Miyuki Imada

Characteristics of information diffusion in blogs, in relation to information source type

A novel method is presented to analyze the dynamics of social media, i.e., information diffusion properties, for information recommendation and ranking. In social media such as blogs, various information diffuses over time. As a result, a network structure is constructed. In an information diffusion network, each influential information source has an affected subnetwork whose nodes are reachable from it. We define three information diffusion properties of the subnetwork using the numbers of three types of directed two-edge connected subgraphs, which are basic structures in a directed acyclic graph such as an information diffusion network. Each basic structure type is related to information scattering, information gathering, or information transmission. We visualized and analyzed the structure of information diffusion networks extracted for various topics. Furthermore, we characterized the information diffusion properties by using the rank correlation coefficient, precision, and mean reciprocal rank and mean average precision of three types of information sources: official sites, news articles, and consumer generated media pages. We found that the three information diffusion properties have different characteristics and give priority to different types of information sources.

Detecting Anomalous Events in Ubiquitous Sensor Environments using Bayesian Networks and Nonparametric Regression

We propose a novel anomaly detection method for a heterogeneous sensor environment in a living space where it is hard to analyze the entire mechanism of the environment and we are unlikely to predict irregular events. By using Bayesian networks and nonparametric regression, our method learns the ordinary behaviors of sensor values and examines the degree of anomaly for each observation according to the estimated variance from the results of learning. We applied our method to data collected in an office room equipped with brightness and motion sensors and obtained the plausible sensor relation networks with no or little prior knowledge. We detected some symptoms of anomalous events and determined the causal sensors by using the network structure.

Full information maximum likelihood estimation in factor analysis with a large number of missing values

We consider the problem of full information maximum likelihood (FIML) estimation in factor analysis when a majority of the data values are missing. The expectation–maximization (EM) algorithm is often used to find the FIML estimates, in which the missing values on manifest variables are included in complete data. However, the ordinary EM algorithm has an extremely high computational cost. In this paper, we propose a new algorithm that is based on the EM algorithm but that efficiently computes the FIML estimates. A significant improvement in the computational speed is realized by not treating the missing values on manifest variables as a part of complete data. When there are many missing data values, it is not clear if the FIML procedure can achieve good estimation accuracy. In order to investigate this, we conduct Monte Carlo simulations under a wide variety of sample sizes.

Topological Feature Mining for Rambling Activities

A method for investigating rambling activities of moving objects is proposed. The goal is to construct common metrics used in various environments for characterizing the trajectory followed by rambling objects. Rambling activities are multi-stop,multi-purpose trips with trajectories with many intersections.Mathematical knot theory is introduced to examine the topological relation between intersections. The trajectories in an environment are represented in a vector space consisting of prime knots. Like a prime number, a prime knot is universal; thus, it is possible to compare the features of rambling activities across environments. An experiment using real-world taxi trajectories demonstrated that our method effectively classifies rambling activities according to daytime, nighttime, and a special event.

Characteristics Estimation of Information Sources by Information Diffusion Analysis

This paper presents a novel method to estimate characteristics of information sources about a topic by analyzing their information diffusion subnetworks in blogspace. In an information diffusion network, each influential information source has an affected subnetwork whose nodes are reachable from it. We define three information diffusion properties of the subnetwork using the numbers of three types of directed 2-edge connected subgraphs, which are basic structures in a directed acyclic graph such as an information diffusion network. Each type of basic structure is related to information scattering, information gathering, or information transmission. We visualized and analyzed the structure of information diffusion networks extracted for other topics. Furthermore, we characterize the information diffusion properties by using the rank correlation coefficient, precision, and mean reciprocal rank (MRR) and mean average precision (MAP) of three types of information sources: official sites, news sites, and CGMs. We found that the three information diffusion properties have different characteristics and give priority to different types of information sources.

Extended another memory: understanding everyday lives in ubiquitous sensor environments

A lifetime recording agent that suggests unusual events to a user is proposed. The goal is to create a memory device that supports human memory by filtering, categorizing, and remembering everyday events. In ubiquitous sensor environments, the agent classifies users’ experiences represented by surrounding objects and predicts typical events that a user will experience next. Unusual events are detected by the awareness of different characteristics as the human brain does. If the prediction is incorrect, the actual event is considered to be unusual. A recurrent neural network that autonomously alters its architecture is introduced to perform event prediction. Experiments confirm: (1) a suitable hierarchical level of event categories for a current situation can be obtained by estimating the event prediction performance, that is, the recall rate and (2) rehearsal sequences dynamically generated by the network can substitute for a sequence of actual events. Thus, the agent easily responds to new environments without forgetting previous memories.

A Flexible Personal Data Disclosure Method Based on Anonymity Quantification

In this paper, we propose a method of controlling personal data disclosure based on LooM (Loosely Managed Privacy Protection Method) that prevents a malicious third party from identifying a person when he/she gets context-aware services using personal data. The basic function of LooM quantitatively evaluates the anonymity level of a person who discloses his/her data, and controls the personal-data disclosure according to the level. LooM uses a normalized entropy value for quantifying the anonymity. In this version of the LooM, the disclosure control is accomplished by adding two new functions. One is an abstracting-function that generates abstractions (or summaries) from the raw personal data to reduce the danger that the malicious third party might identify the person who discloses his/her personal data to the party. The other function is a unique-value-masking function that hides the unique personal data in the database. These functions enhance the disclosure control mechanism of LooM. We evaluate the functions using simulation data and questionnaire data. Then, we confirm the effectiveness of the functions. Finally, we show a prototype of a crime-information-sharing service to confirm the feasibility of these functions.