INTEL QUARTERLY REPORT
MARCS: M2M-Based Anticipatory Reasoning for Contexts and
Services
PI: Li-Chen Fu
1. Team members (please highlight team member modification, e.g., new members join,
or some students graduate)
Our team now has the following members:
 3 Master students: Tsung-Chi Chiang, Hsin-Hui Kuo, Ming-Je Tsai
2. Discussion with champions
We have prepared some slides to overview our recent work on Context Engine,
thus for our champion, Dr. Charlie Tai, to present to the Intel audience.
3. Progress between last month and this month
a. Summary of key findings and innovation
 Single-user data collection for Context Engine in BL313
For the evaluation of context discovery, we have finished a single-user
data collection with 3 subjects, and each of them spent 8 hours for twice
in our semi-real smart-home lab. The dataset contains 7 activities, 40
features and 200,000 instances.
 Context Aggregation and Discovery
Since the simple dataset can be aggregated and discovered well, we have
started to evaluate the complex dataset with 200,000 instances. For the
data values in numerical features, they can be cluster to different level by
their data distribution without quantization by human. After the data
values of each numerical feature are transformed to their corresponding
levels, we can reduce the number of different instances to 50,000 for
further context discovery.
b. Negative results and their consequences
Even after preliminary data clustering for each feature, the number of instances
in the complex dataset is still so huge that we must spend a lot of time to
compute the 50,000 by 50,000 similarity matrix for context discovery.
c. Cross-project synergy
4. Brief plan for the next quarter
a. We will prepare for the upcoming open house meeting.
b. We will try to solve the problem of lots of computation time required by
complex dataset. Two possible solutions are:
1) Dimension Reduction: we will try to further reduce the dimension of the
dataset by aggregating data or clustering data from different perspective.
2) Cloud Computing: we will analyze the algorithm to parallelize the
computation thus to utilize cloud computing to reduce the computation time.
c. We will deploy extra ambient sensors to make information conflicts
2014 年 10 月至 2014 年 12 月與 Intel 計畫相關之：
1.
論文
(1) 國際期刊
(2) 國際會議
1.
Chao-Lin Wu, Chuang-Wen You, Chun-Yuan Chen, Ching-Chi
Chuang, Tsung-Chi Chiang, “Exploring the Collaborative Context
Reasoning in IoT based Intelligent Care Services”, in IEEE
International Workshop on Internet of Things Services in IEEE
International Conference on Service Oriented Computing and
Applications, 2014
(3) 國內期刊
(4) 國內會議
(5) 高引用篇數
2.
擔任國際期刊編輯委員
3.
受邀演講:
4.
重要榮譽/獎項 :
國際期刊論文
國際會議論文
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