Frontiers of Computer Science最新文献_第5页

A program logic for obstruction-freedom 不受阻碍的程序逻辑

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-28 DOI: 10.1007/s11704-023-2774-9

Zhao-Hui Li, Xin-Yu Feng

引用次数: 0

IP2vec: an IP node representation model for IP geolocation IP2vec：用于 IP 地理定位的 IP 节点表示模型

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-28 DOI: 10.1007/s11704-023-2616-9

Fan Zhang, Meijuan Yin, Fenlin Liu, Xiangyang Luo, Shuodi Zu

{"title":"IP2vec: an IP node representation model for IP geolocation","authors":"Fan Zhang, Meijuan Yin, Fenlin Liu, Xiangyang Luo, Shuodi Zu","doi":"10.1007/s11704-023-2616-9","DOIUrl":"https://doi.org/10.1007/s11704-023-2616-9","url":null,"abstract":"IP geolocation is essential for the territorial analysis of sensitive network entities, location-based services (LBS) and network fraud detection. It has important theoretical significance and application value. Measurement-based IP geolocation is a hot research topic. However, the existing IP geolocation algorithms cannot effectively utilize the distance characteristics of the delay, and the nodes’ connection relation, resulting in high geolocation error. It is challenging to obtain the mapping between delay, nodes’ connection relation, and geographical location. Based on the idea of network representation learning, we propose a representation learning model for IP nodes (IP2vec for short) and apply it to street-level IP geolocation. IP2vec model vectorizes nodes according to the connection relation and delay between nodes so that the IP vectors can reflect the distance and topological proximity between IP nodes. The steps of the street-level IP geolocation algorithm based on IP2vec model are as follows: Firstly, we measure landmarks and target IP to obtain delay and path information to construct the network topology. Secondly, we use the IP2vec model to obtain the IP vectors from the network topology. Thirdly, we train a neural network to fit the mapping relation between vectors and locations of landmarks. Finally, the vector of target IP is fed into the neural network to obtain the geographical location of target IP. The algorithm can accurately infer geographical locations of target IPs based on delay and topological proximity embedded in the IP vectors. The cross-validation experimental results on 10023 target IPs in New York, Beijing, Hong Kong, and Zhengzhou demonstrate that the proposed algorithm can achieve street-level geolocation. Compared with the existing algorithms such as Hop-Hot, IP-geolocater and SLG, the mean geolocation error of the proposed algorithm is reduced by 33%, 39%, and 51%, respectively.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"31 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139056534","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

Model gradient: unified model and policy learning in model-based reinforcement learning 模型梯度：基于模型的强化学习中的统一模型和策略学习

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-27 DOI: 10.1007/s11704-023-3150-5

{"title":"Model gradient: unified model and policy learning in model-based reinforcement learning","authors":"","doi":"10.1007/s11704-023-3150-5","DOIUrl":"https://doi.org/10.1007/s11704-023-3150-5","url":null,"abstract":"<h3>Abstract</h3> Model-based reinforcement learning is a promising direction to improve the sample efficiency of reinforcement learning with learning a model of the environment. Previous model learning methods aim at fitting the transition data, and commonly employ a supervised learning approach to minimize the distance between the predicted state and the real state. The supervised model learning methods, however, diverge from the ultimate goal of model learning, i.e., optimizing the learned-in-the-model policy. In this work, we investigate how model learning and policy learning can share the same objective of maximizing the expected return in the real environment. We find model learning towards this objective can result in a target of enhancing the similarity between the gradient on generated data and the gradient on the real data. We thus derive the gradient of the model from this target and propose the Model Gradient algorithm (MG) to integrate this novel model learning approach with policy-gradient-based policy optimization. We conduct experiments on multiple locomotion control tasks and find that MG can not only achieve high sample efficiency but also lead to better convergence performance compared to traditional model-based reinforcement learning approaches.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"32 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139056399","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

ARCHER: a ReRAM-based accelerator for compressed recommendation systems ARCHER：基于 ReRAM 的压缩推荐系统加速器

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-3397-x

Xinyang Shen, Xiaofei Liao, Long Zheng, Yu Huang, Dan Chen, Hai Jin

{"title":"ARCHER: a ReRAM-based accelerator for compressed recommendation systems","authors":"Xinyang Shen, Xiaofei Liao, Long Zheng, Yu Huang, Dan Chen, Hai Jin","doi":"10.1007/s11704-023-3397-x","DOIUrl":"https://doi.org/10.1007/s11704-023-3397-x","url":null,"abstract":"Modern recommendation systems are widely used in modern data centers. The random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and memory. ReRAM-based processing-in-memory (PIM) can resolve this problem by processing embedding vectors where they are stored. However, the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip, which induces off-chip accesses that may offset the PIM profits. Therefore, we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance loss. In this paper, we propose ARCHER, a ReRAM-based PIM architecture that implements fully on-chip recommendations under resource constraints. First, we make a full analysis of the computation pattern and access pattern on the decomposed table. Based on the computation pattern, we unify the operations of each layer of the decomposed model in multiply-and-accumulate operations. Based on the access observation, we propose a hierarchical mapping schema and a specialized hardware design to maximize resource utilization. Under the unified computation and mapping strategy, we can coordinate the inter-processing elements pipeline. The evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system, the state-of-the-art near-memory processing recommendation system RecNMP, and the ReRAM-based recommendation accelerator REREC by 15.79×, 2.21×, and 1.21× in terms of performance and 56.06×, 6.45×, and 1.71× in terms of energy savings, respectively.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"36 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139026723","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

Provable secure authentication key agreement for wireless body area networks 无线体域网络的可证明安全认证密钥协议

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-2548-4

Yuqian Ma, Wenbo Shi, Xinghua Li, Qingfeng Cheng

{"title":"Provable secure authentication key agreement for wireless body area networks","authors":"Yuqian Ma, Wenbo Shi, Xinghua Li, Qingfeng Cheng","doi":"10.1007/s11704-023-2548-4","DOIUrl":"https://doi.org/10.1007/s11704-023-2548-4","url":null,"abstract":"Wireless body area networks (WBANs) guarantee timely data processing and secure information preservation within the range of the wireless access network, which is in urgent need of a new type of security technology. However, with the speedy development of hardware, the existing security schemes can no longer meet the new requirements of anonymity and lightweight. New solutions that do not require complex calculations, such as certificateless cryptography, attract great attention from researchers. To resolve these difficulties, Wang et al. designed a new authentication architecture for the WBANs environment, which was claimed to be secure and efficient. However, in this paper, we will show that this scheme is prone to ephemeral key leakage attacks. Further, based on this authentication scheme, an anonymous certificateless scheme is proposed for lightweight devices. Meanwhile, user anonymity is fully protected. The proposed scheme is proved to be secure under a specific security model. In addition, we assess the security attributes our scheme meets through BAN logic and Scyther tool. The comparisons of time consumption and communication cost are given at the end of the paper, to demonstrate that our scheme performs prior to several previous schemes.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"4 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139025450","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

BVDFed: Byzantine-resilient and verifiable aggregation for differentially private federated learning BVDFed：针对差异化私有联合学习的拜占庭弹性可验证聚合技术

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-3142-5

{"title":"BVDFed: Byzantine-resilient and verifiable aggregation for differentially private federated learning","authors":"","doi":"10.1007/s11704-023-3142-5","DOIUrl":"https://doi.org/10.1007/s11704-023-3142-5","url":null,"abstract":"<h3>Abstract</h3> Federated Learning (FL) has emerged as a powerful technology designed for collaborative training between multiple clients and a server while maintaining data privacy of clients. To enhance the privacy in FL, Differentially Private Federated Learning (DPFL) has gradually become one of the most effective approaches. As DPFL operates in the distributed settings, there exist potential malicious adversaries who manipulate some clients and the aggregation server to produce malicious parameters and disturb the learning model. However, existing aggregation protocols for DPFL concern either the existence of some corrupted clients (Byzantines) or the corrupted server. Such protocols are limited to eliminate the effects of corrupted clients and server when both are in existence simultaneously due to the complicated threat model. In this paper, we elaborate such adversarial threat model and propose BVDFed. To our best knowledge, it is the first Byzantine-resilient and Verifiable aggregation for Differentially private FEDerated learning. In specific, we propose Differentially Private Federated Averaging algorithm (DPFA) as our primary workflow of BVDFed, which is more lightweight and easily portable than traditional DPFL algorithm. We then introduce Loss Score to indicate the trustworthiness of disguised gradients in DPFL. Based on Loss Score, we propose an aggregation rule DPLoss to eliminate faulty gradients from Byzantine clients during server aggregation while preserving the privacy of clients’ data. Additionally, we design a secure verification scheme DPVeri that are compatible with DPFA and DPLoss to support the honest clients in verifying the integrity of received aggregated results. And DPVeri also provides resistance to collusion attacks with no more than t participants for our aggregation. Theoretical analysis and experimental results demonstrate our aggregation to be feasible and effective in practice.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"49 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139026679","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

Label distribution similarity-based noise correction for crowdsourcing 基于标签分布相似性的众包噪声校正

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-2751-3

Lijuan Ren, Liangxiao Jiang, Wenjun Zhang, Chaoqun Li

{"title":"Label distribution similarity-based noise correction for crowdsourcing","authors":"Lijuan Ren, Liangxiao Jiang, Wenjun Zhang, Chaoqun Li","doi":"10.1007/s11704-023-2751-3","DOIUrl":"https://doi.org/10.1007/s11704-023-2751-3","url":null,"abstract":"<h3>Abstract</h3> In crowdsourcing scenarios, we can obtain each instance’s multiple noisy labels from different crowd workers and then infer its integrated label via label aggregation. In spite of the effectiveness of label aggregation methods, there still remains a certain level of noise in the integrated labels. Thus, some noise correction methods have been proposed to reduce the impact of noise in recent years. However, to the best of our knowledge, existing methods rarely consider an instance’s information from both its features and multiple noisy labels simultaneously when identifying a noise instance. In this study, we argue that the more distinguishable an instance’s features but the noisier its multiple noisy labels, the more likely it is a noise instance. Based on this premise, we propose a label distribution similarity-based noise correction (LDSNC) method. To measure whether an instance’s features are distinguishable, we obtain each instance’s predicted label distribution by building multiple classifiers using instances’ features and their integrated labels. To measure whether an instance’s multiple noisy labels are noisy, we obtain each instance’s multiple noisy label distribution using its multiple noisy labels. Then, we use the Kullback-Leibler (KL) divergence to calculate the similarity between the predicted label distribution and multiple noisy label distribution and define the instance with the lower similarity as a noise instance. The extensive experimental results on 34 simulated and four real-world crowdsourced datasets validate the effectiveness of our method.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"10 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139026682","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

Delegable zk-SNARKs with proxies 有代理人的可委托 zk-SNARKs

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-2782-9

{"title":"Delegable zk-SNARKs with proxies","authors":"","doi":"10.1007/s11704-023-2782-9","DOIUrl":"https://doi.org/10.1007/s11704-023-2782-9","url":null,"abstract":"<h3>Abstract</h3> In this paper, we propose the concept of delegable zero knowledge succinct non-interactive arguments of knowledge (zk-SNARKs). The delegable zk-SNARK is parameterized by (μ,k,k′,k″). The delegable property of zk-SNARKs allows the prover to delegate its proving ability to μ proxies. Any k honest proxies are able to generate the correct proof for a statement, but the collusion of less than k proxies does not obtain information about the witness of the statement. We also define k′-soundness and k″-zero knowledge by taking into consider of multi-proxies. We propose a construction of (μ,2t + 1,t,t)- delegable zk-SNARK for the NPC language of arithmetic circuit satisfiability. Our delegable zk-SNARK stems from Groth’s zk-SNARK scheme (Groth16). We take advantage of the additive and multiplicative properties of polynomial-based secret sharing schemes to achieve delegation for zk-SNARK. Our secret sharing scheme works well with the pairing groups so that the nice succinct properties of Groth’s zk-SNARK scheme are preserved, while augmenting the delegable property and keeping soundness and zero-knowledge in the scenario of multi-proxies.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"26 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139026952","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

Contactless interaction recognition and interactor detection in multi-person scenes 多人场景中的非接触式交互识别和交互者检测

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-2418-0

Jiacheng Li, Ruize Han, Wei Feng, Haomin Yan, Song Wang

引用次数: 0

Empirically revisiting and enhancing automatic classification of bug and non-bug issues 以经验为基础，重新审视并加强错误和非错误问题的自动分类

IF 4.2 3区计算机科学

Frontiers of Computer Science Pub Date : 2023-12-23 DOI: 10.1007/s11704-023-2771-z

Zhong Li, Minxue Pan, Yu Pei, Tian Zhang, Linzhang Wang, Xuandong Li

{"title":"Empirically revisiting and enhancing automatic classification of bug and non-bug issues","authors":"Zhong Li, Minxue Pan, Yu Pei, Tian Zhang, Linzhang Wang, Xuandong Li","doi":"10.1007/s11704-023-2771-z","DOIUrl":"https://doi.org/10.1007/s11704-023-2771-z","url":null,"abstract":"A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems (ITSs). Although the existing approaches have shown promising performance, the different design choices, including the different textual fields, feature representation methods and machine learning algorithms adopted by existing approaches, have not been comprehensively compared and analyzed. To fill this gap, we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches. Our experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification, including: (1) Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification; (2) Word embedding with Long Short-Term Memory (LSTM) can better extract features from the textual fields in the issues, and hence, lead to better issue classification models; (3) There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues; (4) The performance of the issue classification model is not sensitive to the choices of ML algorithms. Based on our study outcomes, we further propose an advanced issue classification approach, DeepLabel, which can achieve better performance compared with the existing issue classification approaches.","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"32 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139026681","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