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.

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.

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.

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.

In this paper, we describe the concept, practice, and support systems of “design with discourse” that is a method to design from the “ethics level.” First, we place the ethics level in the hierarchical representation of artifacts. Second, we systematize the concept of design with discourse. Third, we describe practice of design with discourse with two examples. Then, we present an overview of support systems that embody the concept of design with discourse. Finally, we conclude that design with discourse contributes to systematic design from the ethics level.