The Great Principles of Computing

The article of record as published may be found at http://dx.doi.org/The article of record as published may be found at http://dx.doi.org/10.1145/948383.948400American Scientist,. Computing may be the fourth great domain of science, along with the physical, life, and social sciences

Developing Experimental Models for NASA Missions with ASSL

NASA's new age of space exploration augurs great promise for deep space exploration missions whereby spacecraft should be independent, autonomous, and smart. Nowadays NASA increasingly relies on the concepts of autonomic computing, exploiting these to increase the survivability of remote missions, particularly when human tending is not feasible. Autonomic computing has been recognized as a promising approach to the development of self-managing spacecraft systems that employ onboard intelligence and rely less on control links. The Autonomic System Specification Language (ASSL) is a framework for formally specifying and generating autonomic systems. As part of long-term research targeted at the development of models for space exploration missions that rely on principles of autonomic computing, we have employed ASSL to develop formal models and generate functional prototypes for NASA missions. This helps to validate features and perform experiments through simulation. Here, we discuss our work on developing such missions with ASSL.Comment: 7 pages, 4 figures, Workshop on Formal Methods for Aerospace (FMA'09

Decentralised Control Flow: A Computational Model for Distributed Systems

PhD ThesisThis thesis presents two sets of principles for the organisation of distributed computing systems. Details of models of computation based on these principles are together given, with proposals for programming languages based on each model of computation. The recursive control flow principles are based on the concept of recursive control flow computing system structuring. A recursive comprises a group of subordinate computing systems connected together by Each subordinate computing system may either be a communications medium. which a a computing system consists of a processing unit, memory some is itself a recursive component, and input/output devices, or computing components control flow system. The memory of all the computing systems within a recursive control flow computing subordinate system are arranged in a hierarchy. Using suitable addresses, any part of the hierarchy is accessible to any sequence of instructions which may be executed by the processing unit of a subordinate computing system. This rise to serious difficulties in the global accessibility gives understanding of programs written the meaning of in a programming language recursive control flow on the model of computation. based Reasoning about a particular program in isolation is difficult because of the potential interference between the execution different programs cannot be ignored . alternative principles, decentralised control flow, restrict the The accessibility of subordinate global the memory components of the computing The basis of the concept of objects forms the systems. principles. Information channels may flow along unnamed between instances of these objects, this being the only way in which one instance of an object may communicate with some other instance of an object. Reasoning particular program written in a programming language about a based on the decentralised control flow model of computation is easier since it is that there will be no interference between the guaranteed execution of different programs.Science and Engineering Research Council of Great Britain, International Computers Limite

ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

Ethical values to enhance higher education for computing

The evolution and development for every mankind and society is essentially built on education as a main pillar. The current weaknesses and slow development in the developing countries require an efficient and effective higher education system, especially in computing as an essential path toward bridging the gap with developed countries. Computing education has great role in all aspects of human life (politics, economy, health, and education). Moral and ethical values should introduced to and affect all education process’s elements, which lead to better estimate the shape of the next generation of leaders. The increasing problems in societies force many universities to enhance their education process with ethical and moral values. Historically Islamic based ethical and moral values are consider as a great values and principles for human life that leads to build a stable, effective and healthy society that avoid many social diseases. This paper is to prepare a suitable plan to enhance computing higher education with ethical and values that leads to better evolution of society

Theory of variational quantum simulation

The variational method is a versatile tool for classical simulation of a variety of quantum systems. Great efforts have recently been devoted to its extension to quantum computing for efficiently solving static many-body problems and simulating real and imaginary time dynamics. In this work, we first review the conventional variational principles, including the Rayleigh-Ritz method for solving static problems, and the Dirac and Frenkel variational principle, the McLachlan's variational principle, and the time-dependent variational principle, for simulating real time dynamics. We focus on the simulation of dynamics and discuss the connections of the three variational principles. Previous works mainly focus on the unitary evolution of pure states. In this work, we introduce variational quantum simulation of mixed states under general stochastic evolution. We show how the results can be reduced to the pure state case with a correction term that takes accounts of global phase alignment. For variational simulation of imaginary time evolution, we also extend it to the mixed state scenario and discuss variational Gibbs state preparation. We further elaborate on the design of ansatz that is compatible with post-selection measurement and the implementation of the generalised variational algorithms with quantum circuits. Our work completes the theory of variational quantum simulation of general real and imaginary time evolution and it is applicable to near-term quantum hardware.Comment: 41 pages, accepted by Quantu

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics