2020 IEEE International Conference on Web Services (ICWS)最新文献_第2页

WGT: Thwarting Web Attacks Through Web Gene Tree-based Moving Target Defense WGT:通过基于Web基因树的移动目标防御挫败Web攻击

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00054

Yaqin Zhang, Duohe Ma, Xiaoyan Sun, Kai Chen, Feng Liu

引用次数: 2

A Simulation-based Comparison between Industrial Autoscaling Solutions and COCOS for Cloud Applications 基于仿真的工业自动缩放解决方案和云应用COCOS的比较

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00020

L. Baresi, G. Quattrocchi

引用次数: 2

A Category Aware Non-negative Matrix Factorization Approach for App Permission Recommendation 基于类别感知的应用程序权限推荐非负矩阵分解方法

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00038

Xiaocao Hu, Lili Lu, Haoyang Wu

{"title":"A Category Aware Non-negative Matrix Factorization Approach for App Permission Recommendation","authors":"Xiaocao Hu, Lili Lu, Haoyang Wu","doi":"10.1109/ICWS49710.2020.00038","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00038","url":null,"abstract":"The permission mechanism in Android imposes additional requirements on app developers, since developers have to learn not only the APIs to be used, but also the permissions to be declared. Recommending permissions for apps becomes necessary and meaningful to help developers determine suitable permissions to be declared in apps. Previous studies suffer from the cold-start problem and do not consider the fact that categories of APIs invoked by apps may influence permissions required by apps, since APIs with similar usage may request same permissions. To address these issues, this paper proposes a Category aware Non-negative Matrix Factorization (CNMF) framework to recommend app permissions. The framework firstly calculates semantic similarities among APIs based on word embeddings and clusters similar APIs into the same category, and then computes the probabilities of apps using APIs in each category and integrates the app-category information into the non-negative matrix factorization. Experimental results on a real-world dataset show that our framework can achieve better performance than the state-of-the-art approaches.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130197804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

POEM: Position Order Enhanced Model for Session-based Recommendation Service 基于会话的推荐服务的位置订单增强模型

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00024

Mingyou Sun, Jiahao Yuan, Zihan Song, Yuanyuan Jin, Xingjian Lu, Xiaoling Wang

{"title":"POEM: Position Order Enhanced Model for Session-based Recommendation Service","authors":"Mingyou Sun, Jiahao Yuan, Zihan Song, Yuanyuan Jin, Xingjian Lu, Xiaoling Wang","doi":"10.1109/ICWS49710.2020.00024","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00024","url":null,"abstract":"Session-based recommendation, which aims to predict the next action of an anonymous user base on the interaction information in a session, plays a crucial role in many online services. Recent works solve the problem with the latest deep learning techniques and have achieved good performance on some datasets. However, they have some shortcomings that affect their practical application value: a) the drift process of users' interests in the browsing is not well explored; b) the association between a user's current interests and general preferences in the session is not adequately considered. They mostly assume that the last interaction has a significant impact on the next interaction, which makes them work well only in limited scenarios and specific datasets. To address these limitations, we propose a session-based recommendation model called POEM, which explicitly considers the impact of interaction order relationships on recommendations by emphasizing position attributes in the session. Specifically, POEM models the macro and micro importance of each item in the session, the influence of user interaction order on the item-level collaboration, and the session-level collaboration reflected in the user interest drift process, respectively. Extensive experiments of the effectiveness, efficiency, and universality on three real-world datasets show that our method outperforms various state-of-the-art session-based recommendation methods consistently.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127028242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Enhancing Cross-domain Recommendation through Preference Structure Information Sharing 通过偏好结构信息共享增强跨领域推荐

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00076

N. Zhu, Jian Cao

{"title":"Enhancing Cross-domain Recommendation through Preference Structure Information Sharing","authors":"N. Zhu, Jian Cao","doi":"10.1109/ICWS49710.2020.00076","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00076","url":null,"abstract":"Cross-domain recommendation can alleviate data sparsity problems by leveraging data from multiple domains. Hence it is becoming an emerging research topic. Existing approaches based on latent factor models usually attempt to utilize cross-domain information in the form of inner product or Euclidean distance. This kind of form only enables the model to incorporate non-structure similarity relations between entities. However, the studies in the literature of consumer behavior have disclosed that consumers make decisions following a structural characteristic. Inspired by this, we propose a new cross-domain recommendation model, named PSRec, which learns and shares users' inherent preference structure information during their decision making in relation to their product choices in different domains. In PSRec, the latent factors of items of different domains are mapped to a common space so that the factorized user latent vectors have similar preference structures. Moreover, users' preference structure information is transferred from an auxiliary domain to a target domain as a constraint to a non-negative matrix factorization algorithm. Extensive experiments on two real-world datasets demonstrate the effectiveness and competitive performance of PSRec compared to several state-of-the-art solutions.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130472500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Big Service with Network Represent Learning for Quantified Flight Delay Prediction 基于网络表示学习的大服务航班延误量化预测

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00044

Ziyu Guo, Guangxu Mei, Lei Bian, Hongwu Tang, Diansheng Wang, Li Pan, Shijun Liu

{"title":"A Big Service with Network Represent Learning for Quantified Flight Delay Prediction","authors":"Ziyu Guo, Guangxu Mei, Lei Bian, Hongwu Tang, Diansheng Wang, Li Pan, Shijun Liu","doi":"10.1109/ICWS49710.2020.00044","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00044","url":null,"abstract":"An air traffic network is a special and complex Spatio-temporal network. What makes it unique is that multi-data sources-including airports, airlines and air routes-spatial dependence and strong temporal dependence in a dynamic environment. In this paper, we use big service to predict the flight departure delay time in air traffic networks. In the local services layer, we use graph sequences to model the Spatiotemporal network from multi-data sources, what is, using graphs to model the spatial dependence, and using sequences to model the temporal dependence. In the domain-oriented services layer, we use graph neural network to embed the graph sequence. We validate the method on an air Spatiotemporal network. Then, we use the embedding to estimate the departure delay time of the flight based on real-time conditions. In the demand-oriented services layer, we design a weighted cross entropy loss function and use a special evaluation to predict the flight departure delay time by the embedding in the domain-oriented services layer. Evaluated through a series of experiments on a real-world data set, we show that the method produces an effective result on the Spatio-temporal network which is substantially better than state-of-the-art alternative task: flight delay estimation. And it performs well in predicting the departure delay time with a total accuracy of 0.87.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"2019 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132634744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lead Time-Aware Proactive Adaptation for Service-Oriented Systems 面向服务系统的前置时间感知主动适应

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00071

Jingbin Zhang, Meng Ma, Ping Wang

{"title":"Lead Time-Aware Proactive Adaptation for Service-Oriented Systems","authors":"Jingbin Zhang, Meng Ma, Ping Wang","doi":"10.1109/ICWS49710.2020.00071","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00071","url":null,"abstract":"Many service-oriented systems (SoS) operate in uncertain and changing environments. Hence, SoSs should be able to adapt itself during runtime to ensure that they maintain user-expected quality indicators. In real-world environments, some adaptations may have non-negligible latency, and take some lead time to produce their effect. Adapting reactively is an after-the-fact approach, which starts when the system deviates from the expected indicators. It can result in inefficiency and instability due to without anticipating the subsequent adaptation needs. To solve this problem, we propose a novel proactive adaptation solution - LetPa, which makes decisions based on predictions about how adaptations will unfold up to its completion. LetPa divides control parameters into three levels according to the SoS architecture and rates the adaptations considering both goal satisfaction and action penalties. We design a dynamic programming based decision mechanism in LetPa that enables SoS to determine which adaptations need be performed that can prevent and mitigate upcoming problems in the near-future time series. Simulation result implies that LetPa shows good stability and efficiency in SoSs.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132001416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A-SARSA: A Predictive Container Auto-Scaling Algorithm Based on Reinforcement Learning 基于强化学习的预测容器自动缩放算法A- sarsa

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00072

Shubo Zhang, Tianyang Wu, Maolin Pan, Chaomeng Zhang, Yang Yu

{"title":"A-SARSA: A Predictive Container Auto-Scaling Algorithm Based on Reinforcement Learning","authors":"Shubo Zhang, Tianyang Wu, Maolin Pan, Chaomeng Zhang, Yang Yu","doi":"10.1109/ICWS49710.2020.00072","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00072","url":null,"abstract":"Due to the lightweight and flexible characteristics, containers have gradually been used for the application deployment and the basic unit for resource allocation in a cloud platform recently. Reinforcement learning (RL), as a classic algorithm, is widely used in virtual machine scheduling scenarios due to its advantages of adaptability and robustness. However, most RL methods have problems in container scheduling, such as untimely scheduling, lack of accuracy in decision-making and poor dynamics that will lead to a higher SLA violation rate. In order to solve the above problems, a predictive RL algorithm A-SARSA is proposed, which combines the ARIMA model and the neural network model. This algorithm not only ensures the predictability and accuracy of the scaling strategy, but also enables the scaling decisions to adapt to the changing workloads. Through a large number of experiments, the timeliness and effectiveness of the A-SARSA algorithm for container scheduling are verified, which can reduce the SLA violation rate dramatically while keeping the resource utilization rate at a good level.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134221154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Integrating EMD with Multivariate LSTM for Time Series QoS Prediction 集成EMD和多元LSTM的时间序列QoS预测

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00015

Xiuqing Chen, Bing Li, Jian Wang, Yuqi Zhao, Yiming Xiong

引用次数: 4

Trace-driven Modeling and Verification of a Mobility-Aware Service Allocation and Migration Policy for Mobile Edge Computing 移动边缘计算中移动感知服务分配和迁移策略的跟踪驱动建模与验证

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00047

Kaustabha Ray, A. Banerjee

{"title":"Trace-driven Modeling and Verification of a Mobility-Aware Service Allocation and Migration Policy for Mobile Edge Computing","authors":"Kaustabha Ray, A. Banerjee","doi":"10.1109/ICWS49710.2020.00047","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00047","url":null,"abstract":"In recent times, Mobile Edge Computing (MEC) has emerged as a new paradigm allowing low-latency access to services deployed on edge nodes offering computation, storage and communication facilities. Vendors deploy their services on MEC servers to improve performance and mitigate network latencies often encountered in accessing cloud services. An allocation policy determines how to allocate service requests from mobile users to MEC servers. A number of proposals for binding user service requests to nearby edge servers enroute have been proposed in literature. However, none of these proposals, to the best of our knowledge, provide quantitative performance guarantees on the quality of service metrics. Indeed, the evolving environment, along with a large allocation configuration space makes proving performance guarantees for such allocation policies a challenging task. To address such issues, we propose a trace driven approach to derive a formal model of allocation policies and perform quantitative verification to produce probabilistic guarantees on performance metrics. We use benchmark real world MEC server and user datasets and a mobility aware allocation and migration policy from recent literature to validate our model. Experimental results show our model's effectiveness in quantitatively reasoning about service allocation performance metrics in MEC systems.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132745984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2