Reason Maintenance - Conceptual Framework

This paper describes the conceptual framework for reason maintenance developed as part of WP2

An Experiment in Knowledge Acquisition for Software Requirements

The Requirements Apprentice (RA) is a demonstration system that assists a human analyst in the requirements-acquisition phase of the software-development process. By applying the RA to another example it has been possible to show some of the range of applicability of the RA. The same disambiguation, formalization, and contradiction-resolution techniques are useful in the air traffic control and library database domains and some clichés are shared between them. In addition, the need for an extension to the RA is seen: summarization of contradictions could be improved.MIT Artificial Intelligence Laborator

Tea Collector: Web Based Data Tracking Solution for Tea Smallholders

The sustainability of a tea factory also depends on the trustworthiness of the data collection and finance management. Collection of tea leaves and tea processing are the main two processes done by a tea factory. In the process of tea collection, they must handle a large set of data about the tea collection, selling, and payments. Data processing and summarizing can be identified as critical process because these summarizations finally sent to the factory for further analyzes. All these documentation processes are still handled mostly in a manual way. This manual process may lead to malpractices sometimes, especially when handling payments. So, there is a need to develop an easy way to manage all the documentation processes online. In this specific research, our idea is to implement a web application with features like adding, updating, and deleting data with a generation of summary reports. The main reason we need to shift from a normal manual process to an online managed process is that they can consume their time by managing the data more secure and trustworthy way. We have implemented this by using MERN stack as the technology where we use react.js for the frontend and express.js for the backend developments. We used MongoDB as the database and Heroku to host the application

Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

Applying Formal Methods to Networking: Theory, Techniques and Applications

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet which began as a research experiment was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, especially for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification, and an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design---especially, the software defined networking (SDN) paradigm---offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial

explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning

We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different explainable AI methods; as well as (3) refine and optimize the models. Our framework combines an iterative XAI pipeline with eight global monitoring and steering mechanisms, including quality monitoring, provenance tracking, model comparison, and trust building. To operationalize the framework, we present explAIner, a visual analytics system for interactive and explainable machine learning that instantiates all phases of the suggested pipeline within the commonly used TensorBoard environment. We performed a user-study with nine participants across different expertise levels to examine their perception of our workflow and to collect suggestions to fill the gap between our system and framework. The evaluation confirms that our tightly integrated system leads to an informed machine learning process while disclosing opportunities for further extensions.Comment: 9 pages paper, 2 pages references, 5 pages supplementary material (ancillary files

Using Small MUSes to Explain How to Solve Pen and Paper Puzzles

In this paper, we present Demystify, a general tool for creating human-interpretable step-by-step explanations of how to solve a wide range of pen and paper puzzles from a high-level logical description. Demystify is based on Minimal Unsatisfiable Subsets (MUSes), which allow Demystify to solve puzzles as a series of logical deductions by identifying which parts of the puzzle are required to progress. This paper makes three contributions over previous work. First, we provide a generic input language, based on the Essence constraint language, which allows us to easily use MUSes to solve a much wider range of pen and paper puzzles. Second, we demonstrate that the explanations that Demystify produces match those provided by humans by comparing our results with those provided independently by puzzle experts on a range of puzzles. We compare Demystify to published guides for solving a range of different pen and paper puzzles and show that by using MUSes, Demystify produces solving strategies which closely match human-produced guides to solving those same puzzles (on average 89% of the time). Finally, we introduce a new randomised algorithm to find MUSes for more difficult puzzles. This algorithm is focused on optimised search for individual small MUSes