A simulation of selected statistical process control methods :a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology at Massey University

A simulation program, SQC, was developed at the Production Technology Department, Massey University. The program was written in Vax Basic 3.0 which is structured programming language and is run on the Vax computer under the VAX/VMS operating system 4.5. SQC is a menu-driven program which was designed to simulate data from a variety of production processes subject to inherent random variation and predetermined changes; sample selection for statistical quality purposes. Such decisions were made via the available feature to allow for user interactive control of the process parameters and sample selection methods while the chart of selected method was plotted on the terminal screen as well as optionally on the printer. The exercise has been done to test and to observe how the program performed and produced the output on the screen and terminal-format files. Moreover, the program evaluation was carried out by comparing with a published article, which is satisfactorily acceptable. The SQC can be utilized as a teaching tool for students in practising how each statistical process control method performs and how to make a right decision at a right time and as a research tool to observe and use the simulated results to predict and to improve the production process in the future

WavJourney: Compositional Audio Creation with Large Language Models

Large Language Models (LLMs) have shown great promise in integrating diverse expert models to tackle intricate language and vision tasks. Despite their significance in advancing the field of Artificial Intelligence Generated Content (AIGC), their potential in intelligent audio content creation remains unexplored. In this work, we tackle the problem of creating audio content with storylines encompassing speech, music, and sound effects, guided by text instructions. We present WavJourney, a system that leverages LLMs to connect various audio models for audio content generation. Given a text description of an auditory scene, WavJourney first prompts LLMs to generate a structured script dedicated to audio storytelling. The audio script incorporates diverse audio elements, organized based on their spatio-temporal relationships. As a conceptual representation of audio, the audio script provides an interactive and interpretable rationale for human engagement. Afterward, the audio script is fed into a script compiler, converting it into a computer program. Each line of the program calls a task-specific audio generation model or computational operation function (e.g., concatenate, mix). The computer program is then executed to obtain an explainable solution for audio generation. We demonstrate the practicality of WavJourney across diverse real-world scenarios, including science fiction, education, and radio play. The explainable and interactive design of WavJourney fosters human-machine co-creation in multi-round dialogues, enhancing creative control and adaptability in audio production. WavJourney audiolizes the human imagination, opening up new avenues for creativity in multimedia content creation.Comment: Project Page: https://audio-agi.github.io/WavJourney_demopage

Designing an interactive multimedia instructional environment: the civil war interactive

This article describes the rationales behind the design decisions made in creating The Civil War Interactive, an interactive multimedia instructional product based on Ken Burns''s film series The Civil War

Being in Uncertainties: An Inquiry-based Model Leveraging Complexity in Teaching-Learning

Education is traditionally structured as a closed system, privileging result-driven methods that offer control and predictability. In recent decades this reductionist approach has been effectively challenged by interdisciplinary work in complex systems theory, revealing myriad levels of orderly disorder that make either-or, linear instruction an inadequate norm. Narrowing the broad implications of a complexity lens on education, this paper focuses on generative uncertainty in teaching-learning, a paradoxical state of epistemological and creative growth described by English poet John Keats as the negative capability of being in uncertainties, mysteries, doubts. Opportunities to advance this potentiating capacity are especially abundant in constructivist curricula, for example the Methods of Inquiry (MoI) program discussed herein. MoI\u27s open, complexity-based approach foregrounds uncertainty-tolerance and other interactive dispositions, providing a fluid structure for the emergent, often turbulent nature of meaning production. Such dynamic attitudes and strategies are seen as essential for any classroom practice that seeks to transform as well as inform, to guide and also empower

Computer assisted mathematical programming

A Computer Assisted Mathematical Programming (Modelling) System (CAMPS) is described in this paper. The system uses program generator techniques for model creation and contrasts with earlier approaches which use a special purpose language to construct models. Thus no programming skill is required to formulate a model. In designing the system we have first analysed the salient components of the mathematical programming activity. A mathematical programming model is usually constructed by progressive definition of dimensions, data tables, model variables, model constraints and the matrix coefficients which connect the last two entities. Computer assistance is provided to structure the data and the resulting model in the above sequence. In addition to this novel feature and the automatic documentation facility, the system is in line with recent developments, and incorporates a friendly and flexible user interface

Operations research and computers

operational research

E/Valuating new media in language development

This paper addresses the need for a new approach to the educational evaluation of software that falls under the rubric "new media" or "multimedia" as distinct from previous generations of Computer-Assisted Language Learning (CALL) software. The authors argue that present approaches to CALL software evaluation are not appropriate for a new genre of CALL software distinguished by its shared assumptions about language learning and teaching as well as by its technical design. The paper sketches a research-based program called "E/Valuation" that aims to assist language educators to answer questions about the educational effectiveness of recent multimedia language learning software. The authors suggest that such program needs to take into account not only the nature of the new media and its potential to promote language learning in novel ways, but also current professional knowledge about language learning and teaching

Learning to Prove Theorems via Interacting with Proof Assistants

Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics. However, human experts have to construct proofs manually by entering tactics into the proof assistant. In this paper, we study the problem of using machine learning to automate the interaction with proof assistants. We construct CoqGym, a large-scale dataset and learning environment containing 71K human-written proofs from 123 projects developed with the Coq proof assistant. We develop ASTactic, a deep learning-based model that generates tactics as programs in the form of abstract syntax trees (ASTs). Experiments show that ASTactic trained on CoqGym can generate effective tactics and can be used to prove new theorems not previously provable by automated methods. Code is available at https://github.com/princeton-vl/CoqGym.Comment: Accepted to ICML 201