Sub-model aggregation for scalable eigenvector spatial filtering: Application to spatially varying coefficient modeling

This study proposes a method for aggregating/synthesizing global and local sub-models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was used to model spatially varying coefficients and spatial dependence in the residuals by sub-model, while the generalized product-of-experts method was used to aggregate these sub-models. The major advantages of the proposed method are as follows: (i) it is highly scalable for large samples in terms of accuracy and computational efficiency; (ii) it is easily implemented by estimating sub-models independently first and aggregating/averaging them thereafter; and (iii) likelihood-based inference is available because the marginal likelihood is available in closed-form. The accuracy and computational efficiency of the proposed method are confirmed using Monte Carlo simulation experiments. This method was then applied to residential land price analysis in Japan. The results demonstrate the usefulness of this method for improving the interpretability of spatially varying coefficients. The proposed method is implemented in an R package spmoran (version 0.3.0 or later)

Enhanced classification of network traffic data captured by intrusion prevention systems

A common practice in modern computer networks is the deployment of Intrusion Prevention Systems (IPSs) for the purpose of identifying security threats. Such systems provide alerts on suspicious activities based on a predefined set of rules. These alerts almost always contain high percentages of false positives and false negatives, which may impede the efficacy of their use. Therefore, with the presence of high numbers of false positives and false negatives, the analysis of network traffic data can be ineffective for decision makers which normally require concise, and preferably, visual forms to base their decisions upon. Machine learning techniques can help extract useful information from large datasets. Combined with visualisation, classification could provide a solution to false alerts and text-based outputs of IPSs. This research developed two new classification techniques that outperformed the traditional classification methods in accurate classification of computer network traffic captured by an IPS framework. They are also highly effective. The main purpose of these techniques was the effective identification of malicious network traffic and this was demonstrated via extensive experimental evaluation (where many experiments were conducted and results are reported in this thesis). In addition, an enhancement of the principal component analysis (PCA) was presented as part of this study. This enhancement proved to outperform the classical PCA on classification of IPS data. Details of the evaluation and experiments are provided in this thesis. One of the classification methods described in this thesis achieved accuracy values of 98.51% and 99.76% on two computer network traffic dataset settings, whereas the Class-balanced Similarity Based Instance Transfer Learning (CB-SBIT) algorithm achieves accuracy values of 93.56% and 96.25% respectively on the same dataset settings. This means the proposed method outperforms the state-of-the-art algorithm. As for the PCA enhancement mentioned above, using its resulting principal components as inputs to classifiers leads to improved accuracy when compared to the classical PCA

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward