We present a full semester course in which students build a complete computer system - hardware and software - from the ground up. Also known as "Nand to Tetris", the course is presently taught at 100+ universities, and is listed in Coursera's top-rated courses. The course synthesizes many abstractions, algorithms, and data structures learned in CS courses, and makes them concrete by building a complete computer system from first principles. The methodology is based on guiding students through a set of 12 homework assignments that gradually construct and unit-test a simple hardware platform and a modern software hierarchy, yielding a surprisingly powerful computer system. We'll start the workshop by demonstrating some interactive computer games running on this platform. The hardware projects are done in a simple hardware description language and a hardware simulator supplied by us. The software projects (assembler, VM, compiler and OS) can be done in any language, using API's and test programs supplied by us. The result is a general-purpose computer system, simulated on the student's PC. The course is completely self-contained, requiring only programming as a pre-requisite. Also, it is highly modular, and can be viewed as a set of 12 exciting, stand-along homework assignments. The course and the workshop assume no specific knowledge of, or bent for, computer organization/architecture, and are aimed at any instructor who wishes to strengthen his or her courses with engaging programming projects and with an integrated and hands-on understanding of how modern computer systems work, and how they are built.

Engaging students in large system development projects is an important educational objective, since it exposes design and programming challenges that come to play only with scale. Alas, large scale system projects can be monstrously complex - to the extent of being infeasible in academic settings. We describe a set of principles and a framework that enable students to develop large-scale systems, e.g. a complete hardware platform or a compiler, in several semester weeks.

Various forms of virtualization play key roles in the construction and usage of diverse system artifacts ranging from modern compilers to hardware migration to mobile computing. Virtualization is also a powerful concept on cognitive grounds, requiring considerable abstraction skills and modeling abilities. We present a methodology, a software tool, and a set of instructional resources designed to expose students to virtual machines, focusing on both abstraction and implementation issues. An early version of our methodology appeared in a book form [4] in which we describe how to build a complete computer system - hardware and software - in one semester. Recently we've re-written our VM emulation software and consolidated all our VM materials into a web site that supports a stand-alone module on virtual machines [5]. This module can be plugged into many system-oriented CS courses. All the resources that we present are freely available in open source and are presently used successfully in several universities.

We describe a synthesis course that provides a hands-on treatment of many hardware and software topics learned in computer science (CS) programs. Using a modular series of twelve projects, we walk the students through the gradual construction of a simple hardware platform and a modern software hierarchy, yielding a basic yet powerful computer system. In the process of building the computer, the students gain a first-hand understanding of how hardware and software systems are designed and how they work together, as one enterprise. The course web site contains all the materials necessary to run this course in open source, and students and instructors are welcome to use and extend them freely. The course projects are modular and self-contained, and any subset of them can be implemented in any order and in any programming language. Therefore, they comprise a flexible library of exercises that can be used in many applied CS courses. This paper gives a description of the approach and the course, juxtaposed against general educational principles underlying meaningful learning.

No abstract available.

In spite of the increasing sophistication and power of commercial spreadsheet packages, we still lack a formal theory or a methodology to support the construction and maintenance of spreadsheet models. Using a dual logical/physical perspective, we identify four principal components that characterize any spread sheet model: schema, data, editorial, and binding . We present a factoring algorithm for identifying and extracting these components from conventional spreadsheets with minimal user intervention, and a synthesis algorithm that assists users in the construction of executable spreadsheets from reusable model components. This approach opens new possibilities for applying object-oriented and model management techniques to support the construction, sharing, and reuse of spreadsheet models in organizations. Importantly, our approach to model management and the Windows-based prototype that we have developed are designed to coexist with, rather than replace , traditional spreadsheet programs. In other words, the users are not required to learn a new modeling language; instead, their logical models and data sets are extracted from their spreadsheets transparently, as a side-effect of using standard spreadsheet programs.

The Dempster Sharer theory of evidence concerns the elicitation and manipulation of degrees of belief rendered by multiple sources of evidence to a common set of propositions. Information indexing and retrieval applications use a variety of quantitative means—both probabilistic and quasi-probabilistic—to represent and manipulate relevance numbers and index vectors. Recently, several proposals were made to use the Dempster Shafer model as a relevance calculus in such applications. This paper provides a critical review of these proposals, pointing at several theoretical caveats and suggesting ways to resolve them. The methodology is based on expounding a canonical indexing model whose relevance measures and combinations mechanisms are shown to be isomorphic to Shafer's belief functions and to Dempster's rule, respectively. Hence, the paper has two objectives: (i) to describe and resolve some caveats in the way the Dempster Shafer theory is applied to information indexing and retrieval, and (ii) to provide an intuitive interpretation of the Dempster Shafer theory, as it unfolds in the simple context of a canonical indexing model.

Several well-known belief languages in artificial intelligence are reviewed, and both previous work and new insights into their Bayesian interpretations are presented. In particular, the authors focus on three alternative belief-update models: the certainty factors calculus, Dempster-Shafer simple support functions, and the descriptive contrast/inertia model. Important 'dialects' of these languages are shown to be isomorphic to each other and to a special case of Bayesian inference. Parts of the analysis were carried out by other authors; their results were extended and consolidated using an analytic technique designed to study the kinship of belief languages in general.>