The COVID-19 pandemic has catalyzed a rapid transformation in higher education, prompting institutions to embrace online learning and e-learning as essential mechanisms for academic continuity. In this context, Interactive Learning Environments (ILEs), augmented with generative AI chatbots, represent a promising approach to enhancing the effectiveness of virtual education and interactive learning. This paper investigates the efficacy of an ILE integrated with ChatGPT in the context of computer networking education. A pilot experiment was conducted with three graduate students with basic IT networking background. The study examined the impact of integrating ChatGPT within the ILE on students’ engagement, comprehension, and overall learning outcomes. Methodological details, including learning program design, learning outcomes assessment, ILE settings, and ChatGPT integration are presented. Results from pre- and post-assessment tests, quizzes, and students’ feedback on ChatGPT’s utility as a learning aid are discussed. The findings suggest a significant improvement in students’ comprehension and performance following their engagement with the ILE while using ChatGPT as a support tool. Despite a few drawbacks reported by the students in terms of the interfaces’ ease of use, and the timely response and appropriate content delivered by ChatGPT, they were overall satisfied with the experience. The students would recommend ChatGPT for learning purposes under certain controlled environments. This study contributes to the growing body of literature on interactive learning technologies and highlights the potential of generative AI chatbots such as ChatGPT to revolutionize computer networking education in the digital age.

When users engage in online shopping, they often rely on product reviews as a reference. However, efficiently determining the overall evaluation of each product from a large number of reviews is not easy. Previous studies have addressed this issue by automatically scoring each product based on text analysis of product reviews. On the other hand, evaluating the performance of the retail store itself poses challenges. There are usually fewer reviews specifically targeting the store, and evaluations of the store are often embedded within product reviews. As a result, automatically scoring the performance of retail stores is not a straightforward task. Therefore, in this study, we propose a method to extract evaluations of retail stores from product reviews and automatically score them based on store-specific criteria. Additionally, we developed a system that utilizes the calculated store scores, allowing users engaged in online shopping to search for the evaluations of the retail stores that sell the products they are interested in. We present the details of our proposed method and the development of the search system, along with the results of the evaluation experiments conducted using the developed system.

In recent years, there are various review sites on the Web. In online shopping, review sites are important because they strongly influence consumers’ purchasing decisions. We focus on cosmetic reviews and consider the skin type and usability of individual users. In order to realize the cosmetic recommendation system, we are working on the development of a review recommendation method by an automatic scoring system using an evaluative expression dictionary for each cosmetic item classification. Since cosmetic items have detailed classifications, it is necessary to build an evaluative expression dictionary for each cosmetic classification in order to perform automatic scoring. Therefore, it is desirable that the evaluative expression dictionary construction method be efficient and semi-automatic. In this paper, we try to improve the evaluative expression dictionary and examine the efficient method for developing evaluative expression dictionary based on Word2Vec for cosmetics.

In recent years, although cosmetics review-sharing sites have been helpful in decision making by users, it is not easy for users to find reviews that are suitable for them because the quality of skin and taste vary among individuals. We aim to develop a recommender system for cosmetic items and reviews by analyzing cosmetics reviews. In most review sharing sites, reviewers can assign tags to their own reviews. Tags are very useful for users to understand the effects of the items and to filter reviews with specific tags. Thus, we propose a tag recommendation method for a cosmetics review using the results of the automatic scoring method proposed in our previous work. We believe that our proposed method can significantly simplify the task of assigning tags to a review text. Moreover, the results of the experimental evaluation reveal the tendency that "reviewers may select tags if their score for an aspect of a cosmetic item is sufficiently high". Based on this result, we will discuss a threshold to determine whether to recommend tags.

Portal sites supporting online purchases provide commercial items and reviews for them. In the case of purchasing cosmetic items, in particular, reviews have important roles in purchasing decisions, allowing purchasers to avoid becoming annoyed with unsuitable items. Thus, we are trying to develop a recommender system for cosmetic items and analyzing reviews. General recommender systems basically identify similar users based on their preferences against common items. However, owing to the huge number of cosmetic items, it is not easy to use preferences for common items because of the data sparsity problem. Therefore, we propose a method for finding similar users based on their preferences against cosmetic item clusters. Moreover, we evaluate and discuss the proposed method for finding similar users based on experimental evaluations.

Homemaker decide what to cook based on the mood they are in, the ingredients they have in their refrigerators, or the ingredients offered in a supermarket. Most of the existing services for searching recipes allow ingredient names or recipe names as search input. We propose a system that allows searching recipes based on the users' mood. To develop the system, we gather words to express a user's mood when making a menu decision and classify them according to their relationship. We determine six aspects of a user's mood. The result of our preliminary experiment and a questionnaire-based survey show that our method describes a user's mood when deciding for a menu and that the system helps in the decision-making. Furthermore, we propose a method for automatically generating recipe metadata, which we plan to add to our system.

In this paper, we propose a real-time system for measuring the consumption of various types of seasonings. In our system, all seasonings are placed on a scale, and we continuously take images of these items using a camera. Our system estimates the consumption of each condiment by calculating the difference between the weight when the seasoning was picked up and the weight when it was placed back on the scale. Our system identifies the type of seasoning that was used by determining whether or not the seasoning was present on the scale. By using our system, users can automatically log their usage of seasoning. Then, they can adjust the seasoning according to their desired taste.

This paper presents a method for recognizing recipe ingredients based on the load on a chopping board when ingredients are cut. The load is measured by four sensors attached to the board. Each chop is detected by indentifying a sharp falling edge in the load data. The load features, including the maximum value, duration, impulse, peak position, and kurtosis, are extracted and used for ingredient recognition. Experimental results showed a precision of 98.1% in chop detection and 67.4% in ingredient recognition with a support vector machine (SVM) classifier for 16 common ingredients.