Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation

This paper focuses on hierarchical classification problems where the classes to be predicted are organized in the form of a tree. The standard top-down divide and conquer approach for hierarchical classification consists of building a hierarchy of classifiers where a classifier is built for each internal (non-leaf) node in the class tree. Each classifier discriminates only between its child classes. After the tree of classifiers is built, the system uses them to classify test examples one class level at a time, so that when the example is assigned a class at a given level, only the child classes need to be considered at the next level. This approach has the drawback that, if a test example is misclassified at a certain class level, it will be misclassified at deeper levels too. In this paper we propose hierarchical classification methods to mitigate this drawback. More precisely, we propose a method called hierarchical ensemble of hierarchical rule sets (HEHRS), where different ensembles are built at different levels in the class tree and each ensemble consists of different rule sets built from training examples at different levels of the class tree. We also use a particle swarm optimisation (PSO) algorithm to optimise the rule weights used by HEHRS to combine the predictions of different rules into a class to be assigned to a given test example. In addition, we propose a variant of a method to mitigate the aforementioned drawback of top-down classification. These three types of methods are compared against the standard top-down hierarchical classification method in six challenging bioinformatics datasets, involving the prediction of protein function. Overall HEHRS with the rule weights optimised by the PSO algorithm obtains the best predictive accuracy out of the four types of hierarchical classification method.     

Agent Technologies for Sensor Networks

Wireless sensor networks are increasingly seen as a solution to the problem of performing continuous wide-area monitoring in many environmental, security, and military scenarios. The distributed nature of such networks and the autonomous behavior expected of them present many novel challenges. In this article, the authors argue that a new synthesis of electronic engineering and agent technology is required to address these challenges, and they describe three examples where this synthesis has succeeded. In more detail, they describe how these novel approaches address the need for communication and computationally efficient decentralized algorithms to coordinate the behavior of physically distributed sensors, how they enable the real-world deployment of sensor agent platforms in the field, and finally, how they facilitate the development of intelligent agents that can autonomously acquire data from these networks and perform information processing tasks such as fusion, inference, and prediction.     

USE of Railway Analysis Tools from an Australian Perspective

The functions of Australia’s railways are divided between the delivery of suburban passenger railway services, long distance general and intermodal freight services and regional bulk commodity haulage. There is a national interest in the efficient carriage of export freight flows because Australia is a major trading nation. Australia’s railways are expected play their part in hauling export commodities. Unsurprisingly, there are conflicting demands on these railways. Hence, universities, and the industry itself, are directing research and investigation effort into policy, planning, engineering, operational and human factors matters. A question which then arises is what means could be used to analyse the problems identified during this research and investigation. This paper is thus concerned with the inter-relationships between the planning, engineering and operations of railways in Australia. It identifies four areas of analysis associated with the planning and development of railway infrastructure and operations. It then discusses a range of analytical tools which could be applied to different components of these analytical areas and critiques their appropriateness from an Australian perspective. Having made this assessment, the paper uses a recent Australian case study to show how analytical tools could be used and what lessons might be learnt from the process. Figure Use of Railway Analysis Tools from an Australian Perspective

Adaptive neighborhood selection in peer-to-peer networks based on content similarity and reputation

To address the two most critical issues in P2P file-sharing systems: efficient information discovery and authentic data acquisition, we propose a Gnutella-like file-sharing protocol termed Adaptive Gnutella Protocol (AGP) that not only improves the querying efficiency in a P2P network but also enhances the quality of search results at the same time. The reputation scheme in the proposed AGP evaluates the credibility of peers based on their contributions to P2P services and subsequently clusters nodes together according to their reputation and shared content, essentially transforming the P2P overlay network into a topology with collaborative and reputed nodes as its core. By detecting malicious peers as well as free-riders and eventually pushing them to the edge of the overlay network, our AGP propagates search queries mainly within the core of the topology, accelerating the information discovery process. Furthermore, the clustering of nodes based on authentic and similar content in our AGP also improves the quality of search results. We have implemented the AGP with the PeerSim simulation engine and conducted thorough experiments on diverse network topologies and various mixtures of honest/dishonest nodes to demonstrate improvements in topology transformation, query efficiency, and search quality by our AGP.

High-efficiency, argon-laser-pumped Nd:YLF laser system

An Ar-ion laser was used to pump a Nd:YLF laser, in both σ and π polarizations, in a longitudinal scheme. In spite of the small absorption coefficient at the pump (~0.25 cm (-1)), a careful laser design can circumvent this problem, and efficiencies as high as those attained with semiconductor pumping schemes are reported. The laser fundamental parameters were experimentally determined. A double-pass net gain as high as 10(3) was measured, and an output power of 1 W was obtained with a pumping power of 6 W.     

Fusion of ANNs as decoder of retinal spike trains for scene reconstruction

Applied Intelligence - The retina is one of the most developed sensing organs in the human body. However, the knowledge on the coding and decoding of the retinal neurons are still rather limited....     

Use of 3D printing for biofuel production: efficient catalyst for sustainable biodiesel production from wastes

This work deals with the sustainable biodiesel production from low-cost renewable feedstock (waste and non-edible oils) using a heterogeneous catalyst constituted by potassium loaded on an amorphous aluminum silicate naturally occurring as volcanic material (pumice). The main challenge to biodiesel production from low-quality oils (used oils and greases) is the high percentage of free fatty acids (FFAs) and water in the feedstock that causes undesirable side reactions. The catalytic materials studied were tested in the transesterification reaction when using low-quality oils containing a high proportion of free fatty acids (FFAs) and water. Results indicated that the amount of acid and basic sites on the catalytic surface increases upon increasing potassium loading in the catalyst, displaying better performance for biodiesel production. Indeed, the modification of the aluminum silicate substrate upon potassium incorporation results in a catalytic material containing both acidic and basic sites, which are responsible for both triglycerides transesterification and FFA esterification reactions. The studied catalyst not only showed good performance in the biodiesel production reaction but also good tolerance to FFA and water contained in the feedstock for biodiesel production. The catalytic material was microstructured by 3D printing in order to design a catalytic stirring system with high mechanical strength, efficient and reusable. The use of 3D printing in biofuel production is a novelty that brings good solutions for catalyst production.     

Inter-image outliers and their application to image classification

Image variability that is impossible or difficult to restore by intra-image processing, such as the variability caused by occlusions, significantly reduces the performance of image-recognition methods. To address this issue, we propose that the pixels associated with large distances obtained by inter-image pixel-by-pixels comparisons should be considered as inter-image outliers and should be removed from the similarity calculation used for the image classification. When this method is combined with the template-matching method for image recognition, it leads to state-of-the-art recognition performance: 91% with AR database that includes occluded face images, 90% with PUT database that includes pose variations of face images and 100% with EYale B database that includes images with large illumination variation.