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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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Multi-objective optimization on multistage half-band FIR filter design using simulated annealing algorithm 21

Multi-objective optimization on multistage half-band FIR fil...

引用

21st IEEE international Conference on high performance computing and Communications, 17th IEEE international Conference on Smart City and 5th IEEE international Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhang, Jin Hu, Wei Wang, Yonghao Zhu, Xiangyu College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System Wuhan China Digital Media Technology Lab Birmingham City University Birmingham United Kingdom

ISBN: (纸本)9781728120584

The design of a finite impulse response (FIR) digital filter is essentially a multi-parameter and multi-objective optimization problem, different optimization techniques can be utilized to minimize the latency in the live audio processing chain by minimization or maximization of the objective function. Using cascaded FIR filters as anti-aliasing and anti-image filters for high-resolution audio conversion are common. We can optimize the parameters of each stage to achieve the overall performance requirements. the method of modified Simulated Annealing (SA) Algorithm is proposed to resize the best distribution of passband ripple, transition band and stopband attenuation for each level of Multi-stage filter. It shows the proposed a new multi-stage filter design method improves the efficiency of the designed filter in terms of filter cost and group delay comparing with traditional design. © 2019 IEEE.

关键词： FIR filters

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The design and implementation of multi-cloud based distributed storage platform with random linear coding 21

The design and implementation of multi-cloud based distribut...

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作者： He, Bin Wang, Jin Zhou, Jingya Li, Lingzhi Zhou, Wei Zhu, Luqi Zhai, Mengjia School of Computer Science and Technology Soochow University Collaborative Innovation Center of Novel Software Technology and Industrialization China Department of Computer Science Georgia Institue of Technology United States

ISBN: (纸本)9781728120584

Nowadays, distributed storage systems have been widely used in the world, e.g, Google Drive, OneDrive, DropBox, Baidu PCS, etc.. Although these cloud storage services significantly facilitate the file storage for users, the performance of them varies widely when using in different countries. For example, Google Drive has high upload/download speed in America while it has very low upload/download speed in China. Therefore, to enhance the reliability and security of the cloud storage, in this paper, we firstly design a multi-cloud based distributed storage platform, named as, Normal Drive, by utilizing random linear coding and APIs provided by multiple cloud disks. We then implement the Normal Drive and conduct extensive evaluation from different aspects in five different locations around the world to test the performance of it. The experimental results show that the proposed Normal Drive can significantly improve the transmission performance and enhance the reliability of file storage when comparing with single-cloud based distributed storage system. © 2019 IEEE.

关键词： Network coding

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An experimental study of building user profiles for movie recommender system 21

An experimental study of building user profiles for movie re...

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作者： stakhiyevich, Pavel Huang, Zhao School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (纸本)9781728120584

User preferences and interests are usually represented in user profile, which is a crucial part in recommender systems. The more accurate user profile we can build, the better recommendations we can provide for the user. However, challenges in building and representing user profile exist. To overcome these challenges, in this paper, we focus on building user profile and its representation. We compare the different approaches in building user profile and optimization its weights. The study is conducted through experimental work using a real-world dataset, in which top recommendations based on user interests are provided. In addition, we extend the dataset by adding more features with the purposes of comparing its effects on recommendation results. The results show that user profile features and their weights have a great impact on recommendation results. More features we know about an item, more accurate user profile based on user interests we can build. Furthermore, properly optimized weights of the item features can increase the accuracy of recommendations. © 2019 IEEE.

关键词： User profile

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Efficient randomized feature selection Algorithms 21

Efficient randomized feature selection Algorithms

引用

作者： Wang, Zigeng Rajasekaran, Sanguthevar Computer Science and Engineering University of Connecticut 371 Fairfield Road StorrsCT06269 United States

ISBN: (纸本)9781728120584

Feature selection is a core problem in machine learning. It plays an important role in making efficient and explainable machine-driven decisions. Embedded feature selection methods, such as decision trees and LASSO, suffer from learner dependency and cannot be applied well to many popular learners. Wrapper methods, which fit arbitrary learning models, are receiving growing interests in many scientific fields. In order to effectively search relevant features in wrapper methods, many randomized schemes have been proposed. In this paper, we present efficient randomized feature selection algorithms empowered by automatic breadth searching and attention searching adjustments. Our schemes are generic and highly parallelizable in nature and can be easily applied to many related algorithms. Theoretical analysis proves the efficiency of our algorithms. Extensive experiments on synthetic and real dataset show that our techniques achieve significant improvements in the selected features' quality and selection time. © 2019 IEEE.

关键词： Feature Selection

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Fake reviews detection based on text feature and behavior feature 21

Fake reviews detection based on text feature and behavior fe...

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作者： Shuqin, Yin Jing, Feng College of Computer Science and Technology Wuahn University of Technology Wuhan China

ISBN: (纸本)9781728120584

Considering the limitations of comment text in the study of fake reviews recognition, this paper proposes to build a classification model by integrating the features of comment text and user behavior. However, the comment data obtained in reality are mostly unlabeled. Therefore, this paper proposes an MPINPUL (Mixing Population and Individual Nature PU Learning) model based on multiple features to build a fake reviews classification model. In this paper, the MPINPUL model is divided into four steps. Firstly, the constrained k-means algorithm is proposed to calculate a negative example of trust. The advantage of constrained k-means is that it can expand the set of positive examples while identifying the trusted negative examples. Then, LDA and k-means are used to calculate multiple representative samples for positive and negative examples respectively. Then we use the idea of population and individuality to determine the category label of the sample. Finally, the classifier is established. Experimental results on real data sets show that the recognition rate of the MPINPUL model proposed in this paper is higher than that of other single features under fusion feature conditions. © 2019 IEEE.

关键词： Behavioral research

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Super-resolution-based traffic prohibitory sign recognition 21

Super-resolution-based traffic prohibitory sign recognition

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作者： Xiao, Degui Liu, Liang College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

Traffic signs occupy a small proportion in the image, which makes feature extraction difficult. This study focuses on small prohibitory signs. First, we introduce super resolution (SR), which can improve the quality of the image, to solve the recognition problem of small prohibitory signs. Second, we propose a new model that can detect and classify various prohibitory signs compared with a traditional joint model. The proposed model utilizes the color and shape of prohibitory signs in generating proposals and filters the negative samples. The retained proposals are divided into small, medium, and large objects based on the size of prohibitory signs, and the small and medium objects are reconstructed through SR. Finally, the proposals are classified by using support vector machine (SVM) algorithm. Experiments on Tsinghua-Tencent 100K and German Traffic Sign Recognition Benchmark (GTSRB) datasets demonstrate that the proposed method is feasible. The proposed method achieves recognition accuracies of 78% and 67% on the small objects of Tsinghua- Tencent 100K and GTSRB datasets, respectively, which exceed the recognition accuracy without SR. © 2019 IEEE.

关键词： Traffic signs

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DDLB: A dynamic and distributed load balancing strategy 21

DDLB: A dynamic and distributed load balancing strategy

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作者： Liu, Bingqi Chang, Jiulong Xiao, Limin Qin, Guangjun Wei, Bing Huo, Zhisheng State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Smart City College Beijing Union University Beijing100101 China

ISBN: (纸本)9781728120584

In view of the problem of load imbalance for distributed file systems, the current solutions either adopt centralized algorithm and implement load balancing by increasing the number of files as the replica, or lack considerations for network transmission and network bandwidth. Therefore, the current solutions are relatively inefficient in distributed file system. To solve this problem, this paper proposes DDLB, a dynamic and distributed load balancing algorithm which is completely based on distributed load balancing architecture. DDLB can not only monitor the IO load on the data server dynamically and real time, but also use the load prediction model, which based on weighted mean, to reduce the impact of network delay and collection delay on load decision-making. In order to minimize the frequency of remote load collection and decrease network bandwidth occupation, DDLB adopts the remote distributed load collection method based on threshold and the cooling collection mechanism. Moreover, dynamic replica management mechanism of DDLB takes data blocks instead of files as the basic unit for replica replication, and reduces the IO load of the data server by adding replicas of hotspot data blocks. For proving the effectiveness of DDLB, this paper conducts a comprehensive evaluation in the HDFS cluster environment. The results show that compared with the traditional load balancing algorithm, DDLB can effectively balance the IO load between data servers in the distributed file system and improve the data access performance of IO-intensive applications. © 2019 IEEE.

关键词： Decision making

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Dynamic data access control for multi-authority cloud storage 21

Dynamic data access control for multi-authority cloud storag...

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作者： Wang, Jian Ye, Chunxiao Ou, Yangfei Colleage of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9781728120584

Cloud computing have brought a lot of advantages, such as reducing the hardware cost and providing a convenient cloud storage service. More and more people choose to put their private data in the cloud. However, due to data outsourcing and seminal-trusted cloud servers, data access control has becoming a challenging issue. To protect data security and privacy, some ciphertext policy attribute-based encryption (CP-ABE) schemes have been applied to cloud data access control. However, there are two dynamic issues, namely attribute revocation and policy updating, that need to be solved in the existing CP-ABE schemes. In this paper, we propose a fine-grained dynamic multi-authority cloud data access control scheme, which can solve the above two problems. Besides, our scheme can also support user revocation, which make it more practical. The analysis and simulation results show that our scheme is secure in the random oracle model and is efficient. © 2019 IEEE.

关键词： Access control

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A deadline-constrained scheduling algorithm for scientific workflows in clouds 21

A deadline-constrained scheduling algorithm for scientific w...

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作者： Cao, Shujin Deng, Kefeng Ren, Kaijun Li, Xiaoyong Nie, Tengfei Song, Junqiang College of Computer Science and Technology College of Meteorology and Oceanography National University of Defense Technology Changsha China

ISBN: (纸本)9781728120584

Scheduling scientific workflows in clouds under deadline constraint is a challenging problem since public clouds usually provide various types of instances that are rented on-demand and charged based on time period. To solve this problem, the paper proposes DCWS, a deadline-constrained workflow scheduling algorithm for cost-effective execution of scientific workflows in clouds. DCWS is a list-based scheduling algorithm that uses several strategies to reduce the monetary cost under deadline constraint: i) sub-deadlines are assigned for individual tasks by considering the probabilities that tasks are placed together;ii) instance type upgrading and downgrading strategies are designed to accelerate workflow execution and reduce the total cost respectively;iii) task backfilling and sub-deadline violation penalizing are used to improve resource utilization and ensure that the sub-deadlines of the individual tasks are satisfied. Experimental results demonstrate that in comparison with two state-of-the-art algorithms, DCWS is effective on reducing monetary cost under deadline constraint. © 2019 IEEE.

关键词： Scheduling algorithms

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A face privacy protection scheme using CNN based ROI editing 21

A face privacy protection scheme using CNN based ROI editing

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作者： Peng, Fei Ping, Guo-Lou Ge, Shuang-Kui College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

Aiming at the deficiencies of the current face privacy protection based on image editing in terms of protection capability and image utility, a novel face privacy protection scheme using CNN based ROI (Region of interest) editing is proposed. In this paper, a novel face ROI calculation method based on CNN (convolutional neural Network) is first put forward. A set of CNN networks is selected and utilized to calculate the gradient of each face image in an image dataset, and then an average gradient image is obtained from the image gradients. After that, the sub-ROIs are determined from the average gradient image, and different sub-ROIs have different contribution to the output of the CNN. Based on face ROI editing, a privacy protection scheme is put forward, which is adaptive to the existing privacy protection methods such as blurring, pixelization, encryption and etc. Experimental results and analysis show that it can achieve the same privacy protection capability with less modification and better image quality compared with the existing methods. © 2019 IEEE.

关键词： Convolution

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