Personalized interventions through dialog-based health guidance effectively encourage healthy behaviors for clients (i.e., individuals) at risk of lifestyle diseases. However, it is not obvious which dialog policies would enable the best understanding of the client’s characteristics and subsequent provision of tailored interventions. To meet this need, we propose a dialog policy learning method for both supporting professionals and directly motivating clients via chatbots. We model the dialog to identify tailored interventions, considering the heterogeneity among clients, by multitask reinforcement learning and train an agent in a model-based manner utilizing real-world data and reward shaping to ensure the safety of early agents’ statements. A simulation study utilizing real data shows that our approach achieves a success rate of 76% in identifying tailored interventions with few questions, outperforming standard methods, including large language models, which had a 42% success rate.

With the widespread use of fitness trackers, predicting the future of individuals’ health is becoming easier. However, little is known about how presenting a prediction of an individual's future impacts one's behavior. In this study, we targeted walking behavior and aimed to clarify the impact of presenting a prediction of the number of steps on one's behavior. We conducted a five-week experiment with 36 participants using the application “StepUp Forecast”, which presents prediction on the basis of past lifelogs. We found that self-efficacy and the number of steps increased significantly when the predictions were presented compared with when only records of steps were shown. This was because people were motivated to exceed the predicted value. Furthermore, when additional steps were presented along with the step prediction, neither self-efficacy nor the number of steps increased. Our findings suggest that the prediction should be an achievable value in which the user can exceed, and that a positive feedback loop could be possible by enhancing self-efficacy through the experience of achievement.

The importance of developing an application based on intervention technology that leads to a healthier life is widely recognized. A challenging part of realizing the application is the need for planning, i.e., considering a user's health goal (e.g., sleep at 10:00 p.m. to get enough sleep), providing intervention at the appropriate timing to help the user achieve the goal. The reinforcement learning (RL) approach is well suited to this type of problem since it is a methodology for planning; RL finds the optimal strategy as that which maximizes future expected profit. The purpose of this study is to clarify the effects of intervention based on RL to support healthy daily life. Therefore, we (i) collect real daily activity data from participants, (ii) generate a user model that imitates the user's response to system interventions, (iii) examine valuable goals and design them as rewards in RL and (iv) obtain optimal intervention strategies by RL via simulations given a user model and goals. We evaluate a generated user model and verify by simulations whether our method could successfully achieve the goal. In addition, we analyze the cases that demonstrated higher probability of achieving the goal and report the features.

Data integrity constraints are fundamental in various applications, such as data management, integration, cleaning, and schema extraction. In this paper, we address the problem of finding inclusion dependencies on the Web. The problem is important because (1) applications of inclusion dependencies, such as data quality management, are beneficial in the Web context, and (2) such dependencies are not explicitly given in general. In our approach, we enumerate pairs of HTML/XML elements that possibly represent inclusion dependencies and then rank the results for verification. First, we propose a bit-based signature scheme to efficiently select candidates (element pairs) in the enumeration process. The signature scheme is unique in that it supports Jaccard containment to deal with the incomplete nature of data on the Web, and preserves the semiorder inclusion relationship among sets of words. Second, we propose a ranking scheme to support a user in checking whether each enumerated pair actually suggests inclusion dependencies. The ranking scheme sorts the enumerated pairs so that we can examine a small number of pairs for simultaneously verifying many pairs.

We pose a new problem of discovering associations between events in our daily lives and their characteristic items, such as (Halloween, pumpkin) and (Christmas, chimney). To solve the problem, we dopted an approach similar to that of existing research on event detection, which tries to discover events by detecting bursts of occurrence frequency of a relevant term in a document stream, where the term (item) is associated with the discovered event. We extracted events from blog entries available on the Web, while the previous studies mostly used news articles as document streams. Blog entries are shown to have quite different characteristics to news articles. Considering this fact, we developed a method for discovering the associations by integrating existing techniques that can handle and take advantage of the characteristics of blog data. We verified through experiments using actual data that the proposed approach works quite well.

The problems of utilizing an extended vector processor called IDP (Integrated Database Processor) for nonnumerical processing are discussed. IDP has highly pipelined merge and search vector facilities in addition to conventional vector facilities. This extended vector processor can be utilized not only for primitive relational operations but also for the following applications: (1) complex relational query using multiple indexes; (2) multiway merge/sort utility, and (3) generation and test processing. Vectorization methods and performance improvements are described and analyzed.>

A new relational database system architecture based on a vector processing method is presented. The similarity between table and vector structures was studied. This similarity implies that vector processors have high potential for the improvement of Relational Data Base Management System (RDBMS) performance. In commercial RDBMSs, however, data records are internally organized in complex pointer structures, to which vector processing is not directly applicable. We propose a new vectorization method by which data in the pointer structures are dynamically rearranged into vector form, and collectively processed using a pipelined vector processor. A new pipelined vector processor called an Integrated Database Processor (IDP) which is particularly suitable for our vectorization strategy was developed. IDP is designed as an optional hardware for a general purpose computer. The vectorization method and IDP are applied to Hitachi's new, recently-announced, commercial RDBMS (called RDB1/IDP). In this paper, the vectorization method and the architecture of IDP are described. The performance improvement is also described and analyzed.