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

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

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

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

A survey on large language model based autonomous agents 基于大型语言模型的自主代理调查

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-03-22 DOI: 10.1007/s11704-024-40231-1

Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Jirong Wen

{"title":"A survey on large language model based autonomous agents","authors":"Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Jirong Wen","doi":"10.1007/s11704-024-40231-1","DOIUrl":"https://doi.org/10.1007/s11704-024-40231-1","url":null,"abstract":"Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective. We first discuss the construction of LLM-based autonomous agents, proposing a unified framework that encompasses much of previous work. Then, we present a overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"304 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199133","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

SCREEN: predicting single-cell gene expression perturbation responses via optimal transport SCREEN：通过优化传输预测单细胞基因表达扰动反应

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-03-22 DOI: 10.1007/s11704-024-31014-9

Haixin Wang, Yunhan Wang, Qun Jiang, Yan Zhang, Shengquan Chen

引用次数: 0

A biased edge enhancement method for truss-based community search 基于桁架的群体搜索的偏置边缘增强法

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2024-03-15 DOI: 10.1007/s11704-024-2604-8

Yuqi Li, Tao Meng, Zhixiong He, Haiyan Liu, Keqin Li

引用次数: 0