Beyond Market Baskets: Generalizing Association Rules to Dependence Rules

One of the more well-studied problems in data mining is the search for association rules in market basket data. Association rules are intended to identify patterns of the type: A customer purchasing item A often also purchases item B. Motivated partly by the goal of generalizing beyond market basket data and partly by the goal of ironing out some problems in the definition of association rules, we develop the notion of dependence rules that identify statistical dependence in both the presence and absence of items in itemsets. We propose measuring significance of dependence via the chi-squared test for independence from classical statistics. This leads to a measure that is upward-closed in the itemset lattice, enabling us to reduce the mining problem to the search for a border between dependent and independent itemsets in the lattice. We develop pruning strategies based on the closure property and thereby devise an efficient algorithm for discovering dependence rules. We demonstrate our algorithm's effectiveness by testing it on census data, text data (wherein we seek term dependence), and synthetic data.     

Synthesis and characterization of novel blue azo‐dye derivatives containing acrylate group for dye‐based color filters

Abstract— Even though dyes have a fine resolution and good chromaticities, they are not widely used as coloring materials for color filters (CFs) due to their low thermal stability and chemical resistance. A series of azo‐dye derivatives, which consist of two cross‐linkable acrylate or methacrylate groups to improve thermal and chemical properties, have been synthesized and used to fabricate color filters. The spectral properties and chemical/thermal stabilities of the fabricated CFs were investigated by comparing dye‐based CFs, without a complicated dispersion process, but with pigment‐based CFs using dispersed pigment. Also, more properties including the development test and surface morphologies lithographic properties were studied. The synthesized azo dyes were characterized by elemental analysis, UV‐visible spectra, IR, mass, and ¹H‐NMR spectra.     

无锡惠山泥人的造型特征研究

无锡惠山泥人历史悠久,造型优美传神,是我国民间工艺的精粹。论文通过对无锡惠山泥人的造型特征进行分析,总结出其造型、色彩、及构图上的规律,以此为实例分析推动传统工艺文化的保护和发展。     

中国传统色彩在环境空间设计中的应用

传统色彩是某一民族特有的具有地域文化意味的色谱系统及其组合,是自然、社会、审美心理历史积淀的结果。同时,它是现代环境设计的灵感之源,成为深化设计语言的重要途径和手段。中国的色彩文化历史悠久,文化底蕴丰厚,是深化设计语言,提高文化品位的重要组成部分。本文试图探求传统色彩的文化内涵,探讨传统色彩与现代环境设计之间的深层关系,进而在现代设计中进一步传承和发展传统色彩。     

浅析产品形象设计中的型与色

产品形象设计是为实现企业形象总体目标的细化,使产品内在的品质形象、产品外在的视觉形象与社会形象形成统一性而展开的。良好的产品形象有利于树立良好的企业形象,增加客户对品牌的美誉度和信任度,提高企业的竞争实力。本论文是通过对产品设计的色彩与造型两方面进行分析,来阐述其在产品形象设计中的造型价值。     

Color discrimination enhancement for dichromats using self-organizing color transformation

Color deficient persons, especially dichromats, have difficulty in discriminating certain kinds of colors. To help dichromats discriminate colors better, a color transformation method is proposed. The method utilizes the redundancy of color information, changing the colors of a scene into discernable ones for dichromats. The self-organizing map (SOM) algorithm is used to build a nonlinear color map, maintaining the neighboring relations between colors. Results of both subjective observation and quantitative evaluation show that the method can effectively enhance the color discrimination for dichromats. This study provides a new approach to rectify color vision deficiency.     

Learning a color distance metric for region-based image segmentation

In this paper we describe an experiment where we studied empirically the application of a learned distance metric to be used as discrimination function for an established color image segmentation algorithm. For this purpose we chose the Mumford–Shah energy functional and the Mahalanobis distance metric. The objective was to test our approach in an objective and quantifiable way on this specific algorithm employing this particular distance model, without making generalization claims. The empirical validation of the results was performed in two experiments: one applying the resulting segmentation method on a subset of the Berkeley Image Database, an exemplar image set possessing ground-truths and validating the results against the ground-truths using two well-known inter-cluster validation methods, namely, the Rand and BGM indexes, and another experiment using images of the same context divided into training and testing set, where the distance metric is learned from the training set and then applied to segment all the images. The obtained results suggest that the use of the specified learned distance metric provides better and more robust segmentations, even if no other modification of the segmentation algorithm is performed.     

A novel associative memory approach to speech enhancement in a vehicular environment

Numerous attempts have been undertaken to apply the spectral subtraction method to cancel noise perturbations but these efforts have yet to produce an algorithm that is able to adapt well to the environmental changes in the perturbations. In addition, the variants of the spectral subtraction method so far proposed in the literature would require a non-voice activity detector (NVAD), for a single microphone system, to store the perturbation. This is used as an estimate for the reference signal. Inaccuracy in the perturbation estimates causes the cleaned speech to be corrupted by musical artifacts, which is unacceptable. Post processing of signals corrupted by the musical artifacts is very costly. This paper provides an alternative approach that employs associative memory for speech enhancement. Extensive comparison is made using the soft computing approaches for noise cancellation based on associative memories. A set of stereo microphones captures the corrupted speech in a vehicle and is used to point to the closest associative memory location. The Wiener filter approach is used to cancel the noise. The paper discusses novel examples of the associative memories using the cerebellum model for noise modeling. Experimental results show the potential of these novel soft computing architectures in generating and adapting the required Weiner filters to cancel perturbation even at signal to noise ratio (SNR) of less than −13 dB.     

一致性测试的静态需求测试研究

针对一致性测试中的静态需求测试通常采用手工方法而产生的效率低问题,提出一种基于统计学习的方法,包括静态需求的关联规则表示,并对采样的协议数据进行关联规则挖掘,如果得到的规则与需求规则一致,静态需求就可以得到统计上的测试检验。该方法在OSPFv3协议测试中取得了较好的效果。     

基于先验信息的运动物体分割与跟踪

提出一种基于先验信息的运动物体分割与跟踪算法。该算法假设帧间运动物体颜色直方图和大小等信息是相对不变的,利用动态规划思想最小化当前帧目标物体颜色直方图和大小与已知目标物体颜色直方图和大小信息的差距,从而找到较准确的目标物体轮廓。与梯度向量流算法相比,该算法对初始轮廓的鲁棒性较好,能在复杂的背景中找到物体轮廓。