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²ñ¾ì: L1

»Ê²ñ: µÈÌî¡¡¹¬°ìϺ
TRONO EDGAR MARKO SARMIENTO 1461021: D, Ãæ´Öȯɽ °ÂËÜ¡¡·Ä°ì¡¤³Þ¸¶¡¡Àµ¼£¡¤¹ÓÀî¡¡Ë­¡¤¿Ûˬ¡¡Çîɧ¡¤Æ£ËÜ¡¡¤Þ¤Ê¤È
title: Generating Disaster Area Pedestrian Maps using Distributed Computing across a Delay Tolerant Network
abstract: During disasters, maps of the affected area are needed to guide evacuees to safe areas or to lead responders to points-of-interest. However, the generation of digital disaster maps is challenging because it requires heavy processing, such as GPS trace aggregation and event detection from images, and the disaster may have damaged the communication network infrastructure, rendering Cloud-based mapping services inaccessible. The most available form of computing in such Cloud-less environments are mobile devices, but with limited processing speed and energy, these are not suited for mapping. In this study, we propose and evaluate system that leverages distributed computing across a Delay Tolerant Network to generate pedestrian maps. To realize our system, we address the following technical challenges: (1) how to collect data from the disaster area that can be used to generate a map, (2) how to process the collected data to generate maps in a Cloud-less disaster environment, and (3) how to propagate data across the system without a continuous end-to-end network. For the first challenge, we develop an Android application called DTN MapEx, which enables its users (i.e. pedestrian evacuees and on-foot responders) to use their mobile devices to collect disaster area data, specifically GPS traces. For the second challenge, because the Cloud is inaccessible and mobile devices do not have enough computing resources, we distribute data processing tasks to the key component of the system: Computing Nodes (CNs), which are pre-deployed, stationary kiosks with more computing power than mobile devices. The collected data are sent to the CNs, which run a pedestrian map inference program to generate a weighted directed graph that shows pedestrian paths and the average walking speeds along the paths. The generated maps are then sent to querying mobile devices. For the third challenge, we use Delay Tolerant Networking to propagate the collected data and generated maps across the system. We evaluate our system through experimentation and simulations, and posit that our approach can create maps faster than when data is processed locally in mobile devices.
language of the presentation: English
 
²Ï¼¡¡°ìµ± 1451041: M, 2²óÌÜȯɽ °ÂËÜ¡¡·Ä°ì¡¤Ã漡¡Å¯¡¤¹ÓÀî¡¡Ë­¡¤¿Ûˬ¡¡Çîɧ¡¤Æ£ËÜ¡¡¤Þ¤Ê¤È
title: Customer Recommendation to Support Real Estate Salespersons for a Restaurant
abstract: In this study, we propose a method to support the real estate sales for the restaurant.Flow of customers leading to conclusion of a contract is as follows. ­¡They look for real estate in the search site. ­¢They view to the favorite properties. ­£They apply for heir favorite propertie. ­¤They negotiate the property owner and reach the conclusion of constract. Flow­¡¢ª­¢, ­¢¢ª­£¡¡are very difficult, so salesman urge to the next step by phone. In order to improve sales performance, this study recmmend the customer who is easy to apply for the property.
language of the presentation: *** Japanese ***
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ȯɽ³µÍ×: Ëܸ¦µæ¤Ç¤Ï, °û¿©Å¹¸þ¤±¤ÎÉÔÆ°»º±Ä¶È¤ò»Ù±ç¤¹¤ë¼êË¡¤òÄó°Æ¤¹¤ë. °û¿©Å¹µ¯¶È¤òÌܻؤ¹¸ÜµÒ¤¬, ʪ·ï¤ÎÀ®Ìó¤Ë»ê¤ëή¤ì¤Ï°Ê²¼¤ÎÄ̤ê¤Ç¤¢¤ë. ­¡¸¡º÷¥µ¥¤¥È¤ÇÉÔÆ°»º¤òõ¤¹. ­¢¼ÂºÝ¤ËÆ⸫¤ò¹Ô¤¤µ¤¤ËÆþ¤Ã¤¿Êª·ï¤ò±ÜÍ÷¤¹¤ë. ­£Æ⸫¤Ë¹Ô¤Ã¤¿Êª·ï¤ÎÆⵤ¤ËÆþ¤Ã¤¿Êª·ï¤ò¿½¤·¹þ¤ß¤¹¤ë. ­¤Êª·ï¤ÎÂß¼ç¤È¸ò¾Ä¤·, À®Ìó¤Ë»ê¤ë. ­¡¢ª­¢¤Î¸¡º÷¤«¤éÆ⸫¤Ë°Ü¤ë¤³¤È¤ä, ­¢¢ª­£¤ÎÆ⸫¤«¤é¿½¤·¹þ¤ß¤Ë°Ü¤ë¤³¤È¤ÏÆñ¤·¤¤¤¿¤á, ÉÔÆ°»º²ñ¼Ò¤Î±Ä¶È¥Þ¥ó¤¬ÅÅÏäò¤«¤±¤Æ¼¡¤Î¥¹¥Æ¥Ã¥×¤ËÂ¥¤¹. ½¾Íè¤Ç¤ÏÅÅÏñĶȤò¹Ô¤¦ºÝ, ±Ä¶È¥Þ¥ó¤ÏĹǯ¤Î´¶¤È·Ð¸³¤Ë¤è¤Ã¤Æ, ¿ô¤¢¤ë¸ÜµÒ¥ê¥¹¥È¤«¤é¤è¤êÀ®Ìó¤Ë»ê¤ê¤ä¤¹¤¤¸ÜµÒ¤òÁªÄꤷ, ÅÅÏäò¤«¤±¤Æ¤¤¤¿. ¤·¤«¤·, ¿·¿Í¤Î±Ä¶È¥Þ¥ó¤Ç¤Ï, ¤É¤Î¸ÜµÒ¤ËÅÅÏäò¤«¤±¤ë¤Ù¤­¤«¤Î¥Î¥¦¥Ï¥¦¤¬¤¿¤Þ¤Ã¤Æ¤ª¤é¤º, ¤É¤Î¸ÜµÒ¤Ë¤«¤±¤¿¤é¤¤¤¤¤Î¤«Ê¬¤«¤é¤Ê¤¤¤È¤¤¤Ã¤¿ÌäÂ꤬¤¢¤ë. ¤³¤ÎÌäÂê¤ò²ò·è¤¹¤ë¤¿¤á¤Ë, Ëܸ¦µæ¤Ç¤Ïµ¡³£³Ø½¬¤òÍѤ¤¤Æ, ¿½¹þ¤ß¤ò¤·¤ä¤¹¤¤¸ÜµÒ¤ò¿äÄꤷ¡¤±Ä¶È¥Þ¥ó¤ËÅÅÏäò¤«¤±¤ë¤è¤¦¿äÁ¦¤¹¤ë.
 
ƣ߷¡¡Ïµ± 1451089: M, 2²óÌÜȯɽ °ÂËÜ¡¡·Ä°ì¡¤Ã漡¡Å¯¡¤¹ÓÀî¡¡Ë­¡¤¿Ûˬ¡¡Çîɧ¡¤Æ£ËÜ¡¡¤Þ¤Ê¤È
title: Study on Automatic Content Curation System for Multiple Live Sport Video Streams
abstract: Our goal is to create personalized and high-presence multi-channel contents for a sport game through realtime content curation from various media streams captured/created by spectators. We use the live TV broadcast as a training data and construct a machine learning-based model to automatically conduct curation from multiple videos which spectators captured from different angles and zoom levels. As inputs for constructing a model, we use meta data such as image feature data (e.g., a pitcher is on the screen) in each fixed interval of baseball videos and game progress data (e.g., the inning number and the batting order). Output is the camera ID (among multiple cameras of spectators) at each point of time. For evaluation, we targeted Spring-Selection high-school baseball games. As training data, we used image features, game progress data, and the camera position at each point of time in the TV broadcast. Random Forests algorithm is used for learning. We used videos of a baseball game captured from 7 different points in Hanshin Koshien Stadium with handy video cameras and generated sample data set by dividing the videos to fixed interval segments. As a result,our method predicted the camera switching timings with accuracy (F-measure) of 72.53% on weighted average for the base camera work and 92.1% for the fixed camera work.
language of the presentation: Japanese
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²ñ¾ì: L2

»Ê²ñ: ³ÀÆâ¡¡ÀµÇ¯
ÅÄÃ桡ͺ´õ 1451071: M, 2²óÌÜȯɽ ¶âë¡¡½Åɧ¡¤ÃÓÅÄ¡¡Ï»ʡ¤MD.ALTAF-UL-AMIN¡¤¿ù±º¡¡ÃéÃË¡¤º´Æ£¡¡Å¯Âç

title: title: Analysis of efficacy from herbal medicine recipe

abstract:The many countries use herbal medicine which utilizes medical plant. For example Japan (kampo) and Indonesia (Jamu). Estimation of efficacy from their recipe is important for standardrzation of these medicine. Moreover, Knapsack is metabollite database including herbal medicines and compounds in their plant.By combing information of such prediction models and metabolite database will allow us to find compounds which have not discovered in plant of the present recipes.In this presentation , I will introduce the result of prediction and analysis from plants and compounds in Jamu recipe by using Random Forest and Bayesian filter.

language of the presentation: Japanese

 
ÌðÌî¡¡´°¿Í 1451114: M, 2²óÌÜȯɽ ¶âë¡¡½Åɧ¡¤¾®³Þ¸¶¡¡»Ê¡¤MD.ALTAF-UL-AMIN¡¤¿ù±º¡¡ÃéÃË
title: Three dimensional structural analysis of dendritic spines
abstract: Recently, research about the brain have been actively performed. However, we don't know the brain very well. So we need to verify about the brain very well. There are a variety of techniques to analyze the brain. We have analyzed the brains by using two-photon excited fluorescence microscopy. And, I have focused on dendritic spines. I have studied a method of analyzation in the morphological features dendritic spines.
language of the presentation: Japanese
 
LIDWINA AYU ANDARINI 1451125: M, 2²óÌÜȯɽ ¶âë¡¡½Åɧ¡¤¾®³Þ¸¶¡¡»Ê¡¤MD.ALTAF-UL-AMIN¡¤¿ù±º¡¡ÃéÃË¡¤º´Æ£¡¡Å¯Âç

title: Distant Respiration and Heartrate measurement with Doppler Radar

abstract: The use of Doppler radar has proven to be one of the method of measuring heart rate and respiration rate without having to make contact with the subject. In this research the effect of movement and posture is being evaluated in correlation with the accuracy of the measurement. Here we are using 10.5 GHz doppler radar, while measuring the subject in resting positions.

language of the presentation: English

 
»³ºê¡¡ÏË 1451116: M, 2²óÌÜȯɽ ¾®³Þ¸¶¡¡»Ê¡¤²£Ìð¡¡Ä¾Ï¡¤¹â¾¾¡¡½ß
title: Acquisition of Semantic Objects Based on Classifying Motion of Human Hands
abstract: Service robots need to break up a simple instruction into detailed sub-tasks. It is important that robots have the knowledge of the relation between an object and manipulation of it because a sub-task is composed of a set of them. The purpose of my research is acquisition of semantic objects based on classifying the motion of human hands which is acquired from egocentric RGB-D sensor.
language of the presentation: Japanese
ȯɽÂêÌÜ: ¿Í´Ö¤Î¼êÆ°ºîʬÎà¤Ë¤è¤ë¥»¥Þ¥ó¥Æ¥£¥Ã¥¯¥ª¥Ö¥¸¥§¥¯¥È¤Î³ÍÆÀ
ȯɽ³µÍ×: ¥µ¡¼¥Ó¥¹¥í¥Ü¥Ã¥È¤¬´ÊÊؤʻؼ¨¤Ë¤è¤Ã¤Æ¥¿¥¹¥¯¤ò¼Â¹Ô¤¹¤ëºÝ¡¤¼«¿È¤Ç¾ÜºÙ¤ÊÆ°ºî¡¤¤¹¤Ê¤ï¤Á¥µ¥Ö¥¿¥¹¥¯¤Ëʬ²ò¤¹¤ëɬÍפ¬¤¢¤ë¡¥¥µ¥Ö¥¿¥¹¥¯¤ÏʪÂΤȤ½¤ì¤ËÂФ¹¤ëÆ°ºî¤«¤é¤Ê¤Ã¤Æ¤¤¤ë¤¿¤á¡¤¤½¤ì¤é¤Î´Ø·¸À­¤òÃΤ뤳¤È¤ÏÈó¾ï¤Ë½ÅÍפǤ¢¤ë¡¥Ëܸ¦µæ¤Ç¤Ï¡¤°ì¿Í¾Î»ëÅÀRGB-D¥»¥ó¥µ¤òÍѤ¤¤ÆʪÂΤËÂФ¹¤ë¿Í´Ö¤Î¼êÆ°ºî¤ò¼èÆÀ¤·¡¤¤½¤ÎÆ°ºî¤òʬÎष¤ÆʪÂΤËÂбþÉÕ¤±¤ë¤³¤È¤Ç¡¤°ÕÌ£ÉÕ¤±¤µ¤ì¤¿ÊªÂΡ¤¤¹¤Ê¤ï¤Á¥»¥Þ¥ó¥Æ¥£¥Ã¥¯¥ª¥Ö¥¸¥§¥¯¥È¤Î³ÍÆÀ¤òÌÜŪ¤È¤¹¤ë¡¥