MINES: Mutual Information Neuro-Evolutionary System

Mutual information neuro-evolutionary system (MINES) presents a novel self-governing approach to determine the optimal quantity and connectivity of the hidden layer of a three layer feed-forward neural network founded on theoretical and practical basis. The system is a combination of a feed-forward neural network, back-propagation algorithm, genetic algorithm, mutual information and clustering. Back-propagation is used for parameter learning to reduce the system’s error; while mutual information aides back-propagation to follow an effective path in the weight space. A genetic algorithm changes the incoming synaptic connections of the hidden nodes, based on the fitness provided by the mutual information from the error space to the hidden layer, to perform structural learning. Mutual information determines the appropriate synapses, connecting the hidden nodes to the input layer; however, in effect it also links the back-propagation to the genetic algorithm. Weight clustering is applied to reduce hidden nodes having similar functionality; i.e. those possessing same connectivity patterns and close Euclidean angle in the weight space. Finally, the performance of the system is assessed on two theoretical and one empirical problems. A nonlinear polynomial regression problem and the well known two-spiral classification task are used to evaluate the theoretical performance of the system. Forecasting daily crude oil prices are conducted to observe the performance of MINES on a real world application

Are You Tampering With My Data?

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.Comment: 18 page

A Conceptual Framework of Quality-Assured Fabrication, Delivery and Installation Processes for Liquefied Natural Gas (LNG) Plant Construction

© 2014, Springer Science+Business Media Dordrecht. Construction productivity issues in the Liquefied Natural Gas (LNG) construction industry can lead to project cost blowouts. Time wasted by construction personnel getting the right information on megaprojects can be a substantial contributing factor. It appears that the communication on site is not cost effective, judging by the number of large project that have experienced budget overruns in the past. More importantly, as-built design documentation often fails the quality test, resulting in operational inefficiencies once the plant has been handed over from Construction to Operation Phase. Common errors during the static prefabrication, dispatch and installation processes can result in serious rework as a significant amount of construction time and budget is wasted. To minimise these problems, this paper recommends to better control the dynamic natures of construction. This study propagates a conceptual framework for assuring quality of modular construction in LNG plants by introducing a Situation Awareness construction environment with well-defined sensing and tracking technologies. While encountering situations inconsistent with plans during construction, such as time delay, fabrication errors, conflicts in terms of accessibility and constructability issues and so forth, sensors mounted in situ can discover such situations and recursively fed back to field personnel. Automation and robotics technologies, such as real-time path planning, collision detection and deviation examination utilizing as-planned building information model, can assist engineers to rapidly react with inconsistent situations and make acceptable decisions instead of partially or entirely suspending the workforce through massive reworks. In this study, we conduct a preliminary study in demonstrating the feasibility of utilizing sensory devices and automatic planning technologies. The expected results of adopting the framework are the quality-assured modular construction and execution plans during construction stages to save rework construction time and budget