An algorithm for fast mining top-rank-k frequent patterns based on node-list data structure

Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and post-order transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_TopK uses the minimal support threshold for pruning strategy to guarantee that each pattern in the top-rank-k table is really frequent and this further improves the efficiency. Experiments are conducted to compare FP_TopK with iNTK and BTK on four datasets. The results show that FP_TopK achieves better performance

Disks around massive young stellar objects: are they common?

We present K-band polarimetric images of several massive young stellar objects at resolutions $\sim$ 0.1-0.5 arcsec. The polarization vectors around these sources are nearly centro-symmetric, indicating they are dominating the illumination of each field. Three out of the four sources show elongated low-polarization structures passing through the centers, suggesting the presence of polarization disks. These structures and their surrounding reflection nebulae make up bipolar outflow/disk systems, supporting the collapse/accretion scenario as their low-mass siblings. In particular, S140 IRS1 show well defined outflow cavity walls and a polarization disk which matches the direction of previously observed equatorial disk wind, thus confirming the polarization disk is actually the circumstellar disk. To date, a dozen massive protostellar objects show evidence for the existence of disks; our work add additional samples around MYSOs equivalent to early B-type stars.Comment: 9 pages, including 2 figures, 1 table, to appear on ApJ

DMP_MI: an effective diabetes mellitus classification algorithm on imbalanced data with missing values

© 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. As a widely known chronic disease, diabetes mellitus is called a silent killer. It makes the body produce less insulin and causes increased blood sugar, which leads to many complications and affects the normal functioning of various organs, such as eyes, kidneys, and nerves. Although diabetes has attracted high attention in research, due to the existence of missing values and class imbalance in the data, the overall performance of diabetes classification using machine learning is relatively low. In this paper, we propose an effective Prediction algorithm for Diabetes Mellitus classification on Imbalanced data with Missing values (DMP_MI). First, the missing values are compensated by the Naïve Bayes (NB) method for data normalization. Then, an adaptive synthetic sampling method (ADASYN) is adopted to reduce the influence of class imbalance on the prediction performance. Finally, a random forest (RF) classifier is used to generate predictions and evaluated using comprehensive set of evaluation indicators. Experiments performed on Pima Indians diabetes dataset from the University of California at Irvine, Irvine (UCI) Repository, have demonstrated the effectiveness and superiority of our proposed DMP_MI

Security feature measurement for frequent dynamic execution paths in software system

© 2018 Qian Wang et al. The scale and complexity of software systems are constantly increasing, imposing new challenges for software fault location and daily maintenance. In this paper, the Security Feature measurement algorithm of Frequent dynamic execution Paths in Software, SFFPS, is proposed to provide a basis for improving the security and reliability of software. First, the dynamic execution of a complex software system is mapped onto a complex network model and sequence model. This, combined with the invocation and dependency relationships between function nodes, fault cumulative effect, and spread effect, can be analyzed. The function node security features of the software complex network are defined and measured according to the degree distribution and global step attenuation factor. Finally, frequent software execution paths are mined and weighted, and security metrics of the frequent paths are obtained and sorted. The experimental results show that SFFPS has good time performance and scalability, and the security features of the important paths in the software can be effectively measured. This study provides a guide for the research of defect propagation, software reliability, and software integration testing

Dealloying of Platinum-Aluminum Thin Films Part I. Dynamics of Pattern Formation

Applying focused ion beam (FIB) nanotomography and Rutherford backscattering spectroscopy (RBS) to dealloyed platinum-aluminum thin films an in-depth analysis of the dominating physical mechanisms of porosity formation during the dealloying process is performed. The dynamical porosity formation due to the dissolution of the less noble aluminum in the alloy is treated as result of a reaction-diffusion system. The RBS analysis yields that the porosity formation is mainly caused by a linearly propagating diffusion front, i.e. the liquid/solid interface, with a uniform speed of 42(3) nm/s when using a 4M aqueous NaOH solution at room temperature. The experimentally observed front evolution is captured by the normal diffusive Fisher-Kolmogorov-Petrovskii-Piskounov (FKPP) equation and can be interpreted as a branching random walk phenomenon. The etching front produces a gradual porosity with an enhanced porosity in the surface-near regions of the thin film due to prolonged exposure of the alloy to the alkaline solution.Comment: 4 pages, 5 figure

The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models

An adaptive ensemble approach to ambient intelligence assisted people search

Some machine learning algorithms have shown a better overall recognition rate for facial recognition than humans, provided that the models are trained with massive image databases of human faces. However, it is still a challenge to use existing algorithms to perform localized people search tasks where the recognition must be done in real time, and where only a small face database is accessible. A localized people search is essential to enable robot–human interactions. In this article, we propose a novel adaptive ensemble approach to improve facial recognition rates while maintaining low computational costs, by combining lightweight local binary classifiers with global pre-trained binary classifiers. In this approach, the robot is placed in an ambient intelligence environment that makes it aware of local context changes. Our method addresses the extreme unbalance of false positive results when it is used in local dataset classifications. Furthermore, it reduces the errors caused by affine deformation in face frontalization, and by poor camera focus. Our approach shows a higher recognition rate compared to a pre-trained global classifier using a benchmark database under various resolution images, and demonstrates good efficacy in real-time tasks

Sensitivity of inferred climate model skill to evaluation decisions: a case study using CMIP5 evapotranspiration

Confrontation of climate models with observationally-based reference datasets is widespread and integral to model development. These comparisons yield skill metrics quantifying the mismatch between simulated and reference values and also involve analyst choices, or meta-parameters, in structuring the analysis. Here, we systematically vary five such meta-parameters (reference dataset, spatial resolution, regridding approach, land mask, and time period) in evaluating evapotranspiration (ET) from eight CMIP5 models in a factorial design that yields 68 700 intercomparisons. The results show that while model–data comparisons can provide some feedback on overall model performance, model ranks are ambiguous and inferred model skill and rank are highly sensitive to the choice of meta-parameters for all models. This suggests that model skill and rank are best represented probabilistically rather than as scalar values. For this case study, the choice of reference dataset is found to have a dominant influence on inferred model skill, even larger than the choice of model itself. This is primarily due to large differences between reference datasets, indicating that further work in developing a community-accepted standard ET reference dataset is crucial in order to decrease ambiguity in model skill

Success Factors of Small and Medium-Sized International Enterprises in the Chinese Market from the Perspective of Polish Direct Investment (Cultural Approach)

Globalization has resulted in increasing transfer of firms operations, regardless of their size, to other countries. The recent dynamic emergence of China in the global economy, connecting with the vast inflows of foreign direct investment in their territory and common adjustments problems of many Western companies, has resulted in growing interest for best suitable business practices to this culturally and socially different environment. In this article, the key factors critical to the success of international companies in this region are introduced, with particular consideration to indigenous cultural elements and specific operation requirements of small and medium-sized enterprises in Business-to-Business sectors. The presented information are based on the broad literature review, five years of direct observation and thirty eight interviews conducted with Polish managers directly residing in China. In addition, some practical recommendations for managers and further research are given.Globalizacja wymusza na firmach, niezależnie od ich wielkości, coraz częstsze przenoszenie operacji do innych krajów. Dynamiczne pojawienie się Chin w światowej gospodarce i szeroki napł;yw zagranicznych inwestycji bezpośrednich na ich teren oraz problemy adaptacyjne wielu zachodnich przedsiębiorstw, spowodował;y zainteresowanie najlepszymi praktykami biznesowymi dostosowanymi do tego odmiennego kulturowo i społ;ecznie otocznia. W artykule zaprezentowane został;y najważniejsze czynnik mające wpł;yw na osiągnięcie sukcesu przez firmy międzynarodowe na tym obszarze, ze szczególnym uwzględnieniem aspektów kulturowych i specyfiki dział;ania mał;ych i średnich podmiotów na rynkach B2B. Prezentowane informacje są oparte na przeglądzie literatury, pięcioletnich obserwacjach bezpośrednich oraz trzydziestu ośmiu wywiadach przeprowadzonych z menadżerami polskich przedsiębiorstw odpowiedzialnymi za operacje w Chinach. Dodatkowo wskazano kilka praktycznych rekomendacji menadżerskich oraz możliwości dalszych badań