Today, personal data is becoming a new economic asset. Personal data which generated from our smartphone can be used for many purposes such as identification, recommendation system, and etc. The purposes of our research are to discover human behavior based on their smartphone life log data and to build behavior model which can be used for human identification. In this research, we have collected user personal data from 37 students for 2 months which consist of 19 kinds of data sensors. There is still no ideal platform that can collects user personal data continuously and without data loss. The data which collected from user’s smartphone have various situations such as the data came from multiple sensors and multiple source information which sometimes one or more data does not available. We have developed a new approach to building human behavior model which can deal with those situations. Furthermore, we evaluate our approach and present the details in this paper.

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Many of researches in controlling smart home system have been proposed. Most of previous approaches in controlling smart home system requires interventions and commands from user. This paper propose a system about smart home based on mobile sensing that does not requires interventions and commands from the user. Mobile Sensing is used to records daily routine activities of the user. Then the system automatically gives a response to user based on his/her daily routine activities. We have implemented our approach to demonstrate the feasibility and effectiveness of using mobile sensing for controlling smart home system. Furthermore, we evaluate our approach and present the details in this paper.

Abstract. This paper proposed awareness home automation system (HAS) based on mobile sensing. Some of the ideas have been proposed in HAS but most of them still requires human intervention such as click the button, voice commands, etc. We want to design and develop HAS which can understand and comprehend the user desires without having to wait for commands from the user (awareness HAS). In this research we exploit two of android sensors. First, accelerometer sensor for identification and activity recognition, second, magnetic field for user indoor positioning system and defined the context related to physical environment. This paper presenting the result of used both of the sensors for developing awareness HAS.

In insight recommendation systems, obtaining timely and high-quality recommended visual analytics over incomplete data is challenging due to the difficulties in cleaning and processing such data. Failing to address data incompleteness results in diminished recommendation quality, compelling users to impute the incomplete data to a cleaned version through a costly imputation strategy. This paper introduces VizPut scheme, an insight-aware selective imputation technique capable of determining which missing values should be imputed in incomplete data to optimize the effectiveness of recommended visualizations within a specified imputation budget. The VizPut scheme determines the optimal allocation of imputation operations with the objective of achieving maximal effectiveness in recommended visual analytics. We evaluate this approach using real-world datasets, and our experimental results demonstrate that VizPut effectively maximizes the efficacy of recommended visualizations within the user-defined imputation budget.