Visual Transfer Learning: Informal Introduction and Literature Overview

Transfer learning techniques are important to handle small training sets and to allow for quick generalization even from only a few examples. The following paper is the introduction as well as the literature overview part of my thesis related to the topic of transfer learning for visual recognition problems.Comment: part of my PhD thesi

Multi-Source Data Fusion for Cyberattack Detection in Power Systems

Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the cyber-physical nature of the system coupled with the need for high reliability and resilience of the physical system. Conventional rule-based and anomaly-based intrusion detection system (IDS) tools are insufficient for detecting zero-day cyber intrusions in the industrial control system (ICS) networks. Hence, in this work, we show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. Specifically, we present how to recognize and address the barriers that can prevent the accurate use of multiple data sources for fusion-based detection. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle (MiTM) attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features followed by pre-processing such as imputation and encoding before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the IDS. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, we observed the co-training technique performs at par with supervised learning methods when fed with our features

3D Classification of Power Line Scene Using Airborne Lidar Data

Failure to adequately maintain vegetation within a power line corridor has been identified as a main cause of the August 14, 2003 electric power blackout. Such that, timely and accurate corridor mapping and monitoring are indispensible to mitigate such disaster. Moreover, airborne LiDAR (Light Detection And Ranging) has been recently introduced and widely utilized in industries and academies thanks to its potential to automate the data processing for scene analysis including power line corridor mapping. However, today’s corridor mapping practice using LiDAR in industries still remains an expensive manual process that is not suitable for the large-scale, rapid commercial compilation of corridor maps. Additionally, in academies only few studies have developed algorithms capable of recognizing corridor objects in the power line scene, which are mostly based on 2-dimensional classification. Thus, the objective of this dissertation is to develop a 3-dimensional classification system which is able to automatically identify key objects in the power line corridor from large-scale LiDAR data. This dissertation introduces new features for power structures, especially for the electric pylon, and existing features which are derived through diverse piecewise (i.e., point, line and plane) feature extraction, and then constructs a classification model pool by building individual models according to the piecewise feature sets and diverse voltage training samples using Random Forests. Finally, this dissertation proposes a Multiple Classifier System (MCS) which provides an optimal committee of models from the model pool for classification of new incoming power line scene. The proposed MCS has been tested on a power line corridor where medium voltage transmission lines (115 kV and 230 kV) pass. The classification results based on the MCS applied by optimally selecting the pre-built classification models according to the voltage type of the test corridor demonstrate a good accuracy (89.07%) and computationally effective time cost (approximately 4 hours/km) without additional training fees

Improving the resolution of interaction maps: A middleground between high-resolution complexes and genome-wide interactomes

Protein-protein interactions are ubiquitous in Biology and therefore central to understand living organisms. In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps or interactomes for a number of relevant organisms including human. Although the analysis of interaction networks is proving useful, current interactomes provide a blurry and granular picture of the molecular machinery, i.e. unless the structure of the protein complex is known the molecular details of the interaction are missing and sometime is even not possible to know if the interaction between the proteins is direct, i.e. physical interaction or part of functional, not necessary, direct association. Unfortunately, the determination of the structure of protein complexes cannot keep pace with the discovery of new protein-protein interactions resulting in a large, and increasing, gap between the number of complexes that are thought to exist and the number for which 3D structures are available. The aim of the thesis was to tackle this problem by implementing computational approaches to derive structural models of protein complexes and thus reduce this existing gap. Over the course of the thesis, a novel modelling algorithm to predict the structure of protein complexes, V-D2OCK, was implemented. This new algorithm combines structure-based prediction of protein binding sites by means of a novel algorithm developed over the course of the thesis: VORFFIP and M-VORFFIP, data-driven docking and energy minimization. This algorithm was used to improve the coverage and structural content of the human interactome compiled from different sources of interactomic data to ensure the most comprehensive interactome. Finally, the human interactome and structural models were compiled in a database, V-D2OCK DB, that offers an easy and user-friendly access to the human interactome including a bespoken graphical molecular viewer to facilitate the analysis of the structural models of protein complexes. Furthermore, new organisms, in addition to human, were included providing a useful resource for the study of all known interactomes

A Systematic Review of Convolutional Neural Network-Based Structural Condition Assessment Techniques

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent years, a broad range of CNN architectures has been developed by researchers to accommodate the extent of lighting and weather conditions, the quality of images, the amount of background and foreground noise, and multiclass damage in the structures. This paper presents a detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance. The review is categorized into multiple classes depending on the specific application and development of CNNs applied to data obtained from a wide range of structures. The challenges and limitations of the existing literature are discussed in detail at the end, followed by a brief conclusion on potential future research directions of CNN in structural condition assessment