Frontiers of Computer Science最新文献_第6页

Discriminative explicit instance selection for implicit discourse relation classification 用于隐式话语关系分类的辨证显式实例选择

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-3058-2

Wei Song, Hongfei Han, Xu Han, Miaomiao Cheng, Jiefu Gong, Shijin Wang, Ting Liu

{"title":"Discriminative explicit instance selection for implicit discourse relation classification","authors":"Wei Song, Hongfei Han, Xu Han, Miaomiao Cheng, Jiefu Gong, Shijin Wang, Ting Liu","doi":"10.1007/s11704-023-3058-2","DOIUrl":"https://doi.org/10.1007/s11704-023-3058-2","url":null,"abstract":"Discourse relation classification is a fundamental task for discourse analysis, which is essential for understanding the structure and connection of texts. Implicit discourse relation classification aims to determine the relationship between adjacent sentences and is very challenging because it lacks explicit discourse connectives as linguistic cues and sufficient annotated training data. In this paper, we propose a discriminative instance selection method to construct synthetic implicit discourse relation data from easy-to-collect explicit discourse relations. An expanded instance consists of an argument pair and its sense label. We introduce the argument pair type classification task, which aims to distinguish between implicit and explicit argument pairs and select the explicit argument pairs that are most similar to natural implicit argument pairs for data expansion. We also propose a simple label-smoothing technique to assign robust sense labels for the selected argument pairs. We evaluate our method on PDTB 2.0 and PDTB 3.0. The results show that our method can consistently improve the performance of the baseline model, and achieve competitive results with the state-of-the-art models.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"30 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139025427","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

How graph convolutions amplify popularity bias for recommendation? 图卷积如何放大推荐时的人气偏差？

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-2655-2

{"title":"How graph convolutions amplify popularity bias for recommendation?","authors":"","doi":"10.1007/s11704-023-2655-2","DOIUrl":"https://doi.org/10.1007/s11704-023-2655-2","url":null,"abstract":"<h3>Abstract</h3> Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall accuracy, GCNs unfortunately amplify popularity bias — tail items are less likely to be recommended. This effect prevents the GCN-based RS from making precise and fair recommendations, decreasing the effectiveness of recommender systems in the long run. In this paper, we investigate how graph convolutions amplify the popularity bias in RS. Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (i.e., neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential. The two points make popular items get closer to almost users and thus being recommended more frequently. To rectify this, we propose to estimate the amplified effect of popular nodes on each node’s representation, and intervene the effect after each graph convolution. Specifically, we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node, then remove the effect from the node embeddings at each graph convolution layer. Our method is simple and generic — it can be used in the inference stage to correct existing models rather than training a new model from scratch, and can be applied to various GCN models. We demonstrate our method on two representative GCN backbones LightGCN and UltraGCN, verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular items. Codes are open-sourced 1).","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"19 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139026894","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

Blockchain based federated learning for intrusion detection for Internet of Things 基于区块链的联合学习，用于物联网入侵检测

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-3026-8

Nan Sun, Wei Wang, Yongxin Tong, Kexin Liu

引用次数: 0

Optimizing B+-tree for hybrid memory with in-node hotspot cache and eADR awareness 利用节点内热点缓存和 eADR 感知优化混合内存的 B+ 树

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-3344-x

Peiquan Jin, Zhaole Chu, Gaocong Liu, Yongping Luo, Shouhong Wan

{"title":"Optimizing B+-tree for hybrid memory with in-node hotspot cache and eADR awareness","authors":"Peiquan Jin, Zhaole Chu, Gaocong Liu, Yongping Luo, Shouhong Wan","doi":"10.1007/s11704-023-3344-x","DOIUrl":"https://doi.org/10.1007/s11704-023-3344-x","url":null,"abstract":"The advance in Non-Volatile Memory (NVM) has changed the traditional DRAM-only memory system. Compared to DRAM, NVM has the advantages of non-volatility and large capacity. However, as the read/write speed of NVM is still lower than that of DRAM, building DRAM/NVM-based hybrid memory systems is a feasible way of adding NVM into the current computer architecture. This paper aims to optimize the well-known B+-tree for hybrid memory. The novelty of this study is two-fold. First, we observed that the space utilization of internal nodes in B+-tree is generally below 70%. Inspired by this observation, we propose to maintain hot keys in the free space within internal nodes, yielding a new index named HATree (Hotness-Aware Tree). The new idea of HATree is to use the unused space of the parent of leaf nodes (PLNs) as the hotspot data cache. Thus, no extra space is needed, and the in-node hotspot cache can efficiently improve query performance. Second, to further improve the update performance of HATree, we propose to utilize the eADR technology supported by the third-generation Intel Xeon Scalable Processors to enhance HATree with instant log persistence, which results in the new HATree-Log structure. We conduct extensive experiments on real hybrid memory architecture involving DRAM and Intel Optane Persistent Memory to evaluate the performance of HATree and HATree-Log. Three state-of-the-art indices for hybrid memory, namely NBTree, LBTree, and FPTree, are included in the experiments, and the results suggest the efficiency of HATree and HATree-Log.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"112 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139025409","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

Route selection for opportunity-sensing and prediction of waterlogging 机会感应和内涝预测的路线选择

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-18 DOI: 10.1007/s11704-023-2714-8

{"title":"Route selection for opportunity-sensing and prediction of waterlogging","authors":"","doi":"10.1007/s11704-023-2714-8","DOIUrl":"https://doi.org/10.1007/s11704-023-2714-8","url":null,"abstract":"<h3>Abstract</h3> Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’ daily travel. However, due to feedback delays or high costs, existing methods make large-scale, fine-grained waterlogging monitoring impossible. A common method is to forecast the city’s global waterlogging status using its partial waterlogging data. This method has two challenges: first, existing predictive algorithms are either driven by knowledge or data alone; and second, the partial waterlogging data is not collected selectively, resulting in poor predictions. To overcome the aforementioned challenges, this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus routes. This framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and selector. The predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural network. It combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming term. The selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget constraints. The experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost, high accuracy, wide coverage, and fine granularity.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"45 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138743332","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

Multi-user reinforcement learning based task migration in mobile edge computing 移动边缘计算中基于多用户强化学习的任务迁移

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-18 DOI: 10.1007/s11704-023-1346-3

Yuya Cui, Degan Zhang, Jie Zhang, Ting Zhang, Lixiang Cao, Lu Chen

引用次数: 0

HACAN: a hierarchical answer-aware and context-aware network for question generation HACAN：用于生成问题的分层答案感知和上下文感知网络

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-18 DOI: 10.1007/s11704-023-2246-2

Ruijun Sun, Hanqin Tao, Yanmin Chen, Qi Liu

{"title":"HACAN: a hierarchical answer-aware and context-aware network for question generation","authors":"Ruijun Sun, Hanqin Tao, Yanmin Chen, Qi Liu","doi":"10.1007/s11704-023-2246-2","DOIUrl":"https://doi.org/10.1007/s11704-023-2246-2","url":null,"abstract":"Question Generation (QG) is the task of generating questions according to the given contexts. Most of the existing methods are based on Recurrent Neural Networks (RNNs) for generating questions with passage-level input for providing more details, which seriously suffer from such problems as gradient vanishing and ineffective information utilization. In fact, reasonably extracting useful information from a given context is more in line with our actual needs during questioning especially in the education scenario. To that end, in this paper, we propose a novel Hierarchical Answer-Aware and Context-Aware Network (HACAN) to construct a high-quality passage representation and judge the balance between the sentences and the whole passage. Specifically, a Hierarchical Passage Encoder (HPE) is proposed to construct an answer-aware and context-aware passage representation, with a strategy of utilizing multi-hop reasoning. Then, we draw inspiration from the actual human questioning process and design a Hierarchical Passage-aware Decoder (HPD) which determines when to utilize the passage information. We conduct extensive experiments on the SQuAD dataset, where the results verify the effectiveness of our model in comparison with several baselines.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"6 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138743206","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

Embracing connected intelligence with the YuanOS architecture: one OS kit for all 利用 YuanOS 架构实现互联智能：一个操作系统套件满足所有需求

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-18 DOI: 10.1007/s11704-023-3997-5

Haibo Chen, Ning Jia, Jie Yin

引用次数: 0

Semantic-aware entity alignment for low resource language knowledge graph 针对低资源语言知识图谱的语义感知实体对齐

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-18 DOI: 10.1007/s11704-023-2542-x

Junfei Tang, Ran Song, Yuxin Huang, Shengxiang Gao, Zhengtao Yu

{"title":"Semantic-aware entity alignment for low resource language knowledge graph","authors":"Junfei Tang, Ran Song, Yuxin Huang, Shengxiang Gao, Zhengtao Yu","doi":"10.1007/s11704-023-2542-x","DOIUrl":"https://doi.org/10.1007/s11704-023-2542-x","url":null,"abstract":"Entity alignment (EA) is an important technique aiming to find the same real entity between two different source knowledge graphs (KGs). Current methods typically learn the embedding of entities for EA from the structure of KGs for EA. Most EA models are designed for rich-resource languages, requiring sufficient resources such as a parallel corpus and pre-trained language models. However, low-resource language KGs have received less attention, and current models demonstrate poor performance on those low-resource KGs. Recently, researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance, but the relation semantics are often ignored. To address these issues, we propose a novel Semantic-aware Graph Neural Network (SGNN) for entity alignment. First, we generate pseudo sentences according to the relation triples and produce representations using pre-trained models. Second, our approach explores semantic information from the connected relations by a graph neural network. Our model captures expanded feature information from KGs. Experimental results using three low-resource languages demonstrate that our proposed SGNN approach out performs better than state-of-the-art alignment methods on three proposed datasets and three public datasets.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"108 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138715450","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

On the upper bounds of (1,0)-super solutions for the regular balanced random (k,2s)-SAT problem 论常规平衡随机 (k,2s)-SAT 问题的 (1,0)- 超级解的上限

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-18 DOI: 10.1007/s11704-023-2752-2

Yongping Wang, Daoyun Xu, Jincheng Zhou

引用次数: 0