A scalable algorithm for mining maximal frequent sequences using a sample

In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than other algorithms. Congnan Luo received the B.E. degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 1997, the M.S. degree in Computer Science from the Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China, in 2000, and the Ph.D. degree in Computer Science and Engineering from Wright State University, Dayton, OH, in 2006. Currently he is a technical staff at the Teradata division of NCR in San Diego, CA, and his research interests include data mining, machine learning, and databases. Soon M. Chung received the B.S. degree in Electronic Engineering from Seoul National University, Korea, in 1979, the M.S. degree in Electrical Engineering from Korea Advanced Institute of Science and Technology, Korea, in 1981, and the Ph.D. degree in Computer Engineering from Syracuse University, Syracuse, New York, in 1990. He is currently a Professor in the Department of Computer Science and Engineering at Wright State University, Dayton, OH. His research interests include database, data mining, Grid computing, text mining, XML, and parallel and distributed processing.     

On the Competitive Ratio for Online Facility Location

We consider the problem of Online Facility Location, where the demand points arrive online and must be assigned irrevocably to an open facility upon arrival. The objective is to minimize the sum of facility and assignment costs. We prove that the competitive ratio for Online Facility Location is Θ . On the negative side, we show that no randomized algorithm can achieve a competitive ratio better than Ω against an oblivious adversary even if the demands lie on a line segment. On the positive side, we present a deterministic algorithm which achieves a competitive ratio of in every metric space. A preliminary version of this work appeared in the Proceedings of the 30th International Colloquium on Automata, Languages and Programming (ICALP 2003), Lecture Notes in Computer Science 2719. This work was done while the author was at the Max-Planck-Institut für Informatik, Saarbrücken, Germany, and was partially supported by the Future and Emerging Technologies programme of the EU under contract number IST-1999-14186 (ALCOM–FT).     

Biofidelic child head FE model to simulate real world trauma

Biomechanics of human head has been widely studied since several decades. At a mechanical level, the use of engineering allowed investigating injury mechanisms developing numerical models of adult head. For children, the problem is more difficult and evaluating child injury mechanisms using data obtained from scaling adult injury criteria does not account for differences in morphology and structure between adults and children. During growth, child head undergoes different modifications in morphology and structure. The present paper compares the anthropometry and numerical simulations of a child head model based on medical CT scans to a child head model developed by scaling an adult head model using the method proposed by Mertz [H.J. Mertz, A procedure for normalizing impact response data, SAE paper 840884, 1984]. These analysis point out significant differences showing that scaling down an adult head to obtain a child head does not appear relevant. Biofidelic and specific child geometry is needed to investigate child injury mechanisms.     

基于集成的年龄估计方法

近十年来, 由于广泛的应用前景, 关于人脸识别的研究得到了广泛的关注. 但目前有一种影响人脸识别技术的因素尚未被研究者所重视, 那就是年龄变化. 而在适用于年龄变化的人脸识别技术中有一个重要的问题, 即年龄估计. 本文基于典型相关分析和代价敏感学习提出了两种年龄估计算法, 并在此基础上利用集成技术来提高年龄估计的准确性. 最终实验结果验证了本文方法的有效性.     

Feature extraction by autoregressive spectral analysis using maximum likelihood estimation: internal carotid arterial Doppler signals

Abstract: In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model-based methods. Fast Fourier transform (classical method) and autoregressive (model-based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms.     

Motif patterns in 2D

Motif patterns consisting of sequences of intermixed solid and don’t-care characters have been introduced and studied in connection with pattern discovery problems of computational biology and other domains. In order to alleviate the exponential growth of such motifs, notions of maximal saturation and irredundancy have been formulated, whereby more or less compact subsets of the set of all motifs can be extracted, that are capable of expressing all others by suitable combinations. In this paper, we introduce the notion of maximal irredundant motifs in a two-dimensional array and develop initial properties and a combinatorial argument that poses a linear bound on the total number of such motifs. The remainder of the paper presents approaches to the discovery of irredundant motifs both by offline and incremental algorithms.     

A probabilistic alternative to regression suites

Automated regression suites are essential in developing large applications, while maintaining reasonable quality and timetables. The main argument against the automation of regression suites, in addition to the cost of creation and maintenance, is the observation that if you run the same test many times, it becomes increasingly less likely to find bugs. To alleviate such problems, a new regression suite practice, using random test generators to create regression suites on-the-fly, is becoming more common. In this practice, instead of maintaining tests, we generate test suites on-the-fly by choosing several specifications and generating a number of tests from each specification.     

Queueing analysis of a server node in transaction processing middleware systems

Quantitative performance modeling of complex information systems is of immense importance for designing enterprise e-business infrastructures and applications. In this paper, we present a traffic model of a server node in a typical transaction processing middleware system as well as a quantitative framework to model and analyze its performance. A multi-class open queueing network model is presented in which multi-class jobs are admitted to a number of server processes sharing hardware resources including the CPU and the disk. We have developed a viable approximation method, which decomposes the dependent components into their independent counterparts while preserving their relevant characteristics. We have conducted queueing-theoretic delay analyses and verified the approach using simulation. Results demonstrate the strength of our approach in predicting delays, elapsed times and other system performance measures.     

Typology of teacher perception toward distance education issues – A study of college information department teachers in Taiwan

The popularity of distance education has grown rapidly over the last decade in Taiwan’s higher education, yet many fundamental teaching–learning issues are still in debate. While teacher–student interaction is a key success factor in distance education, little work has been done on the teachers. The intent of this research was to clarify teacher’s perceptions on key distance education issues and to develop a discernible typology of different groups of teachers based on their perceptions. Because there may be a gulf between teachers and the technology used in distance education, the target of this study was teachers in information related departments in Taiwan’s colleges, who were more familiar with current technology. Factor analysis and cluster analysis were used to derive the typology. Five higher-level issue constructs emerged from the factor analysis: learning effect, customization, administrative challenges, geographic and resource integration, and instructional design challenges. Four groups of teachers, namely the skeptics, the optimists, the mild-promising group, and the outlier, were identified using cluster analysis of teachers’ perceptions on these five higher-level issue constructs. The profiles of the four groups of teachers were summarized and implications were discussed, which should provide useful insights to the policy makers of higher education on distance education decisions.     

Using adaptive e-news to improve undergraduate programming courses with hybrid format

With the growing popularity of computers and the Internet, most teachers are taking advantage of Internet functions to assist in both teaching and student learning. However, students only login to the assisted learning system once or twice a week on average to surf for relevant references, participate in discussions, or hand in assignments, even though information and interaction are greatly enhanced by these online services. Therefore, increasing the transfer rate of useful information and interaction during the course period would help students learn. To this end, we designed an e-news delivery system that sends personalized e-mails to every student’s mailbox to make information access and peer interaction more convenient and efficient. Through the daily habit of using e-mail, students can receive the latest information, new instructions or peer discussions early. To encourage usage of the system, a user friendly interface was designed. The e-news system takes advantage of webpage hyperlinks to allow students to click on interesting topics while reading e-news; doing so will cause another window to pop-up with detailed content retrieved from the web-based learning system. A mouse-click allows students to login to the web-based learning system and extract the details of the chosen topic. To enhance the efficiency of e-news, learners’ needs and interests were taken into consideration when forming the e-news content. Students’ learning status and reading preferences are assessed to determine which information or content to include in the e-news. Experiment results reveal that e-news and personalized information greatly promote the reading of new information and participation in online activities.