Enhancing reliability with Latin Square redundancy on desktop grids.

Computational grids are some of the largest computer systems in existence today. Unfortunately they are also, in many cases, the least reliable. This research examines the use of redundancy with permutation as a method of improving reliability in computational grid applications. Three primary avenues are explored - development of a new redundancy model, the Replication and Permutation Paradigm (RPP) for computational grids, development of grid simulation software for testing RPP against other redundancy methods and, finally, running a program on a live grid using RPP. An important part of RPP involves distributing data and tasks across the grid in Latin Square fashion. Two theorems and subsequent proofs regarding Latin Squares are developed. The theorems describe the changing position of symbols between the rows of a standard Latin Square. When a symbol is missing because a column is removed the theorems provide a basis for determining the next row and column where the missing symbol can be found. Interesting in their own right, the theorems have implications for redundancy. In terms of the redundancy model, the theorems allow one to state the maximum makespan in the face of missing computational hosts when using Latin Square redundancy. The simulator software was developed and used to compare different data and task distribution schemes on a simulated grid. The software clearly showed the advantage of running RPP, which resulted in faster completion times in the face of computational host failures. The Latin Square method also fails gracefully in that jobs complete with massive node failure while increasing makespan. Finally an Inductive Logic Program (ILP) for pharmacophore search was executed, using a Latin Square redundancy methodology, on a Condor grid in the Dahlem Lab at the University of Louisville Speed School of Engineering. All jobs completed, even in the face of large numbers of randomly generated computational host failures

Volunteer computing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 205-216).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.This thesis presents the idea of volunteer computing, which allows high-performance parallel computing networks to be formed easily, quickly, and inexpensively by enabling ordinary Internet users to share their computers' idle processing power without needing expert help. In recent years, projects such as SETI@home have demonstrated the great potential power of volunteer computing. In this thesis, we identify volunteer computing's further potentials, and show how these can be achieved. We present the Bayanihan system for web-based volunteer computing. Using Java applets, Bayanihan enables users to volunteer their computers by simply visiting a web page. This makes it possible to set up parallel computing networks in a matter of minutes compared to the hours, days, or weeks required by traditional NOW and metacomputing systems. At the same time, Bayanihan provides a flexible object-oriented software framework that makes it easy for programmers to write various applications, and for researchers to address issues such as adaptive parallelism, fault-tolerance, and scalability. Using Bayanihan, we develop a general-purpose runtime system and APIs, and show how volunteer computing's usefulness extends beyond solving esoteric mathematical problems to other, more practical, master-worker applications such as image rendering, distributed web-crawling, genetic algorithms, parametric analysis, and Monte Carlo simulations. By presenting a new API using the bulk synchronous parallel (BSP) model, we further show that contrary to popular belief and practice, volunteer computing need not be limited to master-worker applications, but can be used for coarse-grain message-passing programs as well. Finally, we address the new problem of maintaining reliability in the presence of malicious volunteers. We present and analyze traditional techniques such as voting, and new ones such as spot-checking, encrypted computation, and periodic obfuscation. Then, we show how these can be integrated in a new idea called credibility-based fault-tolerance, which uses probability estimates to limit and direct the use of redundancy. We validate this new idea with parallel Monte Carlo simulations, and show how it can achieve error rates several orders-of-magnitude smaller than traditional voting for the same slowdown.by Luis F.G. Sarmenta.Ph.D

An Energy-Efficient and Reliable Data Transmission Scheme for Transmitter-based Energy Harvesting Networks

Energy harvesting technology has been studied to overcome a limited power resource problem for a sensor network. This paper proposes a new data transmission period control and reliable data transmission algorithm for energy harvesting based sensor networks. Although previous studies proposed a communication protocol for energy harvesting based sensor networks, it still needs additional discussion. Proposed algorithm control a data transmission period and the number of data transmission dynamically based on environment information. Through this, energy consumption is reduced and transmission reliability is improved. The simulation result shows that the proposed algorithm is more efficient when compared with previous energy harvesting based communication standard, Enocean in terms of transmission success rate and residual energy.This research was supported by Basic Science Research Program through the National Research Foundation by Korea (NRF) funded by the Ministry of Education, Science and Technology(2012R1A1A3012227)

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

EVALUATING ARTIFICIAL INTELLIGENCE FOR OPERATIONS IN THE INFORMATION ENVIRONMENT

Recent advances in artificial intelligence (AI) portend a future of accelerated information cycles and intensified technology diffusion. As AI applications become increasingly prevalent and complex, Special Operations Forces (SOF) face the challenge of discerning which tools most effectively address operational needs and generate an advantage in the information environment. Yet, SOF currently lack an end user–focused evaluation framework that could assist information practitioners in determining the operational value of an AI tool. This thesis proposes a practitioner’s evaluation framework (PEF) to address the question of how SOF should evaluate AI technologies to conduct operations in the information environment (OIE). The PEF evaluates AI technologies through the perspective of the information practitioner who is familiar with the mission, the operational requirements, and OIE processes but has limited to no technical knowledge of AI. The PEF consists of a four-phased approach—prepare, design, conduct, recommend—that assesses nine evaluation domains: mission/task alignment; data; system/model performance; user experience; sustainability; scalability; affordability; ethical, legal, and policy considerations; and vendor assessment. By evaluating AI through a more structured, methodical approach, the PEF enables SOF to identify, assess, and prioritize AI-enabled tools for OIE.Outstanding ThesisMajor, United States ArmyApproved for public release. Distribution is unlimited

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area