2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)最新文献

VideoGIS data retrieval based on multi-feature fusion 基于多特征融合的视频信息数据检索

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258831

Haihong Dai, B. Hu, Qian Cui, Z. Zou

{"title":"VideoGIS data retrieval based on multi-feature fusion","authors":"Haihong Dai, B. Hu, Qian Cui, Z. Zou","doi":"10.1109/ISKE.2017.8258831","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258831","url":null,"abstract":"With the rapid development of smart cities and the advance of city safety and defense demands, the question that how to accurately discover and retrieve the data that users request from VideoGIS data faces a series of bottleneck problems. VideoGIS data retrieval is one of the important ways to solve above problems. In order to accelerate the rate of feature matching and improve the efficiency of the video retrieval, a new method of VideoGIS data retrieval based on multi-feature fusion is proposed in this paper. The method firstly use video frame difference based on Euclidean distance to extract the key frames under the spatial and temporal sampling of the video. On the basis of this, the global features (e.g. color, shape, texture) are fused by different weighted coefficients, then the feature vector, as the video multi-feature fusion representation, can be constructed by fusing the global features and local features. Based on the multi-feature fusion, correlation between video features is made full use. Compared with the method of single feature and two-feature fusion, the experimental results indicate that the proposed retrieval method has better retrieval effect.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117152275","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

Some synergized clause selection strategies for contradiction separation based automated deduction 基于矛盾分离的自动推理的协同子句选择策略

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258741

Shuwei Chen, Yang Xu, Yan Jiang, Jun Liu, Xingxing He

引用次数: 4

Classifying segments in edge detection problems 边缘检测问题中的片段分类

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258764

P. Flores-Vidal, D. Gómez, J. Montero, G. Villarino

引用次数: 4

End-to-end trajectory transportation mode classification using Bi-LSTM recurrent neural network 基于Bi-LSTM递归神经网络的端到端轨迹运输模式分类

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258799

Hongbin Liu, Ickjai Lee

引用次数: 32

An improved textual storyline generating framework for disaster information management 一种用于灾害信息管理的改进文本故事线生成框架

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258738

Qifeng Zhou, Ruifeng Yuan, Tao Li

{"title":"An improved textual storyline generating framework for disaster information management","authors":"Qifeng Zhou, Ruifeng Yuan, Tao Li","doi":"10.1109/ISKE.2017.8258738","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258738","url":null,"abstract":"Analyzing and understanding disaster-related situation updates from a large number of disaster-related documents plays an important role in disaster management and has attracted a lot of research attention. Recently several methods have been developed to generate textual storylines from disaster-related documents to help people understand the overall trend and evolution of a disaster event as well as how the disaster affects different areas. These methods are able to help people improve their situation awareness by generating informative summarizes to present the global pictures of disaster events. However, these methods suffer from several limitations including text representation, representative document selection, and summary generation that may affect the quality of the summarized results. To address these limitations, in this paper, we propose an improved two-layer storyline generating framework which generates a global storyline of the disaster events in the first layer, and provides condensed information about specific regions affected by the disaster in the second layer. The proposed framework utilizes the word embedding for text similarity measurement, considers both uniqueness and relevance for representative document selection, and uses Maximal Marginal Relevance to generate summaries from each local document set. The experimental results on four typhoons related events demonstrate the efficacy of our proposed framework on capturing the status information and understanding the situation from a large of documents.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"564 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116286507","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

A specification with performance evaluation for probabilistic timed automata 概率时间自动机性能评价规范

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258788

Yan Ma, Zining Cao, Yang Liu

{"title":"A specification with performance evaluation for probabilistic timed automata","authors":"Yan Ma, Zining Cao, Yang Liu","doi":"10.1109/ISKE.2017.8258788","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258788","url":null,"abstract":"At present, there are various logics used to describe properties of continue-time systems, such as TCTL (timed computation tree logic) and RTCTL (rewards timed computation tree logic), and of continue-time stochastic systems, such as CSL (continuous stochastic logic) and PTCTL (probabilistic timed computation tree logic). When assigned on the systems, these logics are limited to returning only true or false. In order to establish the correctness and the performance of a system one needs a unified framework for expressing the system, and a unified logic for expressing the desired specification. In this word, we propose a new formal specification TCTML (timed computation tree measurement language) to check the correctness and evaluate the performance of a system, which operates on real values to reason over probabilistic continue-time systems. Retaining the conventional semantics of temporal logics, it extends weak until and until with arithmetic operations on real-valued quantities. Moreover, the algorithms of CTML model checking PTAs (probabilistic timed automata) is given and a concrete example is used to demonstrate that it is feasible and effective.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114759879","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

Image representation based on multi-features 基于多特征的图像表示

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258830

Xianzhong Long, Lei Chen, Qun Li

引用次数: 0

Semi advised learning and classification algorithm for partially labeled skin cancer data analysis 部分标记皮肤癌数据分析的半建议学习和分类算法

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258767

A. Masood, Adel Al-Jumaily

{"title":"Semi advised learning and classification algorithm for partially labeled skin cancer data analysis","authors":"A. Masood, Adel Al-Jumaily","doi":"10.1109/ISKE.2017.8258767","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258767","url":null,"abstract":"Development of automated diagnosis systems using machine learning and expert knowledge based data analysis requires effective automated learning models. However, models based on limited expert labeled training data can wrongly affect the results of diagnosis due to insufficient training knowledge acquired. On the other hand, getting more relevant analytical details from all the data used for training is an aspect that can enhance the efficiency of learning algorithms. This paper proposes a semi-advised training and classification algorithm that has the capability to effectively use limited labeled data along with abundant unlabeled data. It demonstrates the capability to use unlabeled data for training the algorithm by obtaining sufficient amount of information through incorporating an advised and/or partially supervised methodology. For comparative analysis, dermatological and histopathalogical images of skin cancer are used as experimental datasets. The proposed algorithm provided very impressive diagnosis outputs for both type of datasets in comparison to several other famous algorithms that are usually used in literature for classification.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126345336","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: 9

On the secure optimized link state routing (SOLSR) protocol for MANETs 面向manet的安全优化链路状态路由(SOLSR)协议

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258757

Mushtaq Ahmad, Qingchun Chen, M. Najam-ul-Islam, M. Iqbal, S. Hussain

引用次数: 3

Modeling of distributed visual knowledge discovery from data process 基于数据过程的分布式可视化知识发现建模

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI: 10.1109/ISKE.2017.8258791

Hamdi Ellouzi, Mounir Ben Ayed, Hela Ltifi

引用次数: 0