Proceedings of the 2nd International Conference on Intelligent Information Processing最新文献

{"title":"Analysis on Land Use Change and Its Driving Forces in Kirin District of Qujing City, 2005-2015","authors":"Wen Dong, Wenju He","doi":"10.1145/3144789.3144813","DOIUrl":"https://doi.org/10.1145/3144789.3144813","url":null,"abstract":"In order to explore the evolution process of land use and the driving forces of Kirin district of Qujing city, the remote sensing images of which in the three years of 2005, 2010 and 2015 were used as data sources. The classification of the objects was classified by supervised classification and unsupervised classification, then the data processing and spatial superposition analysis of the classification results were carried out based on GIS technology. Finally, the evolution of land use and its driving forces in the region from 2005 to 2015 were analyzed through the results data. The results showed that the land use change in Kirin district of Qujing city was significant in ten years, and the area of construction land and forest land continued to increase. The area of cultivated land and other agricultural land decreased significantly, and the area of bare land increased first and then decreased, at the same time the area of water area increased by a certain extent. The changes of land use in the region were caused by the combined effects of natural conditions, economic development, population growth, government policies and traffic factors, among them, economic development and population growth were the main driving forces. The comprehensive use of GIS, RS and statistical analysis method is the main innovation of this research, and the results can provide a decision basis for the land planning and environmental protection of the region.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124085124","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}

{"title":"Better Weather Forecasting through truth discovery Analysis","authors":"Zhiqiang Zhang, Xiangbing Huang, Muhammad Faisal Buland Iqbal, Songtao Ye","doi":"10.1145/3144789.3144797","DOIUrl":"https://doi.org/10.1145/3144789.3144797","url":null,"abstract":"In many real world applications, the same object or event may be described by multiple sources. As a result, conflicts among these sources are inevitable and these conflicts cause confusion as we have more than one value or outcome for each object. One significant problem is to resolve the confusion and to identify a piece of information which is trustworthy. This process of finding the truth from conflicting values of an object provided by multiple sources is called truth discovery or fact-finding. The main purpose of the truth discovery is to find more and more trustworthy information and reliable sources. Because the major assumption of truth discovery is on this intuitive principle, the source that provides trustworthy information is considered more reliable, and moreover, if the piece of information is from a reliable source, then it is more trustworthy. However, previously proposed truth discovery methods either do not conduct source reliability estimation at all (Voting Method), or even if they do, they do not model multiple properties of the object separately. This is the motivation for researchers to develop new techniques to tackle the problem of truth discovery in data with multiple properties. We present a method using an optimization framework which minimizes the overall weighted deviation between the truths and the multi-source observations. In this framework, different types of distance functions can be plugged in to capture the characteristics of different data types. We use weather datasets collected by four different platforms for extensive experiments and the results verify both the efficiency and precision of our methods for truth discovery.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124156462","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}

{"title":"The Effect of Co-authorship Network on Research Performance of Scholars: A Correlation Analysis","authors":"Chuanyi Wang, Zhe Cheng, Chen Chen","doi":"10.1145/3144789.3144824","DOIUrl":"https://doi.org/10.1145/3144789.3144824","url":null,"abstract":"Collaborative research has been increasingly celebrated by the science community, and there are many researches proving that the co-authorship system has positive effect on research performance. In this paper, the correlation between collaborations and research performance was explored in the field of graduate education in China. Results from the analysis of SPSS suggest that the research collaboration system has significant influence on the quantity and quality of research, but not on the h-index. Additionally, it is found that the collaboration papers are conductive to improve the h-index of the author. Consequently, it can be stated that the co-authorship of researchers can be used to assess the performance of researchers in the field of graduate education in China and the scholars in the field should collaborate more and endeavor to be the leader during collaboration process.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125459544","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}

{"title":"On-line Multi-step Prediction of Short Term Traffic Flow Based on GRU Neural Network","authors":"J. Guo, Zijun Wang, Huawei Chen","doi":"10.1145/3144789.3144804","DOIUrl":"https://doi.org/10.1145/3144789.3144804","url":null,"abstract":"Strengthened road traffic flow monitoring and forecasting can ease road traffic congestion and facilitate road traffic safety planning. Multi-step ahead of the ability to predict the traffic flow is particularly important. The monitoring data of road traffic flow is characterized by uncertainty and non-linearity. And using the existing methods to carry out multi-step prediction error will be very large. In this paper, based on these feature, we propose GRU neural network and autocorrelation analysis for multi-step prediction. We make this model dynamically update the network with the input of the measured real-time data, namely on-line prediction, to work effectively and constantly. Through the theoretical derivation and simulation analysis, it is shown that the prediction accuracy of the proposed GRU prediction model is improved. The model can be used as an effective method for multi-step traffic prediction.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752150","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}

{"title":"Adaptive I/O Bandwidth Allocation for Virtualization Environment on Xen Platform","authors":"Phasakorn Sukheepoj, N. Nupairoj","doi":"10.1145/3144789.3144820","DOIUrl":"https://doi.org/10.1145/3144789.3144820","url":null,"abstract":"Virtual machines frequently have various demands of storage I/O and priority. They are desired to operated several services simultaneously in the same system. However, we cannot assure that some virtual machine which required higher I/O bandwidth will gain proper resources. Moreover, existing fair queueing I/O schedulers cannot provide efficient disk resource allocation, which often leads to bottlenecks at a storage level. To solve the upper described problems, we propose AIO, an Adaptive I/O Bandwidth Allocation for Virtualization Environment on Xen platform. We designed AIO by adopted the concept of the traffic policing and shaping to support the bandwidth sharing between virtual machines and ensuring the Quality of Service of disk I/O resource. This framework allows us to allocate disk I/O bandwidth among the groups of virtual domains dynamically according to configuration rate and priority of particular resource classes. Experiments results show that AIO is useful in maintaining the disk I/O bandwidth of the virtual domain and slightly improves the I/O performance of the system.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126969501","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}

{"title":"DAC-SGD: A Distributed Stochastic Gradient Descent Algorithm Based on Asynchronous Connection","authors":"Aijia He, Zehong Chen, Weichen Li, Xingying Li, Hongjun Li, Xin Zhao","doi":"10.1145/3144789.3144815","DOIUrl":"https://doi.org/10.1145/3144789.3144815","url":null,"abstract":"In the data mining practice, it happens that the algorithm used in mining tasks needs to deal with the multiple distributed data source, while the required datasets are located in different companies or organizations and reside in different system and technology environments. In traditional mining solutions or algorithms, data located in different source need to be copied and integrated into a homogenous computation environment, and then the mining can be executed, which leads to large data transmission and high storage costs. Even the data mining can be in feasible due to the data ownership problems. In this paper, a distributed asynchronous connection approach for the well-used stochastic gradient descent algorithm (SGD) was presented, and a distributed implementation for it was done to cope with the multiple distributed data source problems. In which, the main process of the algorithm was executed asynchronously in distributed computation node and the model can be trained locally in multiple data sources based on their own computation environment, so as to avoid the data integration and centralized processing. And the feasibility and performance for the proposed algorithm was evaluated based on experimental studies.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131017598","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}

{"title":"Compressing Deep Convolutional Networks Using K-means Based on Weights Distribution","authors":"Wang Lei, Huawei Chen, Yixuan Wu","doi":"10.1145/3144789.3144803","DOIUrl":"https://doi.org/10.1145/3144789.3144803","url":null,"abstract":"For the application of deep neural networks on devices with limited hardware resources, it is necessary to reduce the computational complexity and storage requirement. Compression is an effective way to achieve this goal. One of the available method is to quantize the weights to enforce weight sharing, which can greatly reduce the parameters of each layer. This paper presents an improved k-means clustering algorithm to compress CNN (convolutional neural networks).By taking weights distribution into consideration when choosing clustering centers to quantize weights, this algorithm automatically chooses and revises centers to compress network. Compared with traditional quantification method, this algorithm can maintain accuracy and increase the compression speed at the same time. Experiments on AlexNet show that using k-means based on weights distribution to quantize the weights can improve compression speed by 5% to 10% and improve accuracy by 6% compared to traditional algorithm. This algorithm provides a better way for the application of convolutional neural networks on mobile devices.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127634916","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}

{"title":"An Improved DBSCAN Clustering Algorithm for Multi-density Datasets","authors":"Tang Cheng","doi":"10.1145/3144789.3144808","DOIUrl":"https://doi.org/10.1145/3144789.3144808","url":null,"abstract":"In this paper, we proposed a DBSCAN-based clustering algorithm called NNDD-DBSCAN with the main focus of handling multi-density datasets and reducing parameter sensitivity. The NNDD-DBSCAN used a new distance measuring method called nearest neighbor density distance (NNDD) which makes the new algorithm can clustering properly in multi-density datasets. By analyzing the relationship between the threshold of nearest neighbor density distance and the threshold of nearest neighborcollection, we give a heuristic method to find the appropriate nearest neighbor density distance threshold and reducing parameter sensitivity. Experimental results show that the NNDD-DBSCAN has a good robustadaptation and can get the ideal clustering result both in single density datasets and multi-density datasets.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128745002","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}

{"title":"AR Furniture: Integrating Augmented Reality Technology to Enhance Interior Design using Marker and Markerless tracking","authors":"Waraporn Viyanon, Thanadon Songsuittipong, Phattarika Piyapaisarn, Suwanun Sudchid","doi":"10.1145/3144789.3144825","DOIUrl":"https://doi.org/10.1145/3144789.3144825","url":null,"abstract":"Purchasing products for interior design always has a problem that the purchased products may not satisfy customers because they cannot put them in their own place before buying. The purpose of this research is to study and develop an android application called 'AR Furniture' with the use of Augmented Reality technology for design and decoration that will help customers visualize how furniture pieces will look and fit (to scale) in their homes and also can provide details of products to support customer decision. This application is a prototype to find out factors affecting the design and tracking of AR applications. This paper presents three factors that are important for designing and tracking AR applications. The principle of the application is started with analyzing images from the rear camera of a smartphone or tablet using marker tracking technique for displaying product's details and markerless tracking technique for displaying 3D models, performing feature tracking, and calculating positions to display a 3D model over the real world image. The implementation of the application can be split into 2 parts: Part 1 Creating 3D Models using Autodesk 3Ds Max and Part 2 Developing the application using Unity3D and Kudan Augmented Reality SDK as an engine for image analysis, image processing and 3D model rendering. Then we performed three experiments to test the application, 1) Image analysis with marker tracking 2) Image analysis with markerless tracking and 3) User's satisfaction of using the application. The results show that image analysis with marker tracking works well using markers which their size should not be less than 200 x 200 pixels, the distance between the camera and the marker should not be far more than 60 cm. Image analysis with markerless tracking works well with surfaces having a lot of features and at light levels of 100--300 lux (indoor light levels) with 70% accuracy. The user experience evaluation shows that the weakness (2.86 out of 5 points) of the application is when a user found a problem in the application they would need time to solve it. The user experience evaluation shows that the strength (3.93 out of 5 points) of the application is the application can show 3D Object that meet user satisfaction. And the average overall user's satisfaction come up with 3.93 out of 5 point evaluation score. From the experiments, the application should be modified for better performance such as develop various maker patterns using QR code or barcode, distinguish walls and ceilings so that the application would show 3D objects on them properly, improve light robustness and make 3D models more realistic.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132375766","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}

{"title":"A Privacy-preserving Cancelable Palmprint Template Generation Scheme Using Noise Data","authors":"Jian Qiu, Hengjian Li, Jiwen Dong, Guang Feng","doi":"10.1145/3144789.3144822","DOIUrl":"https://doi.org/10.1145/3144789.3144822","url":null,"abstract":"In order to achieve more secure and privacy-preserving, a new method of cancelable palmprint template generation scheme using noise data is proposed. Firstly, the random projection is used to reduce the dimension of the palmprint image and the reduced dimension image is normalized. Secondly, a chaotic matrix is produced and it is also normalized. Then the cancelable palmprint feature is generated by comparing the normalized chaotic matrix with reduced dimension image after normalization. Finally, in order to enhance the privacy protection, and then the noise data with independent and identically distributed is added, as the final palmprint features. In this article, the algorithm of adding noise data is analyzed theoretically. Experimental results on the Hong Kong PolyU Palmprint Database verify that random projection and noise are generated in an uncomplicated way, the computational complexity is low. The theoretical analysis of nosie data is consistent with the experimental results. According to the system requirement, on the basis of guaranteeing accuracy, adding a certain amount of noise will contribute to security and privacy protection.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124425515","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}