Takayuki Yumoto

Human Activity Monitoring Using Fuzzified Neural Networks

Abstract For monitoring and estimating our daily activity, some kinds of devices are available. One of such kinds of monitoring devices is a MEMS based prototype which is developed by the Maenaka Human Sensing Fusion Project. We have developed a estimation method of human activity from three-axis acceleration data using the above-mentioned prototype. This method can estimate our unit activities, such as (1) walking, (2) running, (3) sitting, (4) lying, and (5) standing. In this paper, we propose a system that can find unusual situation from ECG data. Our proposed system is based on the fuzzified neural networks. The fuzzified neural network is trained by using sensing data with reliability grade. Since the fuzzified neural network learns normal state of the subject person, we can understand the ECG state of the subject when we analyze fuzzy outputs from the trained fuzzified neural network. This paper shows estimation results by using actual monitoring data which contains normal state, and artificial unusual data. From the results for the actual monitoring data, we can see that our proposed system was able to estimate the testing data as normal. From the results of estimating artificial unusual data, our proposed system can find the subject persons unusual situation.

Fuzzified Neural Network Based Human Physical Condition Monitoring Using MEMS Based Monitoring Devices

In order to understand our physical condition, we need to record the detail of physical condition data like the heart rate. However, for understanding such data, additional information such as what the subject is doing at that time is needed. We propose a combined system which consists of a fuzzified neural network based unusual condition detection and a standard neural network based action estimation. From experimental results, the effectiveness of our proposed system is shown for understanding our conditions.

Extracting and Clustering Related Keywords based on History of Query Frequency

Query-recommendation systems based on inputted queries have become widespread. These services are effective if users cannot input relevant queries. However, the conventional systems do not take into consideration the relevance between recommended queries. This paper proposes a method of obtaining related queries and clustering them by using the history of query frequencies in query logs. We define similarity in queries based on the history of query frequency and use it for clustering queries. We selected various queries and extracted related queries and then clustered them. We found that our method was useful for clustering queries that were used in around the same term.

Evaluating credibility of web information

We describe a new concept and method for evaluating the Web information credibility. The quality control of information (text, image, video etc.) on the Web is generally insufficient due to low publishing barriers. As a result, there is a large amount of mistaken and unreliable information on the Web that can have detrimental effects on users. This calls for technology that facilitates the judging of the credibility (expertise and trustworthiness) of Web content and the accuracy of the information that users encounter on the Web. Such technology should be able to handle a wide range of tasks: extracting several credibility-related features from the target Web content, extracting reputation-related information for the target Web content, such as hyperlinks and social bookmarks and evaluating its distribution, and evaluating features of the target content authors. We propose and describe methodologies of analyzing information credibility of Web information: (1) content analysis, (2) social support analysis and (3) author analysis. We overview our recent research activities on Web information credibility evaluation based on this methodologies.

Discovering Inconsistency in Multimedia News Based on a Material-Opinion Model

The advantages of the multimedia make the video news presented believable and impressed to the viewers when the personal opinions and ideological perspectives hidden in the contents still cause the effect. To reduce the risk of the misleading, based on a Material-Opinion model, we propose a method of detecting the inconsistent news items reporting the same event when the viewer is watching one of them. In the Material-Opinion Model, main participants filmed as the materials are presented to the viewer through the video stream, which is used to support the arguments put forward. Based on this model, given a series of multimedia news items reporting a same event, we explore inconsistency between any two of them by computing their dissimilarities of materials and of opinions. Material-dissimilarity is based on the appearance of the main participants in the video. Opinion-dissimilarity is calculated as the vector difference of two vectors consisting of the argument points extracted from the closed captions. If one of the dissimilarities is high and the other is low, we consider that there exists the inconsistency as a result. We also show some experimental results to validate the proposed methods.

Development of a fault diagnosis system based on fuzzified neural networks

In circulatory systems or systems like chemical plants, failure of piping, sensors or valves causes serious problems. These failures can be prevented by the increase in sensors and operators for condition monitoring. However, since the increase in cost is required by adding sensors and operators, it is not easy to realize. In this paper, a technique of diagnosing target systems is proposed by using a fuzzified neural network which is trained with time-series data with reliability grades recorded by the sensor system which has already existed. Reliability grades are beforehand given to the recorded data by domain experts. The state of a target system is determined based on the fuzzy output value from the trained fuzzified neural network. Our proposed technique makes us determine easily the state of the target systems. Our proposed technique is flexibly applicable to various types of systems by considering some parameters for failure determination of target systems.

Measuring attention intensity to web pages based on specificity of social tags

Social bookmarks are used to find Web pages drawing much attention. However, tendency of pages to collect bookmarks is different by their topic. Therefore, the number of bookmarks can be used to know attention intensity to pages but it cannot be used as the metric of the intensity itself. We define the relative quantity of social bookmarks (RQS) for measuring the attention intensity to a Web page. The RQS is calculated using the number of social bookmarks of related pages. Related pages are found using similarity based on specificity of social tags. We define two types of specificity, local specificity, which is the specificity for a user, and global, which is the specificity common in a social bookmark service.

How-to information search by lightweight analysis of web pages

We propose a method for searching for comprehensible how-to information on the Web. In our how-to information search, we use lightweight analysis of Web pages to extract how-to information from Web pages obtained by conventional Web search engines and rank them according to their easily-viewable-degree. In the extraction process, we focus on expressions in Web page text blocks that describe procedures. In the ranking process, we focus on images, the effect of letter string and the length of the how-to information.

Converting Topics of User Query Sequences for Cooperative Web Search

In this paper, we propose a method of converting a given sequence of search queries about a certain topic into a sequence of search queries about a given different topic. We define the concept of a search skeleton for topic conversion. A search skeleton represents relationships between keywords in a query. A given sequence of search queries is converted into a sequence of search skeletons, which are in turn converted into a sequence of search queries about the target topic. We evaluated our method of search query conversion and found that the precision for deciding types of subtopic keywords in search queries was 84.4%, the precision for finding relational keywords was 35.7%, and the precision for converting dynamic subtopic keywords was 40.0%.

Complex-Valued Associative Memories with Projection and Iterative Learning Rules

Abstract In this paper, we investigate the stability of patterns embedded as the associative memory distributed on the complex-valued Hopfield neural network, in which the neuron states are encoded by the phase values on a unit circle of complex plane. As learning schemes for embedding patterns onto the network, projection rule and iterative learning rule are formally expanded to the complex-valued case. The retrieval of patterns embedded by iterative learning rule is demonstrated and the stability for embedded patterns is quantitatively investigated.