The stock market is a complex system influenced by diverse factors, such as monetary policy, economic cycles, geopolitical risks, and innovation. These dynamics significantly affect investment performance. Detecting such structural changes early and understanding the underlying factors is crucial for effective investment decision-making. This paper proposes an explainable intelligent decision support framework for the detection and interpretation of stock market structural changes. The proposed method monitors the dynamics of correlation-based cluster structures computed from high-frequency return data and detects market-structure changes using an entropy-based indicator. To interpret detected changes, we introduce a structure-aware representation, termed Cluster Structure Embedding (CSE), which maps mathematically grounded market structure signals into a language-compatible space. This representation enables the retrieval of relevant financial news associated with detected structural shifts, thereby providing interpretable explanations for market regime changes. Applying the proposed method to S&P 500 and S&P Mid-Cap 400 constituents demonstrates its ability to detect structural changes not captured by conventional market indicators while providing interpretable explanations to support investment decisions. Furthermore, comparisons with multiple baseline approaches, including LLM-based re-ranking, neural rerankers, and time-series–based methods, demonstrate the utility of the proposed method. These results highlight the effectiveness of the proposed method as an explainable and practically deployable investment decision support system.

Here we show that the activity of people are enhanced if those in the place, in the real space of conversation, share the word “Kamo” in Japanese which means an uncertain belief in one’s interpretation of an observed event or object, corresponding to “maybe.” Two approaches are taken here: (a) observe the length of each utterance in the conversation log of an innovation-oriented workshop with respect to the appearances of “Kamo” (b) the relevance between the number of users of each train station and the tendency that the gen-AI ChatGPT recollects sentences with “Kamo” relevant to the station. Correlations are observed in both approaches, which supports the correlation between the occurrence of “Kamo” and the enhancement of people’s activity. This implies that uncertainty calls for people’s interests, supported by theories about information seeking and the importance of chances as potential opportunities although this is controversial to the belief that human desires to reduce uncertainty for wellbeing and beneficial decision-making.

As in the lessons learned by the authors so far, moving direction entropy (MDE) represents the risk of COVID-19 infection spread as well as the interactive activities of people from different contexts. Here, the MDE is computed from mobility data for each mesh of 100m square in Tokyo to visualize the heatmaps for times in a day. In comparison, the number of moving people and areas of planned redevelopment, such as the extension of train transportation lines, are visualized. As a result of the comparison, we found that the high MDE meshes correspond to the planned areas, which implies the latent risk to enhance the infection expansion in those areas, as well as the expected prosperity of economics. Thus, our message is to combine the policies of urban redevelopment and care for the spread of infection and apply our simple method to compute the MDE.

In modern society, supermarkets serve as an essential infrastructure for daily life, and their sales are greatly influenced by the value judgments of customers regarding products. As digital technologies advance, many companies are leveraging POS (Point of Sales) data analysis to increase their profits. This study analyses POS data using a novel metric called "diversity," defined as the variety of contexts in which a product is purchased. The discussion proceeds by testing the hypothesis that products exhibiting similar temporal changes in diversity tend to share similar characteristics. To measure diversity, the study employs Semantic Cells (SCs), a technique effective for analysing time-series data, and applies a new method that focuses on temporal changes in diversity to perform product clustering. The study found that products with similar patterns of temporal change are indeed related, thereby supporting the proposed hypothesis.

Methods for exchanging datasets and values as payment have been studied so far, where the communication of user’s intension has been positioned as a key for creating trust. However, user’s intension does not stand alone without logical description of the user’s current and desired situations. Here, with referring to the authors’ previous study about tsugo network, it is proposed to provide a prompt including the statement about the intention of data user, composed of his requirement and situation. This design of prompt is shown to play a significant role for obtaining a satisfactory use plan of datasets.

In Japan, a new concept of 'Station and Town Special Design’, an urban development that considers the station and the surrounding town block as a whole and revitalises the vitality of the town as a whole, is attracting attention. We hypothesised that people's movement behaviour in the area would become more active via this Station-Town Space Design project. We analysed the effects using movement direction entropy (MDE), which measures the diversity of people's movement directions. The results showed that MDE increased in the project area. Furthermore, MDE tended to be higher in the surroundings of stations. This suggests that the Station-Town Space Design stimulates people's movement behaviour in the station, station square, and surrounding area. These findings have significant implications for urban planners and developers, as they indicate that MDE can be a useful indicator for measuring the liveliness of a town, and that the Station-Town Space Design design can enhance the vitality of urban areas.

During the COVID-19 pandemic, the human mobility was restricted and significantly impacted, which also had a serious effect on financial markets. Specifically, industries such as energy, utilities, travel, retail, and hospitality were negatively affected by the decrease in population movement, leading to reduced consumer spending and lower demand for services in these sectors, while other sectors, such as healthcare and technology, saw significant growth due to increased demand during the pandemic. Additionally, the challenging market environment led to multiple anomalies in financial markets. When financial markets are abnormal, signs of distress in the market structure often appear before a crisis fully erupts. This study proposes a model based on the Stochastic Block Model (SBM) to detect anomalies by analyzing market changes and underlying spatial structure. We then extended the model to create a multi-layer model, treating the blocks in the lower layer as nodes in the upper layer, using a two-layer approach for cause analysis. We analyzed the structure of the S&P 100 and TOPIX 100 indices during the pandemic and compared it with mobility data from the same period. The results indicate a correlation between population mobility and changes in market structure during the pandemic, offering a new perspective for analyzing financial markets.

Text, as data which can represent actors’ behaviors with the reflection of emotional dimension, is expected to be used combined with physical mobility data for explaining contextual continuity and transitions. Even in the era of LLM, we still desire methods for the preparation of essential parts of texts for finely obtaining the contextual shift which may go from and to interleaving meaningful key-terms rather than sheer change of topics corresponding to word clusters. In this study, the explanatory relay of words in text is represented in the form of archipelagoes. Here, each archipelago is a sequence of islands composed of the occurrences of a certain word. An island here is interpreted as the local sequence where the word is emphasized, and an archipelago of a length comparable to the target text is extracted by using the variation of entropy type-A, the window-based entropy based on the distribution of the word’s occurrences with the width of each time window. The results show that the parts of the target text including the words forming archipelagoes thus extracted, without pre-learned knowledge, form an explanatory part of the text that is of smaller entropy B than the parts extracted by the baseline methods. Here, entropy type-B is the graph-based entropy representing the contextual dispersion of the text. This result means archipelagoes work, for extracting contextual flow with discarding noisy words in the text without a prepared knowledge about stop words in text or noises in sequential data.

Tourism information is crucial for enhancing tourist experiences, yet conventional visualization methods often overlook intersections and peripheral data that could reveal new insights. Boundary Objects and Edges are identified as key elements in discovering these overlooked aspects. Boundary Objects act as bridges between different information sources, facilitating new perspectives, while Edges offer new dimensions of value beyond the usual range of information. Here, we show that by applying our algorithm to identify these elements in tourism data, tourists can discover novel insights and enhanced experiences. This approach contrasts with previous methods that focus primarily on central data, often missing the potential insights at the boundaries. Our findings suggest that integrating Boundary Objects and Edges into tourism information systems can significantly enrich the tourist experience, offering new opportunities for value creation. These results pave the way for more innovative and comprehensive tool in the tourism industry, with broader implications for how information is processed and utilized across various fields.

In the process of data exchange, we assert that the needs of data users should be prioritized and clearly articulated for a trustworthy data marketplace. Here, we present a case study on predicting and discovering chemical-protein interactions, which are crucial for drug discovery, by collecting data tailored to these requirements. We developed a linear sequence model based on a list of amino acid residues to meet this need, and the data were selected for analysis with this model. A convolutional neural network is employed to capture the spatial characteristics of proteins, while also integrating the linear molecular features of drugs and the network topology. The framework’s 3D visualization provides intuitive representations of chemical-protein interactions, enhancing biological interpretation. This approach effectively balances data processing efficiency with rich structural insights, enabling a deeper analysis of critical protein-related features.

Keyphrase extraction is a critical task in natural language processing (NLP) aimed at automatically identifying and extracting essential phrases from a given text. Traditionally, keyphrase extraction has been extensively studied in the context of structured text sources such as articles, documents, and web pages. With the growth of social media platforms like Twitter (X), the demand for effective keyphrase extraction has become increasingly important in not only public opinion and sentiment understanding but also facilitating real-time trend identification and social governance. Keyphrase extraction from social media presents unique challenges due to the informal, unstructured, and highly diverse nature of the data. Existing methods mainly rely on either unsupervised (statistical based) approaches or supervised (deep learning based) techniques, each of which has its own advantages and disadvantages. In order to leverage the strengths of both supervised and unsupervised learning, we propose BERT-MLTKE, a BERT-based multi-task framework designed to significantly enhance keyphrase extraction performance on social media data. Our framework fine-tunes the pre-trained BERT model to effectively capture both contextual information and syntactic structures within sentences. Instead of using traditional unsupervised approaches of extracting candidate phrases and ranking them based on statistical metrics, we introduce a multi-task module with supervised deep learning models that addresses both keyword identification and keyphrase extraction as two progressive tasks, enabling the framework to share essential information while tackling multiple subtasks concurrently. During the implementation of deep learning models, we used several techniques that contribute to model stability. For further enhancement in framework’s performance, we conducted several comparative experiments, optimizing both the architecture of BERT-MLTKE and deep model candidates selection in embedding and feature transformation layers. The results of these experiments were thoroughly analyzed, and we offer possible explanations for the observed variations for different deep models in task performance. We also propose strict evaluation metrics to ensure a more precise assessment of the experimental results in an objective manner.

The study of route selection for cyclists is a field aimed at promoting bicycle use and fostering the development of eco-friendly cities. Such research often focuses on investigating bicycle routes used for commuting. However, there is a lack of research on routes used for recreational cycling, and it is a missed opportunity not to focus on the strolling behaviors of people who enjoy such freedom. This study presents the results of utilizing a route generation model for recreational cycling, with a focus on identifying the elements of roads and surrounding environments that attract individuals to participate in this activity. We discovered how much people value waterfronts and recreational spaces, as well as the impact of railway density on recreational cycling across different areas. These findings could be used to inform policies for creating cities where recreational cycling is easier to enjoy and bicycles, as an eco-friendly mode of transportation, are more readily utilized.

As the world suffered from the Covid-19 pandemic, people struggled to maintain their lives while working hand in hand to find exits, and infection control measures that have been led by the government came to be increasingly left to the decisions of individuals and business organizations. Living individuals sought correct information for the normalization of their lives, and their active intellectual demands increased, while researchers involved in Covid-19-related studies were also concerned about the need to fit the social requirement and deliver the latest research information to people, rather than reporting research content and results within closed institutions. Here we proposed the network linking the intellectual interests of researchers and the general public as a Living Lab where consumers themselves can create their own lifestyle guidelines by adopting scientific findings that mesh with their own requirements, in preparation for a post-corona society and unexpected pandemics by the following methods. (1) clarify the connection between everyday questions and research questions regarding Covid-19, (2) put into practice a Living Lab in which individuals themselves guide the living guidelines according to their locality, and (3) examine the direction of social implementation of scientific findings and new research areas. This paper examines the future potential of this method by means of a case study of applying this method.

The diversity of members of the National Diet in Japan has remained to be low in Japan and the lack of representation has been pointed out. In this study, we propose a model that mathematically measures descriptive representation in the multiple steps of undergoing an election, using the attribute data of members of the parliaments in three democratic countries. First, we modeled the three groups that undergo an election process from a general public group to a candidate group, then to an elected group. Next, by quantifying representation from the distribution of attributes in each group using Kullback-Leibler divergence (KLD) and comparing among the groups, we examined the change in the degree of representation. As a result, we found some consistent results with the current discourse in political science research and proposed further research questions regarding the representation in the candidate group. The results have double implications (1) modelling an election as a multiple steps process enables us to examine in which steps the degree of representation changes and (2) the KLD is useful in measuring and comparing the representation between the candidate group and the elected group.

Previous models for learning the semantic vectors of items and their groups, such as words, sentences, nodes, and graphs, using distributed representation have been based on the assumption that the basic sense of an item corresponds to one vector composed of dimensions corresponding to hidden contexts in the target real world, from which multiple senses of the item are obtained by conforming to lexical databases or adapting to the context. However, there may be multiple senses of an item, which are hardly assimilated and change or evolve dynamically following the contextual shift even within a document or a restricted period. This is a process similar to the evolution or adaptation of a living entity with/to environmental shifts. Setting the scope of disambiguation of items for sensemaking, the author presents a method in which a word or item in the data embraces multiple semantic vectors that evolve via interaction with others, similar to a cell embracing chromosomes crossing over with each other. We obtained a preliminary result: the role of a word that evolves to acquire the largest or lower-middle variance of semantic vectors tends to be explainable by the author of the text.

Since we presented the Stay with Your Community principle for suppressing the infection spread at the social level, policies on SWYC have been considered in the context of the tradeoff between the risk of infection spreading and the benefit of inter-community interaction on productive and innovative social activities. In this paper, Moving Direction Entropy is defined as a value representing the diversity of humans’ moving direction in each region. This value had been found to be positively correlated with the number of COVID-19 positive cases, and here it is shown to be in positive correlation with the regional population of inbound flow and its increase during the analyzed period from 2019 through 2022. The results have double implications (1) the dynamics of regional populations, regarded as the enhancement of inter-community interaction, diversify the movements and (2) such dynamics are likely to foster infection spreading.

Communication regarding data use enhances and sustains trust in the data market. Showing this principle based on the literature on trust, this study proposes a novel method for representing the digest information of datasets to foster the thoughts of potential data users, who attempt to create valuable products, services, or business models using datasets and aid their communication with data providers. Compared with existing metadata, where variable labels in a dataset are listed, the presented metadata, called data leaf (DL), includes events, situations, and/or actions that, as a set, compose scenarios supposed to be active in the target real world of a dataset. This method considers the fitness of metadata corresponding to various datasets to a feature concept (FC), which provides an abstract illustration of the knowledge acquired or expected to be acquired from the data, and plays an essential role in data utilization. In experiments using metadata as elements to be combined in human thought for data federative innovation, DLs significantly outperformed cases using data jackets (DJs) where variable labels were used as attributes of the datasets.

A key issue is how to help buyers and sellers build a trusting relationship with each other on e-commerce platforms. In this regard, researchers have been interested in addressing the information asymmetry between buyers and sellers. This study focuses on figure products often sold at higher than original prices and whether the signals hidden in sellers’ presentations of such products can alleviate this information asymmetry. To this end, we use text analysis and logic models to discover new signals that can reduce information asymmetry, and the effect of the signals varies with price. Such findings may have managerial implications for platform owners, helping them to maintain better buyer-seller relationships and increase trust in the platform.

A co-author of this paper had previously presented the principle of "Stay with Your Community" as a method of countermeasures against COVID-19 infection spread and have been working on its social implementation. This case study paper presents an example of activities to spread the Stay with Your Community principle to citizens and visitors in Shimoda City, Shizuoka Prefecture, in order to control the spread of COVID-19 infection. As a result, the infection cluster was successfully controlled. The authors discuss the effect of the regional workshop as a key to open the way to Organizational Citizenship Behavior of participants.

Over the past two decades, itemset mining techniques have become an integral part of pattern mining in large databases. We present a novel system for mining frequent and rare itemsets simultaneously with supports weighted by cardinality in transactional datasets. Based on our neural item embedding with additive compositionality, the original mining problems are approximately reduced to polynomial-time convex optimization, namely a series of vector subset selection problems in Euclidean space. The numbers of transactions and items are no longer exponential factors of the time complexity under such reduction, except only the Euclidean space dimension, which can be assigned arbitrarily for a trade-off between mining speed and result quality. The efficacy of our method reveals that additive compositionality can be represented by linear translation in the itemset vector space, which resembles the linguistic regularities in word embedding by similar neural modeling. Experiments show that our learned embedding can bring pattern itemsets with higher accuracy than sampling-based lossy mining techniques in most cases, and the scalability of our mining approach triumphs over several state-of-the-art distributed mining algorithms.

Searching and buying items from online shops has become common these days. Although recommender systems have supported consumers to find products online, it is challenging to select suitable products, such as clothes and smartphone cases, which are called Kansei-oriented products. These products have various visual designs but have the same function, and it is arduous for consumers to express their preferences and choose desired products. Although marketers and designers have empirical knowledge regarding the abstract diagrams in relation to product design and consumers’ product selection, its mechanism has not been fully understood. In this study, first, we investigate whether common tendency exists between preference for products and for abstract diagrams. Secondly, to understand and support consumers’ understanding concerning their preferences and product selections, we analyzed the process whereby abstract diagrams affect human cognition and their selections of Kansei-oriented products. As a result of two experiments, we found that the abstract diagrams affect cognition and the ability to choose products. Further, we believe that our findings may be beneficial for the process of preference expression and marketing through the use of abstract diagrams.

Expectations for new businesses and research and development that combine data and technologies from different fields have risen, owing to the global trend of big data and artificial intelligence technology. Under these circumstances, a massive amount of data can be accumulated and reused; value creation through data exchange has been attracting attention as a new source of innovation. However, there is a large gap between the data utilization plan and problem solving using actual data. There are different evaluation indicators and issues to be overcome at each stage of the development process: generation of data utilization plans, collection of actual data, data analysis, and implementation. In this study, we verified the feasibility of a data utilization plan created at the Innovators Marketplace on Data Jackets, a workshop-style forum for discussing data combinations. We set the theme of “indicator formulation for selecting residential areas,” which is an urgent issue in city planning and urban development, as a use case. The demonstration experiment of data combination that was conducted over half a year, the realization process, and the issues considered at each development phase are discussed. This case study is expected to be useful for future innovation creation through interdisciplinary data collaboration.

When and which products to recommend to whom has been the essential issue for retailers. In this field, the topic model is attracting researchers’ attention for extracting customers’ purchase behavior instead of association rules or K-means. However, the optimal number of topics is chosen manually, and there are some limitations to use topic models. In this study, we developed the model by Koltcov et al. for point of sales (POS) data in the supermarket. To grasp the change of topics over time, we divided five-month POS data into ten datasets into two-week intervals and applied the topic model with Renyi entropy separately. The results suggest that splitting data might be a better way to understand customer’s behavior.

Due to the global violence of the novel coronavirus, various industries have been affected and the breakdown between systems has been apparent. To understand and overcome the phenomenon related to this unprecedented crisis caused by the coronavirus infectious disease (COVID-19), the importance of data exchange and sharing across fields has gained social attention. In this study, we use the interactive platform called treasuring every encounter of data affairs (TEEDA) to externalize data requests from data users, which is a tool to exchange not only the information on data that can be provided but also the call for data-what data users want and for what purpose. Further, we analyze the characteristics of missing data in the corona-related confusion stemming from both the data requests and the providable data obtained in the workshop. We also create three scenarios for the data design of unobserved events focusing on variables.

With the development of computers and the rise of data exchange, the expectations for innovation by combining data from different industries, and data exchange platforms that handle different types of data are sprouting up. However, the data handled on such platforms have been obtained and are stored independently by data providers with different purposes, and the maintenance of data catalogs and the spread of schemata are not currently insufficient, making it difficult to understand the relationships between the data on such platforms. In this study, in order to derive the relationships among datasets and to discuss the similarity and combinability of the data on the data exchange platform, we analyze and discuss the relationships between datasets on the platform. We focussed on the data outlines and variables using the metadata of the data exchange platform service, D-Ocean, and found that the similarity of data cannot be measured by a single indicator, and that the items to be referred to calculate the similarity depending on the indicator.

As data are being generated at an incredible speed and scale, the market of data that aims to fully discover the value of data is becoming increasingly important. Data evaluation is a vital part of the data market. It can discover the data's flaws and the value hidden in the data. Nevertheless, there have been few studies investigating how to evaluate data in the data market. Existing methods seldom utilize the multi-level structure in data. Our work proposes a novel hierarchical graph convolutional network for the data evaluation of dynamic graphs, following the anomaly detection paradigm. Our model performs significantly better than existing models on several benchmark datasets. This study provides a more powerful tool for processing dynamic graphs. It could also guide the direction of data evaluation for a broader range of data categories in the data market.

In recent years, data have come to be not only targets of analysis, but also economic goods traded and exchanged among different fields in the digital economy. Under such circumstances, the data marketplace and related businesses/technologies are receiving global attention. However, unlike other well-known markets, the data marketplace is an emerging one, and decision makers in firms are cautious because there is little information regarding the choice to introduce new technologies into the data marketplace. In this study, we discuss the usefulness of two samples (Data Jacket Store and Variable Quest) in order to understand the acceptability of tools for the data marketplace among firms. We conducted a survey using market research online communities (MROCs). The evaluations differed depending on the number of employees in the firms and the positions of the subjects. Moreover, even in firms with the same number of employees, decision makers who are engineers and non-engineers assessed the same tool using different criteria and had different viewpoints.

Event popularity quantification is essential in the determination of current trends in events on social media and the internet. Particularly, it is important during a crisis to ensure appropriate information transmission and prevention of false-rumor diffusion. Here, we propose Net-TF-SW - a noise-robust and explainable topic popularity analysis method. This method is applied to tweets related to COVID-19 and the Fukushima Daiichi Nuclear Disaster, which are two significant crises that have caused significant anxiety and confusion among Japanese citizens. The proposed method is compared to existing methods, and it is verified to be more robust with respect to noise.

The application of deep learning in data mining and knowledge extraction becomes more and more extensive, yet people often criticize deep models for its ”black box” trait. In this paper, we propose a simple algorithm for the model explanation by using the energy-based model theory and subset sampling. This approach can be model-agnostic without knowing the model architecture or be hybrid with the information inside the target model (e.g. the gradients). Through two image classification models, we compared our algorithm with other interpretation methods by testing the effects on the predictions and got an encouraging result.

In the field of disaster prevention, it is crucial to provide the general public with appropriate integrated information before, during, and after the disasters. However, in such situations, individuals are not capable of establishing clear opinions owing to inconsistent and diverse information from multiple sources. As individuals receive varied inconsistent information from several sources, they are puzzled about the credibility of such information. This situation is defined as a belief drift in our study, and it must be alleviated by providing appropriate information. To create a system that provides the required information to individuals with drifting beliefs, we acquired reliable information from medical doctors and implemented the knowledge base and retrieval system to bridge the gap between professionals and nonprofessionals. We focused on certain blood diseases, collected large texts, and extracted the terms representing contexts and variables, and the medical doctors evaluated the relationships among these diseases. In this study, we discussed the design and the implementation of the proposed system and demonstrated its performance.

Data jacket (DJ) emerged as a method to make use of data without revealing any actual content. In a DJ Store, one can search DJs, and learn the types of solution these DJs are used for. In this study, we propose a model that calculates and acquires similar DJs under the assumption that it may be used for similar solutions by acquiring DJs that are similar to a certain DJ. The experimental results show that the similarity model generated by the proposed method is the model that most reflects the similarity of the DJ’s outline.

Proof of Work(PoW) as a critical consensus algorithm plays an essential role in Cryptocurrency fields. In PoW, computation power is the most vital resources. The security of a PoW system heavily relies on the computation power all honest users controlled. PoW systems distribute some rewards among participants according to their contributions as an incentive. Users who focus on earning such rewards are known as miners. Miners can join pools to share and reduce the variance of their rewards. In this work, we will compare different reward distribution mechanisms in a long-term condition. The result shows that all existed mechanisms cannot motivate miners continuously mining in the pool. For solving this flaw, a novel mechanism is proposed to improve the utility and motivation of miners.

In recent years, the expectations for cross-disciplinary data exchange and collaboration have been rising. However, creating a data-driven innovation by coordinating data across different fields first requires a correct understanding of structures and relationships of existing data. It thus is important to investigate the structural characteristics of the population of data rather than analyzing individual data. Data Jacket (DJ) is a framework for describing an overview of data while keeping data itself confidential. This paper utilizes DJs to quantitatively assess overall trends and characteristics of data and to understand the structure and system of data with their variables and contexts. Results of the analysis revealed the networks of data via variables and contexts are different comparing their structural characteristics. The result suggested that even if data cannot be connected only with variables, the common usage contexts can combine the related data for solving problems.

It is useful to extract review sentences based on an assigned viewpoint for purposes such as summarization tasks. Previous studies have considered review extraction using semi-supervised learning or association mining. However, we approach this task using a clustering method. In particular, we focus on a topic model as a clustering method. In the conventional topic model, after randomly initializing the word distribution and the topic distribution, these distributions are estimated in order to minimize the perplexity using Gibbs sampling or variational Bayes. We introduce a new method called the PageRank topic model (PRTM) for estimating multinomial distributions over topics and words using network structure analysis methods. PRTM extracts topics by focusing on the co-occurrence relationships of words and it does not need randomly initialized values. Therefore, it can calculate unique word and topic distributions. In experiments using synthetic data, we showed that PRTM can infer an appropriate number of topics by clustering short sentences, and it was particularly effective when the sentences were covered by a small number of topics. Furthermore, in a real-world review data experiment, we showed that PRTM performed better with a shorter runtime compared with other models that infer the number of topics.

The potential expectation about generating innovative businesses and creating values by combining data from different domains has been increased. However, a major problem for establishing the market of data for cross-disciplinary data collaboration is that both data holders and users do not recognize the values of data as economic goods. Data holders do not know the optimal investment level for data production because of the lack of knowledge about the market values of data. Therefore, in the current situation, the conditions for data exchange, e.g., the prices of data, are the asking prices of data holders. In this paper, we conducted the workshop for observing the utility values of data varying depending on the contexts of data utilizations using Data Jackets. Data Jacket (DJ) is the technique for sharing the information about data by publishing the summary of datasets as meta-data. Even if the data itself is not open in public, DJs enable the stakeholders in the market of data to consider the latent value of datasets by understanding the information about data described on DJs. Referring to the method of behavioral economics, we design a place where intentions and purposes of each stakeholder, especially data users in this experiment, in the data market are reflected. We quantify the values of data with the currency unit used in actual value exchange in the society. The result suggested that the prices of data which data users are willing to pay depend on the usage contexts.

Since the development of Big Data, we have been doing various researches on data utilization, also through data mining, the importance of data value in terms of degree of contribution to business decision-making and planning has been discussed in previous studies. However not enough attention has been paid to the value of the data itself. Therefore, there is a high risk that the data will not be useful to the user even if developed or mined by massive steps. Moreover, once the value of data cannot be fully demonstrated, it will also cause a great loss in terms of business. In this paper, we focus on the present condition that there is no fixed standard in the data value evaluation, the model is structured to evaluate the data from different fields, and the value of each data has been relatively accurate assessed. In order to build such a model, first of all focus on how to determine the value of Data Jackets. Are the values of the Data Jackets fully assessed in the context of the data market? What is the difference between the value evaluated from the virtual data market and the reality of itself? This paper focuses on the gap, and provides a new idea and new method for the re-discovery of the value of Data Jackets.

Argument-based reasoning offers promising interaction and computation mechanisms for multi-agent negotiation and deliberation. Arguments in this context are typically statements of beliefs or actions related to agents' subjective values, preferences and so on. Consequences of such arguments can and should be evaluated using various criteria, and therefore it is desirable that semantics supports these criteria as principles for accepting arguments. This article gives an instance of Dung's abstract argumentation framework to deal with Pareto optimality, i.e. a fundamental criterion for social welfare. We show that the instance allows Dung's acceptability semantics to interpret Pareto optimal arguments, without loss of generality. We discuss the prospects of justified Pareto optimal arguments and Pareto optimal extensions.

Public safety, especially the daily traffic accident is concerned by the public.Previous studies have already discussed accident reasons associated with accidents statistically.There is a method called Innovators Marketplace on Data Jackets created by Professor Ohsawa.This method is used to externalize the value of data via stakeholders' requirement communication.This paper applied the solution from an IMDJ workshop to research this topic creatively.This novel solution suggested to do analysis on the combination of urban data and traffic accident rate to find the impact factors to the traffic accident rate in the urban system.This paper used factor analysis, structure equation modeling and data mining to construct a theoretical frame for traffic accident rate analysis for urban data.Different accident indexes, such as total number of accident, fatality rate, injury rate, and casualty rate are combined to construct a traffic accident risk evaluation model.This paper chosen the urban data as the solution from IMDJ workshop, such as population structure information, vehicle information, road characters, public traffic system information, and the other kinds of data to explore factor meaning, and to identify relationships between different factors.It segmented these urban data based on their categories, and determined accident risk for each section.By doing analysis on not only the original data but also the changing rate of these data each year, the result analytical results showed that traffic accident rate on urban data could be described by the combination of population structure, road characters, public traffic system and public facilities.These four sections affects traffic accident rate significantly during the development of urban; however, the vehicle factor does not have influence on traffic accident rate.And it proves the solution from IMDJ workshop is not only novel but also practical strongly.Making some solution from IMDJ into reality, we will find another new way to affect the world.

Patent analysis is useful to understand the trends of technological problems and develop strategies for technologies.Here patent classification is a method to support the analysis.The purpose of this study is to propose a method for patent classification, with the use of hierarchical clustering based on the structural similarity of problems to be solved.The structural similarity can be calculated with case vectors based on predicate-argument structures of the contents of the patents.The interview survey indicated that this classification plays an essential role in analogical problem solving, by allowing visualization of similar technological problems.

The potential expectation about generating innovative businesses and creating values by combining data from different regions has been increased, however, even the information about data and knowledge of data utilization are not shared. In order to lead data-driven innovations, a market of data is expected to reduce the risks of data utilization, and encourage data exchange. Data Jacket (DJ) has been proposed as the technique for sharing the information about data by publishing the summary of datasets as meta-data. Even if the data itself is not open in public, DJs enable the stakeholders in the market of data to consider the latent value of datasets by understanding the information about data described on DJs. Datasets described in DJs have various sharing policies of data, such as Open Data from governments, or private data from companies or individuals. Although, in general, there is a preconception that “the fee-charging things are better than the free ones”, the relationship of sharing policies and the utility expectations of data has not discussed in the market of data. In this study, we examine which data is expected higher for utilizing, that is, comparing the utility expectation of the data which can be shared in public (Public Data) and the data which cannot be shared (Private Data). Observing the frequency of uses in Innovators Marketplace on Data Jackets, which is the gamified workshop for discussing the data utilization, and analyzing the number of views in DJ Store, which is the retrieval system of DJs, the result shows that the data which is hardly open may be recognized valuable for utilizing.

In recent years, various data has been obtained at di_erent situations due to the improvement of the sensors or the popularization of the Internet and mobile phones. In the field of business, academia or politics, the demand for supporting decision making based on data has been increased. Ohsawa et al. propose the concept of the market of data (MoDAT) for the demand, and provide Innovators Marketplace on Data Jackets (IMDJ) as a method for realizing MoDAT, which is a workshop method for encouraging particippants to utilize and exchange data. However, although participants of the workshop discuss the given theme each other, possible combinations of DJs for satisfying requirements are hardly generated. That is one of the problems of IMDJ. In this paper, in order to solve the problem, we propose the visualization method for representing the similarities between DJs using latent dirichlet allocation (LDA) and multidimensional scaling (MDS) in order to aid users metacognition in IMDJ. The result shows that our proposed method improve the performance of participants to create feasible solutions by combining DJs.

Previous seriation algorithms are confronted with a balance problem. Some approaches provide permutations with perfect wholeness, where matrix rows/columns are associated with increasing or decreasing gradient. However, this smooth permutation may lead to the blurred representation of the data structure, such as clustering structures and detailed structures inside clusters. Some other approaches indicate these structures well by tighter aggregating similar rows/columns, but this aggregation is alway at the cost of losing necessary coherence of the matrix rows/columns. In this paper, we introduce a seriation algorithm that aims at balancing the smoothness of the permutation and the clarity of the matrix structure. The permutation algorithm greedily and recursively replaces high-dissimilar object pairs with low-dissimilar ones, and the optimization algorithm searches the global optimizing solution by applying the simulated annealing algorithm. A comparison study shows both empirical and statistical evidence that Recut can provide more accurate and visually appropriate permutation by considering the balance problem.

A scenario is a series of information derived from data or related knowledge. Action Planning is a workshop method for creating scenarios, which participants resolve conflicts and reduce risks of taking actions, through the process of externalizing and serializing knowledge. In this study, we show our work to create a database for reuse and retrieval of knowledge by converting scenarios created in Action Planning into structured knowledge with RDF. We store information of scenarios, and design the interface for helping users extract accurate information and make decision. By not only structuring knowledge used in the past Action Planning, but also making it possible to access external collective intelligence of DBpedia and Data Jacket store, this system is expected to retrieve important information about solving problems.

In this paper, Tangled String, a method for visualizing sequential data is summarized and exemplified for log text of histories given as timeline lists. Valuable events are detected according to the results, on the assumption that the value of an event or an item in history means the essential impact on the past trend or on the future events that can be understood by qualitative explanation. According to the results, valuable events differ for different granularity in segmenting history into subsequences of events, corresponding to our intuitive sense of the impacts of events for human decisions and actions. This paper also aims to suggest evaluation criteria about the value of events and information in general, where their quality cannot be easily replaced by quantity. Humans’ thoughts should be involved in the process of quality evaluation, so a method such as Tangled String is desired to support this process and improve efficiency.

A communication gap between developers and operators can lead to serious problems on system service continuity, especially for open systems with constant changes of stakeholders' requirements and operating environment. In order to break down such communication barriers, this paper gives general patterns of reasoning, called argumentation schemes, for open system dependability. The schemes are defined in terms of system lifecycle taking into account an iterative developing and operating processes for service continuity. They are used to embody assurance cases written in a form of the goal structuring notation. A collaborative experiment with a software developer shows that the schemes promote communication between developers and operators in such a way that dependable operating measures considered in development phases improve the next operation and dependable developing measures obtained in operation phases improve the next development. Moreover, the schemes contribute to find flaws of development processes taken by the software developer.

Data Jackets, that are small pieces of information containing the abstracts of data that exist but cannot be disclosed, are encouraged to be submitted to the market, by showing a result of the experimental process with Innovators Marketplace on Data Jackets (IMDJ). The process started from participants’ submission of data jackets, on which they then played a creative game where they proposed ideas, to combine DJs and analyze the obtained dataset, and evaluate the expected knowledge to be obtained by each other's idea. Finally we had other group of people including those who did not participate in the game, and had them propose stakeholders who may give more information about the data corresponding to the data jackets. As a result, the conceived value of the data corresponded between in and after the game.

In order to make a social environment where analysts and decision makers in active businesses and sciences can be provided with data they need, we discuss why we should and how we can (re)design an environment called the market of data, where each user or provider of data can externalize and share the value of each part of data so that one can buy/sell it in a reasonable condition, e.g., for a reasonable price or as open source if it may give merits to people in general rather than to particular segments who can pay. Presenting one way to go, that is called Innovators’ Marketplace on Data Jackets, we show a vision for the systematic design of the market of data where existing tools and new technologies are configured, such as the visualization of the relation among databases/datasets for aiding stakeholders’ communication about possible use scenarios of data. By this, this paper calls for potential contributors in the future to the market of data.

Knowledge discovery in texts (KDT) has been widely applied for business data analysis, but it only reveals a common pattern based on large amounts of data. Since 2000, chance discovery (CD) as an extension of KDT has been proposed to detect rare but significant events or situations regarded as chance candidates for human decision making. Key Graph is a useful and important algorithm as well as a tool in CD for mining and visualizing these chances. However, a scenario graph visualized by Key Graph is machine-oriented, causing a bottleneck of human cognition. Traditional knowledge discovery also runs into the similar problem. In this paper, we propose a human-oriented algorithm called IdeaGraph which can generate a rich scenario graph for human's perception, comprehension and even innovation. IdeaGraph not only works on discovering more rare and significant chances, but also focuses on uncovering latent relationships among chances for gaining richer and deeper human insights. Our experiment has validated the advantages of IdeaGraph by comparing with Key Graph.

We report a relation with the time which human thought was seeing the object and the time which human was actually looking at. We conducted experiment of the difference of the ranking which subjects thought was seeing the object and the ranking which subjects were looking at, considering a new idea combining two or more products. The results show the tendency that there was a difference of the ranking of which subjects thought was seeing the object and the ranking which subjects were looking at.

Sticky information proposed by von Hippel is known to well explain the localization of innovations, i.e., the tendency of information to be localized in the hands of either inventors in the industrial side or consumers makes it hard for information about needs and seeds meet in the market. In this paper, we break sticky information into sticky tsugoes, where a tsugo is a triple of the intention and two types of constraints behind each stakeholder’s acting/planning. We accumulated cases of Innovators Marketplace ® (IM hereafter) - a serious gaming process for innovative workshop involving multidisciplinary stakeholders, and visualized the interaction of their tsugoes by using KeyGraph. As a tentative result, the variation of human-tsugo network (HTN) explains the performance of innovations more finely than the graph visualizing the relations among participants and sheer information represented by words in the dialogues.

When reading documents, people often find writer's opinions in documents which are new information for people. Annotation is known as a quite powerful method to find such information. Making annotation on documents, however, is a hard work because people need to read documents very carefully to think which sentences in documents can be annotated. People spent much time for choosing sentences and thinking what annotations they make on sentences. If people are automatically given sentences recommended to make annotations on, people can make annotations on documents easily with shaving off time. New methods for recommending sentences which can be made annotations on are required. This paper proposes an extraction method of sentences recommended to make annotations on for understanding writer's opinions in a document. The method extracts sentences including writer's opinions by evaluating words included in sentences. If a sentence includes words used for showing writer's opinions, the method extracts the sentence to recommend users to make annotations on. Users of the proposed method can understand writer's opinions deeply by reading a document and making annotations on the extracted sentences. We experimented with the proposed method and verified that the proposed method can extract sentences which are made annotations on by participants for understanding writer's opinions deeply.

There are many people who are struggling againstintractable diseases in the world. In general, it takes a long time to cure the intractable diseases because it is difficult to make diagnoses and to cure such diseases. There may be also people who are struggling against unclassified diseases that we have not discovered yet. Though, in Japan, methods for curing 56 intractable diseases have been studied, methods for discovering the unclassified diseases have not been studied. This paper proposes a new system that supports users for thinking new criteria for the unclassified diseases. We propose this system as one of the new diagnosis approach for early detection of intractable diseases. A user of the system inputs names of classified diseases to use the system. The system chooses 20 names from the inputs and show them to the user on its visualizing interface. The user chooses some names of disease whose diagnostics are similar to each other, and makes a group of the diseases. After making groups of diseases, if the user vacillates which group is appropriate for names of disease, the user writes the names and the reasons why the user has vacillated. The system outputs the reasons as candidates of new criteria for classifying unclassified diseases. We experimented the proposed system. Though six doctors majoring eye, hematology, and psychiatry used the proposed system, we obtained new criteria for unclassified diseases only from eye doctors. We validated the efficiency of the system for specific doctors.

Tsugology is the science and technology for dealing with tsugoes, where a Tsugo is the set composed of an intention of a working/living human and constraints which may restrict the way to achieve the intention or other/others' intentions. The incomplete expression by each subject about his/her tsugoes sometimes cause inefficient communications, and may force the restarting of a project because the latent conflicts may get externalized on the way. In Tsugology, we study the expression format of tsugoes for each human, and the process for finding conflict solutions and creating business opportunities of both an individual and of a group of people. In this paper, we present exemplifications to show the expected utility of Tsugology.

We developed a system for extracting Discounted Utterances(DUs) and evaluating them. We defined the DU as “Utterance which was ignored by others in a conversation in spite of its potential.” We aim to mine participants' unique stories and specialized knowledge based on their experiences from DUs. The proposed system is designed for considering emotional effects on others in conversations. We analyzed a meeting of toilet design as a case study. Each participant here has his/her own specialty. The system extracted DUs as triggers for mining the unique stories. We employed double helical model in Chance Discovery to the system. The study shows the significance of the system, and makes it clear that the DU in this paper is not just ignored in the conversation and includes speakers' unspoken stories based on speakers' own experiences. These stories are useful for the development of systems. Besides, some of DUs indicated the sense of existing values. By utilizing DUs, the system can also provide opportunities to improve existing systems.

Many medical accidents and incidents occurred due to communication errors. To avoid such incidents, in this paper, we propose a system for determining communication errors. Especially, we propose a model that can be applied to multi-layered or chained situations. First, we provide an overview of communication errors in nursing activities. Then we describe the warp and woof model for nursing task that was proposed by Harada and considers multi-layered or chained situations. Next we describe a system for determining communication errors based on the warp and woof model for nursing task. The system is capable of generating nursing activity diagrams semi-automatically and compiles necessary nursing activities. We also propose a prototype tagging of the nursing corpus for an effective generation of the diagrams. Then we combine the diagram generation with the Kamishibai KeyGraph to determine possible points of the hidden or potential factors of communication errors.

Mediation is a form of alternative dispute resolution which aims at assisting disputants in reaching an agreement on a disputed matter. Debate ensues on what skills an individual needs to play a mediator's role effectively. Education and training for mediators become complex issues. Then how can mediator's skills be trained in spite that the skills can not be defined clearly? In cognitive science visualization of and reflection on one's behavior is proven effective in such a situation. In this paper we explore a text processing method for reflective visualization of a dialogue. The dialogue is a time sequence of utterances from a mediator and disputants. The method visualizes an inter-topic association which foreshadows the intentional or unintentional subsequent development of topics indicated by temporal topics clusters far apart in time. The method is applied to a mediation case where a dispute between a seller and a buyer on cancelling an purchase transaction at an online auction site is resolved.

By mining and looking at the relations between pieces of knowledge, the talent of human to create an idea from a combination of knowledge comes to be elevated. In this paper, the Innovation Game we have been presenting since last year is introduced as an environment for the communication to elevate this talent. The players called innovators present ideas created from combining prepared basic ideas, and sell the ideas to each other and their stocks to investors. As a result, the values of ideas are evaluated, to reveal latent opportunities in the market. Here we present experimental results showing the niche, i.e., the space between clusters of already used or newly invented ideas of business, tends to enable players to create ideas/products of which the quality can be highly evaluated by the human society.

We are applying the data visualization method using Pictorial KeyGraph to the data on RFID tags attached to books browsed in a book library and apparel stores. From the visualized map, we find (1) customers start from the items which are easy to notice, (2) later converge to items not so outstanding (rare in the RFID data) items but connected from the items in (1), and (3) then notice his/her own latent interest in non-popular items more strongly, by looking at the visualized map and doing some interaction with the items or with us who are designer of the experimental environment. This presents a method for discovering latent interest of customers in the real space of the store, and for enhancing the popularity of non-popular items, by integrating the effects of RFID sensors and the process of chance discovery.

The human eyes are a powerful sensor to detect valuable objects, and of the valuable structure among objects in the environment. Scientists executed experiments on these balls, for investigating the attention of human in searching things to buy and in exploring clues for problem solving. The shift of attention target toward insight has been reported in previous studies, but it has not been clarified how logical ideas of human is emerging for the short time attaining insight. This paper reports the discovery that the orbits of eyes' target movement just before insight can be regarded as auxiliary lines, i.e., lines which externalize the hidden relationship of objects which accelerate the human's deeper thought. The experiments were executed for subjects looking at artificial products and artworks and also for subjects designing a new city on a map. The snapshots of eye movements can be interpreted as reflecting the subjects' interest in combining basic parts to make a valuable structure. By showing the snapshot just before insight to subjects themselves, they tended to notice why they attained insight and could explain the value of the objects to others.

Credit risk management based on portfolio theory becomes popular in recent Japanese financial industry. But consideration and modeling of chain reaction bankruptcy effect in credit portfolio analysis leave much room for improvement even though the importance of the effect is recognized among credit analysis experts. That is partly because method for grasping relations among companies with limited data is underdeveloped. In this article, chance discovery method is applied to estimate structure of industrial relations that are to include companies' relations that transmit chain reaction of bankruptcy. The steps for the data analysis is introduced and result of example analysis with Japanese default events data of 2005 is presented.

Experts of chance discovery have recognized a new class of problems where the previous methods fail to reveal a latent structure behind observation. There are invisible events which play an important role in the dynamics of visible events. A hidden hub person (an invisible leader) in a communication network is a typical example. This paper presents a stable deterministic crystallization algorithm for discovering such hidden hub events. The algorithm is evaluated with the test data generated from a large scale-free random network. It is demonstrated that precision for discovering the hidden hub events remains as high as 80% to 100%, regardless of the prior knowledge and the network structure.

Tools for chance discovery have been used in wide range of applications, e.g. marketing, product designs, earthquake prediction. These tools visualized the map of relations among events, to be used as a scenario map for aiding users' decision in the real world. However, the network models in the basis of these existing tools, represented by KeyGraph, have been sometimes confusing due to the complex structure of the output graph. In this paper, a potential model has been introduced for easily presenting the contextual meaning underlying the links in KeyGraph. The visual outlook of the presented tool KeyBird is like the land surface of a real island with mountains and ridges, so that the users easily interprets the output and understands the deep contextual structures missing in the data. The experimental results show the successful performance of this user interface.

Textual communication in message boards is analyzed for classifying Web communities. We present a communication-content based generalization of an existing business-oriented classification of Web communities, using KeyGraph, a method for visualizing the co-occurrence relations between words and word clusters in text. Here, the text in a message board is analyzed with KeyGraph, and the structure obtained is shown to reflect the essence of the content-flow. The relation of this content-flow with participants' interests is then formalized. Three structure-features of relations between participants and words, determining the type of the community, are shown to be computed and visualized: (1) centralization (2) context coherence and (3) creative decisions. This helps in surveying the essence of a community, e.g. whether the community creates useful knowledge, how easy it is to join the community, and whether/why the community is good for making commercial advertisement.

The phenomenon that a 'little' topic can make a big difference is called 'The Tipping Point'. As a strategy for marketing this phenomenon could be a great economic opportunity. For instance, at is a good strategy to promote items associated with a certain fashion. The Tipping Point refers to a situation where the topic matches potential needs of customers. We analyze the mechanism of The Tipping Point in the context of the WWW, and present an algorithm called 'Expected Activation' for discovering promising new topics on the WWW.

According to interviews with refugees of the south-Hyogo earthquake (1995, M7.2), the human processes leading to activities for protecting themselves from disaster is made of multiple steps. The paper presents a guideline for stimulating essential steps in this process by computer, which aids humans in making life earthquake-proof. Two core tools in the proposed guideline are well coordinated realistic disaster simulations and discovery tools for risks and their causes, playing each role in the overall process. Stimulating the user with these tools helps in shifting the context in the user's mind into realistic disaster situations, motivating his/her protective actions.

The presented KeyGraph for Files (KGF) helps a user to find timely files and their relationships to their major relevant files. KeyGraph, which was presented as a document indexing algorithm, is applied to a history of files accessed by the user, instead of to a document. This strategy of KGF is supported by analogies between a document and a history of one using files. The occurrences of words in a document and of files used in a sequence have common causal structures, and KeyGraph previously indexes documents taking advantage of the causal structure in a document. As an effect, timely files are obtained from the history of a user using files, in a similar manner to obtaining keywords from a document by KeyGraph.

A hypothetical reasoning is an important knowledge system's framework because of its theoretical basis and its usefulness for practical problems including diagnosis, design, etc. One crucial problem with hypothetical reasoning is, however, its slow inference speed. The polynomial-time approximate solution method of equivalent 0-1 integer programming, i.e., pivot and complement method, has been applied to computing hypothetical reasoning. However, to achieve further improvement by considering the knowledge structure of a given problem, it is beneficial to have another inference method which can work in the knowledge domain rather than the mathematical programming domain. For this purpose, the above method is reformalized using a new type of network. By taking advantage of the knowledge structure, this network achieves an inference speed in the order of O(N/sup 2/) against N, where N indicates problem size.