This study proposes a novel method for quantitatively and visually representing individual preferences for piano performance styles. Such preferences are often highly subjective and difficult to articulate, thus limiting effective learning and the development of personalized artistic expressions. Using features such as tempo, dynamics, and their first- and second-order derivatives, and leveraging the Approximate Inverse Model Explanation (AIME) framework from Explainable AI (XAI), the method analyzes preference labels assigned by learners to professional performances, enabling the visualization of integrated, preference-based performance styles independent of specific pieces. This approach allows learners to explicitly visualize and verbalize their preferences, incorporate them into their performances, and identify professional performers whose stylistic tendencies align with their preferences. The experimental results demonstrate that the proposed method effectively captures integrated stylistic tendencies across multiple pieces and reveals stylistic similarities among performers that may remain hidden when using conventional numerical metrics alone. By making the abstract concept of performance style preferences concrete, the proposed method provides a systematic, explainable, and personalized framework that lays the groundwork for future studies aimed at bridging the gap between subjective musical preferences and actual learning outcomes, with potential applications in individualized music education, artistic self-discovery, and enriching musical performance culture.

This paper presents an automated news-analysis method for the U.S. presidential election that combines approximate inverse model explanations (AIME), an explainable AI (XAI) technique, with a large language model (LLM). News articles related to the election were collected using NewsAPI and classified into thematic clusters using k-means clustering. Thereafter, AIME was applied to compute the global feature importance for each cluster, extracting key descriptive terms. Subsequently, an LLM was used to automatically generate appropriate labels for each cluster, significantly enhancing topic interpretability. Visualizing the daily distribution of these clusters enabled the identification of surges in specific topics on specific days and facilitated a detailed analysis of associated news articles. This approach addresses the challenge of interpretability in conventional news analysis clustering, enabling both automated political news classification and time-series trend analysis. This method provides a novel framework for tracking evolving news coverage during extended political events, such as presidential elections, and identifying key events. This methodology is not restricted to election analysis and can be applied to various other domains, including finance, sports, and social news.

The “Law of Act on the Promotion of Understanding of Sexual Orientation and Gender Identity Diversity” was passed in Japan on June 16, 2023, and it has become particularly important to deepen the understanding of the similarities and differences in attitudes toward LGBT (Lesbian, Gay, Bisexual, Transgender) in an international context to promote global understanding. This study proposed a method to analyze news stories in each linguistic area related to LGBT and derived the respective differences in attitudes using approximate inverse model explanations (AIME). We aggregated news articles on LGBT from January 1, 2021, to December 12, 2023, in several languages, such as German, Spanish, French, Italian, Dutch, Russian, Swedish, and Chinese, which were obtained through NewsAPI and translated using DeepL. Consequently, the characteristics (words) of the news in each language were extracted using AIME. Using this method, we aimed to clarify the different focus areas on LGBT and the issues involved in elucidating differences in perceptions of LGBT in different language areas.

In this paper, a dynamic curation method that uses pretrained models of art interpretation is proposed. The proposed method leverages exhibition catalogs (primary classifications and images) as a source of curatorial knowledge to construct a machine learning model, thereby using this model to evaluate, position, and visualize other artwork. This method enables the dynamic curation of artwork in a vast integrated art collection archive. A prototype system for the proposed method was implemented and applied to two exhibition catalogs at the Artizon Museum. In the experiment, artworks matching the purpose and art style of the modeled exhibition from the archives of the Metropolitan Museum of Art in New York City and Paris Musées were evaluated using the constructed model. The experiment demonstrated that the proposed method successfully classifies and enables the visualization of artwork and images available in the open data from the MET and Paris Musées in alignment with the intended theme of the exhibition for which the model was constructed. Feedback from curators and art professionals indicates that the proposed method can be used to compare museums with art collections of the same genre and to organize new exhibitions based on the curatorial model.

We introduced a novel approach to global sign language recognition by leveraging the capabilities of the Editable Mediator. Traditional methods have often been limited to recognizing sign languages from specific linguistic regions, necessitating ad hoc implementation for multilingual regions. Our method aims to bridge this gap by providing a unified framework for recognizing sign languages in various linguistic areas and promoting global communication. At the core of our system is the Editable Mediator, a mechanism that determines the actual sign meaning from various local hand-shape recognitions. Instead of focusing on specific sign language notations, such as HamNoSys, our approach emphasizes the recognition of common primitive actions shared across different sign languages. These primitive actions are recognized by multiple modules, and their combinations are interpreted by the Editable Mediator to determine the intended sign-language message. This architecture not only simplifies the recognition process, but also offers flexibility. By merely editing the Editable Mediator, our system can adapt to various sign languages worldwide without the need for extensive retraining or ad hoc implementation. This innovation reduces barriers to introducing new sign language systems and promotes a more inclusive global communication platform.

Through technology, it is essential to seamlessly bridge the divide between diverse speaking communities (including the signer (the sign language speaker) community). In order to realize communication that successfully conveys emotions, it is necessary to recognize not only verbal information but also non-verbal information. In the case of signers, there are two main types of behavior: verbal behavior and emotional behavior. This paper presents a sign language recognition method by similarity measure with emotional expression specific to signers. We focus on recognizing the sign language conveying verbal information itself and on recognizing emotional expression. Our method recognizes sign language by time-series similarity measure on a small amount of model data, and at the same time, recognizes emotion expression specific to signers. Our method extracts time-series features of the body, arms, and hands from sign language videos and recognizes them by measuring the similarity of the time-series features. In addition, it recognizes the emotional expressions specific to signers from the time-series features of their faces.

This paper presents a weather map prediction method using RGB metaphorical feature extraction for atmospheric pressure patterns. In the field of meteorological science, predicting weather based on the analysis of observational data and the knowledge of weather experts is crucial. Weather experts draw weather maps based on air pressure distribution; hence, we believe that weather maps entail the interpretations of weather experts. In this study, we improved the prediction accuracy by using machine learning to recognize patterns of qualitative expert interpretations that cannot be predicted by analyzing observed data alone. The proposed method can be realized via two steps. The first is developing a module for extracting pressure pattern features from a weather map. Certain features, such as tropical cyclones or atmospheric high/low pressure distributions, are emphasized in weather maps to facilitate better understanding of the weather features. Therefore, we can predict weather features based on the knowledge of weather experts using data that contain their interpretations, particularly weather maps. The developed module extracts the atmospheric pressure features from the current weather map as an RGB metaphorical gradation map. The second step is developing a module to design a predicted weather map using the extracted features. The weather map of the following day is predicted using pix2pix. To the best of our knowledge, our method for extracting features from weather maps is the first to create a predicted weather map automatically.

In this study, we propose an automatic mashup creation method based on the similarity of musical features. The mashup is one of the techniques of uninterrupted composition of music with similar sound quality and characteristics. When mashups can be created algorithmically and automatically, there are more opportunities to listen to music corresponding to one’s sensibilities using streaming services with large amounts of musical content. Our method automatically creates mashup works by decomposing existing music into components defined by their respective instruments. These components are subsequently recombin ed with each other depending on the evaluation of their similarity. In this study, we also implement an automated mashup creation system using our proposed method and validate it using a questionnaire. Our method can change the way people listen to music, and enable them to easily consume the large amount of music available, especially through the Internet.

In this paper, we represent an impression extraction method for music by relationship between chords and impression terms. Our methods extract impression term with weights from chord extracted from music as wav file. In addition, we created a module for further application of the music impression extraction method. We designed the system which enable us to retrieve music from the word which has been input by user. We use a data set consisting of a music file and annotation file with an impression terms to each song in order to create models that relates to chord extracted from music with an impression using cosine-similarity. By using this first model, we can retrieve music based on the words that we put. We will realize a recommendation system based on the chords from music and our impression of the word.

In this paper, we present a new topic variation detection method that combines a topic extraction method with a change point detection method. It extracts topics from time-series text data as the feature of each time and detects change points from the changing patterns of the extracted topics. We applied the method to analyze effective albeit underutilized text data that contained the Japanese Prime Minister's (PM's) detailed daily activities of over 32 years. Our method and data provide novel insights into the empirical analysis of the political business cycle, a classical issue in economics and political science. For example, because our approach enabled us to directly observe and analyze the PM's actions, it overcame empirical challenges that previous researchers encountered owing to the unobservability of the PM's behavior. Our empirical observations are mostly consistent with recent theoretical developments regarding this topic. Despite limitations, by employing a completely new method and data, our approach enhances our understanding and provides new insights into this classic issue.

We propose a new model, which is called the action-demographic interconnection model which realizes an interconnection function between customer actions and their customer demographic attribution. The features of our method are a semantic-dependent web access log analysis for predicting customer attribution. Our method makes predictions from a web access log. Therefore, we can determine what kind of customers are accessing each website. We present experiments for effectiveness with real advertising web access logs. Based on the results of the experiments, we discuss the possibility and the practicability of our proposed model.

In this paper, we introduce an automatic media content creation system according to an impression. The system creates media content such as music, an image, etc. according to an impression given by a user. The impression is represented in words. Generally, there are two systems related to an impression. The first is a recognition system. The recognition system evokes an impression from media content. The other is a creation system. The creation system creates media content according to an impression. We construct a cycle of the impression via the recognition creation systems. In this cycle, it is mutual mapping between impressions and media contents. In this paper, we construct a recognition operator and a creation operator for realizing the impression cycle. Each operator is a relation of inverse calculation. We apply this system to music media. We introduce an automatic music creation system.

In this paper, we propose evaluation of customer journey for contents of Owned Media. In recent years, many companies publish the Owned Media in order to brand its products and services. The Owned Media is useful for provision of novel information correctly and rapidly. On these backgrounds, there is the demand of evaluation for effectiveness of the Owned Media. It is necessary to recognize which detailed content of Owned Media has importance. Our proposed method is able to produce attractive contents through appreciating which contents make approaches to customers. We demonstrate the evaluation using a certain Web-site as Owned Media and show the effectiveness of our methods.

In this paper, we present a new feature selection method for comparison of each event in big data. The method selects appropriate features for comparing or evaluating each event when we prepare an event set. There are massive data on the Internet as big data. We have been able to retrieve appropriate data sets from them by using given keywords. However, it is still difficult to use these data to compare some concepts. For example, when you would like to compare the difference between your company and a rival company, it is difficult to compare them, because evaluation axes are needed to compare them. This method extracts evaluation axes as features in a data-driven manner. We can compare them by using the extracted axes as the features. This means that the system automatically selects the appropriate features which we should focus on when we compare them.

In this paper, we design a new model for Big data analytics – data-driven axes creation model. In Big data environment, the one of the important technologies is a correlation measurement. We cannot define a protocol of measurement on Big data era, because there are many varieties of data. However, almost current data analytics and data mining method cannot apply to Big data environment, because the big data environment is opened assumption and we have to consider new methods for opened assumption. That is, we have to design a new data-driven axes creation model for correlation measurement method. Our proposed model creates axes for correlation measurement on Big data analytics. Specifically, this model infers in the Bayesian network and measures correlation in the coordinate axes. Therefore, this model maps the Bayesian network into measure correlation mutually. This model contributes to a paradigm shift of Big data analytics.

We represent the inconsistencies of the past research on the connections among such heterogeneous fields as Linked Data, Semantic Web, Bridge Ontology, and Schema Mapping as well as our own past researches. Graph structures are commonly represented as links in relationships. For the same domain, the relationships agree with each other in the domain, because the transitive and order relations are defined. However, in most heterogeneous domains, we have to define the new order relation to link heterogeneous sets. This limit exists when we consider the relation among heterogeneous fields in set theory. Three inconsistencies of linking heterogeneous resources exist: 1) the inconsistency that shows that the relation does not guarantee the future; 2) the inconsistency where no transitive relation is true, when anyone connects links for heterogeneous fields; and 3) the inconsistency where no relation in heterogeneous fields can be discovered in set theory. Closed assumption systems have already reached their limit. In the big data era, we must consider a new framework for the Three Opened Assumption's Evil. As one solution, we present a map transformation method from set theory to the Cartesian system of coordinates to interconnect these heterogeneous sets and the Three Opened Assumption's Evil by two easy mathematical proofs of transitive and order relations to interconnect the heterogeneous resources. In addition, we define a new functional predicate as an example of a map transformation from set theory to a Cartesian system of coordinates to interconnect the heterogeneous resources for our solution. We also define a “dependOn” function as an example of this framework.

Specification of collaboration has neglected over a long period although collaboration is one of main conceptions in computer science and computer engineering. We distinguish between collaborations among systems and socio-technical collaboration. Database research has succeeded in developing approaches for collaboration among database systems that incorporate conceptual specification and allow to reason on systems at a far higher abstraction level. Conceptual modelling of socio-technical collaborating systems is however an open research issue. With the advent of web information systems systems became naturally socio-technical and collaborating. Therefore, we need a techniques for conceptual description of collaboration. Collaboration modelling may be based on models for communication, models for coordination, and models for cooperation. In socio-technical systems users work within their local environment and collaborate within the global world. Therefore, users inject their culture and their specific behaviour into collaboration. Users use information, communication, cooperation and coordination services. These services must be highly flexible and adaptable to the needs of users, to the circumstances and contexts of these users, and to the technical infrastructures used.

Abstract Recently, the number of users who use search engines for not only retrieving Web pages but also understanding or learning an arbitrary concept has been increasing. However, it is difficult to understand and learn an arbitrary concept by using the existing search engines. This paper presents a method for the interconnection of heterogeneous knowledge bases on Knowledge Grid for knowledge sharing and provision. In this paper, for realizing the proposed method, we introduce two types of services—intra‐operation services and inter‐operation services—on Knowledge Grid. The system dynamically creates a semantic associative network that connects the user's interests and concepts, and provides correlation views that represent various relationships for connecting related resources. In addition, we employ a link‐free browsing system by this method called correlation browsing, which represents the semantic association utilizing collective knowledge by connecting various knowledge bases. The current web browsing has two actions—browsing and jumping by static hyperlinks. It is difficult to understand the relationship between each page that is connected by static hyperlink. The system navigates the related contents while repeatedly switching between a content browsing mode and a correlation browsing mode. Therefore, a user can access various contents while understanding the relationships. Copyright © 2010 John Wiley & Sons, Ltd.

With the advance of ubiquitous computing and mobile environments, we have begun to continuously monitor changes in real-world condition and environment through wireless sensor networks. Opportunities also exist for people to create information related to the world around them by using mobile phones equipped with sensing devices, and share that information online with others. In this paper, we propose a novel approach for the interconnection of earth observation data and spatiotemporal web contents on the basis of spatiotemporal and thematic relationships. In particular, we use the concept of moving phenomena of interests to link between measurement sensing data and people-centric contents on the basis of spatiotemporal proximity and thematic relevance. This paper also shows a simple application that automatically generates semantic tags with respect to natural geographic phenomena, such as typhoons, climate changes, and air pollution, on the basis of our interconnection approach. We are able to easily understand qualitative meanings with respect to a certain phenomenon expressed by quantitative numeric conditions.

Various knowledge resources are spread to a world-wide scope. Unfortunately, most of them are community-based and never thought to be used among different communities. That makes it difficult to gain “connection merits” in a web-scale information space. This paper presents a three-layered system architecture for computing dynamic associations of events to related knowledge resources. The important feature of our system is to realize dynamic interconnection among heterogeneous knowledge resources by event-driven and event-centric computing with resolvers for uncertainties existing among those resources. This system navigates various associated data including heterogeneous data-types and fields depending on user's purpose and standpoint. It also leads to effective use for the sensor data because the sensor data can be interconnected with those knowledge resources. This paper also represents application to the space weather sensor data.

This paper presents an interconnection method for heterogeneous knowledge bases depending on user's interests as a context. Various knowledge bases have been created in each field by using collaborative working environments such as Wiki. One of the important issues is how to interconnect these knowledge bases and represent the relationships between various concepts in heterogeneous fields. An event affects various aspects of an area, field, or community. In order to understand an arbitrary event or concept, it is necessary to find various relationships over heterogeneous fields. Generally, the relationships over heterogeneous fields strongly depend on contexts and situations. It is important to realize the dynamic interconnection of knowledge bases depending on contexts and situations. Therefore, we design an interrelation management function (IMF) that defines the operator for interconnection data. In this paper, we propose a framework for a context-dependent dynamic interconnection method by using the interrelation management function.

This paper described our development dialog system on Kyoto tourist information assistance. Dialog part of our system helped user to make an appropriate query. Information analysis part would be assisted for user to select the retrieved information. Nowadays we can get most information through the Internet. However, we have a trouble to pick up expected information from the huge results with conventional search engines. Especially in mobile terminal, we are confronted with great difficulties for two factors. One is that most of users cannot make an appropriate query because their request is vague with theirselves. The other is that the retrieved information has huge variation and mobile terminal has small area for displaying them. Therefore, we aim to develop technologies for the users to input their requests by familiar way and clarify what they want to know with displaying the retrieved information with suitable method.

This paper proposes a general framework of for a system with a semantic browsing and visualization interface called Knowledge Communication, Collaboration and Creation Browser (KC3 Browser) integrates multimedia contests and web services on the grid networks, and makes a semantic mash-up called knowledge workspace (k-workspace) with various visual gadgets according to user's contexts (e.g. their interests, purpose and computational environments).