Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence最新文献_第7页

Crowd Counting Using Scale Enhanced Network with Dual Attention Booster 基于双注意助推器的规模增强网络的人群计数

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579655

Xin Zeng, Shizhe Hu, Qiang Guo, Yunpeng Wu, Yangdong Ye

引用次数: 0

Research on Ship target recognition based on attention mechanism 基于注意机制的舰船目标识别研究

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579683

Teng Dong

{"title":"Research on Ship target recognition based on attention mechanism","authors":"Teng Dong","doi":"10.1145/3579654.3579683","DOIUrl":"https://doi.org/10.1145/3579654.3579683","url":null,"abstract":"Abstract: Marine ship target recognition can effectively identify the categories of sailing ships and realize effective management of ships. It is strategically important for both civil and military domains, but it is highly demanding in terms of accuracy. In this paper, a novel neural network ByCTE(Bayesian Classification Transformer-Encoder) is proposed to realize ship target recognition by using track information. First, the raw data is preprocessed to make the processed data more favorable for model learning. Secondly, four BayesianLinear Encoder(BLE) modules are used to learn the complex relationship between different spatial positions of the sequence, so as to capture the long-term dependence relationship between the input sequences, and further extract the deep features of the sequence. Finally, complete the recognition by attention layer and softmax function. We select the best performing model in the training and use open dataset Automatic Identification System (AIS) data from Europe for training and validating the validity of the proposed model. ByCTE can achieve better accuracy by comparison with other methods.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"1074 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116022595","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

Cherry Size and Shape Classification Detection Based On Deep Convolutional Neural Network 基于深度卷积神经网络的樱桃大小和形状分类检测

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579756

Zhi Chai, Yue-Kun Pei, J. Liu, Pei-Pei Cao

引用次数: 1

A Multi-Armed Bandit Recommender Algorithm Based on Conversation and KNN 基于对话和KNN的多臂强盗推荐算法

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579714

Hao-dong Xia, Zhifeng Lu, Wenxing Hong

引用次数: 0

An Efficient Transformer-based Approach for Joint Nuclei Detection and Segmentation in Whole Slide Tissue Images 一种基于变换的全切片组织图像关节核检测与分割方法

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579727

Hengxu Zhang, Pengpeng Liang, Zhiyong Sun, Bo Song, Erkang Cheng

{"title":"An Efficient Transformer-based Approach for Joint Nuclei Detection and Segmentation in Whole Slide Tissue Images","authors":"Hengxu Zhang, Pengpeng Liang, Zhiyong Sun, Bo Song, Erkang Cheng","doi":"10.1145/3579654.3579727","DOIUrl":"https://doi.org/10.1145/3579654.3579727","url":null,"abstract":"The detection and segmentation of cell nuclei in whole slide tissue images plays an important role in disease diagnosis and treatment. Automatic detection and segmentation of nuclei is very challenging due to high nuclei density, low contrast, overlapping and adhesion between cells. Recently, Transformer-based object detection and instance segmentation methods have made great progress on traditional computer vision datasets. These Transformer-based approaches are effective by removing the need for many hand-crafted components in the network, and post-processing steps like non-maximum suppression (NMS). However, such approaches consume tremendous amount of memory in detection and segmentation of cell nuclei in whole slide tissue images due to large number of cells in the images. Also, those methods may suffer from inferior performance on small cell instances. Inspired by Deformable DETR which makes use of small set of key sampling points in the attention module to reduce the computation and the usage of multi-scale feature maps, we propose an efficient Transformer-based approach for joint nuclei detection and segmentation in whole slide tissue images. Specifically, In Transformer decoder, it directly outputs the object detection results and instance segmentation masks. We evaluate our proposed method on the public MoNuSeg dataset, and the experimental results show that our method achieves promising performance on nuclei detection and segmentation.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127424152","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

A Novel Prediction Method for Metal-Ion Binding Sites in Protein Sequence Based on Ensemble Learning 基于集成学习的蛋白质序列金属离子结合位点预测新方法

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579694

Chuyi Song, Jing-qing Jiang

{"title":"A Novel Prediction Method for Metal-Ion Binding Sites in Protein Sequence Based on Ensemble Learning","authors":"Chuyi Song, Jing-qing Jiang","doi":"10.1145/3579654.3579694","DOIUrl":"https://doi.org/10.1145/3579654.3579694","url":null,"abstract":"The identification of metal ion-binding sites is important for detecting the protein structures and understanding its biological functions. However, in Protein Data Bank (PDB) which collects the known crystal structures of proteins, only less than one percent are membrane proteins even though they play a significant role in material exchange for cells and have a close relationship in drug target design. In this work, we develop an efficient prediction method for six different types of metal ion-binding sites in membrane proteins. In order to solve the imbalance problem in the dataset, multiple random down-sampling technique is used to obtain multiple training subsets with equal number of binding residues and non-binding residues. The support vector machines (SVM) and random forest (RF) classification models are built based on these subsets and their results are combined by ensemble learning algorithm which efficiently reduce the number of false positive samples in the final prediction. On an independent testing set, our proposed method achieves the average accuracy of 0.991 and average MCC of 0.681 which outperform a recently proposed prediction method, . The superiority in performance has demonstrated that our proposed method is expected to be an accurate tool for prediction of metal ion-binding sites in membrane proteins and it should provide assistant in design of new drug targets.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121689969","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

Prediction of Protein-ATP Binding Sites Based on Word Vector Convolution Model 基于词向量卷积模型的蛋白质- atp结合位点预测

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579660

Zerui Song, Chuyi Song, Jiazhi Song, Jing-qing Jiang

引用次数: 0

θ is all you need: Revisiting SVD in caputuring changes in matrices θ 即可：在把握矩阵变化中重新审视 SVD

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579773

Yanwen Zhang, Jichang Zhao

{"title":"θ is all you need: Revisiting SVD in caputuring changes in matrices","authors":"Yanwen Zhang, Jichang Zhao","doi":"10.1145/3579654.3579773","DOIUrl":"https://doi.org/10.1145/3579654.3579773","url":null,"abstract":"Given a series of matrices (e.g., frames in video) vary over time, figuring out the spatially changing regions (e.g., moving objects) is a critical issue both in theory and applications. In this article, we propose a change detection scheme based on E-SVD, a theory uses Givens transformation, that only determined by the rotation angle θ, to reduce the number of parameters representing a matrix after singular value decomposition (SVD) compression. Inspired by the close relationship between SVD and principal component analysis (PCA), we firstly provide the analytical dependence between θ and matrix elements when changes happen, which guarantees the theoretical rationality of our selection of target θ to efficiently capture these changes. Secondly, we present our detection scheme which is implemented to accurately locate the changing regions of a matrix spatially. The proposed methodology is verified using both simulation and empirical data, results of which show its efficiency and effectiveness. In order to clarify the realistic application of this scheme in object detection without supervision, an additional experiment is also conducted using surveillance video to further demonstrate its potential. Our findings in both theory and application give a new perspective of figuring out spatial variation in a matrix, leading to a wider usage of matrix factorization methods in the domain of unsupervised object detection.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127060876","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

High-Quality-High-Quantity Semantic Distillation for Incremental Object Detection 面向增量目标检测的高质量、高数量语义蒸馏

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579682

Mengxue Kang, Jinpeng Zhang, Xiashuang Wang, Xuhui Huang

引用次数: 0

Infrared Small Target Detection Based on the Difference Variance Weighted Enhanced Local Contrast Measure 基于差分方差加权增强局部对比度的红外小目标检测

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579731.3579813

Xiaofeng Lu, Jiaming Liu, Xiaofei Bai, Sixun Li

引用次数: 0