Raster graphics for interactive programming environments

Hierarchical routing for large networks Performance evaluation and optimization

A packet-switching computer network used in the C/sup 3/ system is introduced. This network analyzes packet delays in channels with various data rates, and optimum channel capacity is given in various packet input rates. The result obtained from a simulated testing system is the same as that of the theoretical analysis. It proves that the analysis is universally applicable. >

6 - Protection Mechanisms and Secure Design Principles

Part 1 Putting knowledge-based systems in context: artificial intelligence - a brief overview knowledge-based systems other fruits of AI research an applications point of view differences between KBS's and conventional computer systems. Part 2 Knowledge-based systems - a solution in search of a problem?: what sort of problems may be addressed using KBS's what factors determine an application's suitability for a KBS approach? operational KBS's the key features of KBS construction. Part 3 KBS/AI concepts and tools: KBS concepts tools used to build KBS's - an overview knowledge acquisition knowledge representation system execution - for the knowledge engineer system execution - for the end-user maintenance. Part 4 Conventional software engineering: why are structured development methods relevant to KBS's? the conventional systems development life-cycle (SDLC) and formal methods extensions to the SDLC prototyping for decision support systems prototyping for non-DSS's contrasting features of DP and KB system development prototyping in KBS development. Part 5 ORSA methodology: why worry about the business objectives? what is operational research? a soft OR methodology soft systems methodology soft methodologies in general. Part 6 Lessons from other KBS work: current KBS development methodology - the textbook view AI methodology compared with DP methodologies a project management perspective. Part 7 The Intellipse experience: the original project specification - the main project phase the key changes to the proposal the concept of Intellipse functional specification the task compraring SSD Intellipse architecture knowledge bases is SSD a suitable domain for KBS's? advisor designer. Part 8 Measuring the success of a knowledge-based system : Why measure the success? what needs to be done? how to measure the success-sources of inspiration how to measure the success of a KBS-our recommendations. Part 9 A methodology for the practical engineering of knowledge-based systems: a POLITE engineering methodology for KBSs' standards for the POLITE methodology-the ITAM investigation POLITE in action.

The work concerns the selection of the programming language and environment for the needs of neural modeling of the power and electricity demand generation system in terms of uninhabited factories. Therefore, the main goal of the conducted research is to obtain the best possible Artificial Neural Network, i.e. to teach it a model of a real system, which is a system for generating demand for power and electricity based on numerical data on parts of the power system operation in terms of uninhabited factories. The learning capabilities of artificial neural networks were checked by comparing the MSE error and the Regression Index R2. In each of the examined programming languages and related programming environments, i.e. Matlab, Python and Wolfram, an Artificial Neural Network with the same structure and properties was designed and implemented, i.e. with the same number of input and output neurons, the number of hidden layers and the number of neurons in them, the activation function of neurons and the learning method. In addition to the ANN training of the system model, testing and validation as well as comparative studies were carried out.

Prograph is a programming environment and language which has been available on the Apple Macintosh platform for more than 5 years. It provides a sophisticated application builder, together with a visual programming language, supported by a powerful program development environment. The programming language uses an object-oriented model for data abstraction and the logic is based on a dataflow model of computation, specified graphically.Graphical dataflow gives programmers a clear view of the potential for exploitation of concurrency and so the Prograph language appears to give some leverage for the programming of parallel or distributed systems. This still leaves many issues unresolved, however, since for example dataflow is usually associated with fine-grain parallelism, but expected target architectures are unlikely to support fine-grain parallelism efficiently. This paper discusses the preliminary investigation of the issues of Prograph and parallelism.KeywordsParallel EvaluationApple MacintoshDataflow GraphDataflow ModelRemote ExecutionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this dissertation we develop numerical algorithms and software tools to facilitate parallel solutions of nonlinear programming (NLP) problems. In particular, we address large-scale, block-structured problems with an intrinsic decomposable configuration. These problems arise in a great number of engineering applications, including parameter estimation, optimal control, network optimization, and stochastic programming. The structure of these problems can be leveraged by optimization solvers to accelerate solutions and overcome memory limitations, and we propose variants to two classes of optimization algorithms: augmented Lagrangian (AL) schemes and Schur-complement interior-point methods. The convergence properties of augmented Lagrangian decomposition schemes like the alternating direction method of multipliers (ADMM) and progressive hedging (PH) are well established for convex optimization but convergence guarantees in non-convex settings are still poorly understood. In practice, however, ADMM and PH often perform satisfactorily in complex non-convex NLPs. In this work, we study connections between the method of multipliers (MM), ADMM, and PH to derive benchmarking metrics that explain why PH and ADMM work in practice. We illustrate the concepts using challenging dynamic optimization problems. Our exposition seeks to establish more formalism in benchmarking ADMM, PH, and AL schemes and to motivate algorithmic improvements. The effectiveness of nonlinear interior-point solvers for solving large-scale problems relies quite heavily on the solution of the underlying linear algebra systems. The schur-complement decomposition is very effective for parallelizing the solution of linear systems with modest coupling. However, for systems with large number of coupling variables the schur-complement method does not scale favorably. We implement an approach that uses a Krylov solver (GMRES) preconditioned with ADMM to solve block-structured linear systems that arise in the interior-point method. We show that this ADMM-GMRES approach overcomes the well-known scalability issues of Schur decomposition. One important drawback of using decomposition approaches like ADMM and PH is their convergence rate. Unlike Schur-complement interior-point algorithms that have super-linear convergence, augmented Lagrangian approaches typically exhibit linear and sublinear rates. We exploit connections between ADMM and the Schur-complement decomposition to derive an accelerated version of ADMM. Specifically, we study the effectiveness of performing a Newton-Raphson algorithm to compute multiplier estimates for augmented Lagrangian methods. We demonstrate using two-stage stochastic programming problems that our multiplier update achieves convergence in fewer iterations for MM on general nonlinear problems. In the case of ADMM, the newton update significantly reduces the number of subproblem solves for convex quadratic programs (QPs). Moreover, we show that using newton multiplier updates makes the method robust to the selection of the penalty parameter. Traditionally, state-of-the-art optimization solvers are implemented in low-level programming languages. In our experience, the development of decomposition algorithms in these frameworks is challenging. They present a steep learning curve and can slow the development and testing of new numerical algorithms. To mitigate these challenges, we developed PyNumero, a new open source framework implemented in Python and C++. The package seeks to facilitate development of optimization algorithms for large-scale optimization within a high-level programming environment while at the same time minimizing the computational burden of using Python. The efficiency of PyNumero is illustrated by implementing algorithms for problems arising in stochastic programming and optimal control. Timing results are presented for both serial and parallel implementations. Our computational studies demonstrate that with the appropriate balance between compiled code and Python, efficient implementations of optimization algorithms are achievable in these high-level languages.

Conventional computer systems currently used by architecture and landscape architecture are not addressing complex decision making, system interface, dynamic manipulation and real time visualization of data. This paper identifies a strategy by which Canadian Schools could form a supportive network, incorporate and expand their research development. Within this larger framework schools would have better tools, a larger research base and access to funding as a group. The following discussion is an idea of what we at the Canadian Schools need to do differently over the next five years in our research and teaching in order to make a unique contribution to our fields.

In the era of big data, massive data requires processing efficiently. However, the limited data bandwidth between the memory and the processor in conventional computer systems could not meet the requirement of data transferring. Computing in-memory has been considered an effective solution to address this problem. In this paper, based on the spin transfer torque-magnetic random access memory (STT-MRAM), a computing in-memory architecture with as few peripheral circuits as possible is proposed. This computing in-memory architecture gives the specific reference cell so that two rows in one array can be activated simultaneously to perform bitwise logic operations, such as OR/NOR and AND/NAND. In addition, with technology scaling down, STT-MRAM suffers from high sensitivity to process variation, which results in more device mismatch in a sense circuit. Additionally, the negative bias temperature instability (NBTI) seriously affects the life of PMOS transistors used in a sense circuit. In this paper, a high-reliability sense amplifier for computing in-memory with STT-MRAM is proposed. By using two self-enabled switching transistors, the proposed sense amplifier not only can decrease the NBTI effect on PMOS transistors but also can achieve a low sensing error rate. Using a CMOS 40[Formula: see text]nm design-kit and an accurate compact model of the STT magnetic tunnel junction (MTJ), mixed transient and statistical simulations have been present to demonstrate the functionality and performance of the proposed circuits.

This paper describes the design and the implementation of OOPE, the graphical programming environment of the prototype version of O2, an object-oriented database system. One of the distinguishing features of this environment is that it mixes the functionalities of programming environments, of both databases and programming languages. Thus, it facilitates and fastens not only the schema design, but also the development of application programs. Another interesting characteristic is that it is being developed using as much as possible the functionalities provided by the O2 system, namely the programming language, the database and the graphics functionalities.

It is well known that students face many difficulties when they have to learn programming. Generally, these difficulties arise from two main reasons: i) the kind of exercises proposed by the teacher, and ii) the programming language used for solving those problems. The first problem is overcome by selecting an interesting application domain for the students. The second problem is tackled by using programming languages specialized for teaching. Nowadays, there are many programming languages aimed at simplifying the learning process. However, many of them still have the same drawbacks of traditional programming languages: the language used to write the statements is different from the programmers' native language; and the syntactic rules impose many tricky restrictions not easy to follow. This paper presents an approach for solving the problems previously mentioned. The approach consists of using: an application domain motivating for the student, the Project Hoshimi (PH); and a programming environment, PH-Helper that is a simple and user-friendly syntax-directed editor and compiler for Hoshimi Language (HL), the actual PH programming language.

22 - Self-Learning Knowledge Systems and Fuzzy Systems and Their Applications

Alien is a generator for interactive language-specific programming environments. The system makes a language definition environment available to the language definer and supports him with an interactive generator. Using these tools every syntactic unit of a language can be made available. This can be done by a method of stepwise testing, correction and expansion. Fully defined environments can easily be restricted to subsets of a language.This paper introduces a method making a large software product executable on small UNIX machines as well as a survey of the system's components. These are designed to make the implementation on PCs effective and to develop a powerful generator for programming environments. Therefore the results are of interest to all projects in the programming environment field.ALIEN has been implemented in C running on Siemens PC-X, PC-X10, PC-MX-2 machines and Atari PC. Implementation on Apollo and SUN workstations is still in progress and on IBM PC is planned.The project is supported by the Stiftung Volkswagen-werk under grant 1/61 602.

In the 1960s, Papert introduced Logo language that commanded a graphic turtle. These were the first advances in the field of educational robotics. Many environments were created in this theme; however, most programming environments are highly graphical, so they do not provide accessible interfaces for visually impaired users. Thus, our research presents challenges and recommendations in terms of usability and accessibility in designing a robot programming environment for people who are visually impaired. We conjectured that by using a programming language to guide a robot in a virtual environment, the person who is visually impaired could better understand orientation and mobility skills. Hence, we created the GoDonnie programming language based on the Logo language. GoDonnie runs in a programming environment called Donnie. We show all the design process of Donnie, that was based on interactive design. Participates in this study, people who are visually impaired and sighted programming professors. Results indicate that GoDonnie and Donnie have good usability, supports the development of orientation and mobility in visually impaired people, and meets the expectations regarding the programming environment. Finally, we created a set of design guidelines for developing robot programming environments for people who are visually impaired.

An interactive software development environment can be viewed as a structure-based editor, provided that structure is broadly interpreted. The user sees and manipulates complex objects at various levels of detail. Many of the implications of changes are analyzed and made available to the user as frequently as the user wishes, without a mode change from editing to compiling or linking. To help provide these services in a uniform way that can readily respond to changes in the programming language(s) supported or in the preferences of individual users, some programming environments use attribute grammars.