Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program, 1994, volume 1

The JSC NASA/ASEE Summer Faculty Fellowship Program was conducted by Texas A&M University and JSC. The objectives of the program, which began nationally in 1964 and at JSC in 1965 are to: (1) further the professional knowledge of qualified engineering and science faculty members, (2) stimulate an exchange of ideas between participants and NASA, (3) enrich and refresh the research and teaching activities of participants' institutions, and (4) contribute to the research objectives of the NASA centers. Each faculty fellow spent at least 10 weeks at JSC engaged in a research project in collaboration with a NASA JSC colleague. This document is a compilation of the final reports on the research projects completed by the faculty fellows during the summer of 1994

Advanced document analysis and automatic classification of PDF documents

This thesis explores the domain of document analysis and document classification within the PDF document environment The main focus is the creation of a document classification technique which can identify the logical class of a PDF document and so provide necessary information to document class specific algorithms (such as document understanding techniques). The thesis describes a page decomposition technique which is tailored to render the information contained in an unstructured PDF file into a set of blocks. The new technique is based on published research but contains many modifications which enable it to competently analyse the internal document model of PDF documents. A new level of document processing is presented: advanced document analysis. The aim of advanced document analysis is to extract information from the PDF file which can be used to help identify the logical class of that PDF file. A blackboard framework is used in a process of block labelling in which the blocks created from earlier segmentation techniques are classified into one of eight basic categories. The blackboard's knowledge sources are programmed to find recurring patterns amongst the document's blocks and formulate document-specific heuristics which can be used to tag those blocks. Meaningful document features are found from three information sources: a statistical evaluation of the document's esthetic components; a logical based evaluation of the labelled document blocks and an appearance based evaluation of the labelled document blocks. The features are used to train and test a neural net classification system which identifies the recurring patterns amongst these features for four basic document classes: newspapers; brochures; forms and academic documents. In summary this thesis shows that it is possible to classify a PDF document (which is logically unstructured) into a basic logical document class. This has important ramifications for document processing systems which have traditionally relied upon a priori knowledge of the logical class of the document they are processing

Fingerprint-based biometric recognition allied to fuzzy-neural feature classification.

The research investigates fingerprint recognition as one of the most reliable biometrics identification methods. An automatic identification process of humans-based on fingerprints requires the input fingerprint to be matched with a large number of fingerprints in a database. To reduce the search time and computational complexity, it is desirable to classify the database of fingerprints into an accurate and consistent manner so that the input fingerprint is matched only with a subset of the fingerprints in the database. In this regard, the research addressed fingerprint classification. The goal is to improve the accuracy and speed up of existing automatic fingerprint identification algorithms. The investigation is based on analysis of fingerprint characteristics and feature classification using neural network and fuzzy-neural classifiers.The methodology developed, is comprised of image processing, computation of a directional field image, singular-point detection, and feature vector encoding. The statistical distribution of feature vectors was analysed using SPSS. Three types of classifiers, namely, multi-layered perceptrons, radial basis function and fuzzy-neural methods were implemented. The developed classification systems were tested and evaluated on 4,000 fingerprint images on the NIST-4 database. For the five-class problem, classification accuracy of 96.2% for FNN, 96.07% for MLP and 84.54% for RBF was achieved, without any rejection. FNN and MLP classification results are significant in comparison with existing studies, which have been reviewed

Content sensitivity based access control model for big data

Big data technologies have seen tremendous growth in recent years. They are being widely used in both industry and academia. In spite of such exponential growth, these technologies lack adequate measures to protect the data from misuse or abuse. Corporations that collect data from multiple sources are at risk of liabilities due to exposure of sensitive information. In the current implementation of Hadoop, only file level access control is feasible. Providing users, the ability to access data based on attributes in a dataset or based on their role is complicated due to the sheer volume and multiple formats (structured, unstructured and semi-structured) of data. In this dissertation an access control framework, which enforces access control policies dynamically based on the sensitivity of the data is proposed. This framework enforces access control policies by harnessing the data context, usage patterns and information sensitivity. Information sensitivity changes over time with the addition and removal of datasets, which can lead to modifications in the access control decisions and the proposed framework accommodates these changes. The proposed framework is automated to a large extent and requires minimal user intervention. The experimental results show that the proposed framework is capable of enforcing access control policies on non-multimedia datasets with minimal overhea

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Functionality-based application confinement: A parameterised and hierarchical approach to policy abstraction for rule-based application-oriented access controls

Access controls are traditionally designed to protect resources from users, and consequently make access decisions based on the identity of the user, treating all processes as if they are acting on behalf of the user that runs them. However, this user-oriented approach is insufficient at protecting against contemporary threats, where security compromises are often due to applications running malicious code, either due to software vulnerabilities or malware. Application-oriented access controls can mitigate this threat by managing the authority of individual applications. Rule-based application-oriented access controls can restrict applications to only allow access to the specific finely-grained resources required for them to carry out their tasks, and thus can significantly limit the damage that can be caused by malicious code. Unfortunately existing application-oriented access controls have policy complexity and usability problems that have limited their use. This thesis proposes a new access control model, known as functionality-based application confinement (FBAC). The FBAC model has a number of unique features designed to overcome problems with previous approaches. Policy abstractions, known as functionalities, are used to assign authority to applications based on the features they provide. Functionalities authorise elaborate sets of finely grained privileges based on high-level security goals, and adapt to the needs of specific applications through parameterisation. FBAC is hierarchical, which enables it to provide layers of abstraction and encapsulation in policy. It also simultaneously enforces the security goals of both users and administrators by providing discretionary and mandatory controls. An LSM-based (Linux security module) prototype implementation, known as FBAC-LSM, was developed as a proof-of-concept and was used to evaluate the new model and associated techniques. The policy requirements of over one hundred applications were analysed, and policy abstractions and application policies were developed. Analysis showed that the FBAC model is capable of representing the privilege needs of applications. The model is also well suited to automaiii tion techniques that can in many cases create complete application policies a priori, that is, without first running the applications. This is an improvement over previous approaches that typically rely on learning modes to generate policies. A usability study was conducted, which showed that compared to two widely-deployed alternatives (SELinux and AppArmor), FBAC-LSM had significantly higher perceived usability and resulted in significantly more protective policies. Qualitative analysis was performed and gave further insight into the issues surrounding the usability of application-oriented access controls, and confirmed the success of the FBAC model