarks, such as the passive IRS-aided AF relay network, the proposed HP-SDR-FP and WF-GPI-GRR methods achieve significant rate improvements. In particular, the HP-SDR-FP and WF-GPI-GRR methods yield more than a 135.0%rate gain when the transmit power Ps of the source is 10 dBm. Furthermore, the proposed HP-SDR-FP method outperforms the WF-GPI-GRR method in terms of rate performance.

关键词： double-fading intelligent reflecting surface active elements passive elements hybrid IRS AF relay

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Twitter Stance Detection via Neural Production Systems 48

Twitter Stance Detection via Neural Production Systems

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Bowen Ding, Daijun Xu, Guangning Guo, Jinjin Huang, Zhichao Huang, Xu Shenzhen Technology University College of Big Data and Internet Shenzhen China Harbin Institute of Technology Computer Science & Technology Shenzhen China Jd Intelligent Cities Research Beijing China

ISBN: (纸本)9781728163277

Stance detection is an important task, which aims to classify the attitude of an opinionated text toward a given target. In this paper, we develop an interpretable neural production system for stance detection (NPS4SD). NPS4SD is an end-to-end deep learning model, which consists of a set of knowledge rules that are applied by binding with specific entities. NPS4SD consists of two main components: a pretrained model for learning the text representation and a variable binding network (VBN) to bind the knowledge rules with text entities. Extensive experiments are conducted to evaluate the effectiveness of the proposed NPS4SD model on three real-world datasets with in-domain, cross-target and zero-shot setups. Experimental results demonstrate that NPS4SD achieves substantially better performance than the strong competitors for the stance detection task. © 2023 IEEE.

关键词： deep neural network neural production system stance detection

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The Application of big data and AI Analytics in Rating Industrial and Smart Manufacturing Districts: Challenges and Opportunities 3

The Application of Big Data and AI Analytics in Rating Indus...

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3rd International Conference on big data, Information and Computer Network, BDICN 2024

作者： Zhou, Chenchen Wang, Wei Tian, Maohong Liang, Jian Zhang, Dequan Li, Hualin School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center Chongqing China Shu Yi Xin Credit Management Co LTD Chongqing China

ISBN: (纸本)9798350363821

As an important carrier and component of industrial clusters, industrial districts are an important way for regional economic growth, and the economic effects of industrial districts have attracted more and more attention. Therefore, how to identify high-quality districts with good development trends and obvious competitive advantages from a large amount of district information has become an increasingly important topic. This article takes industrial and intelligent manufacturing districts as an example and proposes an innovative industrial and intelligent manufacturing zone rating method that combines rule-based modeling, Analytic Hierarchy Process (AHP), and Artificial Intelligence (AI) models. Collect structured and unstructured data, including real-Time operational data, historical records, market trends, and expert opinions, from multiple sources through big data technology. Apply rule-based models and Analytic Hierarchy Process (AHP) to prioritize and weight various factors that affect regional performance, and obtain district ratings;And use the Kmeans method for clustering to obtain district level classification. Finally, the paper combines the outputs of the rule-based models, AHP, and AI models to create an overall rating system. This system continuously monitors district performance, providing accurate and comprehensive ratings to support decision-making in industry and government. © 2024 IEEE.

关键词： Smart manufacturing

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Knowledge Graph for China's Genealogy1

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IEEE Transactions on Knowledge and data engineering 2023年第1期35卷 634-646页

作者： Wu, Xindong Jiang, Tingting Zhu, Yi Bu, Chenyang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei230009 China Hefei University of Technology Research Institute of Big Knowledge Anhui Hefei230009 China Mininglamp Technology Mininglamp Academy of Sciences Beijing100102 China Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei230009 China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230009 China

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descendant paths, and explore the origins of a family. However, the heterogenous, autonomous, complex, and evolving natures of genealogical data bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical data may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical datasets verify the feasibility and effectiveness of our proposed model. © 1989-2012 IEEE.

关键词： History

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BDLR:lncRNA identification using ensemble learning

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BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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