EXPERIMENTAL RESULT CONCLUSION ICTS2005 The Proceeding

Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005 ISSN 1858-1633 2005 ICTS 120 Figure 7. Venn Diagram of Objectivity Case 6: In this case the number of objects which has similar objectivity’s characteristic or vocabulary’s characteristic is more than 70 of the total objects .For this specific condition, the system will specify the detail feature of each objects to get their uniqueness and their distribution. . The system manages a first question based on both of these characteristic. Table 3 shows all of general rule system of robot question in any cases. Table 3. General rule system Question Condition Content The Type The total objects 3 Subjectivity “yesno” question Case 1 Vocabulary “what” question Case 2 Vocabulary “yesno” question Case 3 Objectivity “yesno” question Case 4 vocabulary “yesno” question Case 5 Objectivity “what” question Case 6 uniqueness or object distribution “yesno” question A. Dynamic Knowledgebase The system records the results and their process to a dynamic database. This database contains some features of the objects. In this research, system can exactly record the result only for objective characteristic because subjective characteristic is not stable and relative. Position feature is usually changed for every case and size is dependent to other objects. The knowledgebase of robot will be increased if it does a new task. This dynamic knowledgebase also become perfect if there are various feature in one objects. If the human command to the robot to bring an apple, the robot will know that the color of the apple is usually red and its shape is circle. This knowledge is never changed until the robot has a new task to bring green or yellow apple. Having this dynamic knowledge, the robot will be helpful for human assistance in the future.

3. EXPERIMENTAL RESULT

For example, we have four objects which have 2 similar color and shape. The user points to one direction where some objects are located. He wants a red apple which located among the objects is brought by the robot. This example refers to case 2 at table 2. The system makes some questions to confirm to the user. Robot : Is the target object red? User : Yes Robot : Is the target object in the up side? User : No. Robot : This one is the target object. What should I do then? There are some steps to find the target objects: 1. The system merges some nearest color region for removing background and than labels the objects. In this process, the number of candidate target objectss, their size and shape are determined but not appeared. After all of objects recognized by the system, it asks the user based on general rule. In this case, the system asks a yesno question that contain about color to the user. 2. The system removes unnecessary objects based on user answer and than determines the current condition of target objects. 3. The system formulates a next question in current condition by acting on general rule system. There are only two objects less than 3 in the current scene. Therefore, the system makes a question based on subjectivity characteristic feature that has been explained before. 4. The system asks the second question and remove unnecessary object to get a real target objects. Figure 8 shows a visualization of this case. a b vocabulary objectivity color size position shape Comfortable Dialog for Object Detection – Rahmadi Kurnia ISSN 1858-1633 2005 ICTS 121 c d Figure 8. Human robot interaction in case 2. a Segmentation result. b Merging nearest color region result c AaR method result d.Final result

5. CONCLUSION

Robots need vision to carry out their tasks. However, conventional vision systems cannot work in complex scenes. We have proposed to use human users assistance to solve this problem. The robot asks a question to the user when it cannot detect the target object. It generates a sequence of utterances that can lead to determine the object efficiently and user- friendly. It determines what and how to ask the user by considering the image processing results and the characteristics of object image attributes. We obtain promising experimental results. The current system is a small system to examine whether or not the approach is promising. Since this has been confirmed, we will develop a larger system that can deal with more complex situations. REFERENCES [1] Takuya Takahashi, Satoru Nakanishi, Yoshinori Kuno and Yoshiaki Shirai,”Human Robot Intervace by Verbal and Nonverbal Behaviors” in Proc. IEEERSJ International Conference on Intelligent Robots and Systems , pp.924-929, 1998 [2] G. Damnati, F. Panaget, Adding NewWords in a Spoken DialogueSystem Vocavulary Using Conceptual Information and Derived Class- based LM, in Proceeding of Workshop on Automatic Speech Recognition and Understanding, 1999 [3] M. Nagata, A Part of Speech Estimation Method for Japanese Unknown Word Using a Statistical Model of Morphology and Context , in Proc. 37th Annual Meeting of the Association for Computational Linguistic , 1999, pp. 277-278 [4] Masao Takizawa, Yasushi Makihara, Nobutaka Shimada, Jun Miura and Yoshiaki Shirai, A Service Robot with Interactive Vision- Objects Recognition Using Dialog with User, In Proc. of Int. WS on Language Understanding and Agents for Real World Interaction , 2003, pp.16- 23 [5] Tomohiro Kawaji, Kei Okada, Masayuki Inaba, Hirochika Inoue, Human Robot Interaction through Intergrating Visual Auditory Information with Relaxation Method, in Proceeding of IEEE International Conference on Multisensor Fusion on Integration for Inteligent Systems Tokyo, 2003 , pp 323 - 328. [6] Comaniciu, D. and P. Meer,” Mean Shift : A Robust Approach Toward Feature Space Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, Vol. 24, p. 603 – 619. [7] H.D. Cheng, X.H. Jiang, Y Sun, Jingli Wang,” Color Image Segmentation : advances and prospects,” Pattern Recognition Journal 34 2001, pp. 2259 – 2251. [8] R. C. Gonzales, R. E. Woods, Digital Image Processing , Addison-Wesley, Reading, Massachusetts,1992 [9] P. McGuire, J.Fritsch, J.J. Steil, F. Roothling, G.A. Fink, S. Wachsmuth, G. Sagerer, H. Ritter,” Multi-Modal Human Machine Communication for Instructiong Robot Grasping Tasks”, Proceeding of International Conference on Intelligent Robots and Systems 2002, pp. 1082-1089. [10] Y. Kuniyoshi, M Inaba and H Inouue, Learning by Watching : Extracting Reusable Task Knowledge from Visual Observation of Human Performance, in IEEE Trans. Robotic Automation , 1994, 106 : 779-882, [11] P. Bakker and Y. Kuniyoshi, Robot see, Robot Do : An Overview of Robot Imitation, In Proc. AISB Workshop Learining in Robots and Animals , 1996 pp 3 -11, . [12] C. Breazal and B. Scassellati, Challenges in Building Robots that Imatate People , In K Dautenhahn and C. Nehaniv, editors, Imitation in Animals and Artifacts. MIT Press. [13] Jonker, C. M., Treur, J. , and, Wijngaards, W. C. A., “An Executable Model of the Interaction between Verbal and Non-Verbal Communication”. In: Dignum, F., Chaib-draa, Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005 ISSN 1858-1633 2005 ICTS 122 B., and, Weigand, H. eds, Proceedings of the Agent Communication Languages Workshop 1999, ACL99. Published in: Dignum, F., and Greaves, M., eds., Issues in Agent Communication. Lecture Notes in AI, v ol. 1916, Springer Verlag, 2000, pp. 331-350. [14] M. Yoshizaki, Y. Kuno, and A. Nakamura, Mutual Assistance between Speech and Vision for Human-Robot Interface, Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems, CD-ROM, 2002. Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005 ISSN 1858-1633 2005 ICTS 123 A SOCIAL INFORMATICS OVERVIEW OF E-GOVERNMENT IMPLEMENTATION: ITS SOCIAL ECONOMICS AND RESTRUCTURING IMPACT Irwan Sembiring, Krismiyati Faculty of Information Technology Satya Wacana Christian University. email : irwanmybiring.com, xme_blessedyahoo.com ABSTRACT Social informatics is a new field of study concerning the design, implementation, and the use of ICT that take into account its interaction in contextual and cultural context. This kind of study usually uses three approaches which can be combined in its application. They are normative orientation, analytical orientation and critical orientation. This study will investigate the social economics and restructuring impacts of E-Government implementation in Selayar Regency South Sulawesi. The result of the study shows that globally the advantages got from the implementation is still very little compared to the investment spent on this project. Keywords : social informatics, E-government in selayar regency.

1. INTRODUCTION