Our work applies the dataflow algorithm to an area outside its traditional scope: functional languages. Our approach relies on a monadic intermediate language that provides low-level, imperative features like computed jumps and explicit allocations, while at the same time supporting high-level, functional-language features like case discrimination and partial application. We prototyped our work in Haskell using the HOOPL library and this dissertation shows numerous examples demonstrating its use. We prove the efficacy of our approach by giving a novel description of the uncurrying optimization in terms of the dataflow algorithm, as well as a complete implementation of the optimization using HOOPL.

Historically, hydrologic models have been available only on mainframe computers and have therefore not been used by local water managers to support daily decisions. The development of the microcomputer presents an opportunity for that to change. Mathematical models of soil moisture and river routing are selected, developed and tested for use on a microcomputer as an aid to local water management. The models are then integrated with an appropriate data base in a computer program written for a microcomputer. The result is a Decision Support System for local water managers. In order to evaluate the feasibility of using the Decision Support System, an application of the program to a small hypothetical river and irrigation system is carried out. The speed of execution indicates that the use of the Decision Support System on this class of computer under the present configuration may be possible if software or hardware changes can reduce data transfer times to and from disk.

Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1976 / Bibliography: leaf 115. / M.S. / M.S. Massachusetts Institute of Technology, Sloan School of Management

In this thesis, we try to bridge the gap between which program transformations are possible at source-level and which are possible at binary-level. While binaries are typically seen as opaque artifacts, our binary recompiler Egalito (ASPLOS 2020) enables users to parse and modify stripped binaries on existing systems. Our technique of binary recompilation is not robust to errors in disassembly, but with an accurate analysis, provides near-zero transformation overhead. We wrote several demonstration security tools with Egalito, including code randomization, control-flow integrity, retpoline insertion, and a fuzzing backend. We also wrote Nibbler (ACSAC 2019, DTRAP 2020), which detects unused code and removes it. Many of these features, including Nibbler, can be combined with other defenses resulting in multiplicatively stronger or more effective hardening. Enabled by our recompiler, an overriding theme of this thesis is our focus on deployable software transformation. Egalito has been tested by collaborators across tens of thousands of Debian programs and libraries. We coined this term egalitarian in the context of binary security. Simply put, an egalitarian analysis or security mechanism is one that can operate on itself (and is usually more deployable as a result). As one demonstration of this idea, we created a strong, deployable defense against code reuse attacks. Shuffler (OSDI 2016) randomizes function addresses, moving functions periodically every few milliseconds. This makes an attacker's job extremely difficult, especially if they are located across a network (which necessitates ping time) -- JIT-ROP attacks take 2.3 to 378 seconds to complete. Shuffler is egalitarian and defends its own code and target code simultaneously; Shuffler actually shuffles itself. We hope our deployable, egalitarian binary defenses will allow others to improve upon state-of-the-art and paint binaries as far more malleable than they have been in the past.

No description available.

Critical media literacy (Kellner & Share, 2005) may better equip children to interpret videogame content and to create games that are nonviolent and socially just. Videogames are growing in popularity in classrooms. Yet educators and parents have concerns about the violent and stereotypical content they include. An earlier study based on the curriculum Beyond Blame: Challenging Violence in the Media (Webb, Martin, Afifi, & Kraus, 2009) examined the value of a media awareness curriculum. In this mixed-method study, I explored the effectiveness of a critical media literacy program that incorporated collaboratively creating nonviolent or sociallyjust games in teaching fourth-grade students the factors of awareness of violence, marketing, and critical media literacy. Qualitative data collected from teacher reflection notes, student journals, Scratch projects, and interviews revealed the positive effects of the program. Quantitative data supported these conclusions. This highlights the need for schools to engage students in computer programming as a means to learn academics, while educating students in critical media literacy to better enable them to navigate wisely the media saturated world in which they live. In learning programming, students engage in collaborative work, their interactions helping them to collectively create meaning for the symbols they create. Set in a framework of critical media literacy and symbolic interactionism (Blumer, 1969; Mead, 1934), this study provides an innovative model for teaching computer programming and critical media literacy skills to students.

The proposed project, sCool, is an adaptive game-based learning experience designed for STEM education. In this work, we present a new iteration of sCool in efforts to further examine contributing factors of engagement, usability, and comprehension. The newly developed game experience for acquiring object-oriented programming skills is divided into two parts: concept learning and practical challenge. The concept learning part teaches students theoretical lessons of programming through fun gameplay. The practical challenge part allows students to practice programming by completing tasks. This project presents several new game types for both the concept learning and practical challenge parts. The development of these game types spreads across two phases. The first phase introduces two new game types and focuses on extending sCool to support learning object-oriented programming and improve student’s learning comprehension. The second phase builds off of the first phase, introducing another new game type to improve the object-oriented programming learning experience and the game’s overall usability and engagement. During the first phase, three experiments were conducted in a classroom setting with a computer science teacher. Conducting a study involving a total of 39 school students and three teachers, we are able to successfully display an enhanced understanding of different programming concepts. During the second phase, a single experiment was held remotely among a wide group of people, and the participants were self-guided by an instruction document and the sCool application. Conducting a study with 25 participants, we are able to show a significant improvement in the game’s usability and engagement. For future works, further evaluations in-classroom and over a longer course will be useful in assessing the new game type’s effectiveness in teaching object oriented programming. Furthermore, the game should be expanded to support learning more complex concepts in object oriented programming.

A significant part of the software development effort is spent on detecting deviations between software implementations and specifications, and subsequently locating the sources of such errors. This thesis illustrates that is possible to identify a significant number of errors during static analysis of declarative object-oriented equation-based modeling languages that are typically used for system modeling and simulation. Detecting anomalies in the source code without actually solving the underlying system of equations provides a significant advantage: a modeling error can be corrected before trying to get the model compiled or embarking on a computationally expensive symbolic or numerical solution process. The overall objective of this work is to demonstrate that debugging based on static analysis techniques can considerably improve the error location and error correcting process when modeling with equation-based languages. A new method is proposed for debugging of over- and under-constrained systems of equations. The improved approach described in this thesis is to perform the debugging process on the flattened intermediate form of the source code and to use filtering criteria generated from program annotations and from the translation rules. Each time when an error is detected in the intermediate code and the error fixing solution is elaborated, the debugger queries for the original source code before presenting any information to the user. In this way, the user is exposed to the original language source code and not burdened with additional information from the translation process or required to inspect the intermediate code. We present the design and implementation of debugging kernel prototypes, tightly integrated with the core of the optimizer module of a Modelica compiler, including details of the novel framework required for automatic debugging of equation-based languages. This thesis establishes that structural static analysis performed on the underlying system of equations from object-oriented mathematical models can effectively be used to statically debug real Modelica programs. Most of our conclusions developed in this thesis are also valid for other equation-based modeling languages. / <p>Report code: LiU-Tek-Lic-2002:37.</p>

Instruction that purposefully develops emergent learning is inherently efficient and is well-supported in behavior science research. This type of instruction is underpinned by two contemporary theories of human language and learning - Relational Frame Theory (RFT) and Stimulus Equivalence. Unfortunately, RFT is not widely taught in higher education in research and practice. Consequently, the adoption of these teaching methodologies at a meaningful scale is out of proportion with their potential benefits for learners across many populations (Dixon, et al., 2018). Most research that does make use of these theories in computer-based instruction involves proprietary or costly software and is therefore unlikely to be replicable. Few low-cost solutions have been proposed to date to address barriers to adoption and application, and the solutions that have been proposed are missing critical features (e.g., Blair & Shawler, 2020). This project 1) determined specific barriers to implementing computer-based derived stimulus relations research and practice, 2) attempted to fill this gap, by developing instruction and training for researchers and practitioners to code their own web-based RFT/equivalence-based instruction tasks, and 3) demonstrated the effectiveness of the above web-based solution via training on derived stimulus relations key concepts and terms. This project supports the methods of behavior science researchers to align with open science standards and provide a tool for researchers and instructors to efficaciously deliver instruction to meet the needs of their learners.

This research report investigates the use of an expert system to aid project engineers at the Naval Training Systems Center in making decisions concerning the requirements of the computer systems used in simulators. For a prototype system domain, the author chose an expert system that would generate a software development cost estimate. This system questions the user about the features and options required on the training system. The expert system then analyzes the information to generate a “lines of code” estimate. A selected model will combine various factors to generate s value answer for the user. The capabilities and features of current expert system development tools are reviewed as to what features would best address this problem domain. EXSYS, a rule-based expert system shell that runs on both Zenith and IBM PCs, was selected to develop the prototype because of its capability to meet the requirements of the software cost estimation domain. The COCOMO estimation model was selected to generate the user answers. The technique of using a rule-based system in combination with other management decision tools, such as spreadsheets, holds a potential of being an excellent approach for providing a tool for storing and utilizing estimation data and heuristics.