The Experts below are selected from a list of 1055244 Experts worldwide ranked by ideXlab platform
Yang Yang - One of the best experts on this subject based on the ideXlab platform.
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service migration for deadline varying user Generated Data in mobile edge clouds
World Congress on Services, 2018Co-Authors: Zhipeng Gao, Jie Meng, Qian Wang, Yang YangAbstract:Mobile edge computing is a promising paradigm to compensate for the lack of traditional cloud computing, which has a variety of application scenarios. However, the migration of user-Generated Data in edge networks is a key issue which involves in transmission costs, the mobility of users, transmission resources, etc. In this paper, we focus on migrating deadline-varying user-Generated Data to edge servers, considering the tasks characteristics and contact patterns between nodes. We design a heuristic algorithm and propose the online algorithm using real-time information to save the cost of transmission. Further, we conduct the extensive simulations to demonstrate the effectiveness of our algorithms.
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SERVICES - Service Migration for Deadline-Varying User-Generated Data in Mobile Edge-Clouds
2018 IEEE World Congress on Services (SERVICES), 2018Co-Authors: Zhipeng Gao, Jie Meng, Qian Wang, Yang YangAbstract:Mobile edge computing is a promising paradigm to compensate for the lack of traditional cloud computing, which has a variety of application scenarios. However, the migration of user-Generated Data in edge networks is a key issue which involves in transmission costs, the mobility of users, transmission resources, etc. In this paper, we focus on migrating deadline-varying user-Generated Data to edge servers, considering the tasks characteristics and contact patterns between nodes. We design a heuristic algorithm and propose the online algorithm using real-time information to save the cost of transmission. Further, we conduct the extensive simulations to demonstrate the effectiveness of our algorithms.
Anton Konushin - One of the best experts on this subject based on the ideXlab platform.
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ACIVS - Evaluation of Traffic Sign Recognition Methods Trained on Synthetically Generated Data
Advanced Concepts for Intelligent Vision Systems, 2013Co-Authors: Boris Moiseev, Artem Konev, Alexander Chigorin, Anton KonushinAbstract:Most of today's machine learning techniques requires large manually labeled Data. This problem can be solved by using synthetic images. Our main contribution is to evaluate methods of traffic sign recognition trained on synthetically Generated Data and show that results are comparable with results of classifiers trained on real Dataset. To get a representative synthetic Dataset we model different sign image variations such as intra-class variability, imprecise localization, blur, lighting, and viewpoint changes. We also present a new method for traffic sign segmentation, based on a nearest neighbor search in the large set of synthetically Generated samples, which improves current traffic sign recognition algorithms.
Keke Chen - One of the best experts on this subject based on the ideXlab platform.
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ESORICS (1) - Confidential Boosting with Random Linear Classifiers for Outsourced User-Generated Data
Lecture Notes in Computer Science, 2019Co-Authors: Sagar Sharma, Keke ChenAbstract:User-Generated Data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a Data owner can continuously record Data Generated by distributed users and build various predictive models from the Data to improve its operations, services, and revenue. Due to the large size and evolving nature of users Data, a Data owner may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-Generated Data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for Data owners that want to learn predictive models from aggregated user-Generated Data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive Data. SecureBoost allows users to submit encrypted or randomly masked Data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud’s processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the Data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-Generated Data.
Massimo Franceschet - One of the best experts on this subject based on the ideXlab platform.
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xpathmark an xpath benchmark for the xmark Generated Data
International XML Database Symposium, 2005Co-Authors: Massimo FranceschetAbstract:We propose XPathMark, an XPath benchmark on top of the XMark Generated Data. It consists of a set of queries which covers the main aspects of the language XPath 1.0. These queries have been designed for XML documents Generated under XMark, a popular benchmark for XML Data management. We suggest a methodology to evaluate the XPathMark on a given XML engine and, by way of example, we evaluate two popular XML engines using the proposed benchmark.
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XSym - XPathMark: an XPath benchmark for the XMark Generated Data
Database and XML Technologies, 2005Co-Authors: Massimo FranceschetAbstract:We propose XPathMark, an XPath benchmark on top of the XMark Generated Data. It consists of a set of queries which covers the main aspects of the language XPath 1.0. These queries have been designed for XML documents Generated under XMark, a popular benchmark for XML Data management. We suggest a methodology to evaluate the XPathMark on a given XML engine and, by way of example, we evaluate two popular XML engines using the proposed benchmark.
Zhipeng Gao - One of the best experts on this subject based on the ideXlab platform.
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service migration for deadline varying user Generated Data in mobile edge clouds
World Congress on Services, 2018Co-Authors: Zhipeng Gao, Jie Meng, Qian Wang, Yang YangAbstract:Mobile edge computing is a promising paradigm to compensate for the lack of traditional cloud computing, which has a variety of application scenarios. However, the migration of user-Generated Data in edge networks is a key issue which involves in transmission costs, the mobility of users, transmission resources, etc. In this paper, we focus on migrating deadline-varying user-Generated Data to edge servers, considering the tasks characteristics and contact patterns between nodes. We design a heuristic algorithm and propose the online algorithm using real-time information to save the cost of transmission. Further, we conduct the extensive simulations to demonstrate the effectiveness of our algorithms.
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SERVICES - Service Migration for Deadline-Varying User-Generated Data in Mobile Edge-Clouds
2018 IEEE World Congress on Services (SERVICES), 2018Co-Authors: Zhipeng Gao, Jie Meng, Qian Wang, Yang YangAbstract:Mobile edge computing is a promising paradigm to compensate for the lack of traditional cloud computing, which has a variety of application scenarios. However, the migration of user-Generated Data in edge networks is a key issue which involves in transmission costs, the mobility of users, transmission resources, etc. In this paper, we focus on migrating deadline-varying user-Generated Data to edge servers, considering the tasks characteristics and contact patterns between nodes. We design a heuristic algorithm and propose the online algorithm using real-time information to save the cost of transmission. Further, we conduct the extensive simulations to demonstrate the effectiveness of our algorithms.
