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A Novel Linear Cellular Automata-Based data Clustering Algorithm

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4th international Work-conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： de Lope, Javier Maravall, Dario Univ Politecn Madrid Cognit Robot Grp Dept Artificial Intelligence E-28040 Madrid Spain Univ Politecn Madrid Dept Appl Intelligent Syst E-28040 Madrid Spain

ISBN: (纸本)9783642213441

In this paper we propose a novel data clustering algorithm based on the idea of considering the individual data items as cells belonging to an uni-dimensional cellular automaton. Our proposed algorithm combines insights from both social segregation models based on Cellular Automata theory, where the data items themselves are able to move autonomously in lattices, and also from Ants Clustering algorithms, particularly in the idea of distributing at random the data items to be clustered in lattices. We present a series of experiments with both synthetic and real datasets in order to study empirically the convergence and performance results. these experimental results are compared to the obtained by conventional clustering algorithms.

关键词： Cellular Automata machine learning pattern recognition data mining data Clustering Social Segregation Models Ants Clustering

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Multi-relational pattern mining Based-on Combination of Properties with Preserving their Structure in Examples

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20th international conference on Inductive Logic Programming (ILP)

作者： Nakano, Yusuke Inuzuka, Nobuhiro Nagoya Inst Technol Showa Ku Nagoya Aichi 4668555 Japan

ISBN: (纸本)9783642212949;9783642212956

We propose an algorithm for multi-relational pattern mining through the problem established in WARMR. In order to overcome the combinatorial problem of large pattern space, another algorithm MAPIX restricts patterns into combination of basic patterns, called properties. A property is defined as a set of literals appeared in examples and is of an extended attribute-value form. Advantage of MAPIX is to make patterns from pattern fragments occurred in examples. Many patterns which are not appeared in examples are not tested. Although the range of patterns is clear and MAPIX enumerates them efficiently, a large part of patterns are out of the range. the proposing algorithm keeps the advantage and extends the way of combination of properties. the algorithm combines properties as they appeared in examples, we call it structure preserving combination.

关键词： data mining

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Proc. of the Int. Workshop on Soft Computing Applications and Knowledge Discovery 2011, SCAKD 2011 - In Conjunction with the 4th Int. Conf. on pattern recognition and machine Intell., PReMI 2011

Proc. of the Int. Workshop on Soft Computing Applications an...

引用

international Workshop on Soft Computing Applications and Knowledge Discovery 2011, SCAKD 2011 - In Conjunction with the 4th international conference on pattern recognition and machine Intelligence, PReMI 2011

the proceedings contain 15 papers. the topics discussed include: estimating probability of failure of a complex system based on partial information about subsystems and components, with potential applications to aircraft maintenance;stepwise feature selection using multiple kernel learning;empirical reconstruction of fuzzy model of experiment in the Euclidean metric;SVM based offline handwritten gurmukhi character recognition;obtaining of a minimal polygonal representation of a curve by means of a fuzzy clustering;KDDClus: a simple method for multi-density clustering;intelligent data mining for turbo-generator predictive maintenance: an approach in real-world;handwritten script identification from a bi-script document at line level using gabor filters;image recognition using kullback-leibler information discrimination;beyond analytical modeling, gathering data to predict real agents' strategic interaction;and construction of enzyme network of arabidopsis thaliana using graph theory.

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Classification methods using Winners-Take-All neural networks

Classification methods using Winners-Take-All neural network...

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作者： Brenych, Yana Informational Technologies Department Volyn State University Named after Lesia Ukrainka Volia av. 13 Lutsk Ukraine

ISBN: (纸本)9789662191196

Winner-Take-All (WTA) and its extended version K-Winner-Take-All (KWTA) networks have been frequently used as the classifiers in neural networks. they are very important tools in data mining, machine learning and pattern recognition. there are a lot of scientific works devoted to this technology. the elimination of limitations is main aim of the most number of these researches. KWTA is the unique strategy for solving classification problems in different branches of science. this unique character is presented in the research. © 2011 LVIV POLYTECHNIC NATL UNIV.

关键词： pattern recognition

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What's your current stress level? Detection of stress patterns from GSR sensor data

What's your current stress level? Detection of stress patter...

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11th IEEE international conference on data mining Workshops, ICDMW 2011

作者： Bakker, Jorn Pechenizkiy, Mykola Sidorova, Natalia Department of Computer Science Eindhoven University of Technology P.O. Box 513 Eindhoven 5600MB Netherlands

ISBN: (纸本)9780769544090

the problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care specialists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. Stress management should start far before the stress start causing illnesses. the current state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. In this paper we (1) formulate the problem of stress identification and categorization from the sensor data stream mining perspective, (2) consider a reductionist approach for arousal identification as a drift detection task, (3) highlight the major problems of dealing with GSR data, collected from a watch-style stress measurement device in normal (i.e. in non-lab) settings, and propose simple approaches how to deal with them, and (4) discuss the lessons learnt from the conducted experimental study on real GSR data collected during the recent field study. © 2011 IEEE.

关键词： pattern recognition

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pattern recognition and classification for multivariate time series

Pattern recognition and classification for multivariate time...

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5th international Workshop on Knowledge Discovery from Sensor data, SensorKDD'11 - Held in Conjunction with the 17th ACM SIGKDD conference on Knowledge Discovery and data mining, KDD-2011

作者： Spiegel, Stephan Gaebler, Julia Lommatzsch, Andreas De Luca, Ernesto Albayrak, Sahin DAI-Labor Technische Universitaet Berlin Ernst-Reuter-Platz 7 10587 Berlin Germany

ISBN: (纸本)9781450308328

Nowadays we are faced with fast growing and permanently evolving data, including social networks and sensor data recorded from smart phones or vehicles. Temporally evolving data brings a lot of new challenges to the data mining and machine learning community. this paper is concerned with the recognition of recurring patterns within multivariate time series, which capture the evolution of multiple parameters over a certain period of time. Our approach first separates a time series into segments that can be considered as situations, and then clusters the recognized segments into groups of similar context. the time series segmentation is established in a bottom-up manner according the correlation of the individual signals. Recognized segments are grouped in terms of statistical features using agglomerative hierarchical clustering. the proposed approach is evaluated on the basis of real life sensor data from different vehicles recorded during car drives. According to our evaluation it is feasible to recognize recurring patterns in time series by means of bottom-up segmentation and hierarchical clustering. Copyright 2011 ACM.

关键词： Singular value decomposition

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Monitoring Insiders Activities in Cloud Computing Using Rule Based learning

Monitoring Insiders Activities in Cloud Computing Using Rule...

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Int Joint conference of 10th IEEE Int Conf on Trust, Security and Privacy in Computing and Communications (TrustCom) / 8th IEEE Int Conf on Embedded Software and Systems (ICESS) / 6th Int Conf on Frontier of Computer Science and Technology (FCST)

作者： Khorshed, Md Tanzim Ali, A. B. M. Shawkat Wasimi, Saleh A. CQUniv Sch Informat & Commun Technol Rockhampton Qld 4702 Australia

ISBN: (纸本)9780769546001

One of the essential but formidable tasks in cloud computing is to detect malicious attacks and their types. A cloud provider's constraints or inability in monitoring its employees, and lack of transparency, may make the detection process even harder. We found these insiders' activities form similar pattern in the monitoring systems as some other cyber attacks because these also uses huge computer resources. In this paper we first provide a brief overview on the importance of monitoring insiders' activities through a literature survey on cloud computing security. then, we observe some of the real life insiders' activities that can be detected from the performance data in a hypervisor and its guest operating systems. Rule based learning is successfully used for identification of these activities in this research. We further observe that some of these insiders' activities can on occasions turn into a malicious insider's attack, and thus, need constant monitoring in the cloud environment.

关键词： Security threats machine learning cyber attacks cloud computing insiders

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Evaluation of a Resource Allocating Network with Long Term Memory Using GPU

Evaluation of a Resource Allocating Network with Long Term M...

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10th international conference on Artificial Neural Networks and Genetic Algorithms

作者： Ribeiro, Bernardete Quintas, Ricardo Lopes, Noel Univ Coimbra Dept Informat Engn P-3000 Coimbra Portugal CISUC P-3000 Coimbra Portugal

ISBN: (纸本)9783642202667

Incremental learning has recently received broad attention in many applications of pattern recognition and data mining. With many typical incremental learning situations in the real world where a fast response to changing data is necessary, developing a parallel implementation (in fast processing units) will give great impact to many applications. Current research on incremental learning methods employs a modified version of a resource allocating network (RAN) which is one variation of a radial basis function network (RBFN). this paper evaluates the impact of a Graphics Processing Units (GPU) based implementation of a RAN network incorporating Long Term Memory (LTM) [4]. the incremental learning algorithm is compared with the batch RBF approach in terms of accuracy and computational cost, both in sequential and GPU implementations. the UCI machine learning benchmark datasets and a real world problem of multimedia forgery detection were considered in the experiments. the preliminary evaluation shows that although the creation of the model is faster with the RBF algorithm, the RAN-LTM can be useful in environments with the need of fast changing models and high-dimensional data.

关键词： Incremental learning CPU Computing

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Interpreting 3D Faces for Augmented Human-Computer Interaction 1

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6th international conference on Universal Access in Human-Computer Interaction (UAHCI)/14th international conference on Human-Computer Interaction (HCI)

作者： Cadoni, Marinella Grosso, Enrico Lagorio, Andrea Tistarelli, Massimo Univ Sassari Comp Vis Lab Tramariglio Alghero Italy

ISBN: (数字)9783642216633

ISBN: (纸本)9783642216626;9783642216633

Human-machine interaction requires the ability to analyze and discern human faces. Due to the nature of the 3D to 2D projection, the recognition of human faces from 2D images, in presence of pose and illumination variations, is intrinsically an ill-posed problem. the direct measurement of the shape for the face surface is now a feasible solution to overcome this problem and make it well-posed. this paper proposes a completely automatic algorithm for 3D face registration and matching based on the extraction of stable 3D facial features characterizing the face and the subsequent construction of a signature manifold. the facial features are extracted by performing a continuous-to-discrete scale-space analysis. Registration is driven from the matching of triplets of feature points and the registration error is computed as shape matching score. A major advantage of the proposed method is that no data pre-processing is required. Despite of the high dimensionality of the data (sets of 3D points, possibly with the associate texture), the signature and hence the template generated is very small. therefore, the management of the biometric data associated to the user data, not only is very robust to environmental changes, but it is also very compact. the method has been tested against the Bosphorus 3D face database and the performances compared to the ICP baseline algorithm. Even in presence of noise in the data, the algorithm proved to be very robust and reported identification performances in line with the current state of the art.

关键词： Face recognition 3D faces pattern recognition Scale-space theory Geometric invariants

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A quantitative approach to Triaging in Mobile Forensics

A quantitative approach to Triaging in Mobile Forensics

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作者： Marturana, Fabio Me, Gianluigi Berte, Rosamaria Tacconi, Simone Univ Roma Tor Vergata Dept Comp Sci Syst & Prod Rome Italy Polizia Stato & Comunicaz Rome Italy

ISBN: (纸本)9780769546001

Forensic study of mobile devices is a relatively new field, dating from the early 2000s. the proliferation of phones (particularly smartphones) on the consumer market has caused a growing demand for forensic examination of the devices, which could not be met by existing Computer Forensics techniques. As a matter of fact, Law enforcement are much more likely to encounter a suspect with a mobile device in his possession than a PC or laptop and so the growth of demand for analysis of mobiles has increased exponentially in the last decade. Early investigations, moreover, consisted of live analysis of mobile devices by examining phone contents directly via the screen and photographing it with the risk of modifying the device content, as well as leaving many parts of the proprietary operating system inaccessible. the recent development of Mobile Forensics, a branch of Digital Forensics, is the answer to the demand of forensically sound examination procedures of gathering, retrieving, identifying, storing and documenting evidence of any digital device that has both internal memory and communication ability [1]. Over time commercial tools appeared which allowed analysts to recover phone content with minimal interference and examine it separately. By means of such toolkits, moreover, it is now possible to think of a new approach to Mobile Forensics which takes also advantage of "data mining" and "machine learning" theory. this paper is the result of study concerning cell phones classification in a real case of pedophilia. Based on Mobile Forensics "Triaging" concept and the adoption of self-knowledge algorithms for classifying mobile devices, we focused our attention on a viable way to predict phone usage's classifications. Based on a set of real sized phones, the research has been extensively discussed with Italian law enforcement cybercrime specialists in order to find a viable methodology to determine the likelihood that a mobile phone has been used to commit the specific

关键词： Triaging Mobile Forensics data mining Knowledge Analysis machine learning

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