Frontiers of Computer Science最新文献_第9页

CW-YOLO: joint learning for mask wearing detection in low-light conditions CW-YOLO:微光条件下口罩佩戴检测联合学习

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-02 DOI: 10.1007/s11704-023-3351-y

Mingqiang Guo, Hongting Sheng, Zhizheng Zhang, Ying Huang, Xueye Chen, Cunjin Wang, Jiaming Zhang

引用次数: 0

Alignment efficient image-sentence retrieval considering transferable cross-modal representation learning 考虑可转移跨模态表示学习的对齐高效图像句子检索

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-02 DOI: 10.1007/s11704-023-3186-6

Yang Yang, Jinyi Guo, Guangyu Li, Lanyu Li, Wenjie Li, Jian Yang

{"title":"Alignment efficient image-sentence retrieval considering transferable cross-modal representation learning","authors":"Yang Yang, Jinyi Guo, Guangyu Li, Lanyu Li, Wenjie Li, Jian Yang","doi":"10.1007/s11704-023-3186-6","DOIUrl":"https://doi.org/10.1007/s11704-023-3186-6","url":null,"abstract":"Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities, thereby to search similar instances in one modality according to the query from another modality in result. The basic assumption behind these methods is that parallel multi-modal data (i.e., different modalities of the same example are aligned) can be obtained in prior. In other words, the image-sentence cross-modal retrieval task is a supervised task with the alignments as ground-truths. However, in many real-world applications, it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs, leading the non-parallel multi-modal data and existing methods cannot be used directly. On the other hand, there actually exists auxiliary parallel multi-modal data with similar semantics, which can assist the non-parallel data to learn the consistent representations. Therefore, in this paper, we aim at “Alignment Efficient Image-Sentence Retrieval” (AEIR), which recurs to the auxiliary parallel image-sentence data as the source domain data, and takes the non-parallel data as the target domain data. Unlike single-modal transfer learning, AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel data. Specifically, AEIR learns the image-sentence consistent representations in source domain with parallel data, while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial loss. Consequently, we can effectively learn the consistent representations for target domain considering both the structure and semantic transfer. Furthermore, extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"21 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534574","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

A survey on federated learning: a perspective from multi-party computation 联邦学习研究综述:多方计算视角

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-02 DOI: 10.1007/s11704-023-3282-7

Fengxia Liu, Zhiming Zheng, Yexuan Shi, Yongxin Tong, Yi Zhang

引用次数: 0

Improved differential-neural cryptanalysis for round-reduced SIMECK32/64 SIMECK32/64的改进差分神经密码分析

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-02 DOI: 10.1007/s11704-023-3261-z

Liu Zhang, Jinyu Lu, Zilong Wang, Chao Li

引用次数: 1

Adaptive fusion of structure and attribute guided polarized communities search 结构与属性自适应融合引导极化群落搜索

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-02 DOI: 10.1007/s11704-023-2776-7

Fanyi Yang, Huifang Ma, Wentao Wang, Zhixin Li, Liang Chang

引用次数: 0

A sampling method based on forecasting and combinatorial optimization for high performance A/B testing 基于预测和组合优化的抽样方法，用于高性能 A/B 测试

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-01 DOI: 10.1007/s11704-023-3460-7

Fan Li, Tiancheng Zhang, Shengjia Cui, Hengyu Liu, Zhibin Ren, Donglin Di, Xiao Wang, Po Zhang, Gensitskiy Yu.

引用次数: 0

Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks 基于时空图卷积网络的动态旅行时间预测

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-01 DOI: 10.1007/s11704-023-2704-x

Fangshu Chen, Yufei Zhang, Lu Chen, Xiankai Meng, Yanqiang Qi, Jiahui Wang

引用次数: 0

Announcement of the 2023 FCS Paper Awards 宣布 2023 年 FCS 论文奖

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-01 DOI: 10.1007/s11704-023-3998-4

引用次数: 0

Scattering-based hybrid network for facial attribute classification 基于散射的人脸属性分类混合网络

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-11-25 DOI: 10.1007/s11704-023-2570-6

Na Liu, Fan Zhang, Liang Chang, Fuqing Duan

{"title":"Scattering-based hybrid network for facial attribute classification","authors":"Na Liu, Fan Zhang, Liang Chang, Fuqing Duan","doi":"10.1007/s11704-023-2570-6","DOIUrl":"https://doi.org/10.1007/s11704-023-2570-6","url":null,"abstract":"Face attribute classification (FAC) is a high-profile problem in biometric verification and face retrieval. Although recent research has been devoted to extracting more delicate image attribute features and exploiting the inter-attribute correlations, significant challenges still remain. Wavelet scattering transform (WST) is a promising non-learned feature extractor. It has been shown to yield more discriminative representations and outperforms the learned representations in certain tasks. Applied to the image classification task, WST can enhance subtle image texture information and create local deformation stability. This paper designs a scattering-based hybrid block, to incorporate frequency-domain (WST) and image-domain features in a channel attention manner (Squeeze-and-Excitation, SE), termed WS-SE block. Compared with CNN, WS-SE achieves a more efficient FAC performance and compensates for the model sensitivity of the small-scale affine transform. In addition, to further exploit the relationships among the attribute labels, we propose a learning strategy from a causal view. The cause attributes defined using the causality-related information can be utilized to infer the effect attributes with a high confidence level. Ablative analysis experiments demonstrate the effectiveness of our model, and our hybrid model obtains state-of-the-art results in two public datasets.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"27 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534581","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

Simulation study on the security of consensus algorithms in DAG-based distributed ledger 基于dag的分布式账本共识算法安全性仿真研究

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-11-25 DOI: 10.1007/s11704-023-2497-y

Shuzhe Li, Hongwei Xu, Qiong Li, Qi Han

{"title":"Simulation study on the security of consensus algorithms in DAG-based distributed ledger","authors":"Shuzhe Li, Hongwei Xu, Qiong Li, Qi Han","doi":"10.1007/s11704-023-2497-y","DOIUrl":"https://doi.org/10.1007/s11704-023-2497-y","url":null,"abstract":"Due to the advantages of high volume of transactions and low resource consumption, Directed Acyclic Graph (DAG)-based Distributed Ledger Technology (DLT) has been considered a possible next-generation alternative to block-chain. However, the security of the DAG-based system has yet to be comprehensively understood. Aiming at verifying and evaluating the security of DAG-based DLT, we develop a Multi-Agent based IOTA Simulation platform called MAIOTASim. In MAIOTASim, we model honest and malicious nodes and simulate the configurable network environment, including network topology and delay. The double-spending attack is a particular security issue related to DLT. We perform the security verification of the consensus algorithms under multiple double-spending attack strategies. Our simulations show that the consensus algorithms can resist the parasite chain attack and partially resist the splitting attack, but they are ineffective under the large weight attack. We take the cumulative weight difference of transactions as the evaluation criterion and analyze the effect of different consensus algorithms with parameters under each attack strategy. Besides, MAIOTASim enables users to perform large-scale simulations with multiple nodes and tens of thousands of transactions more efficiently than state-of-the-art ones.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"19 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138534580","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