https://onlineresource.ucsy.edu.mm/handle/123456789/23 Mon, 08 Jun 2026 10:28:31 GMT 2026-06-08T10:28:31Z https://onlineresource.ucsy.edu.mm/handle/123456789/2548 Table of Contents of ICCA 2017 UCSY Presents the table of contents page of the proceedings of the 15th International Conference on Computer Applications 2017 Wed, 01 Feb 2017 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2548 2017-02-01T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/2534 Detection the Urban Change Areas of Yangon City Using Landsat Time series Images Aung, Thida; Sein, Myint Myint Urban growth is the critical task for city planning of the developing country. It can estimate to know the increasing rate of the building area of the certain township during the specific year. The system proposes a method combining the Morphological Building Index (MBI) and Slow Feature Analysis (SFA). It can find the urban changing areas of the Yangon city using the Landsat 7 ETM+ time series images from 2003 to 2015. In MBI, it leads to a number of false alarms involving non-building urban structures such as soil and roads. In SFA, it alone is not suitable for building change detection since it provides high commission error. The purposed system combines these two method to overcome the weakness of MBI and SFA. The experimental result shows the comparative accuracies of MBI and SFA method only with the proposed method. Fri, 17 Feb 2017 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/2534 2017-02-17T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/897 Robot Language Acquisition Based on Sequence-to-Sequence Learning Thu, Ye Kyaw; Takabuchi, Kenta; Fukai, Kaisei; Iwahashi, Naoto; Kunishima, Takeo Language acquisition for robot is a challenging topic in the artificial intelligence research area and essential for natural communication between robot and human. In this paper, we proposed language acquisition directly from motion video and user’s utterance with multimodal machine learnings without prior knowledge of linguistic or language specific information. Translation between acquired conceptual structure and syllable sequences of a human language (e.g. Japanese language) was carried out by applying machine translation methodologies including sequence-to-sequence learning. Experiments on language acquisition with 500 videos show Encoder- Decoder, Encoder-Decoder with Attention models are able to achieve equal translation performance of baselines that was prepared manually. This work was supported by JSPS KAKENHI (grant number 15K00244) and JST CREST (“Symbol Emergence in Robotics for Future Human-Machine Collaboration”). Thu, 16 Feb 2017 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/897 2017-02-16T00:00:00Z https://onlineresource.ucsy.edu.mm/handle/123456789/896 Cross-modal Sentiment Information Expression of Voice Source Characteristics using Image Texture Features Kyaw, Win Thuzar; SAGISAKA, Yoshinori Following the successful findings of high correlations between speech and color such as F0 and Value, Loudness and Saturation and Spectrum and Hue, we analyzed the correlations between voice source characteristics and the image parameters showing textural differences in this paper for better scientific understanding of their correlations and effective use in visualization of speech information. Through sentiment association experiments, we could have observed high positive correlations between H1*- H2* (amplitude difference between first and second harmonics corrected for vocal tract effects), H1-A1 (amplitude difference between first harmonic and first formant) and Contrast, high negative correlations between H1*-H2*, H1-A1, H1-A2, H1-A3, Harmonicto- Noise Ratio (HNR) in 0 to 3500Hz frequency band and Variance, Prominence and negative correlations between H1*-A3*, HNR in 0 to 500 Hz and Prominence. These results show the possibility of direct visualization of speech characteristics which cannot be effectively carried out by conventional mapping using discrete language expressions. This work was partly supported by Grand-inaid for Science Research B, NO. 23320091 of JSPS. Thu, 16 Feb 2017 00:00:00 GMT https://onlineresource.ucsy.edu.mm/handle/123456789/896 2017-02-16T00:00:00Z