Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

Emotion and Adaptation

Low molecular weight heparins are complex polycomponent drugs that have recently become amenable to top-down analysis using liquid chromatography-mass spectrometry. Even using open source deconvolution software, DeconTools, and automatic structural assignment software, GlycReSoft, the comparison of two or more low molecular weight heparins is extremely time-consuming, taking about a week for an expert analyst and provides no guarantee of accuracy. Efficient data processing tools are required to improve analysis. This study uses the programming language of Microsoft Excel™ Visual Basic for Applications to extend its standard functionality for macro functions and specific mathematical modules for mass spectrometric data processing. The program developed enables the comparison of top-down analytical glycomics data on two or more low molecular weight heparins. The current study describes a new program, GlycCompSoft, which has a low error rate with good time efficiency in the automatic processing of large data sets. The experimental results based on three lots of Lovenox®, Clexane® and three generic enoxaparin samples show that the run time of GlycCompSoft decreases from 11 to 2 seconds when the data processed decreases from 18000 to 1500 rows.

/pdf/glyccompsoft-software-for-automated-comparison-of-low-i5tt5025v8.pdf

GlycCompSoft: Software for Automated Comparison of Low Molecular Weight Heparins Using Top-Down LC/MS Data.

At present, the accuracy of text detection from natural scene images based on deep learning is much higher than that of traditional text detection algorithm. However, most of the text detectors based on deep learning should use large-scale neural network models (such as VGG and ResNet) for improving the accuracy. Because the weight files of these models are very large (VGG is about 500MB and ResNet is about 100MB), they are not suitable for the computationally limited platforms in daily life. Based on the small-scale neural network model named MobileNet V2, and U-Net that is used in semantic segmentation commonly, a text detector for natural scene by using neural network with low complexity is proposed in this paper. The weight file of the designed neural network model is about 16MB, which is suitable for running on mobile platforms. The experiment result based on the ICDAR 2013 dataset proves that this model has good performance in text detection.

Text Detection for Natural Scene based on MobileNet V2 and U-Net

Understanding the information need or intent encoded within a query has long been regarded as an essential factor of effective information retrieval. For better query representation and understanding, two intent roles (kernel-object and modifier) are introduced to structurally parse a class of role-explicit queries, which constitute a majority of common user queries. Furthermore, we focus on two research problems: RP-1: Given a role-explicit query, how to identify the kernel-object and modifier, namely intent role annotation; RP-2: How to determine whether an arbitrary query is role-explicit or not. To solve RP-1, we propose a simplified word n-gram role model (SWNR), which quantifies the generating probability of a role-explicit query and performs intent role annotation effectively. Using a set of discriminative features, we build classifiers to address RP-2 in a supervised manner. The experimental results show that: (1) SWNR can achieve a satisfactory performance, more than 73% in terms of different metrics; (2) The classifiers can achieve more than 90% precision in identifying role-explicit queries; (3) Compared with traditional techniques for query representation and understanding, e.g., name entity recognition in query and class-level query intent inference, intent role annotation provides a more flexible framework and a number of applications can benefit from annotating role-explicit queries, such as intent mining and diversified document ranking.

Role-explicit query identification and intent role annotation

Bi-text alignment is useful to many Natural Language Processing tasks such as machine translation, bilingual lexicography and word sense disambiguation. This paper presents a Chinese-Japanese alignment at the level of clause. After describing some characteristics in Chinese-Japanese bilingual texts, we first investigate some statistical properties of Chinese-Japanese bilingual corpus, including the correlation test of text lengths between two languages and the distribution test of length ratio data. We then pay more attention to n-m(n>1 or m>1) alignment modes which are prone to mismatch. We propose a similarity measure based on Hanzi characters information for these kinds of alignment modes. By using dynamic programming, we combine statistical information and Hanzi character information to find the overall least cost in aligning. Experiments show our algorithm can achieve good alignment accuracy.

https://www.cicling.org/2005/Abstracts/34060392.pdf

Chinese-Japanese clause alignment

https://repo.lib.tokushima-u.ac.jp/files/public/11/113246/20190424152428527185/caai_4_1_64.pdf

Fuji Ren

Papers

GlycCompSoft: Software for Automated Comparison of Low Molecular Weight Heparins Using Top-Down LC/MS Data.

Text Detection for Natural Scene based on MobileNet V2 and U-Net

Role-explicit query identification and intent role annotation

Chinese-Japanese clause alignment

Slang feature extraction by analysing topic change on social media