Automatic text categorisation of racist webpages

Automatic Text Categorisation (TC) involves the assignment of one or more predefined categories to text documents in order that they can be effectively managed. In this thesis we examine the possibility of applying automatic text categorisation to the problem of categorising texts (web pages) based on whether or not they are racist. TC has proven successful for topic-based problems such as news story categorisation. However, the problem of detecting racism is dissimilar to topic-based problems in that lexical items present in racist documents can also appear in anti-racist documents or indeed potentially any document. The mere presence of a potentially racist term does not necessarily mean the document is racist. The difficulty is finding what discerns racist documents from non-racist. We use a machine learning method called Support Vector Machines (SVM) to automatically learn features of racism in order to be capable of making a decision about the target class of unseen documents. We examine various representations within an SVM so as to identify the most effective method for handling this problem. Our work shows that it is possible to develop automatic categorisation of web pages, based on these approache

Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

Automating allocation of development assurance levels: An extension to HiP-HOPS

Controlling the allocation of safety requirements across a system's architecture from the early stages of development is an aspiration embodied in numerous major safety standards. Manual approaches of applying this process in practice are ineffective due to the scale and complexity of modern electronic systems. In the work presented here, we aim to address this issue by presenting an extension to the dependability analysis and optimisation tool, HiP-HOPS, which allows automatic allocation of such requirements. We focus on aerospace requirements expressed as Development Assurance Levels (DALs); however, the proposed process and algorithms can be applied to other common forms of expression of safety requirements such as Safety Integrity Levels. We illustrate application to a model of an aircraft wheel braking system

Automated speech and audio analysis for semantic access to multimedia

The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives

Class-based storage location assignment : an overview of the literature

Storage, per se, is not only an important process in a warehouse, also it has the greatest influence on the most expensive one, i.e., order picking. This study aims to give a literature overview on class-based storage location assignment (CBSLAP). In this paper, we discuss storage policies and present a classification of storage location assignment problem. Next, different configuration of classes are presented. We identify the research gaps in the literature and conclude with promising future research directions

Substitution-based approach for linguistic steganography using antonym

Steganography has been a part of information technology security since a long time ago. The study of steganography is getting attention from researchers because it helps to strengthen the security in protecting content message during this era of Information Technology. In this study, the use of substitution-based approach for linguistic steganography using antonym is proposed where it is expected to be an alternative to the existing substitution approach that using synonym. This approach still hides the message as existing approach but its will change the semantic of the stego text from cover text. A tool has been developed to test the proposed approach and it has been verified and validated. This proposed approach has been verified based on its character length stego text towards the cover text, bit size types of the secret text towards the stego text and bit size types of the cover text towards the stego text. It has also been validated using four parameters, which are precision, recall, f-measure, and accuracy. All the results showed that the proposed approach was very effective and comparable to the existing synonym-based substitution approach

DCU at MediaEval 2010 – Tagging task WildWildWeb

We describe our runs and results for the fixed label Wild Wild Web Tagging Task at MediaEval 2010. Our experiments indicate that including all words in the ASR transcripts of the document set results in better labeling accuracy than restricting the index to only words with recognition confidence above a fixed level. Additionally our results show that tagging accuracy can be improved by incorporating additional metadata describing the documents where it is available

Automatic editing of manuals

The documentation problem is discussed that arises in getting all the many items included in a computer program prepared in a timely fashion and keeping them all correct and mutually consistent during the life of the program. The proposed approach to the problem is to collect all the necessary information into a single document, which is maintained with computer assistance during the life of the program and from which the required subdocuments can be extracted as desired. Implementation of this approach requires a package of programs for computer editorial assistance and is facilitated by certain programming practices that are discussed