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9th IEEE International Conference on Cognitive Informatics (ICCI'10)最新文献

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Combining KNN algorithm and other classifiers 结合KNN算法和其他分类器
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-10-11 DOI: 10.1109/COGINF.2010.5599804
Zhiyong Yan, Congfu Xu
{"title":"Combining KNN algorithm and other classifiers","authors":"Zhiyong Yan, Congfu Xu","doi":"10.1109/COGINF.2010.5599804","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599804","url":null,"abstract":"In this paper, we propose KNC algorithm for combining KNN algorithm and other three classifiers (C4.5 algorithm, Naive Bayes classifier and SVM) based on their classification capabilities on different types of instances. According to labels of instances and their K nearest neighbors, we divide instances into three types, S-, DS- and D-type. The classification capabilities of KNN algorithm on S-type instances are the best, while ones on D-type and DS-type are usually worse than other three classifiers. KNC algorithm uses KNN algorithm to classify S- and DS-type instances, and uses other classifiers to classify D-type instances. KNC algorithm utilizes classification capability of KNN algorithm on S-type instances and utilizes classification capabilities of other three classifiers on D-type instances. Experimental results on 20 UCI data sets demonstrate utility and feasibility of KNC algorithm.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134631554","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: 19
Locally Assembled Binary feature with feed-forward cascade for pedestrian detection in intelligent vehicles 基于前馈级联的局部组合二值特征在智能车辆行人检测中的应用
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599695
Liping Yu, Feng Zhao, Zhiyong An
{"title":"Locally Assembled Binary feature with feed-forward cascade for pedestrian detection in intelligent vehicles","authors":"Liping Yu, Feng Zhao, Zhiyong An","doi":"10.1109/COGINF.2010.5599695","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599695","url":null,"abstract":"Detecting pedestrians in images is a challenging task, especially for the intelligent vehicle environment where there is a real-time requirement which limits the computational complexity of algorithms. In this paper, we demonstrate a near real-time and robust pedestrian detection system in the context of intelligent vehicle. This is achieved by integrating Locally Assembled Binary (LAB) features with a feed-forward cascade structure. LAB feature, which comprises several neighboring binary Haar features with a similar idea to Local Binary Pattern (LBP), is not only efficient in evaluation, but also very discriminative for pedestrian/non-pedestrian classification. Furthermore, a feed-forward cascade structure, which can exploit both the stage-wise and the cross-stage information, is presented to build on an efficient detector. Experimental results demonstrate the effectivity and efficiency of the proposed method.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127529480","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: 11
Cognitive skills promotion by means of collaborative technology Web 2.0 tools 借助协同技术Web 2.0工具提升认知技能
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599775
I. Bernabe-Munoz
{"title":"Cognitive skills promotion by means of collaborative technology Web 2.0 tools","authors":"I. Bernabe-Munoz","doi":"10.1109/COGINF.2010.5599775","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599775","url":null,"abstract":"This work aims to launch a line of research dedicated to investigate the psychological aspects involved in teacher learning mediated by information and communication technologies (ICTs). This study aims to contribute to the analysis of the cognitive, emotional and motivational specific aspects of learning with ICT. The line of investigation starts up with a study on the development of creativity through ICT. The study focuses on proposing a theoretical model applicable to learning through ICT, alternative to traditional learning theories that do not address the complexity of incorporating new technologies and also aims to respond to real needs of the current educational community. Knowing which is the ICT role in the teaching and learning process will allow to fully enjoy its benefits while minimizing the potential difficulties, so as to improve both the teaching and learning.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126146667","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
Mining for high complexity regions using entropy and box counting dimension quad-trees 利用熵和盒计数维四叉树挖掘高复杂度区域
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599745
R. Vetro, W. Ding, D. Simovici
{"title":"Mining for high complexity regions using entropy and box counting dimension quad-trees","authors":"R. Vetro, W. Ding, D. Simovici","doi":"10.1109/COGINF.2010.5599745","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599745","url":null,"abstract":"This paper introduces an algorithm for capturing high complexity regions of a data domain. In this work, we focus on domains in R2. In particular, we analyze 2-dimensional image domains. Two different methods for mining are considered. The first method performs an information-theoretic analysis based on entropy to find diverse areas. The second method applies the concept of box-counting dimension related to fractal geometry. We propose the use of a quad-tree as main search structure where complex areas are represented by leaves with high feature1 values at the highest level on the tree. Nodes that refer to specific sub-domains are split when the level of the analyzed feature exceeds a chosen threshold. The relationship between the threshold and the number of pixels located in high value feature sub-domains at the highest level on the resultant quad-tree is demonstrated on test images for both methods. Experimental results also show the relation between the former measurements and characteristics of the images. Finally, we identify a correlation between the methods presented.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114261830","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
Predicting embedding strength in audio steganography 预测音频隐写术中的嵌入强度
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599777
Mengyu Qiao, A. Sung, Qingzhong Liu
{"title":"Predicting embedding strength in audio steganography","authors":"Mengyu Qiao, A. Sung, Qingzhong Liu","doi":"10.1109/COGINF.2010.5599777","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599777","url":null,"abstract":"As a serious concern of information security, steganography provides a covert communication channel for cyber-terrorism and cyber-crime. The widespread use enables MP3 compressed audio to become an excellent carrier for audio steganography on the Internet. Since embedding capacity is an important measure to evaluate the performance of steganographic systems, and embedding ratio is commonly used when comparing the accuracy of different steganalysis algorithms. In this paper, we present a scheme to predict embedding strength based on feature mining in MDCT transform domain. We generate reference signals by reversing and repeating quantification process, and derive the reference based accumulative features from the difference between source signal and reference signal. Finally, a dynamic evolving neuron-fuzzy inference system is applied to predict embedding strength of MP3 compressed audio. Experimental results show that our approach obtains good prediction of the embedding strength in the steganograms.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116601589","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: 5
A DRM model based on proactive secret sharing scheme for P2P networks 基于主动秘密共享方案的P2P网络DRM模型
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599791
Ling Ma, Shouxun Liu, Yongbin Wang
{"title":"A DRM model based on proactive secret sharing scheme for P2P networks","authors":"Ling Ma, Shouxun Liu, Yongbin Wang","doi":"10.1109/COGINF.2010.5599791","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599791","url":null,"abstract":"The development of P2P technology has brought a significant revolution to Internet, which effectively improves data-sharing in networks. However, it also enables piracy products to spread quickly all over the networks. Therefore, it is urgent to design digital rights management (DRM) for P2P network. Recently, many DRM products for P2P have been investigated, but there are some disadvantages. A new DRM model based on proactive secret sharing scheme is proposed in this paper. This verifiable DRM model ensures the validation of sub-keys. The model enables the users to obtain the key effectively and utilize the advantage of P2P networks.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129587075","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: 7
Who dominates the retinotectal mapping? 谁主导着视网膜顶的映射?
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599831
Sanming Song, H. Yao
{"title":"Who dominates the retinotectal mapping?","authors":"Sanming Song, H. Yao","doi":"10.1109/COGINF.2010.5599831","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599831","url":null,"abstract":"Retina ganglia cells (RGCs) connect to the tectum neurons in the midbrain through 1–1 strict topic mapping, which benefits the precise information transmission and is called retinotopic mapping. It's significant to understand the underlying mechanisms that contributing to the topic mapping. Inspired by the experimental observations given by [Nicol et al 2007 Nat. Neurosci. 10(3):340], we divide the retinotectal mapping into three almost independent stages, axon overshooting, axon retraction, and the final refinement of axons and arborization. In order to analyze the roles that forward signaling, reverse signaling and neural activity plays in different stages, an axon retraction model and a refinement procedure have been proposed to simulate the retinotopic mapping process. We testify the asymmetry of gradients between forward signaling and reverse signaling, and find that it is the forward signaling that dominates the axon retraction. Also, the refinement procedure demonstrates that the reverse-signaling-induced gradient branching and the synapse-dependent neural activity lead the RGC axons projecting to the topic target tectum neurons.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129866891","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
Weighting imputation methods and their evaluation under shell-neighbor machine 壳邻机下的加权插值方法及其评价
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599790
Shichao Zhang, Manlong Zhu
{"title":"Weighting imputation methods and their evaluation under shell-neighbor machine","authors":"Shichao Zhang, Manlong Zhu","doi":"10.1109/COGINF.2010.5599790","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599790","url":null,"abstract":"The paper studies three typical weighting strategies for Shell-Neighbor Imputation (SNI) algorithm, while there are many weighting modes that can be used in the SNI. To best capture the imputation efficiency, a new metrics, called goodess, is proposed for evaluating imputation algorithms. We conduct some experiments for examining the proposed approached, and demonstrate that (1) distance-frequency-weighting strategy is the best one for the shell-neighbor imputation; (2) the goodness is much better than the RMSE if there is a few individual values of serious deviation, otherwise, the goodness is the same as the RMSE at measuring the imputation efficiency.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124175900","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
Improvement of bearing-only tracking fusion strategy based on bionics with infrared sensor 基于仿生与红外传感器的全方位跟踪融合策略改进
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599768
Hui Chen, Zhanming Li
{"title":"Improvement of bearing-only tracking fusion strategy based on bionics with infrared sensor","authors":"Hui Chen, Zhanming Li","doi":"10.1109/COGINF.2010.5599768","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599768","url":null,"abstract":"This paper∗ mainly studies some improvements of bearing-only data association with a single passive sensor to overcome the existing problems in the applications of JPDA approaches for bearing-only target locating and tracking. First, some adaptive modified rules are presented by deep analysis of JPDA application. This bearing-only association approach reduces computational complexity clearly under the premise of better tracking precision. Then, an effective track management strategy is designed. This strategy helps to set up a clear tracking level and manage historical the information of the target. The application of the proposed approach in a simulation and engineering system proves its effectiveness and practicability.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123346048","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
In search for effective granularity with DTRS 在寻找有效的粒度与DTRS
9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599696
Bing Zhou, Yiyu Yao
{"title":"In search for effective granularity with DTRS","authors":"Bing Zhou, Yiyu Yao","doi":"10.1109/COGINF.2010.5599696","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599696","url":null,"abstract":"Decision Theoretic Rough Set (DTRS) model provides a three-way decision approach to classification problems, which allows the classifier to make a deferment decision on the hard cases, rather than forced to make an immediate determination. The deferred cases must be reexamined by collecting further information. Although the formulation of DTRS is intuitively appealing, a fundamental question that remains is how to determine the classification of the deferment cases. In this paper, we introduce an adaptive learning method that automatically deals with the deferred cases by searching for the effective granularity. A decision tree is constructed for classification. At each level, we sequentially choose the attributes that provide the most effective granularity. A subtree is added recursively if the conditional probability lies in between of the two threshold values. A branch reaches its leaf node until all the examples are correctly classified. This learning process is illustrated by a given example.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123655187","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
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