JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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Az Azrinudin Alidin and Fabio Crestani Fig.8 indicates the patterns of different sound categories. These patterns captured the sound pressure power or decibel from the microphone instead of using sound recognition. There are four sound patterns captured in this experiment, which are: “quiet”, “conversation”, “noisy” and “very noisy”. The “quiet” pattern is probably difficult to capture because the consistently quiet surrounding sound is not easy to obtain. The “conversation” pattern shows that the lowest decibel almost reaches zero decibel because while people are conversing, there are moments when people are silent for brief seconds. The “very noisy” and “noisy” patterns can be differentiated by identifying the lowest and the highest decibels. CONCLUSION AND FUTURE WORK Fig.8: Sound patterns The results presented in this paper have shown how context could be identified and captured by using embedded sensors in smartphone. The results collected in this paper will be used as prior patterns to automatically recognize context in a later development without using complex recognition algorithm. This could ensure that smartphone can be used to identify context without draining its battery life. The next development in this research focuses on context interpretation, context utilization in JIT-MobIR and user’s verification of the JIT-MobIR. The current implementation in this study emphasizes on identifying and capturing user’s context based on the analysis of the embedded sensors. In order to develop a feasible JIT-MobIR system, three different studies are outlined, and these are: 1. Generating context interpretation, where user’s context is automatically interpreted by using the patterns collected in this paper. 2. User’s validation, where the system is employed in real situation and the user’s validation of his context is collected. 3. Relationship between context and queries, where the system needs to understand how queries can be substituted by context. 236 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
ACKNOWLEDGMENTS Context Modelling for Just-In-Time Mobile Information Retrieval (JIT-MobIR) The authors gratefully acknowledged the Ministry of Higher Education, Malaysia for the inancial support provided for this study. REFERENCES Allan, J. Aslam, J., Belkin, N., Buckley, C., Callan, J., Croft, B., Dumais, S., Fuhr, N., Harman, D., & Harper, D. (2004). Challenges in information retrieval and language modelling. Report of a Workshop held at the Centre for Intelligent Information Retrieval. University of Massachusetts Amherst, September 2002. Computing Reviews, 45(7), 437. Bierig, R., & Göker, A. (2006). Time, location and interest: an empirical and user-centred study. Paper presented at the 1st international conference on Information interaction in context, IIiX, pp.79–87. Brown, P., & Jones, G. (2001). Context-aware retrieval: exploring a new environment for information retrieval and information filtering. Personal and Ubiquitous Computing, 5(4), 253–263. Brown, P. J., & Jones, G. J. F. (2002). Exploiting contextual change in context-aware retrieval. Paper presented at the ACM Symp. on Applied computing, SAC ’02. pp.650–656. Church, K., Smyth, B., & Keane, M. T. (2006). Evaluating interfaces for intelligent mobile search. Paper presented at the International cross-disciplinary workshop on Web accessibility (W4A): Building the mobile web: rediscovering accessibility? W4A’06. pp.69–78. Dey, A., & Abowd, G. (2000). Towards a Better Understanding of Context and Context-Awareness. Paper presented at the CHI’2000 Workshop on The What, Who, Where, When, and How of Context- Awareness. pp.304–307. Fuhr, N. (2005). Information Retrieval-From Information Access to Contextual Retrieval. Designing Information Systems. Festschrift für Jürgen Krause, 47. Jones, G., & Brown, P. (2004). The role of context in information retrieval. Paper presented at the SIGIR 2004 Information Retrieval in Context. pp.20–22. Korpipaa, P., Mantyjarvi, J., Kela, J., Keranen, H., & Malm, E. (2003). Managing context information in mobile devices. IEEE Pervasive Computing, 2(3), 42–51. Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010). A survey of mobile phone sensing. Communications Magazine, IEEE, 48(9), 140 –150. Leake, D. B., Scherle, R., Budzik, J., & Hammond, K. (1999). Selecting task-relevant sources for justin-time retrieval. Paper presented at the AAAI-99 Workshop on Intelligent Information Systems, Menlo Park, CA. Mizzaro, S. (1998). How many relevances in information retrieval? Interacting with Computers, 10(3), 303 – 320. Raento, M., Oulasvirta, A., Petit, R., & Toivonen, H. (2005). Contextphone: A prototyping platform for context-aware mobile applications. IEEE Pervasive Computing, 4(2), 51–59. Razzaque, M. A., Dobson, S., & Nixon, P. (2006). Categorization and modelling of quality in context information. Paper presented at the IJCAI 2005 Workshop on AI and Autonomic Communications. Schilit, B., Adams, N., & Want, R. (1994). Context-aware computing applications. Paper presented at the Workshop on Mobile Computing Systems and Applications. pp.85–90. Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 237
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Selected Articles from UPM-Malaysia
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Surface Roughness Y. Yusuf, J. M. J
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