Annals of Data Science最新文献_第2页

Drinkers Voice Recognition Intelligent System: An Ensemble Stacking Machine Learning Approach 饮酒者语音识别智能系统：集合堆叠机器学习方法

Annals of Data Science Pub Date : 2024-07-07 DOI: 10.1007/s40745-024-00559-8

P. Terlapu

引用次数: 0

A New Kernel Density Estimation-Based Entropic Isometric Feature Mapping for Unsupervised Metric Learning 用于无监督度量学习的基于核密度估计的新熵等距特征映射法

Annals of Data Science Pub Date : 2024-07-06 DOI: 10.1007/s40745-024-00548-x

Alaor Cervati Neto, A. Levada, Michel Ferreira Cardia Haddad

引用次数: 0

Power Evaluation of Some Tests for Inverse Rayleigh Distribution 反瑞利分布某些测试的功率评估

Annals of Data Science Pub Date : 2024-07-05 DOI: 10.1007/s40745-024-00536-1

Vahideh Ahrari, P. Hasanalipour

引用次数: 0

Research on Pricing of Data Based on Bi-level Programming Model 基于双层编程模型的数据定价研究

Annals of Data Science Pub Date : 2024-06-16 DOI: 10.1007/s40745-024-00549-w

Yurong Ding, Yingjie Tian

{"title":"Research on Pricing of Data Based on Bi-level Programming Model","authors":"Yurong Ding, Yingjie Tian","doi":"10.1007/s40745-024-00549-w","DOIUrl":"10.1007/s40745-024-00549-w","url":null,"abstract":"<div><p>Effective value measurement and pricing methods can greatly promote the healthy development of data sharing, exchange and reuse. However, the uncertainty of data value and neglect of interactivity lead to information asymmetry in the transaction process. A perfect pricing system and well-designed data trading market (hereafter called data market) can widely promote data transactions. We take the three-agents data market as an example to construct a sound data trading process. The data owner who provides data records, the model buyer who is interested in buying machine learning (ML) model instances, and the data broker who interacts between the data owner and the model buyer. Based on the characteristics of data market, like truthfulness, revenue maximization, version control, fairness and non-arbitrage, we propose a data pricing methods based on different model versions. Firstly, we utilize market research and construct a revenue maximization (RM) problem to price the different versions of ML models and solve it with the RM-ILP process. However, the RM model based on market research has two major problems: one is that the model buyer has no incentive to tell the truth, that is, the model buyer will lie in the market research to obtain a lower model price; the other is that it asks the data broker to release version menu in advance, resulting in an inefficient operation of the data market. In view of the defects of the RM transaction model, we propose a model buyers behavior analysis, establish the revenue maximization function based on different data versions to establish a bi-level linear programming model. We further add the incentive compatibility constraint and the individual rationality constraint, taking the utility of the model buyer and the revenue of the data broker into account. This reflects the consumer driven model in the data transaction mode. Finally, the RM-BLP process is proposed to transform RM problem into an equivalent single-level integer programming problem and we solve it with the “Gurobi” solver. The validity of the model is verified by experiments.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142412038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UAV-YOLOv5: A Swin-Transformer-Enabled Small Object Detection Model for Long-Range UAV Images UAV-YOLOv5：斯温变换器支持的远距离无人机图像小目标检测模型

Annals of Data Science Pub Date : 2024-05-25 DOI: 10.1007/s40745-024-00546-z

Jun Li, Chong Xie, Sizheng Wu, Yawei Ren

{"title":"UAV-YOLOv5: A Swin-Transformer-Enabled Small Object Detection Model for Long-Range UAV Images","authors":"Jun Li, Chong Xie, Sizheng Wu, Yawei Ren","doi":"10.1007/s40745-024-00546-z","DOIUrl":"10.1007/s40745-024-00546-z","url":null,"abstract":"<div><p>This paper tackle the challenges associated with low recognition accuracy and the detection of occlusions when identifying long-range and diminutive targets (such as UAVs). We introduce a sophisticated detection framework named UAV-YOLOv5, which amalgamates the strengths of Swin Transformer V2 and YOLOv5. Firstly, we introduce Focal-EIOU, a refinement of the K-means algorithm tailored to generate anchor boxes better suited for the current dataset, thereby improving detection performance. Second, the convolutional and pooling layers in the network with step size greater than 1 are replaced to prevent information loss during feature extraction. Then, the Swin Transformer V2 module is introduced in the Neck to improve the accuracy of the model, and the BiFormer module is introduced to improve the ability of the model to acquire global and local feature information at the same time. In addition, BiFPN is introduced to replace the original FPN structure so that the network can acquire richer semantic information and fuse features across scales more effectively. Lastly, a small target detection head is appended to the existing architecture, augmenting the model’s proficiency in detecting smaller targets with heightened precision. Furthermore, various experiments are conducted on the comprehensive dataset to verify the effectiveness of UAV-YOLOv5, achieving an average accuracy of 87%. Compared with YOLOv5, the mAP of UAV-YOLOv5 is improved by 8.5%, which verifies that it has high-precision long-range small-target UAV optoelectronic detection capability.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial Data Analysis for Robust Classification of Network Topology Through Synthetic Combinatorics 通过合成组合学对网络拓扑结构进行稳健分类的空间数据分析

Annals of Data Science Pub Date : 2024-05-20 DOI: 10.1007/s40745-024-00523-6

Samrat Hore, Stabak Roy, Malabika Boruah, Saptarshi Mitra

引用次数: 0

Unified Image Harmonization with Region Augmented Attention Normalization 利用区域增强注意力归一化统一图像协调

Annals of Data Science Pub Date : 2024-05-11 DOI: 10.1007/s40745-024-00531-6

Junjie Hou, Yuqi Zhang, Duo Su

{"title":"Unified Image Harmonization with Region Augmented Attention Normalization","authors":"Junjie Hou, Yuqi Zhang, Duo Su","doi":"10.1007/s40745-024-00531-6","DOIUrl":"10.1007/s40745-024-00531-6","url":null,"abstract":"<div><p>The image harmonization task endeavors to adjust foreground information within an image synthesis process to achieve visual consistency by leveraging background information. In academic research, this task conventionally involves the utilization of simple synthesized images and matching masks as inputs. However, obtaining precise masks for image harmonization in practical applications poses a significant challenge, thereby creating a notable disparity between research findings and real-world applicability. To mitigate this disparity, we propose a redefinition of the image harmonization task as “Unified Image Harmonization,” where the input comprises only a single image, thereby enhancing its applicability in real-world scenarios. To address this challenge, we have developed a novel framework. Within this framework, we initially employ inharmonious region localization to detect the mask, which is subsequently utilized for harmonization tasks. The pivotal aspect of the harmonization process lies in normalization, which is accountable for information transfer. Nonetheless, the current background-to-foreground information transfer and guidance mechanisms are limited by single-layer guidance, thereby constraining their effectiveness. To overcome this limitation, we introduce Region Augmented Attention Normalization (RA2N), which enhances the attention mechanism for foreground feature alignment, consequently leading to improved alignment and transfer capabilities. Through qualitative and quantitative comparisons on the iHarmony4 dataset, our model exhibits exceptional performance not only in unified image harmonization but also in conventional image harmonization tasks.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140989549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the Functional Changes in Protein Mutations Through the Application of BiLSTM and the Self-Attention Mechanism 通过应用 BiLSTM 和自注意机制预测蛋白质突变的功能变化

Annals of Data Science Pub Date : 2024-04-25 DOI: 10.1007/s40745-024-00530-7

Zixuan Fan, Yan Xu

{"title":"Predicting the Functional Changes in Protein Mutations Through the Application of BiLSTM and the Self-Attention Mechanism","authors":"Zixuan Fan, Yan Xu","doi":"10.1007/s40745-024-00530-7","DOIUrl":"10.1007/s40745-024-00530-7","url":null,"abstract":"<div><p>In the field of bioinformatics, changes in protein functionality are mainly influenced by protein mutations. Accurately predicting these functional changes can enhance our understanding of evolutionary mechanisms, promote developments in protein engineering-related fields, and accelerate progress in medical research. In this study, we introduced two different models: one based on bidirectional long short-term memory (BiLSTM), and the other based on self-attention. These models were integrated using a weighted fusion method to predict protein functional changes associated with mutation sites. The findings indicate that the model's predictive precision matches that of the current model, along with its capacity for generalization. Furthermore, the ensemble model surpasses the performance of the single models, highlighting the value of utilizing their synergistic capabilities. This finding may improve the accuracy of predicting protein functional changes associated with mutations and has potential applications in protein engineering and drug research. We evaluated the efficacy of our models under different scenarios by comparing the predicted results of protein functional changes across various numbers of mutation sites. As the number of mutation sites increases, the prediction accuracy decreases significantly, highlighting the inherent limitations of these models in handling cases involving more mutation sites.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140656386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Intelligent Courses in English Education based on Neural Networks 基于神经网络的英语教育智能课程研究

Annals of Data Science Pub Date : 2024-04-25 DOI: 10.1007/s40745-024-00528-1

Huimin Yao, Haiyan Wang

{"title":"Research on Intelligent Courses in English Education based on Neural Networks","authors":"Huimin Yao, Haiyan Wang","doi":"10.1007/s40745-024-00528-1","DOIUrl":"10.1007/s40745-024-00528-1","url":null,"abstract":"<div><p>Accurately predicting students’ performance plays a crucial role in achieving the intellectualization of courses. This paper studied intelligent courses in English education based on neural networks and designed a firefly algorithm-back propagation neural network (FA-BPNN) method. The correlation between various features and final grades was calculated using the students’ online learning data. Features with higher correlation were selected as the input for the FA-BPNN algorithm to estimate the final score that students achieved in the “College English” course. It was found that the training time of the FA-BPNN algorithm was 3.42 s, the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values of the FA-BPNN algorithm were 0.986, 0.622, and 0.205, respectively. They were lower than those of the BPNN, genetic algorithm (GA)-BPNN, and particle swarm optimization (PSO)-BPNN algorithms, as well as the adaptive neuro-fuzzy inference system approach. The results indicated the efficacy of the FA for optimizing the parameters of the BPNN algorithm. The comparison between the predicted results and actual values suggested that the average error of the FA-BPNN algorithm was only 0.5, which was the smallest. The experimental results demonstrate the reliability of the FA-BPNN algorithm for performance prediction and its practical application feasibility.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140653938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Inference for the Entropy of the Rayleigh Model Based on Ordered Ranked Set Sampling 基于有序排序集合采样的雷利模型熵的贝叶斯推断

Annals of Data Science Pub Date : 2024-02-27 DOI: 10.1007/s40745-024-00514-7

Mohammed S. Kotb, Haidy A. Newer, Marwa M. Mohie El-Din

引用次数: 0