2017 International Conference on Computing Intelligence and Information System (CIIS)最新文献

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An Auditory-Based Monaural Feature for Noisy and Reverberant Speech Enhancement 基于听觉的噪声和混响语音增强单声特征
Yi-jiao Jiang, Runsheng Liu, Ya Bai
{"title":"An Auditory-Based Monaural Feature for Noisy and Reverberant Speech Enhancement","authors":"Yi-jiao Jiang, Runsheng Liu, Ya Bai","doi":"10.1109/CIIS.2017.23","DOIUrl":"https://doi.org/10.1109/CIIS.2017.23","url":null,"abstract":"The deep neural networks (DNN) based speech enhancements is a hot topic in machine learning and speech enhancement application. Even with deep neural network, it is still hard to improve the speech quality on noisy and reverberant conditions. For machine learning based system, auditory feature extraction becomes the key point in speech enhancement and recognition. In this paper, we proposed a speech enhancement framework based on an auditory-based monaural feature, which model the function of human hearing auditory system. The auditory based feature is extracted from the data passing the gammatone filter banks, which has more detail on low frequency than normal filters. Systemic tests show the better performance of the proposed auditory based monaural feature than the mel-frequency cepstral coefficients (MFCC) in noise and reverberant environment.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123112751","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
Design and Development of Management Information System for Research Project Process Based on Front-End and Back-End Separation 基于前端与后端分离的科研项目流程管理信息系统的设计与开发
Kun Liu, Jinmin Jiang, Xiaohan Ding, Hui Sun
{"title":"Design and Development of Management Information System for Research Project Process Based on Front-End and Back-End Separation","authors":"Kun Liu, Jinmin Jiang, Xiaohan Ding, Hui Sun","doi":"10.1109/CIIS.2017.55","DOIUrl":"https://doi.org/10.1109/CIIS.2017.55","url":null,"abstract":"With the development of information technology and the improvement on the quality of network services, it is possible to achieve the internet-based project process information management. To reduce the degree of coupling between the model, view and control in the system, a management information system for research project process based on the front-end and back-end separation was proposed in this study. By guaranteeing the correct and orderly processing of research project, the system could realize the hierarchical management and monitoring of management information system for research project process. The system architecture was based on JavaEE technology, which could provide the full and convenient data supports in the process of system operation and ensure the precise and standard operation of research project. The system was mainly designed for the managerial personnel of research project, project evaluation expert, department of research project management and undertaker of research project, covering the all-round management of establishment, schedule, conclusion and evaluation. Meanwhile, relying on the integrated management of research project, the science authorities could reasonably allocate the resources for the research project, guarantee the proper implementation of research project and track the achievements, and thus promote the scientific research efficiency and management effectiveness of research institutions.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127863399","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: 6
A Combination Forecasting Model of Extreme Learning Machine Based on Genetic Algorithm Optimization 基于遗传算法优化的极限学习机组合预测模型
Zhiheng Yu, Chengli Zhao
{"title":"A Combination Forecasting Model of Extreme Learning Machine Based on Genetic Algorithm Optimization","authors":"Zhiheng Yu, Chengli Zhao","doi":"10.1109/CIIS.2017.14","DOIUrl":"https://doi.org/10.1109/CIIS.2017.14","url":null,"abstract":"After studying the working principle of feed-forward neural network and analyzing network structure and the learning mechanism of BP neural network and the extreme learning machine (ELM), a prediction model, GA-ELM, is proposed based on genetic algorithm to optimize the learning machine limit. The genetic algorithm is used to select the weights and thresholds of ELM neural network, and the optimal weights and thresholds are used to determine the connection weights between the hidden layer and the output layer. Further, this model is combined with the grey system model to correct the residual of GM, and then GM-GA-ELM combination forecasting model is established. Compared with BP model, GA-BP model and standard ELM model, it is further verified that the predicting accuracy and running time of the proposed model are better.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131468623","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
Research on Application of Artificial Intelligence Algorithm in Directed Graph 人工智能算法在有向图中的应用研究
Yuanbo Zhou, Fangqin Xu
{"title":"Research on Application of Artificial Intelligence Algorithm in Directed Graph","authors":"Yuanbo Zhou, Fangqin Xu","doi":"10.1109/CIIS.2017.26","DOIUrl":"https://doi.org/10.1109/CIIS.2017.26","url":null,"abstract":"With the development of artificial intelligence algorithm, the combination of intelligent algorithm and directed graph has become an important tool of current path planning. The application of the intelligent algorithm affects the optimal path planning in the directed graph, including the length of the optimal path and the time of the operation of the algorithm. The following studies are carried out through this paper based on the application of intelligent algorithm in directed graph. In the experimental environment of MATLAB, the coordinates of 30 target points are generated by random numbers. The ant colony algorithm and genetic algorithm are used to make the optimal path planning for the 30 target points, and the starting point and the end point are fixed to form a directed and closed graph. The parameters of the two algorithms are adjusted accordingly. Comparison of the two algorithms of the optimal path diagram and the algorithm running time, so as to draw the conclusion of the optimal algorithm.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122863818","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
Dynamic Community Detection Using Nonnegative Matrix Factorization 基于非负矩阵分解的动态社区检测
Feng Gao, Limengzi Yuan, Wenjun Wang, Huandong Chang
{"title":"Dynamic Community Detection Using Nonnegative Matrix Factorization","authors":"Feng Gao, Limengzi Yuan, Wenjun Wang, Huandong Chang","doi":"10.1109/CIIS.2017.56","DOIUrl":"https://doi.org/10.1109/CIIS.2017.56","url":null,"abstract":"Community detection is of great importance in the study of complex networks, which motivates a body of new work in this domain. However, almost all networks change over time; traditional methods for static networks are not able to track evolutionary behaviors in temporal networks. To address this problem, we present a novel dynamic community detection model ENMF using nonnegative matrix factorization (NMF), which can not only track the temporal evolutions but also maintain the quality of detecting communities. Specifically, we propose gradient descent algorithm to optimize object function and evaluate the performance of the algorithm on one synthetic datasets. The results show that our proposed model outperforms other NMF methods.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129211345","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: 4
Workflow Execution Plan Generation in the Cloud Computing Environment Based on an Improved List Scheduling Algorithm 基于改进列表调度算法的云计算环境下工作流执行计划生成
Xiaoying Wang, Chengshui Niu, Yu-an Zhang, Lei Zhang
{"title":"Workflow Execution Plan Generation in the Cloud Computing Environment Based on an Improved List Scheduling Algorithm","authors":"Xiaoying Wang, Chengshui Niu, Yu-an Zhang, Lei Zhang","doi":"10.1109/CIIS.2017.59","DOIUrl":"https://doi.org/10.1109/CIIS.2017.59","url":null,"abstract":"Focusing on the higher ratio of processor utilization and lower execution cost of a scientific workflow in the cloud environment, an improved list scheduling algorithm was proposed in this paper. This algorithm combines the ideas of both list scheduling and task duplication. According to the priority of the tasks, choosing reasonable parent task to replicate can help reduce the overhead between tasks. To properly insert tasks during processor idling time can help to increase the processor utilization. Based on these, we proposed an improved strategy to generate the workflow execution plan, called EPGILS. Experiment results show that the algorithm is feasible and efficient in reducing the task completion time and improving the utilization ratio of the processor.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129337780","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
Background Suppression Based on Improved Top-Hat and Saliency Map Filtering for Infrared Ship Detection 基于改进顶帽滤波和显著性滤波的红外舰船探测背景抑制
Baorong Xie, Lingna Hu, Wentao Mu
{"title":"Background Suppression Based on Improved Top-Hat and Saliency Map Filtering for Infrared Ship Detection","authors":"Baorong Xie, Lingna Hu, Wentao Mu","doi":"10.1109/CIIS.2017.71","DOIUrl":"https://doi.org/10.1109/CIIS.2017.71","url":null,"abstract":"Infrared ship detection aiming at remote-sensing image is important in image processing to get priority in current war today. A background suppression method based on improved Top-Hat filtering and saliency map is presented for the detection. Firstly, Top-Hat filtering with linear combination of the open and close operators is utilized on the infrared remote-sensing image input. The filtering employs different structure operators with the same shape as the object image. Then the architecture of the Itti-Koch saliency-map model is utilized on the grayscale infrared image to intensify the interesting objects by visual effect principle. The results show that the improved background suppression method proposed can project the salient domain and detect more possible ship targets cleary.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"39 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128668991","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: 4
Study on Population Comprehensive Information Sharing Platform 人口综合信息共享平台研究
J. Zeng, X. Liang
{"title":"Study on Population Comprehensive Information Sharing Platform","authors":"J. Zeng, X. Liang","doi":"10.1109/CIIS.2017.58","DOIUrl":"https://doi.org/10.1109/CIIS.2017.58","url":null,"abstract":"Population is the major strategy of long-term development, and it is great challenge as well. Population data is an important part of the national resources informatization. After analyzing in present situation of population information system and existing system impefection in collaboration, data exchange and so on, a new integrated population information sharing platform were built to realize resources sharing, complementary advantages and integrated application from aspects of establishing data standard, synchronization of heterogeneous database access, distributed business data integration, and performance of running system etc.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127213623","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
Integrated Transfer Learning Algorithm Using Multi-source TrAdaBoost for Unbalanced Samples Classification 基于多源TrAdaBoost的非平衡样本分类集成迁移学习算法
Zhixiang Yuan, Damang Bao, Zekai Chen, Ming Liu
{"title":"Integrated Transfer Learning Algorithm Using Multi-source TrAdaBoost for Unbalanced Samples Classification","authors":"Zhixiang Yuan, Damang Bao, Zekai Chen, Ming Liu","doi":"10.1109/CIIS.2017.37","DOIUrl":"https://doi.org/10.1109/CIIS.2017.37","url":null,"abstract":"To solve the binary classification transfer learning problem with similar data distributions and class imbalance between positive and negative samples in the target and source domains, we present an integrated transfer learning algorithm for multi-source unbalanced samples classification. We try to avoid the negative transfer problem by utilizing multiple source domains, and propose the new sample weights initialization and weights updating strategies to solve the class imbalance problem. Moreover, we propose a new elimination mechanism to eliminate the redundant samples in the multiple source domains, and then the time and memory costs of the classifier could be significantly reduced. Experimental results on standard UCI datasets show that the proposed algorithm outperforms the state-of-the-arts transfer learning algorithms in terms of F1-measure and AUC evaluations metrics.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130817701","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: 14
A Fast Image Encryption Scheme Based on Public Image and Chaos 一种基于公共图像和混沌的快速图像加密方案
Yong Zhang, Qiong Zhang, Hancheng Liao, Wenhua Wu, Xueqian Li, Hui-Chong Niu
{"title":"A Fast Image Encryption Scheme Based on Public Image and Chaos","authors":"Yong Zhang, Qiong Zhang, Hancheng Liao, Wenhua Wu, Xueqian Li, Hui-Chong Niu","doi":"10.1109/CIIS.2017.69","DOIUrl":"https://doi.org/10.1109/CIIS.2017.69","url":null,"abstract":"A novel image cryptosystem based on public image and chaotic systems is proposed in this paper. In proposed system, with the help of piecewise linear map and Chen's chaotic system, a public key is used to generate a public image, and a secret key is used to generate three key streams for image encryption. Then, the public image and one key stream are used for covering image. The other two key streams are used for image diffusion. The image encryption system includes two covering modules, two plaintext-unrelated diffusion modules and one plaintext-related confusion module. With the use of public image and plain image related confusion operation, the proposed system can resist the chosen/known plaintext attacks. Each encryption process uses a new public key, then the public key and cipher image are transmitted to the receiver through the public information channel. Even for the same plain image, each encryption process will produce totally different cipher images. When the public key is authenticated, the image cryptosystem can prevent active attacks. The simulation results show that the proposed scheme possesses the merits of fast encryption/decryption speed and high information security, and can be used to protect the image information on the internet.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128312005","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
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