Thesis (M. Phil.)--University of Hong Kong, 2007. / Also available online.

Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention,<br /> marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process.<br /> <br /> This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns.<br /> <br /> Our investigation shows the learning ability of both techniques to discriminate user call patterns / the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data<br /> from a real mobile telecommunication network.

Text communication is often perceived as lacking some components of communication that are essential in sustaining interaction or conversation. This interaction incoherency tends to make text communication plastic. It is traditionally devoid of intonation, pitch, gesture, facial expression and visual or auditory cues. Nevertheless, Instant Messaging (IM), a form of text communication is on the upward uptake both on PCs and on mobile handhelds. There is a need to rubberise this plastic text messaging to improve co-presence for text communications thereby improving synchronous textual discussion, especially on handheld devices. <br /> <br /> One element of interaction is gesture, seen as a natural way of conversing. Attaining some level of interaction naturalism requires improving synchronous communication spontaneity, partly achieved by enhancing input mechanisms. To enhance input mechanisms for interactive text-based chat on mobile devices, there is a need to facilitate gesture input. Enhancement is achievable in a number of ways, such as input mechanism redesigning and input offering adaptation. This thesis explores affective gesture mode on interface redesign as an input offering adaptation. This is done without a major physical reconstruction of handheld devices.<br /> <br /> This thesis presents a text only IM system built on Session Initiation Protocol (SIP) and SIP for Instant Messaging and Presence Leveraging Extensions (SIMPLE). It was developed with a novel user-defined hotkey implemented as a one-click context menu to &ldquo / fast-track&rdquo / text-gestures and emoticons.<br /> <br /> A hybrid quantitative and qualitative approach was taken to enable data triangulation. Results from experimental trials show that an Affective Gesture (AG) approach improved IM chat spontaneity/response. Feedback from the user trials affirms that AG hotkey improves chat responsiveness, thus enhancing chat spontaneity.

The purpose of this thesis is to explore the following research question: How does action research serve as a research method in the discipline of Communications? Specifically, this study will approach the question by analyzing existing literature on action research and also performing an action research trial of a new marketing approach using digital telephony. The study finds that action research has a combination of four characteristics that make it a discrete method of research. Action research involves collaboration, invokes change, requires a researcher's vested interest, and allows a kind of knowing that can only come from direct involvement in a change. / Department of Telecommunications

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 122-126). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 181-193).

Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention,<br /> marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process.<br /> <br /> This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns.<br /> <br /> Our investigation shows the learning ability of both techniques to discriminate user call patterns / the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data<br /> from a real mobile telecommunication network.

Text communication is often perceived as lacking some components of communication that are essential in sustaining interaction or conversation. This interaction incoherency tends to make text communication plastic. It is traditionally devoid of intonation, pitch, gesture, facial expression and visual or auditory cues. Nevertheless, Instant Messaging (IM), a form of text communication is on the upward uptake both on PCs and on mobile handhelds. There is a need to rubberise this plastic text messaging to improve co-presence for text communications thereby improving synchronous textual discussion, especially on handheld devices. <br /> <br /> One element of interaction is gesture, seen as a natural way of conversing. Attaining some level of interaction naturalism requires improving synchronous communication spontaneity, partly achieved by enhancing input mechanisms. To enhance input mechanisms for interactive text-based chat on mobile devices, there is a need to facilitate gesture input. Enhancement is achievable in a number of ways, such as input mechanism redesigning and input offering adaptation. This thesis explores affective gesture mode on interface redesign as an input offering adaptation. This is done without a major physical reconstruction of handheld devices.<br /> <br /> This thesis presents a text only IM system built on Session Initiation Protocol (SIP) and SIP for Instant Messaging and Presence Leveraging Extensions (SIMPLE). It was developed with a novel user-defined hotkey implemented as a one-click context menu to &ldquo / fast-track&rdquo / text-gestures and emoticons.<br /> <br /> A hybrid quantitative and qualitative approach was taken to enable data triangulation. Results from experimental trials show that an Affective Gesture (AG) approach improved IM chat spontaneity/response. Feedback from the user trials affirms that AG hotkey improves chat responsiveness, thus enhancing chat spontaneity.

Thesis (PhD)--University of South Australia, 2008.

This thesis presents the findings from a study on the adoption of mobile phones across ASEAN countries.