Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Trusted community : a novel multiagent organisation for open distributed systems

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NASA/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program 1992

Since 1964, the National Aeronautics and Space Administration (NASA) has supported a program of summer faculty fellowships for engineering and science educators. In a series of collaborations between NASA research and development centers and nearby universities, engineering faculty members spend 10 weeks working with professional peers on research. The Summer Faculty Program Committee of the American Society for Engineering Education supervises the programs. Objectives of the program are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participants' institutions; and (4) to contribute to the research objectives of the NASA center

Authentication Schemes\u27 Impact on Working Memory

Authentication is the process by which a computing system validates a user’s identity. Although this process is necessary for system security, users view authentication as a frequent disruption to their primary tasks. During this disruption, primary task information must be actively maintained in working memory. As a result, primary task information stored in working memory is at risk of being lost or corrupted while users authenticate. For over two decades, researchers have focused on developing more memorable passwords by replacing alphanumeric text with visual graphics (Biddle et al., 2012). However, very little attention has been given to the impact authentication has on working memory. A recent exploratory study suggests that working memory can be disrupted during graphical authentication (Still & Cain, 2019). In this study, we take the next step by controlling for task difficulty and contrasting performance with conventional password-based authentication. Baddeley’s model was employed to examine the impact of authentication on verbal, visuospatial, and central executive working memory (Baddeley & Hitch, 1974). Our findings may help designers select authentication systems that minimize adverse effects on users’ critical primary task performance. For instance, we revealed that conventional passwords do not have a greater negative impact on verbal primary task information compared to graphical passcodes. We also replicated findings reported by Still and Cain (2019), where visuospatial was least impaired by authentication. These findings are not intuitive, highlighting the need for further investigation of how authentication impacts primary task information in working memory

Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

Phrasing Bimanual Interaction for Visual Design

Architects and other visual thinkers create external representations of their ideas to support early-stage design. They compose visual imagery with sketching to form abstract diagrams as representations. When working with digital media, they apply various visual operations to transform representations, often engaging in complex sequences. This research investigates how to build interactive capabilities to support designers in putting together, that is phrasing, sequences of operations using both hands. In particular, we examine how phrasing interactions with pen and multi-touch input can support modal switching among different visual operations that in many commercial design tools require using menus and tool palettes—techniques originally designed for the mouse, not pen and touch. We develop an interactive bimanual pen+touch diagramming environment and study its use in landscape architecture design studio education. We observe interesting forms of interaction that emerge, and how our bimanual interaction techniques support visual design processes. Based on the needs of architects, we develop LayerFish, a new bimanual technique for layering overlapping content. We conduct a controlled experiment to evaluate its efficacy. We explore the use of wearables to identify which user, and distinguish what hand, is touching to support phrasing together direct-touch interactions on large displays. From design and development of the environment and both field and controlled studies, we derive a set methods, based upon human bimanual specialization theory, for phrasing modal operations through bimanual interactions without menus or tool palettes

New Statistical Algorithms for the Analysis of Mass Spectrometry Time-Of-Flight Mass Data with Applications in Clinical Diagnostics

Mass spectrometry (MS) based techniques have emerged as a standard forlarge-scale protein analysis. The ongoing progress in terms of more sensitive machines and improved data analysis algorithms led to a constant expansion of its fields of applications. Recently, MS was introduced into clinical proteomics with the prospect of early disease detection using proteomic pattern matching. Analyzing biological samples (e.g. blood) by mass spectrometry generates mass spectra that represent the components (molecules) contained in a sample as masses and their respective relative concentrations. In this work, we are interested in those components that are constant within a group of individuals but differ much between individuals of two distinct groups. These distinguishing components that dependent on a particular medical condition are generally called biomarkers. Since not all biomarkers found by the algorithms are of equal (discriminating) quality we are only interested in a small biomarker subset that - as a combination - can be used as a fingerprint for a disease. Once a fingerprint for a particular disease (or medical condition) is identified, it can be used in clinical diagnostics to classify unknown spectra. In this thesis we have developed new algorithms for automatic extraction of disease specific fingerprints from mass spectrometry data. Special emphasis has been put on designing highly sensitive methods with respect to signal detection. Thanks to our statistically based approach our methods are able to detect signals even below the noise level inherent in data acquired by common MS machines, such as hormones. To provide access to these new classes of algorithms to collaborating groups we have created a web-based analysis platform that provides all necessary interfaces for data transfer, data analysis and result inspection. To prove the platform's practical relevance it has been utilized in several clinical studies two of which are presented in this thesis. In these studies it could be shown that our platform is superior to commercial systems with respect to fingerprint identification. As an outcome of these studies several fingerprints for different cancer types (bladder, kidney, testicle, pancreas, colon and thyroid) have been detected and validated. The clinical partners in fact emphasize that these results would be impossible with a less sensitive analysis tool (such as the currently available systems). In addition to the issue of reliably finding and handling signals in noise we faced the problem to handle very large amounts of data, since an average dataset of an individual is about 2.5 Gigabytes in size and we have data of hundreds to thousands of persons. To cope with these large datasets, we developed a new framework for a heterogeneous (quasi) ad-hoc Grid - an infrastructure that allows to integrate thousands of computing resources (e.g. Desktop Computers, Computing Clusters or specialized hardware, such as IBM's Cell Processor in a Playstation 3)

Network emulation focusing on QoS-Oriented satellite communication

This chapter proposes network emulation basics and a complete case study of QoS-oriented Satellite Communication

Design and implementation of a multi-agent opportunistic grid computing platform

Opportunistic Grid Computing involves joining idle computing resources in enterprises into a converged high performance commodity infrastructure. The research described in this dissertation investigates the viability of public resource computing in offering a plethora of possibilities through seamless access to shared compute and storage resources. The research proposes and conceptualizes the Multi-Agent Opportunistic Grid (MAOG) solution in an Information and Communication Technologies for Development (ICT4D) initiative to address some limitations prevalent in traditional distributed system implementations. Proof-of-concept software components based on JADE (Java Agent Development Framework) validated Multi-Agent Systems (MAS) as an important tool for provisioning of Opportunistic Grid Computing platforms. Exploration of agent technologies within the research context identified two key components which improve access to extended computer capabilities. The first component is a Mobile Agent (MA) compute component in which a group of agents interact to pool shared processor cycles. The compute component integrates dynamic resource identification and allocation strategies by incorporating the Contract Net Protocol (CNP) and rule based reasoning concepts. The second service is a MAS based storage component realized through disk mirroring and Google file-system’s chunking with atomic append storage techniques. This research provides a candidate Opportunistic Grid Computing platform design and implementation through the use of MAS. Experiments conducted validated the design and implementation of the compute and storage services. From results, support for processing user applications; resource identification and allocation; and rule based reasoning validated the MA compute component. A MAS based file-system that implements chunking optimizations was considered to be optimum based on evaluations. The findings from the undertaken experiments also validated the functional adequacy of the implementation, and show the suitability of MAS for provisioning of robust, autonomous, and intelligent platforms. The context of this research, ICT4D, provides a solution to optimizing and increasing the utilization of computing resources that are usually idle in these contexts