{"title":"The Hybrid Approach of Image Segmentation Using MeanShift and Saliency Maps","authors":"Tuan A. Ta, T. Cao, Tiep V. Nguyen","doi":"10.1109/KSE.2012.20","DOIUrl":"https://doi.org/10.1109/KSE.2012.20","url":null,"abstract":"Image segmentation is an important problems in the field of image processing. In this paper, we propose a hybrid approach of image segmentation, combining MeanShift, an image segmentation method, and Saliency Maps, a regions-of-interest detection method. Our proposed method uses low-level features of color image, such as luminance, color and spatial. The results from experiments showed that our proposed method solves two problems, one is determining the regions of objects with high saliency in image and the other is associating discrete broken regions based on the relationship between low-level features and saliency feature.","PeriodicalId":122680,"journal":{"name":"2012 Fourth International Conference on Knowledge and Systems Engineering","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117132882","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 Constraint Neighborhood Based Approach for Co-location Pattern Mining","authors":"T. V. Canh, Michael Gertz","doi":"10.1109/KSE.2012.16","DOIUrl":"https://doi.org/10.1109/KSE.2012.16","url":null,"abstract":"Driven by the ever increasing amount of spatial data collected by observations and GPS-enabled devices, mining such data for interesting or previously unknown patterns has become a major challenge. Among the many possible patterns, co-location patterns describing the frequently occurring spatial proximity of objects possessing some features are of particular interest. While several approaches have been proposed to discover such patterns, so called self co-location patterns where objects having the same feature (among others) are in spatial proximity, however, have not been effectively addressed. Furthermore, most of the co-location discovery methods suffer from expensive computations, such as spatial joins. To address these problems, in this paper, we propose a novel constraint neighborhood based approach to find co-location patterns. This approach can discover both star and clique co-location patterns, including single and complex self co-locations. Based on the constraint neighborhood idea, our method neither needs to perform spatial or instance joins nor checks for cliques to find co-location instances. To demonstrate the effectiveness of our proposed framework, we conducted experiments using both real-world and synthetic data sets. As our evaluations show, the constraint neighborhood based approach outperforms the well-known joinless approach with respect to the types of co-location patterns discovered and runtime complexity.","PeriodicalId":122680,"journal":{"name":"2012 Fourth International Conference on Knowledge and Systems Engineering","volume":"413 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123568399","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":"Shift Error Analysis in Image Based 3D Skull Feature Reconstruction","authors":"T. Ma, T. D. Bui, T. K. Dang","doi":"10.1109/KSE.2012.38","DOIUrl":"https://doi.org/10.1109/KSE.2012.38","url":null,"abstract":"3D skull is crucial in skull-based 3D facial reconstruction [1, 2, 3, 4, 5, 6, 7, 8]. In 3D reconstruction, especially in skull-based 3D facial reconstruction, features usually play an important role. Because, the accuracy in feature detection strongly affects the accuracy of the 3D final model. In this paper, we concentrate on accuracy of 3D reconstructed skull, one important part in skull-based 3D facial reconstruction. We discuss a cause of errors called shift errors when taking sequence of skull images. In addition, we analysis the effect of shift error in 3D reconstruction and propose solution to limit the effect.","PeriodicalId":122680,"journal":{"name":"2012 Fourth International Conference on Knowledge and Systems Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121673807","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 Study on 4D GIS Spatio-Temporal Data Model","authors":"N. Anh, Phuoc Tran Vinh, H. K. Duy","doi":"10.1109/KSE.2012.29","DOIUrl":"https://doi.org/10.1109/KSE.2012.29","url":null,"abstract":"The GIS applications require the attributes of semantic, space and time data. Spatial data describe the shape, size, location. Temporal data record the time of creation, loss of the space object and history of the changes are stored in the database. The modeling stage is very important first step to build a database of space-time in a GIS application. There have been many models of the authors have proposed in the past, the paper presents a summary of this model. This paper proposes a new 4D space-time data model, it was called TUDM. Time dimension in TUDM integrated into a known 3D GIS space model. TUDM is the pure model temporal-spatial. Time dimension in the model may be either instant or interval time. Born, died time of an object in the model can be happened time in the real world or database. The new model can be represent and store not only explicit evolution history of objects 0D, 1D, 2D but also object 3D in their lifecycle. The TUDM is designed to integrate easy to semantic object when merging to the specified application. The paper compares it to other models. Final part is the experiments with queries on sample data.","PeriodicalId":122680,"journal":{"name":"2012 Fourth International Conference on Knowledge and Systems Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132150937","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":"Evolving Block-Based Neural Network and Field Programmable Gate Arrays for Host-Based Intrusion Detection System","authors":"Quang-Anh Tran, F. Jiang, Quang Minh Ha","doi":"10.1109/KSE.2012.31","DOIUrl":"https://doi.org/10.1109/KSE.2012.31","url":null,"abstract":"In this paper, we design a prototype with hybrid software-enabled detection engine on the basis of an evolving block-based neural network (BBNN), and integrate it with a Field Programmable Gate Arrays (FPGA) board to enable a real-time host-based intrusion detection system (IDS). The established prototype can feed sequence of system calls obtained from a server directly into the BBNN based IDS. The structure and weights of BBNN are evolved by Genetic Algorithms. Experimental performance comparisons have been conducted against four major Support Vector Machines (SVMs) by carrying out leave-one-out cross validation. The results show that the improved BBNN outperforms other algorithms with respect to the classification and detection performances. The false alarm rate is successfully reduced as low as 2.22% while the detection rate 100% is still maintained. The running times of the proposed hardware based IDS versus other software based systems are also discussed.","PeriodicalId":122680,"journal":{"name":"2012 Fourth International Conference on Knowledge and Systems Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133693698","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 Pessimistic Approach for Solving a Multi-criteria Decision Making","authors":"Hieu Nguyen Van, L. Utkin, D. D. Thang","doi":"10.1109/KSE.2012.12","DOIUrl":"https://doi.org/10.1109/KSE.2012.12","url":null,"abstract":"An extension of the DS/AHP method in the paper. The extension assumes that expert judgments concerning the criteria are often imprecise and incomplete. The proposed extension also uses groups of experts or decision makers for comparing decision alternatives and criteria. However, it does not require assigning favorable values for different groups of decision alternatives and criteria. The computation procedure for processing and aggregating the incomplete information about criteria and decision alternatives is reduced to solving a finite set of linear programming problems. Main results are explained and illustrated by numerical examples.","PeriodicalId":122680,"journal":{"name":"2012 Fourth International Conference on Knowledge and Systems Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133872526","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":"Exploring Neighborhood Influence in Text Classification","authors":"N. Le, T. Tran, M. Tran","doi":"10.1109/KSE.2012.35","DOIUrl":"https://doi.org/10.1109/KSE.2012.35","url":null,"abstract":"Standard supervised learning approaches have been widely applied on the text classification problem. These standard approaches exploit only the local content of the document. However, the additional information in the relationship between the items can be used to improve the overall accuracy of the classification process. To make use of this information, the authors propose a statistical model to capture both the contents and labels from each link the neighborhood. This link model is then incorporated with the Markov Random Field model to form the soft labeling model for text classification. This new approach has combined both the local content and the influence from the neighborhood. The results of soft labeling model on standard data sets are also promising. Moreover, the new model can be applied on not only the text classification problem but also many kinds of richly structured data sets.","PeriodicalId":122680,"journal":{"name":"2012 Fourth International Conference on Knowledge and Systems Engineering","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117243125","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}
Mai-Vu Tran, M. Nguyen, Sy-Quan Nguyen, Minh-Tien Nguyen, X. Phan
{"title":"VnLoc: A Real -- Time News Event Extraction Framework for Vietnamese","authors":"Mai-Vu Tran, M. Nguyen, Sy-Quan Nguyen, Minh-Tien Nguyen, X. Phan","doi":"10.1109/KSE.2012.34","DOIUrl":"https://doi.org/10.1109/KSE.2012.34","url":null,"abstract":"Event Extraction is a complex and interesting topic in Information Extraction that includes event extraction methods from free text or web data. The result of event extraction systems can be used in several fields such as risk analysis systems, online monitoring systems or decide support tools. In this paper, we introduce a method that combines lexico -- semantic and machine learning to extract event from Vietnamese news. Furthermore, we concentrate to describe event online monitoring system named VnLoc based on the method that was proposed above to extract event in Vietnamese language. Besides, in experiment phase, we have evaluated this method based on precision, recall and F1 measure. At this time of experiment, we on investigated on three types of event: FIRE, CRIME and TRANSPORT ACCIDENT.","PeriodicalId":122680,"journal":{"name":"2012 Fourth International Conference on Knowledge and Systems Engineering","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124782463","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}