Horizons in Computer Science Research. Volume 18

Thomas S. Clary (Editor)

Series: Horizons in Computer Science
BISAC: COM014000



Volume 10

Issue 1

Volume 2

Volume 3

Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick


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Horizons in Computer Science Research. Volume 18 first discusses intermittently connected networks, a form of the delay tolerant network, where there never exists a complete end-to-end path between two nodes wishing to communicate.

The authors provide an overview of the current algorithms (both analytic and iterative) for the generation of tomographic images, including comparisons of the basic characteristics of image quality.

An intelligent scheduling framework is proposed to alleviate the problems encountered in modern job scheduling. The challenges of high performance computing scheduling and state-of-art scheduling methods to overcome these challenges are investigated.

The state of art developments in quantum cryptography are presented, as well as the fundamental laws of quantum physics that quantum key distribution is based on. Subsequently, three main protocols of key distribution are introduced which enable users to share a secret between them.

Three iterative methods for dose reconstruction in protoacoustics are discussed, including: gradient-descent algorithm, SART and MLEM, which are widely used in other medical imaging modalities such as PET, SPECT and CT.

Additionally, the authors evaluate the behaviour of the concurrent execution of tasks in different implementations by Java virtual machines. The performance measurements were analysed by rigorous statistical techniques: analysis of Variance and HSD comparison test of Tukey averages.

A mathematical model for finding the distribution of threads in local pools that minimises the makespan in integration processes is also presented. Makespan is a metric that calculates the average time a message takes to be executed by an integration process.

In closing, varied knowledge sources for aspect clustering are explored a new method, OpCluster-PT, is proposed. To support this research, annotated corpora and some related resources that are publicly available to the interested reader have been included.
(Imprint: Nova)


Chapter 1. A Brief History on Intermittently Connected Networks
(S. Ramesh, Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India)

Chapter 2. Quality of Reconstructed Images from Iterative Reconstruction Algorithms for Digital Tomosynthesis
(Tsutomu Gomi, PhD, School of Allied Health Sciences, Kitasato University, Sagamihara, Japan)

Chapter 3. Job Scheduling in High Performance Computing
(Yuping Fan, Illinois Institute of Technology, Chicago, IL, US)

Chapter 4. Quantum Key Distribution: Towards Unconditional Security
(Aymen Ghilen and Mostafa Azizi, Computer Department, Community College of Dammam, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia)

Chapter 5. Iterative Reconstruction Methods in Protoacoustic Imaging
(Clara Freijo, Daniel Sánchez-Parcerisa, Alejandro Lopez-Montes, Jose Manuel Udias and Joaquin L. Herraiz, Nuclear Physics Group, Department EMFTEL ans IPARCOS, Complutense, University of Madrid and CEI Moncloa, Madrid, Spain, and others))

Chapter 6. Experimental Study for Evaluating the Performance of Java Virtual Machines in Application Integration
(Daniela L. Freire, Rafael Z. Frantz, Eldair F. Dornelles, Fabricia Roos-Frantz, and Sandro Sawicki, Unijuí University, Department of Exact Sciences and Engineering, Ijuí, RS, Brazil)

Chapter 7. Threads Distribution on Local Pools of Enterprise Integration Platforms to Achieve an Optimal Makespan in Integration Processes
(Fernando Parahyba, Fabricia Roos-Frantz, Rafael Z. Frantz and Sandro Sawicki, Unijuí University, Department of Exact Sciences and Engineering, Ijuí, RS, Brazil)

Chapter 8. Aspect Clustering for Sentiment Analysis
(Francielle Alves Vargas, Interinstitutional Center for Computational Linguistics (NILC), Institute of Mathematical and Computer Sciences, University of Sao Paulo, Sao Carlos, Brazil)


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