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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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Deep web repeated pattern discovering based on the largest block strategy

Deep web repeated pattern discovering based on the largest b...

引用

2012 IEEE 12th international conference on Computer and Information Technology, CIT 2012

作者： Ye, Feiyue Tang, Haibo Luo, Xiangfeng School of Computer Engineering and Science Shanghai Univercity Shanghai China

ISBN: (纸本)9780769548586

Repeated pattern is a common phenomenon in query result pages of deep web sites. the deep web back-end data can be accessed by mining repeated patterns. So far, most of the algorithms of discovering repeated pattern use traditional web information extraction methods. But the recall percentage and accuracy are not high. How to obtain the repeated pattern accurately and completely is still a difficulty. We propose a method based on the largest block strategy to discover such pattern. the core of the method is using the largest block strategy to discover the repeated pattern layer. We can quickly navigate to the region of the entity data, and then analyze the sub tree in this area, finally, get the simplified repeated pattern of the deep web site. According to the results of the experiment, this method can get the repeated pattern data more accurately and more completely than the traditional methods. It can also address the multi-pattern problem which has not been solved yet in other methods. © 2012 IEEE.

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Advanced Research in Applied Artificial Intelligence 2012

引用

丛书名： Lecture Notes in Computer Science

2012年

作者： He Jiang Wei Ding Moonis Ali Xindong Wu

ISBN: (数字)9783642310874

ISBN: (纸本)9783642310867

this volume constitutes the thoroughly refereed conference proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligend Systems, IEA/AIE 2012, held in Dalian, China, in June 2012. the total of 82 papers selected for the proceedings were carefully reviewed and selected from numerous submissions. the papers are organized in topical sections on machine learning methods; cyber-physical system for intelligent transportation applications; AI applications; evolutionary algorithms, combinatorial optimization; modeling and support of cognitive and affective human processes; natural language processing and its applications; social network and its applications; mission-critical applications and case studies of intelligent systems; AI methods; sentiment analysis for asian languages; aspects on cognitive computing and intelligent interaction; spatio-temporal datamining, structured learning and their applications; decision making and knowledge based systems; pattern recognition; agent based systems; decision making techniques and innovative knowledge management; machine learning applications.

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Run-time composite event recognition 12

Run-time composite event recognition

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6th ACM international conference on Distributed Event-Based Systems, DEBS'12

作者： Artikis, Alexander Sergot, Marek Paliouras, Georgios Demokritos Athens 15310 Greece Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9781450313155

Events are particularly important pieces of knowledge, as they represent activities of special significance within an organisation: the automated recognition of events is of utmost importance. We present RTEC, an Event Calculus dialect for run-time event recognition and its Prolog implementation. RTEC includes a number of novel techniques allowing for efficient run-time recognition, scalable to large data streams. It can be used in applications where data might arrive with a delay from, or might be revised by, the underlying event sources. We evaluate RTEC using a real-world application. Copyright © 2012 ACM.

关键词： pattern matching

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A new multi-pattern matching algorithm based on frequently used group

A new multi-pattern matching algorithm based on frequently u...

引用

2012 IEEE 12th international conference on Computer and Information Technology, CIT 2012

作者： Yang, Guangming Li, Shuang Tan, Zhenhua Yan, Xiangshuai Hou, Xiangjun Software College Northeastern University Shenyang China

ISBN: (纸本)9780769548586

Along with the rapid development of computer technology, people's lives are increasingly dependent on computers. At the same time, the computer system is facing increasingly complex and diverse sabotage and attacks. Destruction of computer viruses is most widespread and severe among them, so studying anti-virus technology is imminent. Virus scanning engine is the kernel of anti-virus software, it uses signature database to identify known viruses. the pattern matching algorithm is the core algorithm of the entire anti-virus software. this paper first introduces some background knowledge of anti-virus software, pattern matching algorithm and hash algorithm. then it proposes a new type of multi-pattern matching algorithm with automata based on frequently matching hash values. Combining the advantages of fast calculation of hash function and parallel pattern matching of automata, it has significant performance advantages in the circumstance of virus signature matching. It can also be applied to other similar circumstances after making a little improvement, such as gene sequence alignment where patterns are also very long. © 2012 IEEE.

关键词： pattern matching

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Scale-adaptive feature points extraction and dual-scale based high-precision image registration

Scale-adaptive feature points extraction and dual-scale base...

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2012 IEEE 12th international conference on Computer and Information Technology, CIT 2012

作者： Gao, Zunqi Ming, Delie Xiao, Liping Wang, Hai Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China National Laboratory of Aerospace Intelligence Control Technology Beijing Aerospace Automatic Control Institute Beijing China Department of Computer Science and Engineering Pohang University of Science and Technology Pohang Korea Republic of

ISBN: (纸本)9780769548586

the SIFT algorithm had a profound impact on the area of image local feature extraction and computer vision, since its inception. Because of its robustness and anti-interference performance, it has been widely applied in engineering. However, in practical usage of SIFT, there are actually no sufficient feature points that can be extracted to perform the image registration. For tiny scale targets, this situation becomes even worse. Based on the analysis of the scale-space, this paper puts forward a scale-adaptive feature points extraction method (SFPE). SFPE can extract more feature points than traditional SIFT algorithm, especially when the target scale is tiny. Furthermore, a dual-scale based high-precision image registration process (DHIR) is proposed in this paper. In DHIR, a small-scale matching (precise matching) process is performed after the large-scale registration. Experimental results show that the DHIR is more effective to eliminate mismatches than traditional SIFT algorithm. © 2012 IEEE.

关键词： Feature extraction

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Elastic Net for Paralinguistic Speech recognition 12

Elastic Net for Paralinguistic Speech Recognition

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14th ACM international conference on Multimodal Interaction (ICMI)

作者： Fewzee, Pouria Karray, Fakhri Univ Waterloo Ctr Pattern Anal & Machine Intelligence Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781450314671

Given the fact that the length of the feature vector that is being used for the paralinguistic recognition of speech has exceeded some thousands, the importance of a sparse representation of a model becomes notable. the importance of a sparse representation is mainly due to the more interpretability, higher generalization capability, and numerically more efficiency of such a model. In this work, as an endeavor to search for a sparse representation of speech features used for paralinguistic speech modeling, we make use of the elastic net. As for the benchmark, we use the frameworks of the second audio/visual emotion challenge and the Interspeech 2012 speaker trait challenge. Also proposed in this work is the use of part-of-speech tags as syntactic features of speech for emotional speech recognition. Results of this work show that despite the relatively small number of features that is used for the modeling tasks, generalization capability of the suggested models is comparable to those of other models that use thousands of features and more elaborate learning algorithms.

关键词： Affective computing Emotion recognition Trait recognition Speech Dimensionality Reduction

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Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification

Outlier Detection in Logistic Regression: A Quest for Reliab...

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12th IEEE international conference on Data Mining (ICDM)

作者： Nurunnabi, Abdul West, Geoff Curtin Univ Dept Spatial Sci Perth WA 6845 Australia

ISBN: (纸本)9780769549255;9781467351645

Logistic regression is well known to the data mining research community as a tool for modeling and classification. the presence of outliers is an unavoidable phenomenon in data analysis. Detection of outliers is important to increase the accuracy of the required estimates and for reliable knowledge discovery from the underlying databases. Most of the existing outlier detection methods in regression analysis are based on the single case deletion approach that is inefficient in the presence of multiple outliers because of the well known masking and swamping effects. To avoid these effects the multiple case deletion approach has been introduced. We propose a group deletion approach based diagnostic measure for identifying multiple influential observations in logistic regression. At the same time we introduce a plotting technique that can classify data into outliers, high leverage points, as well as influential and regular observations. this paper has two objectives. First, it investigates the problems of outlier detection in logistic regression, proposes a new method that can find multiple influential observations, and classifies the types of outlier. Secondly, it shows the necessity for proper identification of outliers and influential observations as a prelude for reliable knowledge discovery from modeling and classification via logistic regression. We demonstrate the efficiency of our method, compare the performance with the existing popular diagnostic methods, and explore the necessity of outlier detection for reliability and robustness in modeling and classification by using real datasets.

关键词： data mining high leverge point influential observation knowledge discovery outlier pattern recognition regression reliability statistical computing

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Detecting Pedestrian Flocks by Fusion of Multi-Modal Sensors in Mobile Phones 12

Detecting Pedestrian Flocks by Fusion of Multi-Modal Sensors...

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14th ACM international conference on Ubiquitous computing (UbiComp)

作者： Kjaergaard, Mikkel Baun Wirz, Martin Roggen, Daniel Troester, Gerhard Swiss Fed Inst Technol Wearable Comp Lab Zurich Switzerland

ISBN: (纸本)9781450312240

Previous work on the recognition of human movement patterns has mainly focused on movements of individuals. this paper addresses the joint identification of the indoor movement of multiple persons forming a cohesive whole - specifically a flock - with clustering approaches operating on features derived from multiple sensor modalities of modern smartphones. Automatic detection of flocks has several important applications, including evacuation management and socially aware computing. the novelty of this paper is, firstly, to use data fusion techniques to combine several sensor modalities (WiFi, accelerometer and compass) to improve recognition accuracy over previous unimodal approaches. Secondly, improve the recognition of flocks using hierarchical clustering. We use a dataset comprising 16 subjects forming one to four flocks walking in a building on single and multiple floors. With the best settings, we achieve a F-score accuracy of up to 87 percent an improvement of up to twelve percent points over existing approaches.

关键词： pattern recognition crowd behavior sensing mobile sensing signal strength-based methods

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GamEMO: How Physiological Signals Show your Emotions and Enhance your Game Experience 12

GamEMO: How Physiological Signals Show your Emotions and Enh...

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14th ACM international conference on Multimodal Interaction (ICMI)

作者： Guillaume, Chanel Konstantina, Kalogianni thierry, Pun Univ Geneva Swiss Ctr Affect Sci CH-1205 Geneva Switzerland

ISBN: (纸本)9781450314671

the proposed demonstration is an automatic emotion assessment installation used for game's dynamic difficulty adjustment. the goal of the system is to maintain the player of the game in a state of entertainment and engagement where his/her skills match the difficulty level of the game. the player's physiological signals are recorded while playing a Tetris game and signal processing, feature extraction and classification techniques are applied to the signals in order to detect when the player is anxious or bored. the level of the Tetris game is then adjusted according to the player's detected emotional state. the demonstration will also serve as an experimental protocol to test the player's experience through their interaction with the proposed platform.

关键词： Affective computing Games Emotion Assessment Physiological Signal Processing pattern recognition Classification

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Evaluating Semi-automatic Annotation of Domestic Energy Consumption as a Memory Aid 12

Evaluating Semi-automatic Annotation of Domestic Energy Cons...

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14th ACM international conference on Ubiquitous computing (UbiComp)

作者： Richardson, Darren P. Costanza, Enrico Ramchurn, Sarvapali D. Univ Southampton Southampton Hants England

ISBN: (纸本)9781450312240

Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants.

关键词： Energy management eco-feedback sustainable HCI consumption pattern recognition human-agent interaction memory aid

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