Weka: A machine learning workbench for data mining

The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. The system is written in Java and distributed under the terms of the GNU General Public License

A Software Tool for Parameter Estimation from Flight Test Data

A software package called FIDA is developed and implemented in PC MATLAB for estimating aircraft stability and control derivatives from flight test data using different system identification techniques. FIDA also contains data pre-processing tools to remove wild points and high frequency noise components from measured flight data. FIDA is a menu driven and user interactive software which is useful to scientists/flight test engineers/pilots who are engaged in experimental flights and analysis of flight test data. Also it has an educational value for students and practising engineers who are new to the field of aircraft parameter estimation

Interactive Three Dimensional Computer Graphics Simulation of the Kinematics of the Human Thumb (Biomechanics, Functional Anatomy, Skeletal Kinematics).

This dissertation describes the development of a computer graphical simulation of thumb kinematics as a clinical design tool. The simulation utilizes a realistic data structure of the thumb bones, an optimized model of joint kinematics, and a variety of experimental cadaver measurements. It was developed with a concurrent processing arrangement in which graphical transformations and interactive manipulations are handled on a distributed graphics system, and musculoskeletal dynamics calculations are carried out on a minicomputer. The thumb simulation includes interactive entry of patient related data, simulation of two types of tendon transfer operations, calculation and display of resultant muscle mechanics during movement, and a summary and analysis of transfer results. The skeletal structure was derived from controlled longitudinal radiographs of an excised cadaver specimen mounted in an index fixture. Radiographs were traced and digitized in a vector format suitable for the graphics system. These longitudinal views constitute the bottom level nodes in a hierarchical structure built to simulate the motions of the distal three bones. The structure was improved by replacing each set of raw vectors with a mathematical spline applied to each view. This provided both a smoothing of the data and a decrease in vector density. Real time interfacing to the thumb structure for manipulation and viewing was designed on the graphics system to provide rotation and translation of the entire view and rotations at each joint. Excursions and moment arms are based on an optimized model of the carpometacarpal joint. Other experimental measurements such as muscle mass, mass fraction, tension fraction, and muscle fiber lengths derived from fresh cadaver studies are built into the simulation as determinants of overall moment and muscle excursion capacities. This interactive system, combined with the mathematical model, results in a realistic depiction of thumb motion in normal and impaired states. Individual anatomical parameters, and muscle pulley and insertion points can be altered resulting in the simulation of typical clinical conditions as well as muscle-tendon transfer operations. This type of computer modeling, utilizing a realistic, three-dimensional data structure, meaningful musculoskeletal kinematics, and interactive programming, shows great potential for bringing mathematical modeling into a useful clinical application

Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System

abstract: Arctic cyclone activity has a significant association with Arctic warming and Arctic ice decline. Cyclones in the North Pole are more complex and less developed than those in tropical regions. Identifying polar cyclones proves to be a task of greater complexity. To tackle this challenge, a new method which utilizes pressure level data and velocity field is proposed to improve the identification accuracy. In addition, the dynamic, simulative cyclone visualized with a 4D (four-dimensional) wind field further validated the identification result. A knowledge-driven system is eventually constructed for visualizing and analyzing an atmospheric phenomenon (cyclone) in the North Pole. The cyclone is simulated with WebGL on in a web environment using particle tracing. To achieve interactive frame rates, the graphics processing unit (GPU) is used to accelerate the process of particle advection. It is concluded with the experimental results that: (1) the cyclone identification accuracy of the proposed method is 95.6% when compared with the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data; (2) the integrated knowledge-driven visualization system allows for streaming and rendering of millions of particles with an interactive frame rate to support knowledge discovery in the complex climate system of the Arctic region

A scalable analysis framework for large-scale RDF data

With the growth of the Semantic Web, the availability of RDF datasets from multiple domains as Linked Data has taken the corpora of this web to a terabyte-scale, and challenges modern knowledge storage and discovery techniques. Research and engineering on RDF data management systems is a very active area with many standalone systems being introduced. However, as the size of RDF data increases, such single-machine approaches meet performance bottlenecks, in terms of both data loading and querying, due to the limited parallelism inherent to symmetric multi-threaded systems and the limited available system I/O and system memory. Although several approaches for distributed RDF data processing have been proposed, along with clustered versions of more traditional approaches, their techniques are limited by the trade-off they exploit between loading complexity and query efficiency in the presence of big RDF data. This thesis then, introduces a scalable analysis framework for processing large-scale RDF data, which focuses on various techniques to reduce inter-machine communication, computation and load-imbalancing so as to achieve fast data loading and querying on distributed infrastructures. The first part of this thesis focuses on the study of RDF store implementation and parallel hashing on big data processing. (1) A system-level investigation of RDF store implementation has been conducted on the basis of a comparative analysis of runtime characteristics of a representative set of RDF stores. The detailed time cost and system consumption is measured for data loading and querying so as to provide insight into different triple store implementation as well as an understanding of performance differences between different platforms. (2) A high-level structured parallel hashing approach over distributed memory is proposed and theoretically analyzed. The detailed performance of hashing implementations using different lock-free strategies has been characterized through extensive experiments, thereby allowing system developers to make a more informed choice for the implementation of their high-performance analytical data processing systems. The second part of this thesis proposes three main techniques for fast processing of large RDF data within the proposed framework. (1) A very efficient parallel dictionary encoding algorithm, to avoid unnecessary disk-space consumption and reduce computational complexity of query execution. The presented implementation has achieved notable speedups compared to the state-of-art method and also has achieved excellent scalability. (2) Several novel parallel join algorithms, to efficiently handle skew over large data during query processing. The approaches have achieved good load balancing and have been demonstrated to be faster than the state-of-art techniques in both theoretical and experimental comparisons. (3) A two-tier dynamic indexing approach for processing SPARQL queries has been devised which keeps loading times low and decreases or in some instances removes intermachine data movement for subsequent queries that contain the same graph patterns. The results demonstrate that this design can load data at least an order of magnitude faster than a clustered store operating in RAM while remaining within an interactive range for query processing and even outperforms current systems for various queries

Cloud-scale VM Deflation for Running Interactive Applications On Transient Servers

Transient computing has become popular in public cloud environments for running delay-insensitive batch and data processing applications at low cost. Since transient cloud servers can be revoked at any time by the cloud provider, they are considered unsuitable for running interactive application such as web services. In this paper, we present VM deflation as an alternative mechanism to server preemption for reclaiming resources from transient cloud servers under resource pressure. Using real traces from top-tier cloud providers, we show the feasibility of using VM deflation as a resource reclamation mechanism for interactive applications in public clouds. We show how current hypervisor mechanisms can be used to implement VM deflation and present cluster deflation policies for resource management of transient and on-demand cloud VMs. Experimental evaluation of our deflation system on a Linux cluster shows that microservice-based applications can be deflated by up to 50\% with negligible performance overhead. Our cluster-level deflation policies allow overcommitment levels as high as 50\%, with less than a 1\% decrease in application throughput, and can enable cloud platforms to increase revenue by 30\%.Comment: To appear at ACM HPDC 202

Mechanisms for the generation and regulation of sequential behaviour

A critical aspect of much human behaviour is the generation and regulation of sequential activities. Such behaviour is seen in both naturalistic settings such as routine action and language production and laboratory tasks such as serial recall and many reaction time experiments. There are a variety of computational mechanisms that may support the generation and regulation of sequential behaviours, ranging from those underlying Turing machines to those employed by recurrent connectionist networks. This paper surveys a range of such mechanisms, together with a range of empirical phenomena related to human sequential behaviour. It is argued that the empirical phenomena pose difficulties for most sequencing mechanisms, but that converging evidence from behavioural flexibility, error data arising from when the system is stressed or when it is damaged following brain injury, and between-trial effects in reaction time tasks, point to a hybrid symbolic activation-based mechanism for the generation and regulation of sequential behaviour. Some implications of this view for the nature of mental computation are highlighted

Sunfall: a collaborative visual analytics system for astrophysics

Computational and experimental sciences produce and collect ever-larger and complex datasets, often in large-scale, multi-institution projects. The inability to gain insight into complex scientific phenomena using current software tools is a bottleneck facing virtually all endeavors of science. In this paper, we introduce Sunfall, a collaborative visual analytics system developed for the Nearby Supernova Factory, an international astrophysics experiment and the largest data volume supernova search currently in operation. Sunfall utilizes novel interactive visualization and analysis techniques to facilitate deeper scientific insight into complex, noisy, high-dimensional, high-volume, time-critical data. The system combines novel image processing algorithms, statistical analysis, and machine learning with highly interactive visual interfaces to enable collaborative, user-driven scientific exploration of supernova image and spectral data. Sunfall is currently in operation at the Nearby Supernova Factory; it is the first visual analytics system in production use at a major astrophysics project