Automated Reasoning and Presentation Support for Formalizing Mathematics in Mizar

This paper presents a combination of several automated reasoning and proof presentation tools with the Mizar system for formalization of mathematics. The combination forms an online service called MizAR, similar to the SystemOnTPTP service for first-order automated reasoning. The main differences to SystemOnTPTP are the use of the Mizar language that is oriented towards human mathematicians (rather than the pure first-order logic used in SystemOnTPTP), and setting the service in the context of the large Mizar Mathematical Library of previous theorems,definitions, and proofs (rather than the isolated problems that are solved in SystemOnTPTP). These differences poses new challenges and new opportunities for automated reasoning and for proof presentation tools. This paper describes the overall structure of MizAR, and presents the automated reasoning systems and proof presentation tools that are combined to make MizAR a useful mathematical service.Comment: To appear in 10th International Conference on. Artificial Intelligence and Symbolic Computation AISC 201

Mobile collaborative working environment of product design

In response to the arrival of new Web/Internet environments, one of the most attractive challenges in current research is to exploit wireless computing technologies in collaborative product design, and hence to build a ubiquitous mobile information system to enable the collaborative product design within a mobile environment. However, the literature review reveals that although the progress of mobile technologies on wireless networks has largely changed the way people access the Internet; little has been achieved in mobile computing for collaborative product design. The reason is that, due to the distinct features of mobile devices and wireless networks (such as small display screen, limited bandwidth, unreliability of wireless networks, etc.), the methodologies and technologies used in stationary networks are not always applicable to mobile systems. The aim of this research is to establish a Wireless Internet-based Collaborative Working Environment for product design through the combination of multiple technologies, by including: Web services, Parametric Design, the Semantic Web, Agent and Flex Technologies. In order to create, deploy, and manage the distributed resources, Web service is used to implement design resource integration in a platform-independent manner. In addition, Semantic Web Technology is used to create a general knowledge base. This approach includes two components: (1) ontology is used to represent abstract views of product data and (2) added semantic rules are also used to represent relationships among product data. Therefore, an ontology-based description model is thus proposed to facilitate expression and organisation of product information in order to manage and deploy the distributed design resources

Development of Hole Recognition System Using Rule-Based Technique

The effective integration of CAD/CAM (Computer Aided Design/Computer Aided Manufacturing) is a cornerstone of automation progress. The 'islands of automation' such as CAD, CAPP (Computer Aided Process Planning) and CAM are facing the ineffective communication problem. The CAM cannot be integrated directly into CAD due to the lower level geometrical data in CAD and the higher level manufacturing data in CAM. It is important that each 'island of automation' be linked together in order to achieve the goal of integrated CAD/CAM systems. For the past decades, feature-based representation has become a basic part of research in the CAD/CAM integration. The work on feature-based modelling has developed two main approaches, namely, design by features and feature recognition. The feature recognition approach is developed to extract the manufacturing information that is recognized from the CAD database into the CAM database. The features can be used to subtract higher level manufacturing data from lower level or geometrical computer aided data. The Hole Recognition System is developed to solve the communication problem between CAD and CAM. Kappa-PC expert system is used in developing the rule of holes. In this work, the Hole Recognition System is retrieves the geometrical data from the UniGraphics (UG) CAD/CAM system indirectly. The Hole Recognition System is designed to generalise and recognise the feature from neutral format file such as Data Exchange File (DXF), Initial Graphics Exchange Specification (IGES) and Standard for the Exchange of Product Model Data (STEP). The neutral format file can be created hy CAD/CAM system such as UG, CATIA, ProEngineer, etc. For this work, the neutral data transfer standards, namely STEP is used. The STEP file is post processed by UG CAD/CAM system after the solid model has been created. A filtering program is developed to extract the geometrical data feature recognition process. The filtering program has been developed because the Kappa-PC expert system cannot read the STEP file directly. The output from the filtering program is fed to the Hole Recognition System. The rule-based technique is applied to recognise holes. There are two features, namely, blind hole and through hole to be considered. The work presented in this thesis and the Hole Recognition System developed is able to overcome the communication problem in CAD/CAM. The output from the Hole Recognition System is useful for multiple downstream manufacturing activities such as machine tool selection and cutting tool selection

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

A Survey of Automated Process Planning Approaches in Machining

Global industrial trend is shifting towards next industrial revolution Industry 4.0. It is becoming increasingly important for modern manufacturing industries to develop a Computer Integrated Manufacturing (CIM) system by integrating the various operational and information processing functions in design and manufacturing. In spite of being active in research for almost four decades, it is clear that new functionalities are needed to integrate and realize a completely optimal process planning which can be fully compliant towards Smart Factory. In order to develop a CIM system, Computer Aided Process Planning (CAPP) plays a key role and therefore it has been the focus of many researchers. In order to gain insight into the current state-of-the-art of CAPP methodologies, 96 research papers have been reviewed. Subsequent sections discuss the different CAPP approaches adopted by researchers to automate different process planning tasks. This paper aims at addressing the key approaches involved and future directions towards Smart Manufacturing

Mem Tri: Memory Forensics Triage Tool

This work explores the development of MemTri. A memory forensics triage tool that can assess the likelihood of criminal activity in a memory image, based on evidence data artefacts generated by several applications. Fictitious illegal suspect activity scenarios were performed on virtual machines to generate 60 test memory images for input into MemTri. Four categories of applications (i.e. Internet Browsers, Instant Messengers, FTP Client and Document Processors) are examined for data artefacts located through the use of regular expressions. These identified data artefacts are then analysed using a Bayesian Network, to assess the likelihood that a seized memory image contained evidence of illegal activity. Currently, MemTri is under development and this paper introduces only the basic concept as well as the components that the application is built on. A complete description of MemTri coupled with extensive experimental results is expected to be published in the first semester of 2017

Conceptual roles of data in program: analyses and applications

Program comprehension is the prerequisite for many software evolution and maintenance tasks. Currently, the research falls short in addressing how to build tools that can use domain-specific knowledge to provide powerful capabilities for extracting valuable information for facilitating program comprehension. Such capabilities are critical for working with large and complex program where program comprehension often is not possible without the help of domain-specific knowledge.;Our research advances the state-of-art in program analysis techniques based on domain-specific knowledge. The program artifacts including variables and methods are carriers of domain concepts that provide the key to understand programs. Our program analysis is directed by domain knowledge stored as domain-specific rules. Our analysis is iterative and interactive. It is based on flexible inference rules and inter-exchangeable and extensible information storage. We designed and developed a comprehensive software environment SeeCORE based on our knowledge-centric analysis methodology. The SeeCORE tool provides multiple views and abstractions to assist in understanding complex programs. The case studies demonstrate the effectiveness of our method. We demonstrate the flexibility of our approach by analyzing two legacy programs in distinct domains