Continuous Restricted Boltzmann Machines

Restricted Boltzmann machines are a generative neural network. They summarize their input data to build a probabilistic model that can then be used to reconstruct missing data or to classify new data. Unlike discrete Boltzmann machines, where the data are mapped to the space of integers or bitstrings, continuous Boltzmann machines directly use floating point numbers and therefore represent the data with higher fidelity. The primary limitation in using Boltzmann machines for big-data problems is the efficiency of the training algorithm. This paper describes an efficient deterministic algorithm for training continuous machines

Automatic generation of human machine interface screens from component-based reconfigurable virtual manufacturing cell

Increasing complexity and decreasing time-tomarket require changes in the traditional way of building automation systems. The paper describes a novel approach to automatically generate the Human Machine Interface (HMI) screens for component-based manufacturing cells based on their corresponding virtual models. Manufacturing cells are first prototyped and commissioned within a virtual engineering environment to validate and optimise the control behaviour. A framework for reusing the embedded control information in the virtual models to automatically generate the HMI screens is proposed. Finally, for proof of concept, the proposed solution is implemented and tested on a test rig

Protein Folding: Search for Basic Physical Models

How a unique three-dimensional structure is rapidly formed from the linear sequence of a polypeptide is one of the important questions in contemporary science. Apart from biological context of in vivo protein folding (which has been studied only for a few proteins), the roles of the fundamental physical forces in the in vitro folding remain largely unstudied. Despite a degree of success in using descriptions based on statistical and/or thermodynamic approaches, few of the current models explicitly include more basic physical forces (such as electrostatics and Van Der Waals forces). Moreover, the present-day models rarely take into account that the protein folding is, essentially, a rapid process that produces a highly specific architecture. This review considers several physical models that may provide more direct links between sequence and tertiary structure in terms of the physical forces. In particular, elaboration of such simple models is likely to produce extremely effective computational techniques with value for modern genomics

The Effect of Wavelet Families on Watermarking

With the advance of technologies such as the Internet, Wi-Fi Internet availability and mobile access, it is becoming harder than ever to safeguard intellectual property in a digital form. Digital watermarking is a steganographic technique that is used to protect creative content. Copyrighted work can be accessed from many different computing platforms; the same image can exist on a handheld personal digital assistant, as well as a laptop and desktop server computer. For those who want to pirate, it is simple to copy, modify and redistribute digital media. Because this impacts business profits adversely, this is a highly researched field in recent years. This paper examines a technique for digital watermarking which utilizes properties of the Discrete Wavelet Transform (DWT). The digital watermarking algorithm is explained. This algorithm uses a database of 40 images that are of different types. These images, including greyscale, black and white, and color, were chosen for their diverse characteristics. Eight families of wavelets, both orthogonal and biorthogonal, are compared for their effectiveness. Three distinct watermarks are tested. Since compressing an image is a common occurrence, the images are compacted to determine the significance of such an action. Different types of noise are also added. The PSNR for each image and each wavelet family is used to measure the efficacy of the algorithm. This objective measure is also used to determine the influence of the mother wavelet. The paper asks the question: “Is the wavelet family chosen to implement the algorithm of consequence?” In summary, the results support the concept that the simpler wavelet transforms, e.g. the Haar wavelet, consistently outperform the more complex ones when using a non-colored watermark

High Performance Attack Estimation in Large-Scale Network Flows

Network based attacks are the major threat to security on the Internet. The volume of traffic and the high variability of the attacks place threat detection squarely in the domain of big data. Conventional approaches are mostly based on signatures. While these are relatively inexpensive computationally, they are inflexible and insensitive to small variations in the attack vector. Therefore we explored the use of machine learning techniques on real flow data. We found that benign traffic could be identified with high accuracy