Frontiers of Computer Science最新文献

A comprehensive survey of federated transfer learning: challenges, methods and applications 联合转移学习综合调查：挑战、方法和应用

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-07-23 DOI: 10.1007/s11704-024-40065-x

Wei Guo, Fuzhen Zhuang, Xiao Zhang, Yiqi Tong, Jin Dong

{"title":"A comprehensive survey of federated transfer learning: challenges, methods and applications","authors":"Wei Guo, Fuzhen Zhuang, Xiao Zhang, Yiqi Tong, Jin Dong","doi":"10.1007/s11704-024-40065-x","DOIUrl":"https://doi.org/10.1007/s11704-024-40065-x","url":null,"abstract":"Federated learning (FL) is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing. In practice, FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target participant. Therefore, many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying distribution. Meanwhile, the differences in their local devices (system heterogeneity), the continuous influx of online data (incremental data), and labeled data scarcity may further influence the performance of these methods. To solve this problem, federated transfer learning (FTL), which integrates transfer learning (TL) into FL, has attracted the attention of numerous researchers. However, since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants, FTL faces many unique challenges that are not present in TL. In this survey, we focus on categorizing and reviewing the current progress on federated transfer learning, and outlining corresponding solutions and applications. Furthermore, the common setting of FTL scenarios, available datasets, and significant related research are summarized in this survey.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"93 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

DMFVAE: miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder DMFVAE：基于变异自动编码器的深度矩阵因式分解方法的 miRNA-疾病关联预测

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-07-12 DOI: 10.1007/s11704-023-3610-y

Pijing Wei, Qianqian Wang, Zhen Gao, Ruifen Cao, Chunhou Zheng

{"title":"DMFVAE: miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder","authors":"Pijing Wei, Qianqian Wang, Zhen Gao, Ruifen Cao, Chunhou Zheng","doi":"10.1007/s11704-023-3610-y","DOIUrl":"https://doi.org/10.1007/s11704-023-3610-y","url":null,"abstract":"MicroRNAs (miRNAs) are closely related to numerous complex human diseases, therefore, exploring miRNA-disease associations (MDAs) can help people gain a better understanding of complex disease mechanism. An increasing number of computational methods have been developed to predict MDAs. However, the sparsity of the MDAs may hinder the performance of many methods. In addition, many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor nodes. In this study, we propose a deep matrix factorization model with variational autoencoder (DMFVAE) to predict potential MDAs. DMFVAE first decomposes the original association matrix and the enhanced association matrix, in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method, to obtain sparse vectors and dense vectors, respectively. Then, the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors, and meanwhile, node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense vectors. Finally, sample features are acquired by combining the latent vectors and network structure embedding vectors, and the final prediction is implemented by convolutional neural network with channel attention. To evaluate the performance of DMFVAE, we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs well. Furthermore, case studies on lung neoplasms, colon neoplasms, and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"218 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141610926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Graph foundation model 图形基础模型

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-07-05 DOI: 10.1007/s11704-024-40046-0

Chuan Shi, Junze Chen, Jiawei Liu, Cheng Yang

引用次数: 0

SEOE: an option graph based semantically embedding method for prenatal depression detection SEOE：基于选项图的产前抑郁症语义嵌入检测方法

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-06-27 DOI: 10.1007/s11704-024-3612-4

Xiaosong Han, Mengchen Cao, Dong Xu, Xiaoyue Feng, Yanchun Liang, Xiaoduo Lang, Renchu Guan

{"title":"SEOE: an option graph based semantically embedding method for prenatal depression detection","authors":"Xiaosong Han, Mengchen Cao, Dong Xu, Xiaoyue Feng, Yanchun Liang, Xiaoduo Lang, Renchu Guan","doi":"10.1007/s11704-024-3612-4","DOIUrl":"https://doi.org/10.1007/s11704-024-3612-4","url":null,"abstract":"Prenatal depression, which can affect pregnant women’s physical and psychological health and cause postpartum depression, is increasing dramatically. Therefore, it is essential to detect prenatal depression early and conduct an attribution analysis. Many studies have used questionnaires to screen for prenatal depression, but the existing methods lack attributability. To diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires, we present the semantically enhanced option embedding (SEOE) model to represent questionnaire options. It can quantitatively determine the relationship and patterns between options and depression. SEOE first quantifies options and resorts them, gathering options with little difference, since Word2Vec is highly dependent on context. The resort task is transformed into an optimization problem involving the traveling salesman problem. Moreover, all questionnaire samples are used to train the options’ vector using Word2Vec. Finally, an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from depression. To verify the model, we compare it with other deep learning and traditional machine learning methods. The experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of 0.8. The most relevant factors of depression found by SEOE are also verified in the literature. In addition, our model is of low computational complexity and strong generalization, which can be widely applied to other questionnaire analyses of psychiatric disorders.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"3 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

WPIA: accelerating DNN warm-up in Web browsers by precompiling WebGL programs WPIA：通过预编译 WebGL 程序加速 DNN 在网络浏览器中的预热

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-06-25 DOI: 10.1007/s11704-024-40066-w

Deyu Tian, Yun Ma, Yudong Han, Qi Yang, Haochen Yang, Gang Huang

引用次数: 0

FedTop: a constraint-loosed federated learning aggregation method against poisoning attack FedTop：针对中毒攻击的限制松散联合学习聚合方法

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-06-25 DOI: 10.1007/s11704-024-3767-z

Che Wang, Zhenhao Wu, Jianbo Gao, Jiashuo Zhang, Junjie Xia, Feng Gao, Zhi Guan, Zhong Chen

引用次数: 0

Audio-guided self-supervised learning for disentangled visual speech representations 针对分离视觉语音表征的音频引导自监督学习

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-06-25 DOI: 10.1007/s11704-024-3787-8

Dalu Feng, Shuang Yang, Shiguang Shan, Xilin Chen

引用次数: 0

JAPO: learning join and pushdown order for cloud-native join optimization JAPO：学习连接和下推顺序，实现云原生连接优化

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-06-25 DOI: 10.1007/s11704-024-3937-z

Yuchen Yuan, Xiaoyue Feng, Bo Zhang, Pengyi Zhang, Jie Song

引用次数: 0

TV100: a TV series dataset that pre-trained CLIP has not seen TV100：预训练 CLIP 未见过的电视剧数据集

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-06-06 DOI: 10.1007/s11704-024-40217-z

Da-Wei Zhou, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan

引用次数: 0

HeterMM: applying in-DRAM index to heterogeneous memory-based key-value stores HeterMM：将内存内索引应用于基于异构内存的键值存储

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-04-08 DOI: 10.1007/s11704-024-3713-0

Yunhong Ji, Wentao Huang, Xuan Zhou

引用次数: 0