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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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Dual-Aspect Noise-Based Regularization for Multi-Modal Relation Extraction in Media Posts

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1324-1336页

作者： Kai Sun Bin Shi Samuel Mensah Wenjian Liu Bo Dong School of Computer Science and Technology and Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Technology The University of Sheffield Sheffield U.K. Faculty of Data Science City University of Macau Macau China School of Continuing Education and Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

Multi-Modal Relation Extraction (MMRE) plays a key role in various multimedia applications including, recommendation and information retrieval systems. MMRE aims to extract the semantic relation between entities by leveraging context from a text-image pair. By utilizing context from images, the challenge of learning from noisy images in MMRE emerges as a research problem by itself. For instance, subtle variations in similar images can act as noise and potentially impact the predictions made by MMRE models. To tackle this problem, current work utilizes attention mechanisms to fuse relevant text and image features or devise data augmentation techniques (e.g., via generative models) to improve generalization. However, the current performance still remains unsatisfactory. In an effort to improve upon the performance, we propose a Dual-Aspect Noise-based Regularization framework that encompasses two techniques: 1) noise removal through an adaptive gating mechanism, 2) fighting noise with noise to improve feature stability in the learning process. We find that combining these techniques encourages the model to focus on more relevant image features for MMRE. We carry out extensive experiments and demonstrate that our proposed model is further enhanced by exploring data augmentation techniques. This additional improvement leads the model to achieve state-of-the-art performance on the widely-used Multi-modal Neural Relation Extraction (MNRE) dataset, and show its effectiveness and generalizability on the Multi-Modal Named Entity Recognition task.

关键词： Feature extraction Noise data mining Noise measurement Social networking (online) Adaptation models Transformers Training Predictive models data models

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Rankings vs Realities: Exploring Competency Differences in Graduate data science Programs

Rankings vs Realities: Exploring Competency Differences in G...

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Frontiers in Education (FIE) Conference

作者： Duo Li Elizabeth Milonas Qiping Zhang Department of Big Data Management and Application Shenyang Institute of Technology Fushun China Computer Systems Technology New York City College of Technology (CUNY) Brooklyn USA Palmer School of Library and Information Science Long Island University Brookville USA

Numerous educational institutions, including those specializing in computer science, engineering, business, and science, now offer graduate programs in data science. However, it remains uncertain whether the data science programs provided by universities with different rankings deliver similar or distinct data science competencies. This study aims to compare the competencies of graduate data science programs offered in the United States. It addressed the research question of “Are there any differences in graduate data science program competencies based on their university rankings?” The program competencies of 228 data science graduate programs as well as their U.S. News university rankings were collected and compared. The findings indicate that regardless of their national rankings, all universities offer most of the competencies required for data science programs, except for Machine Learning and Sensor Networks. Forty-six percent of data science programs are offered in universities that hold national rankings within the top 75, focusing on majors such as data science and business analytics. Furthermore, the top-ranked universities provide data science programs across all departments or schools, with the most prominent ones being Business, Computer science, Information science & technology, and Math/Statistics. The primary research contribution of this study revolves around conducting a comprehensive analysis of data science program competencies within the United States. The implications of the findings have significant relevance to computing education. The study provides valuable guidelines for educational practices in the field of data science: 1) naming the new majors as either “data science” or “Business Analytics”; 2) housing the new data science programs within existing departments or schools specializing in Business, Computer science, Information science & technology, and Math/Statistics; 3) incorporating all ACM data science competencies into the curriculum

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Predict-and-Update Network: Audio-Visual Speech Recognition Inspired by Human Speech Perception

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 11-22页

作者： Jiadong Wang Xinyuan Qian Haizhou Li Shenzhen Research Institute of Big data School of Data Science The Chinese University of Hong Kong Shenzhen China Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science and Technology University of Science and Technology Beijing Beijing China

Audio and visual signals complement each other in human speech perception, and the same applies to automatic speech recognition. The visual signal is less evident than the acoustic signal, but more robust in a complex acoustic environment, as far as speech perception is concerned. It remains a challenge how we effectively exploit the interaction between audio and visual signals for automatic speech recognition. There have been studies using visual signals as redundant or complementary information to audio input in a synchronous manner. However, human studies suggest another mechanism that visual signal primes the listener in advance, indicating when and which frequency to attend to. To simulate such a visual cueing mechanism, we propose a Predict-and-Update Network (P&U net) for Audio-Visual Speech Recognition (AVSR). In particular, we first predict the character posteriors of the spoken words, i.e. the visual embedding, based on the visual signal. The audio signal is then conditioned on the visual embedding via a novel cross-modal Conformer, that updates the character posteriors. We validate the effectiveness of the visual cueing mechanism through extensive experiments. The proposed P&U net outperforms the state-of-the-art AVSR methods on both LRS2-BBC and LRS3-BBC datasets, with the relative Word Error Rate (WER) reductions exceeding 10% and 40% under clean and noisy conditions, respectively.

关键词： Visualization Speech recognition Noise measurement Lips Feature extraction Interference Fuses Vectors Signal to noise ratio Sensors

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Zero-Shot Learning with Noisy Labels 10

Zero-Shot Learning with Noisy Labels

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10th International Conference on Information technology and Quantitative Management, ITQM 2023

作者： Zhao, Pan Tang, Long Pan, Zhigeng School of Artificial Intelligence Nanjing University of Information Science andTechnology Nanjing210044 China Research Institute of Talent Big Data Nanjing University of Information Science and Technology Nanjing210044 China

Zero-shot learning (ZSL) is an attractive technique that can recognize novel object classes without any visual examples, but most existing methods assume that the class labels of the training instances from seen classes are accurate and reliable. However, in some real-world scenarios, the label quality may be compromised by various factors, and ZSL may suffer from performance degradation under label noise. In this paper, we propose an effective approach that utilizes a robust loss function to handle label noise in seen classes. Specifically, we construct a new denoising framework to reduce the impact of outliers and mislabeled samples. To mitigate overfitting to noisy labelled samples, a robust loss function called ramp-style loss is employed to filter out the negative influence of data with anomalous loss values, using only samples within a normal loss range for training, thereby enhancing the overall robust performance. Additionally, we introduce the concave-convex procedure (CCCP) method to address the non-convexity issue of ramp-style loss and devise an efficient update scheme based on ADMM. Extensive experiments conducted on several benchmark datasets (AWA2, SUN, CUB) demonstrate the superior performance of our framework over state-of-the-art ZSL methods in various noisy-label environments. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information technology and Quantitative Management.

关键词： Benchmarking

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Analysis of MFCC and Chromagram Features in Lightweight Bidirectional LSTM and Attention Models for Music Genre Classification 21

Analysis of MFCC and Chromagram Features in Lightweight Bidi...

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21st International Computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Huang, Renxiang Li, Xu Li, Jianping School of Artificial Intelligence and Big Data Sichuan University of Arts and Science China Khoury College of Computer Science Northeastern university BostonMA02115 United States School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798331519254

Music genre classification is essential for organizing music libraries and enhancing recommendation systems. This paper evaluates four lightweight models combining Mel Frequency Cepstral Coefficients (MFCCs) and Chromagram features with Bidirectional LSTM and Multi-head Attention architectures. Using the GTZAN dataset, we found that models with Multi-head Attention, outperformed with MFCCs features, achieving a test accuracy of 85.6%. MFCCs excel in capturing timbral characteristics critical for genre classification, while Chromagram features proved less effective. Although overfitting occurred, future studies can focus on incorporating methods like data augmentation and regularization to improve performance. These lightweight models offer a balance between accuracy and computational efficiency, making them suitable for real-time applications. © 2024 IEEE.

关键词： Real time systems

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Review of Research on Risk Early Warning Methods for Industrial Product Quality and Safety 10

Review of Research on Risk Early Warning Methods for Industr...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Zhang, Junwei Wang, Jinguo Han, Chunxiao Xu, Bin Wang, Lihao Certification and Market Regulation Big Data Center China Cybersecurity Review Beijing China Taiji Computer Co. Beijing China College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798331510138

The quality and safety of industrial products is related to people's daily life, and serious industrial product quality and safety accidents can cause great harm to people, so it is necessary to reduce the occurrence of industrial product quality and safety accidents. Compared with the formulation of policies and strict supervision, the risk warning of industrial product quality and safety can effectively prevent the occurrence of quality and safety accidents, in which the quality and safety risk warning method determines the real-time and accuracy of the early warning, therefore, we have studied the risk warning methods related to industrial product quality and safety, summarised and analysed the strengths and weaknesses of the methods as well as the research trends, and carried out the subsequent research content and perspectives. © 2024 IEEE.

关键词： early warning methods industrial product quality and safety risk warning

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Mask R-CNN Network for Back Recognition on Adam's Bending Images 21

Mask R-CNN Network for Back Recognition on Adam's Bending Im...

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21st International Computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Yuxin, Pu Hongbin, Cai Jipeng, Fan Xin, Zheng School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China Chengdu Chengdian Goldisc HealthData Technology Co. Ltd. China School of Artificial Intelligence and Big Data Sichuan University of Arts and Science China

ISBN: (纸本)9798331519254

Back recognition is critical for adolescent idiopathic scoliosis screening. Existing methods mainly rely on manual work which is time-consuming and laborious and may introduce errors and mismatching impacting the accuracy of subsequent screening outcomes. In this work, we propose to utilize the Mask R-CNN network as the framework for auto-extraction of the back region on Adam's bending images. However, due to the inherent limit receptive fields, initial Mask R-CNN can hardly learn long-range dependencies which are crucial for object detection and segmentation. To this end, we introduce the explicit visual center module into Mask R-CNN. In the EVC module, a lightweight MLP is devised to extract global long-range dependencies. To compensate for MLP's lack of ability to capture features of local corner regions, a learnable visual center is incorporated. By aggregating global long-range dependencies and discriminative feature representation of local key regions, our Mask R-CNN can achieve better performance on Adam's bending images. Moreover, we collect and annotate an Adam's bending image dataset for back recognition. Experimental results on our annotated dataset demonstrate that our improved Mask R-CNN considerably improves the average precision in detection and segmentation of Mask R-CNN from 78.6% to 83.6% and 85.8% to 87.0%, respectively. © 2024 IEEE.

关键词： Object detection

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A Study on Privacy Protection technology for Cross-border Transactions Based on Blockchain technology 6

A Study on Privacy Protection Technology for Cross-border Tr...

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6th International Conference on Computer Information science and Application technology, CISAT 2023

作者： Wang, Wennan Zhu, Linkai Song, Shiyang Academy of Management Guangdong University of Science and Technology Dongguan China Information Technology School Hebei University of Economics and Business Shijiazhuang China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9781510668546

With the acceleration of global economic integration, cross-border transactions are becoming more and more frequent. However, traditional cross- border transaction systems generally suffer from inefficiency, high cost and privacy leakage. In this paper, we study the privacy protection problem in cross-border transactions and propose a privacy protection scheme for cross-border transactions based on block-chain technology. First, this paper briefly introduces the basic principles of block-chain technology and its application scenarios in cross-border transactions. Second, to address the shortcomings of existing privacy protection technologies, this paper proposes a privacy protection scheme that combines data encryption technology, zero-knowledge proof and hybrid network. The scheme achieves automatic execution and verification through smart contracts, which effectively protects the privacy of cross-border transaction participants. Finally, the effectiveness of the privacy protection scheme proposed in this paper in improving the level of privacy protection for cross- border transactions is verified through experimental evaluation and result analysis. This study is important for promoting the development of cross- border transactions, improving transaction efficiency and protecting user privacy. © 2023 SPIE.

关键词： Smart contract

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