Data Mining - Concepts and Techniques.

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

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.

Learning under Concept Drift: A Review

On-line construction of suffix trees

A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth mover's distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases.

Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach

Particle swarm optimization of tuned mass dampers

In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are inserted in a video. For human clothing advertising, we design a deep convolutional neural network using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothing retrieval. Second, we develop a heuristic algorithm to solve the proposed optimization problem. For comparison, we also employ the genetic algorithm to find solutions approaching the global optimum. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object-level video advertising.

Object-Level Video Advertising: An Optimization Framework

There are many traditional methods to find the optimum parameters of a tuned mass damper (TMD) subject to stationary base excitations It is very difficult to obtain the optimum parameters of a TMD subject to non-stationary base excitations using these traditional optimization techniques In this paper, by applying particle swarm optimization (PSO) algorithm as a novel evolutionary algorithm, the optimum parameters including the optimum mass ratio, damper damping and tuning frequency of the TMD system attached to a viscously damped single-degree-of-freedom main system subject to non-stationary excitation can be obtained when taking either the displacement or the acceleration mean square response, as well as their combination, as the cost function For simplicity of presentation, the non-stationary excitation is modeled by an evolutionary stationary process in the paper By means of three numerical examples for different types of non-stationary ground acceleration models, the results indicate that PSO can be used to find the optimum mass ratio, damper damping and tuning frequency of the non-stationary TMD system, and it is quite easy to be programmed for practical engineering applications

Haijun Zhang

Papers

Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach

Particle swarm optimization of tuned mass dampers

Object-Level Video Advertising: An Optimization Framework

Particle swarm optimization of TMD by non‐stationary base excitation during earthquake

CNN-based encoder-decoder networks for salient object detection: A comprehensive review and recent advances