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Review Spam Detection Based on Multi-dimensional Features 9th

Review Spam Detection Based on Multi-dimensional Features

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Deng, Liming Wei, Jingjing Liang, Shaobin Wen, Yuhan Liao, Xiangwen College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Digital Fujian Institute of Financial Big Data Fuzhou350116 China College of Electronics and Information Science Fujian Jiangxia University Fuzhou350108 China

ISBN: (纸本)9783030596040

Review spam detection aims to detect the reviews with false information posted by the spammers on social media. The existing methods of review spam detection ignore the importance of the information hidden in the user interactive behaviors and fail to extract the indistinct contextual features caused by irregular writing style of reviews. In this paper, a new review spam detection method based on multi-dimensional features is proposed. The method utilizes the principal component analysis to get low-dimensional features to characterize the user-product relationship. Then, a neural network constructed with nested LSTM and capsule network is trained to extract textual context features and spatial structure features. Finally, the model combines the text and user behavioral features as the overall features, which are used as the input to the classification module to detect spam reviews. Experimental results show that the F1 value of our proposed method is 1.6%~3.5% higher than the existing methods, indicating the efficiency and effectiveness of our model, especially on the natural distribution datasets. © 2020, Springer Nature Switzerland AG.

关键词： Feature extraction

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A Novel Framework for Concept Detection on Large Scale Video Database and Feature Pool

A Novel Framework for Concept Detection on Large Scale Video...

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2011中国人工生命与智能机器人会议

作者： LV Gang ZHENG Cheng Key Laboratory of Network and Intelligent Information Processing Hefei University Hefei Key of Intelligent Computing & Signal Processing Ministry of EducationAnhui University Hefei

Large-scale semantic concept detection from large video database suffers from the large variations among different semantic concepts as well as their corresponding effective low-level features. In this paper, we propose a novel framework to deal with this obstacle. The proposed framework consists of four major components: feature pool construction, pre-filtering, modeling, and classification. First, a large low-level feature pool is constructed, from which a specific set of features are selected for the latter steps automatically or semi-automatically. Then, to deal with the unbalance problem in training set, a pre-filtering classifier is generated, which aim at achieving a high recall rate and a certain precision rate nearly 50％ for a certain concept. Thereafter, from the pre-filtered training samples, a SVM classifier is built based on the selected features in the feature pool. After that, the SVM classifier is applied to classification of semantic concept. This framework is flexible and extensible in terms of adding new features into the feature pool, introducing human interactions on selecting features, building models for new concepts and adopting active learning.

关键词： Semantic Concept Detection Feature Pool Pre-filtering User-defined Concepts

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A Cloud computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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Semantic keyword searchable proxy re-encryption for postquantum secure cloud storage

Semantic keyword searchable proxy re-encryption for postquan...

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作者： Yang, Yang Zheng, Xianghan Chang, Victor Tang, Chunming College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Province University Key Laboratory of Information Security of Network Systems Fuzhou University Fuzhou350108 China Key Laboratory of Information Security School of Mathematics and Information Science Guangzhou University Guangzhou510006 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China IBSS Xian Jiaotong-Liverpool University Suzhou215123 United Kingdom

With the advent of cloud computing, more and more consumers prefer to use the cloud services with the pay-as-you-consume mode. The cloud storage brings about great convenience to users, who store data in cloud and access to it using the smart devices anytime and anywhere. Consumers' information should be encrypted to guarantee the data privacy. Flexible searching on ciphertext is a critical challenge to be solved for effective data utilization. In this paper, we propose a novel semantic keyword searchable proxy re-encryption scheme for secure cloud storage. A highlight of this work is that the scheme is quantum attack resistant, while most of the available searchable encryption schemes are not. It supports not only exact keyword search but also synonym keyword search. Moreover, the data owner is capable to delegate his search right to another user using the proxy re-encryption mechanism. In the generation process of re-encryption key, the delegator and delegatee do not need to be interactive with each other. The scheme is also collusion resistant. Under the learning with errors hardness problem, this scheme is proved secure in standard model. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Semantics

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An architecture-based autonomous engine for services configuration and deployment in hybrid clouds 2nd

An architecture-based autonomous engine for services configu...

引用

2nd International Conference on Cloud computing and Big Data, CloudCom-Asia 2015

作者： Liu, Yanping Lan, Xingtu Chen, Xing Feng, Xin Zheng, Xianghan Guo, Wenzhong College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of ZhiCheng Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783319284293

As cloud computing offers scalability, extensibility, elasticity, flexibility and cost savings to the customers of cloud service providers, there is a growing trend towards migrating services to the cloud. Hybrid clouds, which comprise nodes both in the private cloud and in the public cloud, have emerged as a new model for service providers to deploy their services. However, to deploy services in hybrid clouds is a complex task as services are in essence distributed applications. What is more, there is heterogeneity among hybrid clouds. This article proposes an autonomous engine for services configuration and deployment in hybrid clouds. The automation is enabled by the definition of generic information model, which describes all the information relevant to the deployment and configuration of services with the same abstractions, including the required resources, service dependencies and business objectives. In addition, to shield the heterogeneity of hybrid clouds, we define mapping rules for model transformation. We also deploy a three-layer architecture application on Openstack and CloudStack to validate the correctness of our approach. © Springer International Publishing Switzerland 2015.

关键词： Engines

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A Genetic Algorithm Based Piecewise Linear Representation of Time Series 14

A Genetic Algorithm Based Piecewise Linear Representation of...

引用

14th IEEE International Conference on intelligent Systems and Knowledge Engineering, ISKE 2019

作者： Yang, Xiyang Zhai, Changxin Li, Fang Liu, Longshu Zhang, Youhua Quanzhou Normal University Key Laboratory of Intelligent Computing and Information Processing of Fujian Province Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Quanzhou China Beijing Normal University School of Mathematical Science Beijing China

ISBN: (纸本)9781728123486

Line Segment Representation (LSR) refers to represents a time series by a few of line segments, such that the original time series and the piecewise line segment series have shapes as similar as possible. Because of its simple expression, LSR based time series are often easier to be understood and computed for some time series datamining tasks than the original raw data. Two kinds of continuous LSR methods, namely, 11 trend filtering and mix-integer programming (MILP) method, are discussed in this paper. To overcome the poor representation ability of l1 trend filtering, and the high computational complexity of MILP, this paper proposes a hybrid method combining GA and linear programming (GA-LP) to find the optimal LSR time series efficiently. In GA-LP, locations of the breakpoints of the piecewise linear segment are fixed by GA, and values on these locations are fixed by a LP method. Numerical experiments reveal that GA-LP can reduce representation error by comparisons with l1 trend filtering and MILP method, and its computing time is much less than that of MILP. © 2019 IEEE.

关键词： Time series

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Learning from multi metrics for stereoscopic 3D image quality assessment

Learning from multi metrics for stereoscopic 3D image qualit...

引用

International Conference on 3D Imaging (IC3D)

作者： Jiamei Zhan Yuzhen Niu Yize Huang College of Mathematics and Computer Science. Fuzhou University Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing Fujian China

Quality assessment of stereoscopic 3D images is much more complex than that of 2D images. Quality assessment metrics for 3D images have achieved good performance on symmetric distorted 3D images, while given poor performance on asymmetric distorted 3D images. To further improve the perception consistency for symmetric and asymmetric distorted 3D images, this paper presents a machine learning-based full-reference 3D image quality assessment method that learns from multi IQA metrics. Considering that changes in left view, right view and depth information all impact human perception, our method considers the quality of two views and depth information. Since symmetric and asymmetric distortions impact left and right views of a 3D image differently, we propose different features for symmetric and asymmetric distorted 3D images. The experimental results show that our method has better performance than both 2D-based 3D quality assessment metrics and the state of the art specially designed 3D quality assessment metrics.

关键词： Three-dimensional displays Measurement Distortion Indexes Two dimensional displays Image quality Stereo image processing

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Text Guided Road Marking Point Cloud Completion

Text Guided Road Marking Point Cloud Completion

引用

Next Generation Data-driven networks (NGDN), International Conference on

作者： Xu He Qihong Xu Huan Luo College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

With the continuous development of intelligent transportation technologies such as autonomous driving and navigation, accurate perception of road markings becomes crucial. However, due to limitations in sensor perspectives and obstacles blocking the view, the scanned point cloud of road markings is often incomplete, potentially leading to erroneous decisions in intelligent transportation systems. Therefore, it becomes imperative to recover complete road markings from these incomplete point clouds. This paper proposes a text-guided road marking completion method, which integrates text information with point cloud data using attention mechanisms. By leveraging text information to guide the network in completing road marking point clouds, the proposed method aims to enhance the perception accuracy and completeness of road markings. Experimental validation on road marking datasets demonstrates the effectiveness and feasibility of the proposed approach.

关键词： Point cloud compression Accuracy Attention mechanisms Navigation Fuses Roads Transportation Diffusion models Robustness Next generation networking

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Multi-path SDN route selection subject to multi-constraints

Multi-path SDN route selection subject to multi-constraints

引用

Third International Conference on Cyberspace Technology (CCT 2015)

作者： Qingfeng Pan Xianghan Zheng Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350108 China College of Mathematics and Computer Science Fuzhou University 350108 China

ISBN: (纸本)9781510821873

Multi-constrained optimal routing problem and disjoint path selection are NP-hard problem. The conventional single-path and single constraint routing can not meet people's needs. The reliability of network data transmission and packet loss rate can be decreased when the route or the transmission fails. To solve the problems, we need to study the routing path selection strategies. This paper optimizes the routing path that meets the QoS constraints under the SDN pre planning routing, and proposes a routing mechanism that can solve problems in polynomial time. Firstly, the paper combined with Clustering and Gauss algorithm to simulate the network simulation, obtain the initial experimental data. It can reflect the network status more accurately. Secondly, simplify the multi-objectives based on the AHP, It reduces the complexity of the problem and meets QoS requirements. Finally, select the disjoint communication paths and backup paths with the heuristic algorithms in order to balance the load of network and improve the transmission reliability. The selection mechanism can improve the efficiency of data transmission, and the survival of network communication.

关键词： Analytic Hierarchy Process Disjoint Path Multi constraints Optimal Path QoS Routing

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An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning

An SDN Traffic Engineering Approach Based on Traffic Unsuper...

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Next Generation Data-driven networks (NGDN), International Conference on

作者： Ruiyu Yang Qi Tang Yingya Guo College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

With the rapid development of Internet technology and the continuous explosive growth of network traffic, Traffic Engineering (TE), as a viable method for optimizing network traffic distribution and improving network performance, attracts widespread attention from both industry and academia. Software Defined networks (SDN), which decouples the data plane and the control plane, realizes a flexible routing and improves the TE performance. Existing TE approaches in SDN mainly utilize Reinforcement Learning (RL) methods to learn the mapping relationship between network traffic and routing policies. However, due to the continuous expansion of network size and dynamic changes in traffic, the enlargement of traffic state space hinders RL from converging to the optimal routing policy, leading to a decline in network performance. To address these issues, this paper presents a TE method based on unsupervised contrastive representation and RL. This method first shrinks the original traffic state space by efficiently extracting traffic features through Contrastive Learning (CL), aiding quick convergence of RL. It then uses RL to directly learn the mapping from traffic features to traffic splitting policies. Finally, through numerous experiments on real network traffic and topology, it demonstrates that the proposed TE method can effectively achieve load balancing of network traffic under complex and volatile dynamic traffic demands, thereby enhancing network performance.

关键词： Training network topology Telecommunication traffic Reinforcement learning Feature extraction Routing Load management Topology Software defined networking Next generation networking

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