Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Survival data analysis using neural networks

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Feed-forward neural networks and topographic mappings for exploratory data analysis

A recent novel approach to the visualisation and analysis of datasets, and one which is particularly applicable to those of a high dimension, is discussed in the context of real applications. A feed-forward neural network is utilised to effect a topographic, structure-preserving, dimension-reducing transformation of the data, with an additional facility to incorporate different degrees of associated subjective information. The properties of this transformation are illustrated on synthetic and real datasets, including the 1992 UK Research Assessment Exercise for funding in higher education. The method is compared and contrasted to established techniques for feature extraction, and related to topographic mappings, the Sammon projection and the statistical field of multidimensional scaling.

Short fatigue crack growth behaviour:data analysis effects

Consideration of the influence of test technique and data analysis method is important for data comparison and design purposes. The paper highlights the effects of replication interval, crack growth rate averaging and curve-fitting procedures on crack growth rate results for a Ni-base alloy. It is shown that an upper bound crack growth rate line is not appropriate for use in fatigue design, and that the derivative of a quadratic fit to the a vs N data looks promising. However, this type of averaging, or curve fitting, is not useful in developing an understanding of microstructure/crack tip interactions. For this purpose, simple replica-to-replica growth rate calculations are preferable. © 1988.

Decision theory to support evacuation in advance of catastrophic disaster including modular influence diagrams and spatial data analysis.

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Towartds Computer-aided Editing of Scientific and Technical Texts

The paper discusses facilities of computer systems for editing scientific and technical texts, which partially automate functions of human editor and thus help the writer to improve text quality. Two experimental systems LINAR and CONUT developed in 90s to control the quality of Russian scientific and technical texts are briefly described; and general principles for designing more powerful editing systems are pointed out. Features of an editing system being now under development are outlined, primarily the underlying linguistic knowledge base and procedures controlling the text.

Intelligent System for Computer Aided Assembly Process Planning

This paper presents the concepts of the intelligent system for aiding of the module assembly technology. The first part of this paper presents a project of intelligent support system for computer aided assembly process planning. The second part includes a coincidence description of the chosen aspects of implementation of this intelligent system using technologies of artificial intelligence (artificial neural networks, fuzzy logic, expert systems and genetic algorithms).

Computer aided licence selection

A model of multiple criteria decision making is presented for selecting the “best” of a finite number of alternatives. Techniques of scoring the alternatives and weighting the criteria are combined with different evaluating procedures and amalgamated in an interactive algorithm. Application of this method for choosing the best tender in a competitive bidding is discussed and a case is presented in some detail.