Intelligent Data Analysis最新文献_第3页

MusicNeXt: Addressing category bias in fused music using musical features and genre-sensitive adjustment layer MusicNeXt：利用音乐特征和体裁敏感调整层解决融合音乐中的类别偏差问题

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-16 DOI: 10.3233/ida-230428

Shiting Meng, Qingbo Hao, Yingyuan Xiao, Wenguang Zheng

{"title":"MusicNeXt: Addressing category bias in fused music using musical features and genre-sensitive adjustment layer","authors":"Shiting Meng, Qingbo Hao, Yingyuan Xiao, Wenguang Zheng","doi":"10.3233/ida-230428","DOIUrl":"https://doi.org/10.3233/ida-230428","url":null,"abstract":"Convolutional neural networks (CNNs) have been successfully applied to music genre classification tasks. With the development of diverse music, genre fusion has become common. Fused music exhibits multiple similar musical features such as rhythm, timbre, and structure, which typically arise from the temporal information in the spectrum. However, traditional CNNs cannot effectively capture temporal information, leading to difficulties in distinguishing fused music. To address this issue, this study proposes a CNN model called MusicNeXt for music genre classification. Its goal is to enhance the feature extraction method to increase focus on musical features, and increase the distinctiveness between different genres, thereby reducing classification result bias. Specifically, we construct the feature extraction module which can fully utilize temporal information, thereby enhancing its focus on music features. It exhibits an improved understanding of the complexity of fused music. Additionally, we introduce a genre-sensitive adjustment layer that strengthens the learning of differences between different genres through within-class angle constraints. This leads to increased distinctiveness between genres and provides interpretability for the classification results. Experimental results demonstrate that our proposed MusicNeXt model outperforms baseline networks and other state-of-the-art methods in music genre classification tasks, without generating category bias in the classification results.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"31 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139268509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A lightweight method of pose estimation for indoor object 室内物体姿态估计的轻量级方法

IF 1.7 4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-16 DOI: 10.3233/ida-230278

Sijie Wang, Yifei Li, Diansheng Chen, Jiting Li, Xiaochuan Zhang

引用次数: 0

ATIN: Attention-embedded time-aware imputation networks for production data anomaly detection ATIN:用于生产数据异常检测的注意力嵌入式时间感知输入网络

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-02 DOI: 10.3233/ida-230301

Xi Zhang, Hu Chen, Rui Li, Zhaolei Fei, Fan Min

引用次数: 0

Unsupervised multi-source domain adaptation for person re-identification via sample weighting 基于样本加权的无监督多源域自适应人物再识别

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-11-02 DOI: 10.3233/ida-230178

Qing Tian, Yao Cheng

引用次数: 0

HSNF: Hybrid sampling with two-step noise filtering for imbalanced data classification HSNF:用于不平衡数据分类的混合采样和两步噪声滤波

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-10-19 DOI: 10.3233/ida-227111

Lilong Duan, Wei Xue, Xiaolei Gu, Xiao Luo, Yongsheng He

{"title":"HSNF: Hybrid sampling with two-step noise filtering for imbalanced data classification","authors":"Lilong Duan, Wei Xue, Xiaolei Gu, Xiao Luo, Yongsheng He","doi":"10.3233/ida-227111","DOIUrl":"https://doi.org/10.3233/ida-227111","url":null,"abstract":"Imbalanced data classification has received much attention in machine learning, and many oversampling methods exist to solve this problem. However, these methods may suffer from insufficient noise filtering, overlap between synthetic and original samples, etc., resulting in degradation of classification performance. To this end, we propose a hybrid sampling with two-step noise filtering (HSNF) method in this paper, which consists of three modules. In the first module, HSNF denoises twice according to different noise discrimination mechanisms. Note that denoising mechanism is essentially based on the Euclidean distance between samples. Then in the second module, the minority class samples are divided into two categories, boundary samples and safe samples, respectively, and a portion of the boundary majority class samples are removed. In the third module, different oversampling methods are used to synthesize instances for boundary minority class samples and safe minority class samples. Experimental results on synthetic data and benchmark datasets demonstrate the effectiveness of HSNF in comparison with several popular methods. The code of HSNF will be released.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135820864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Small object detection based on attention mechanism and enhanced network 基于注意机制和增强网络的小目标检测

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-10-19 DOI: 10.3233/ida-227154

Bingbing Wang, Fengxiang Zhang, Kaipeng Li, Kuijie Shi, Lei Wang, Gang Liu

{"title":"Small object detection based on attention mechanism and enhanced network","authors":"Bingbing Wang, Fengxiang Zhang, Kaipeng Li, Kuijie Shi, Lei Wang, Gang Liu","doi":"10.3233/ida-227154","DOIUrl":"https://doi.org/10.3233/ida-227154","url":null,"abstract":"Small object detection has a broad application prospect in image processing of unmanned aerial vehicles, autopilot and remote sensing. However, some difficulties exactly exist in small object detection, such as aggregation, occlusion and insufficient feature extraction, resulting in a great challenge for small object detection. In this paper, we propose an improved algorithm for small object detection to address these issues. By using the spatial pyramid to extract multi-scale spatial features and by applying the multi-scale channel attention to capture the global and local semantic features, the spatial pooling pyramid and multi-scale channel attention module (SPP-MSCAM) is constructed. More importantly, the fusion of the shallower layer with higher resolution and a deeper layer with more semantic information is introduced to the neck structure for improving the sensitivity of small object features. A large number of experiments on the VisDrone2019 dataset and the NWPU VHR-10 dataset show that the proposed method significantly improves the Precision, mAP and mAP50 compared to the YOLOv5 method. Meanwhile, it still preserves a considerable real-time performance. Undoubtedly, the improved network proposed in this paper can effectively alleviate the difficulties of aggregation, occlusion and insufficient feature extraction in small object detection, which would be helpful for its potential applications in the future.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135781553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An in-depth study on key nodes in social networks 对社交网络关键节点的深入研究

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-10-19 DOI: 10.3233/ida-227018

Chengcheng Sun, Zhixiao Wang, Xiaobin Rui, Philip S. Yu, Lichao Sun

{"title":"An in-depth study on key nodes in social networks","authors":"Chengcheng Sun, Zhixiao Wang, Xiaobin Rui, Philip S. Yu, Lichao Sun","doi":"10.3233/ida-227018","DOIUrl":"https://doi.org/10.3233/ida-227018","url":null,"abstract":"In social network analysis, identifying the important nodes (key nodes) is a significant task in various applications. There are three most popular related tasks named influential node ranking, influence maximization, and network dismantling. Although these studies are different due to their own motivation, they share many similarities, which could confuse the non-domain readers and users. Moreover, few studies have explored the correlations between key nodes obtained from different tasks, hindering our further understanding of social networks. In this paper, we contribute to the field by conducting an in-depth survey of different kinds of key nodes through comparing these key nodes under our proposed framework and revealing their deep relationships. First, we clarify and formalize three existing popular studies under a uniform standard. Then we collect a group of crucial metrics and propose a fair comparison framework to analyze the features of key nodes identified by different research fields. From a large number of experiments and deep analysis on twenty real-world datasets, we not only explore correlations between key nodes derived from the three popular tasks, but also summarize insightful conclusions that explain how key nodes differ from each other and reveal their unique features for the corresponding tasks. Furthermore, we show that Shapley centrality could identify key nodes with more generality, and these nodes could also be applied to the three popular tasks simultaneously to a certain extent.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135823985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MFF-SC: A multi-feature fusion method for smart contract classification MFF-SC:一种多特征融合的智能合约分类方法

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-10-19 DOI: 10.3233/ida-227186

Gang Tian, Xiaojin Wang, Rui Wang, Qiuyue Yu, Guangxin Zhao

{"title":"MFF-SC: A multi-feature fusion method for smart contract classification","authors":"Gang Tian, Xiaojin Wang, Rui Wang, Qiuyue Yu, Guangxin Zhao","doi":"10.3233/ida-227186","DOIUrl":"https://doi.org/10.3233/ida-227186","url":null,"abstract":"The classification of the smart contract can effectively reduce the search space and improve retrieval efficiency. The existing classification methods are based on natural language processing technologies. Because the processing of source code by these technologies lacks extraction and processing in the software engineering field, there is still a lot of room for improvement in their methods of feature extraction. Therefore, this paper proposes a multi-feature fusion method for smart contract classification (MFF-SC) based on the code processing technology. From the source code perspective, source code processing method and attention mechanism are used to extract local code features. Structure-based traversal method are used to extract global code features from abstract syntax tree. Local and global code features introduce attention mechanism to generate code semantic features. From the perspective of account transaction, the feature of account transaction is extracted by using TransR. Next, the code semantic features and account transaction features generate smart contract semantic features by an attention mechanism. Finally, the smart contract semantic features are fed into a stacked denoising autoencoder and a softmax classifier for classification. Experimental results on a real dataset show that MFF-SC achieves an accuracy rate of 83.9%, compared with other baselines and variants.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135821032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resformer: Combine quadratic linear transformation with efficient sparse Transformer for long-term series forecasting 变压器:结合二次线性变换和高效稀疏变压器进行长期序列预测

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-10-19 DOI: 10.3233/ida-227006

Gongguan Chen, Hua Wang, Yepeng Liu, Mingli Zhang, Fan Zhang

{"title":"Resformer: Combine quadratic linear transformation with efficient sparse Transformer for long-term series forecasting","authors":"Gongguan Chen, Hua Wang, Yepeng Liu, Mingli Zhang, Fan Zhang","doi":"10.3233/ida-227006","DOIUrl":"https://doi.org/10.3233/ida-227006","url":null,"abstract":"With the continuous development of deep learning, long sequence time-series forecasting (LSTF) has attracted more and more attention in power consumption prediction, traffic prediction and stock prediction. In recent studies, various improved models of Transformer are favored. While these models have made breakthroughs in reducing the time and space complexity of Transformer, there are still some problems, such as the predictive power of the improved model being slightly lower than that of Transformer. And these models ignore the importance of special values in the time series. To solve these problems, we designed a more concise network named Resformer, which has four significant characteristics: (1) The fully sparse self-attention mechanism achieves O⁢(𝐿𝑙𝑜𝑔𝐿) time complexity. (2) The AMS module is used to process the special values of time series and has comparable performance on sequences dependency alignment. (3) Using quadratic linear transformation, a simple LT module is designed to replace the self-attention mechanism. It effectively reduces redundant information. (4) The DistPooling method based on data distribution is proposed to suppress redundant information and noise. A large number of experiments on real data sets show that the Resformer method is superior to the existing improved model and standard Transformer method.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135823983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A lightweight vision transformer with symmetric modules for vision tasks 一个轻量级的视觉转换器，具有用于视觉任务的对称模块

4区计算机科学

Intelligent Data Analysis Pub Date : 2023-10-19 DOI: 10.3233/ida-227205

Shengjun Liang, Mingxin Yu, Wenshuai Lu, Xinglong Ji, Xiongxin Tang, Xiaolin Liu, Rui You

{"title":"A lightweight vision transformer with symmetric modules for vision tasks","authors":"Shengjun Liang, Mingxin Yu, Wenshuai Lu, Xinglong Ji, Xiongxin Tang, Xiaolin Liu, Rui You","doi":"10.3233/ida-227205","DOIUrl":"https://doi.org/10.3233/ida-227205","url":null,"abstract":"Transformer-based networks have demonstrated their powerful performance in various vision tasks. However, these transformer-based networks are heavyweight and cannot be applied to edge computing (mobile) devices. Despite that the lightweight transformer network has emerged, several problems remain, i.e., weak feature extraction ability, feature redundancy, and lack of convolutional inductive bias. To address these three problems, we propose a lightweight visual transformer (Symmetric Former, SFormer), which contains two novel modules (Symmetric Block and Symmetric FFN). Specifically, we design Symmetric Block to expand feature capacity inside the module and enhance the long-range modeling capability of attention mechanism. To increase the compactness of the model and introduce inductive bias, we introduce convolutional cheap operations to design Symmetric FFN. We compared the SFormer with existing lightweight transformers on several vision tasks. Remarkably, on the image recognition task of ImageNet [13], SFormer gains 1.2% and 1.6% accuracy improvements compared to PVTv2-b0 and Swin Transformer, respectively. On the semantic segmentation task of ADE20K [64], SFormer delivers performance improvements of 0.2% and 0.7% compared to PVTv2-b0 and Swin Transformer, respectively. On the cityscapes dataset [11], SFormer delivers performance improvements of 2.5% and 4.2% compared to PVTv2-b0 and Swin Transformer, respectively. The code is open-source and available at: https://github.com/ISCLab-Bistu/Symmetric_Former.git.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135781751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0