Metaheuristic Based Clustering Algorithms for Biological Hypergraphs

Hypergraphs are widely used for modeling and representing relationships between entities, one such field where their application is prolific is in bioinformatics. In the present era of big data, sizes and complexity of these hypergraphs grow exponentially, it is impossible to process them manually or even visualize their interconnectivity superficially. A common approach to tackle their complexity is to cluster similar data nodes together in order to create a more comprehensible representation. This enables similarity discovery and hence, extract hidden knowledge within the hypergraphs. Several state-of-the-art algorithms have been proposed for partitioning and clustering of hypergraphs. Nevertheless, several issues remain unanswered, improvement to existing algorithms are possible, especially in scalability and clustering quality. This article presents a concise survey on hypergraph-clustering algorithms with the emphasis on knowledge-representation in systems biomedicine. It also suggests a novel approach to clustering quality by means of cluster-quality metrics which combines expert knowledge and measurable objective distances in existing biological ontology

Measuring data locality ratio in virtual MapReduce cluster using WorkflowSim

The data locality is significant factor which has a direct impact on the performance of MapReduce framework. Several previous works have proposed alternative scheduling algorithms for improving the performance by increasing data locality. Nevertheless, their studies had focused the data locality on physical MapReduce cluster. As more and more deployment of MapReduce cluster have been on virtual environment, a more suitable evaluation of MapReduce cluster may be necessary. This study adopts a simulation based approach. Five scheduling algorithms were used for the simulation. WorkflowSim is extended by inclusion of three implemented modules to assess the new performance measure called `data locality ratio'. Comparison of their results reveals interesting findings. The proposed implementation can be used to assess `data locality ratio' and allows users prior to efficiently select and tune scheduler and system configurations suitable for an environment prior to its actual physical MapReduce deployment

Scheduling Deep Learning Training in GPU Cluster Using the Model-Similarity-Based Policy

peer reviewedTraining large neural networks with huge amount of data using multiple Graphic Processing Units (GPUs) became widespread with the emergence of Deep Learning (DL) technology. It is usually operated in datacenters featuring multiple GPU clusters, which are shared amongst users. However, different GPU architectures co-exist on the market and differ in training performance. To maximise the utilisation of a GPU cluster, the scheduler plays an important role in managing the resources by dispatching the jobs to the GPUs. An efficient scheduling strategy should take into account that the training performance of each GPU architecture varies for the different DL models. In this work, an original model-similarity-based scheduling policy is introduced that takes into account the GPU architectures that match with the DL models. The results show that using the model-similarity-based scheduling policy for distributed training across multiple GPUs of a DL model with a large batch size can reduce the makespan

19th Asia Pacific Symposium

This PALO volume constitutes the Proceedings of the 19th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2015), held in Bangkok, Thailand, November 22-25, 2015. The IES series of conference is an annual event that was initiated back in 1997 in Canberra, Australia. IES aims to bring together researchers from countries of the Asian Pacific Rim, in the fields of intelligent systems and evolutionary computation, to exchange ideas, present recent results and discuss possible collaborations. Researchers beyond Asian Pacific Rim countries are also welcome and encouraged to participate. The theme for IES 2015 is “Transforming Big Data into Knowledge and Technological Breakthroughs”. The host organization for IES 2015 is the School of Information Technology (SIT), King Mongkut’s University of Technology Thonburi (KMUTT), and it is technically sponsored by the International Neural Network Society (INNS). IES 2015 is collocated with three other conferences; namely, The 6th International Conference on Computational Systems-Biology and Bioinformatics 2015 (CSBio 2015), The 7th International Conference on Advances in Information Technology 2015 (IAIT 2015) and The 10th International Conference on e-Business 2015 (iNCEB 2015), as a major part of series of events to celebrate the SIT 20th anniversary and the KMUTT 55th anniversary

Zone-of-Interest Strategy for the Creation of High-Resolution Adversarial Images Against Convolutional Neural Networks

peer reviewedTrained convolutional neural networks (CNNs) are among the leading tools used for the automatic classification of images. They are nevertheless exposed to attacks: Given an input clean image classified by a CNN in a category, carefully designed adversarial images may lead CNNs to erroneous classifications, although humans would still classify 'correctly' the constructed adversarial images in the same category as the input image. Currently most attacks are performed in the image input size domain of the considered CNN, which is usually small. However, due to privacy concerns with personal images on social media, there is a demand for generating large adversarial images that preserve the visual information of original images with the highest possible quality, while preventing automatic tracking and personal identification. Creating large-size adversarial images is difficult due to speed, adversity, and visual quality challenges, in particular if a requirement on adversarial images is the inability for humans to notice any difference between them and the original clean images. This paper describes the zone-of-interest generic strategy that aims at increasing drastically the efficiency of any type of attack (white-box or black-box, untargeted or targeted) and any specific attack (FGSM, PGD, BIM, SimBA, AdvGAN, EA-based attacks, etc.) on CNNs. Instead of exploring the full image size, the strategy identifies zones on which to focus the attacks. Although applying to any image size, the strategy is especially valuable for large high-resolution images. This strategy can be combined with other generic approaches, like the noise blowing-up method, to further improve attacks' performances

Chaotic Traversal (CHAT): Very Large Graphs Traversal Using Chaotic Dynamics

Graph Traversal algorithms can find their applications in various fields such as routing problems, natural language processing or even database querying. The exploration can be considered as a first stepping stone into knowledge extraction from the graph which is now a popular topic. Classical solutions such as Breadth First Search (BFS) and Depth First Search (DFS) require huge amounts of memory for exploring very large graphs. In this research, we present a novel memoryless graph traversal algorithm, Chaotic Traversal (CHAT) which integrates chaotic dynamics to traverse large unknown graphs via the Lozi map and the Rössler system. To compare various dynamics effects on our algorithm, we present an original way to perform the exploration of a parameter space using a bifurcation diagram with respect to the topological structure of attractors. The resulting algorithm is an efficient and nonresource demanding algorithm, and is therefore very suitable for partial traversal of very large and/or unknown environment graphs. CHAT performance using Lozi map is proven superior than the, commonly known, Random Walk, in terms of number of nodes visited (coverage percentage) and computation time where the environment is unknown and memory usage is restricted