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"K computer"
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Deep thinking : where machine intelligence ends and human creativity begins
The former world chess champion who played, and lost, against Deep Blue, a supercomputer, in 1997 discusses why he thinks humans should embrace the competition between themselves and machine intelligence.
BOOK
Simulation of a Human-Scale Cerebellar Network Model on the K Computer
Computer simulation of the human brain at an individual neuron resolution is an ultimate goal of computational neuroscience. The Japanese flagship supercomputer, K, provides unprecedented computational capability toward this goal. The cerebellum contains 80% of the neurons in the whole brain. Therefore, computer simulation of the human-scale cerebellum will be a challenge for modern supercomputers. In this study, we built a human-scale spiking network model of the cerebellum, composed of 68 billion spiking neurons, on the K computer. As a benchmark, we performed a computer simulation of a cerebellum-dependent eye movement task known as the optokinetic response. We succeeded in reproducing plausible neuronal activity patterns that are observed experimentally in animals. The model was built on dedicated neural network simulation software called MONET (Millefeuille-like Organization NEural neTwork), which calculates layered sheet types of neural networks with parallelization by tile partitioning. To examine the scalability of the MONET simulator, we repeatedly performed simulations while changing the number of compute nodes from 1,024 to 82,944 and measured the computational time. We observed a good weak-scaling property for our cerebellar network model. Using all 82,944 nodes, we succeeded in simulating a human-scale cerebellum for the first time, although the simulation was 578 times slower than the wall clock time. These results suggest that the K computer is already capable of creating a simulation of a human-scale cerebellar model with the aid of the MONET simulator.
Journal Article
Outcomes and challenges of global high-resolution non-hydrostatic atmospheric simulations using the K computer
This article reviews the major outcomes of a 5-year (2011–2016) project using the K computer to perform global numerical atmospheric simulations based on the non-hydrostatic icosahedral atmospheric model (NICAM). The K computer was made available to the public in September 2012 and was used as a primary resource for Japan’s Strategic Programs for Innovative Research (SPIRE), an initiative to investigate five strategic research areas; the NICAM project fell under the research area of climate and weather simulation sciences. Combining NICAM with high-performance computing has created new opportunities in three areas of research: (1) higher resolution global simulations that produce more realistic representations of convective systems, (2) multi-member ensemble simulations that are able to perform extended-range forecasts 10–30 days in advance, and (3) multi-decadal simulations for climatology and variability. Before the K computer era, NICAM was used to demonstrate realistic simulations of intra-seasonal oscillations including the Madden-Julian oscillation (MJO), merely as a case study approach. Thanks to the big leap in computational performance of the K computer, we could greatly increase the number of cases of MJO events for numerical simulations, in addition to integrating time and horizontal resolution. We conclude that the high-resolution global non-hydrostatic model, as used in this five-year project, improves the ability to forecast intra-seasonal oscillations and associated tropical cyclogenesis compared with that of the relatively coarser operational models currently in use. The impacts of the sub-kilometer resolution simulation and the multi-decadal simulations using NICAM are also reviewed.
Journal Article
Factors for successful use of social networking sites in Higher Education
Social networking sites are extremely popular online destinations that offer users easy ways to build and maintain relationships with each other, and to disseminate information in an activity referred to as social networking. Students, lecturers, teachers, parents and businesses, in increasing numbers, use tools available on social networking sites to communicate with each other in a fast and cost-effective manner. The use of social networking sites to support educational initiatives has received much attention. However, the full potential of social network sites has yet to be achieved as users continue to strive for optimal ways of using these sites, as well as battle to overcome the negative characteristics (for example, privacy, security, governance, user behaviour, information quality) of these sites. This paper proposes factors for successful use of social networking sites in higher educational institutions. These success factors need to be adopted by users in order to develop the positive aspects of social networking, while at the same time mitigating the negative characteristics. An initial set of factors for successful use of social networking sites, as well as measures to test successful use of social networking sites were derived from the literature. These factors were tested by means of an online survey of students at a university, the results of which informed the final factors for successful use of social networking sites. The factors enable users to overcome the negative characteristics associated with social networking sites. If used successfully, social networking sites can offer lecturers and students a useful tool with which to develop their relationship and contribute to their learning experience.
Journal Article
Multiple View Geometry in Computer Vision
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
eBook
Brave NUI world : designing natural user interfaces for touch and gesture
Touch and gestural devices have been hailed as next evolutionary step in human-computer interaction. As software companies struggle to catch up with one another in terms of developing the next great touch-based interface, designers are charged with the daunting task of keeping up with the advances in new technology and this new aspect to user experience design. Product and interaction designers, developers and managers are already well versed in UI design, but touch-based interfaces have added a new level of complexity. They need quick references and real-world examples in order to make informed decisions when designing for these particular interfaces. Brave NUI World is the first practical book for product and interaction developers and designing touch and gesture interfaces. Written by developers of industry-first, multi-touch, multi-user products, this book gives you the necessary tools and information to integrate touch and gesture practices into your daily work, presenting scenarios, problem solving, metaphors, and techniques intended to avoid making mistakes. *Provides easy-to-apply design guidance for the unique challenge of creating touch- and gesture-based user interfaces *Considers diverse user needs and context, real world successes and failures, and a look into the future of NUI *Presents thirty scenarios, giving practitioners a multitude of considerations for making informed design decisions and helping to ensure that missteps are never made again
eBook
2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors
Object recognition is a key research area in the field of image processing and computer vision, which recognizes the object in an image and provides a proper label. In the paper, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented Fast and Rotated BRIEF (ORB) are used for experimental work of an object recognition system. A comparison among these three descriptors is exhibited in the paper by determining them individually and with different combinations of these three methodologies. The amount of the features extracted using these feature extraction methods are further reduced using a feature selection (k-means clustering) and a dimensionality reduction method (Locality Preserving Projection). Various classifiers i.e. K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest are used to classify objects based on their similarity. The focus of this article is to present a study of the performance comparison among these three feature extraction methods, particularly when their combination derives in recognizing the object more efficiently. In this paper, the authors have presented a comparative analysis view among various feature descriptors algorithms and classification models for 2D object recognition. The Caltech-101 public dataset is considered in this article for experimental work. The experiment reveals that a hybridization of SIFT, SURF and ORB method with Random Forest classification model accomplishes the best results as compared to other state-of-the-art work. The comparative analysis has been presented in terms of recognition accuracy, True Positive Rate (TPR), False Positive Rate (FPR), and Area Under Curve (AUC) parameters.
Journal Article
Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications
The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner.
Journal Article
An efficient network intrusion detection model for IoT security using K-NN classifier and feature selection
The Internet of Things (IoT) interconnects billions of sensors and actuators to serve a meaningful purpose. However, it is always vulnerable to various menaces. Thus, IoT security represents a big concern in the research field. Various tools were developed to mitigate these security issues. So, Intrusion detection systems (IDS) have gained much attention in the research community due to their critical role in maintaining network security. In this work, we integrate a network IDS (NIDS) to enhance IoT security. This paper presents a network intrusion detection model for IoT environments using a K-Nearest Neighbors (K-NN) classifier and feature selection. We built the NIDS using the K-NN algorithm to improve the IDS accuracy (ACC) and detection rate (DR). Furthermore, the principal component analysis (PCA), univariate statistical test, and genetic algorithm (GA) are used for feature selection separately to improve the data quality and select the ten best performing features. The performance evaluation of our model is performed on the Bot-IoT dataset. After applying the feature selection, the models have shown promising results regarding ACC, DR, false alarm rate (FAR), and predicting time. Our proposed model provided 99.99% ACC and maintained its superior performance for the ten selected features. Furthermore, we calculated the prediction time, as we consider it critical in building IDS for IoT, and by applying feature selection, we reduced it significantly from 51,182.22 s to under a minute. This novel model presents many advantages and reliable performances compared with previous models relying on the same dataset.
Journal Article
Transfer learning for image classification using VGG19: Caltech-101 image data set
Image classification is getting more attention in the area of computer vision. During the past few years, a lot of research has been done on image classification using classical machine learning and deep learning techniques. Presently, deep learning-based techniques have given stupendous results. The performance of a classification system depends on the quality of features extracted from an image. The better is the quality of extracted features, the more the accuracy will be. Although, numerous deep learning-based methods have shown enormous performance in image classification, still due to various challenges deep learning methods are not able to extract all the important information from the image. This results in a reduction in overall classification accuracy. The goal of the present research is to improve the image classification performance by combining the deep features extracted using popular deep convolutional neural network, VGG19, and various handcrafted feature extraction methods, i.e., SIFT, SURF, ORB, and Shi-Tomasi corner detector algorithm. Further, the extracted features from these methods are classified using various machine learning classification methods, i.e., Gaussian Naïve Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBClassifier) classifier. The experiment is carried out on a benchmark dataset Caltech-101. The experimental results indicate that Random Forest using the combined features give 93.73% accuracy and outperforms other classifiers and methods proposed by other authors. The paper concludes that a single feature extractor whether shallow or deep is not enough to achieve satisfactory results. So, a combined approach using deep learning features and traditional handcrafted features is better for image classification.
Journal Article