Profy: Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice

The quality of piano performance depends on nuanced timing, articulation, and dynamic control, but practice feedback is often summary-based and hard to act on. We introduce Profy, a weakly supervised system that learns from take-level labels derived from aggregated listener ratings (expert-labeled vs. amateur-labeled) to produce time-aligned highlights for review during piano practice. We collected synchronized 1 kHz key-motion and audio from 73 pianists and used 1,083 valid takes for modeling and evaluation. The model outputs clip-level predictions together with evidence scores on a shared resampled model time base for visualization. On 20 amateur clips from short technique studies annotated by 21 expert pianists, the displayed highlight score aligns with passages that expert pianists marked for review despite training without localized labels (Pearson r=0.61, ROC-AUC 0.75). Rather than summarizing a take with a single global score, Profy helps learners decide where to inspect next by supporting scrubbing, looping, and focused replay of time-localized passages associated with expert-amateur differences.

Offline Contextual Bandits in the Presence of New Actions

Automated decision-making algorithms drive applications such as recommendation systems and search engines. These algorithms often rely on off-policy contextual bandits or off-policy learning (OPL). Conventionally, OPL selects actions that maximize the expected reward from an existing action set. However, in many real-world scenarios, actions, such as news articles or video content, change continuously, and the action space evolves over time after data collection. We define actions introduced after deploying the logging policy as new actions and focus on OPL with new actions. Existing OPL methods identify optimal actions from the existing set effectively but cannot learn and select new actions because no relevant data are logged. To address this limitation, we propose a new OPL method that leverages action features. We first introduce the Local Combination PseudoInverse (LCPI) estimator for the policy gradient, generalizing the PseudoInverse estimator initially proposed for off-policy evaluation of slate bandits. LCPI controls the trade-off between reward-modeling condition and the condition for data collection regarding the action features, capturing the interaction effects among different dimensions of action features. Furthermore, we propose a generalized algorithm called Policy Optimization for Effective New Actions (PONA), which integrates LCPI, a component specialized for new action selection, with Doubly Robust (DR), which excels at learning within existing actions. We define PONA as a weighted sum of the LCPI and DR estimators, optimizing both the selection of existing and new actions, and allowing the proportion of new action selections to be adjusted by the weight parameter. Through extensive experiments, we demonstrate that PONA efficiently selects new actions while maintaining the overall policy performance as opposed to most existing methods that cannot select new actions.

Off-Policy Evaluation for Ranking Policies under Deterministic Logging Policies

Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using only offline logged data collected under a different, logging policy. Existing estimators, such as the ranking-wise and position-wise inverse propensity score (IPS) estimators, require the data collection policy to be sufficiently stochastic and suffer from severe bias when the logging policy is fully deterministic. In this paper, we propose novel estimators, Click-based Inverse Propensity Score (CIPS), exploiting the intrinsic stochasticity of user click behavior to address this challenge. Unlike existing methods that rely on the stochasticity of the logging policy, our approach uses click probability as a new form of importance weighting, enabling low-bias OPE even under deterministic logging policies where existing methods incur substantial bias. We provide theoretical analyses of the bias and variance properties of the proposed estimators and show, through synthetic and real-world experiments, that our estimators achieve significantly lower bias compared to strong baselines, for a range of experimental settings with completely deterministic logging policies.

SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills

While current AI illustration tools can generate high-quality images from text prompts, they rarely reveal the step-by-step procedure that human artists follow. We present SakugaFlow, a four-stage pipeline that pairs diffusion-based image generation with a large-language-model tutor. At each stage, novices receive real-time feedback on anatomy, perspective, and composition, revise any step non-linearly, and branch alternative versions. By exposing intermediate outputs and embedding pedagogical dialogue, SakugaFlow turns a black-box generator into a scaffolded learning environment that supports both creative exploration and skills acquisition.

Parallel and Mini-Batch Stable Matching for Large-Scale Reciprocal Recommender Systems

Reciprocal recommender systems (RRSs) are crucial in online two-sided matching platforms, such as online job or dating markets, as they need to consider the preferences of both sides of the match. The concentration of recommendations to a subset of users on these platforms undermines their match opportunities and reduces the total number of matches. To maximize the total number of expected matches among market participants, stable matching theory with transferable utility has been applied to RRSs. However, computational complexity and memory efficiency quadratically increase with the number of users, making it difficult to implement stable matching algorithms for several users. In this study, we propose novel methods using parallel and mini-batch computations for reciprocal recommendation models to improve the computational time and space efficiency of the optimization process for stable matching. Experiments on both real and synthetic data confirmed that our stable matching theory-based RRS increased the computation speed and enabled tractable large-scale data processing of up to one million samples with a single graphics processing unit graphics board, without losing the match count.

HTML-LSTM: Information Extraction from HTML Tables in Web Pages using Tree-Structured LSTM

In this paper, we propose a novel method for extracting information from HTML tables with similar contents but with a different structure. We aim to integrate multiple HTML tables into a single table for retrieval of information containing in various Web pages. The method is designed by extending tree-structured LSTM, the neural network for tree-structured data, in order to extract information that is both linguistic and structural information of HTML data. We evaluate the proposed method through experiments using real data published on the WWW.

FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts

Quickly understanding lengthy lecture videos is essential for learners with limited time and interest in various topics to improve their learning efficiency. To this end, video summarization has been actively researched to enable users to view only important scenes from a video. However, these studies focus on either the visual or audio information of a video and extract important segments in the video. Therefore, there is a risk of missing important information when both the teacher's speech and visual information on the blackboard or slides are important, such as in a lecture video. To tackle this issue, we propose FastPerson, a video summarization approach that considers both the visual and auditory information in lecture videos. FastPerson creates summary videos by utilizing audio transcriptions along with on-screen images and text, minimizing the risk of overlooking crucial information for learners. Further, it provides a feature that allows learners to switch between the summary and original videos for each chapter of the video, enabling them to adjust the pace of learning based on their interests and level of understanding. We conducted an evaluation with 40 participants to assess the effectiveness of our method and confirmed that it reduced viewing time by 53\% at the same level of comprehension as that when using traditional video playback methods.

AIx Speed: Playback Speed Optimization Using Listening Comprehension of Speech Recognition Models

Since humans can listen to audio and watch videos at faster speeds than actually observed, we often listen to or watch these pieces of content at higher playback speeds to increase the time efficiency of content comprehension. To further utilize this capability, systems that automatically adjust the playback speed according to the user's condition and the type of content to assist in more efficient comprehension of time-series content have been developed. However, there is still room for these systems to further extend human speed-listening ability by generating speech with playback speed optimized for even finer time units and providing it to humans. In this study, we determine whether humans can hear the optimized speech and propose a system that automatically adjusts playback speed at units as small as phonemes while ensuring speech intelligibility. The system uses the speech recognizer score as a proxy for how well a human can hear a certain unit of speech and maximizes the speech playback speed to the extent that a human can hear. This method can be used to produce fast but intelligible speech. In the evaluation experiment, we compared the speech played back at a constant fast speed and the flexibly speed-up speech generated by the proposed method in a blind test and confirmed that the proposed method produced speech that was easier to listen to.

DDSupport: Language Learning Support System that Displays Differences and Distances from Model Speech

When beginners learn to speak a non-native language, it is difficult for them to judge for themselves whether they are speaking well. Therefore, computer-assisted pronunciation training systems are used to detect learner mispronunciations. These systems typically compare the user's speech with that of a specific native speaker as a model in units of rhythm, phonemes, or words and calculate the differences. However, they require extensive speech data with detailed annotations or can only compare with one specific native speaker. To overcome these problems, we propose a new language learning support system that calculates speech scores and detects mispronunciations by beginners based on a small amount of unannotated speech data without comparison to a specific person. The proposed system uses deep learning--based speech processing to display the pronunciation score of the learner's speech and the difference/distance between the learner's and a group of models' pronunciation in an intuitively visual manner. Learners can gradually improve their pronunciation by eliminating differences and shortening the distance from the model until they become sufficiently proficient. Furthermore, since the pronunciation score and difference/distance are not calculated compared to specific sentences of a particular model, users are free to study the sentences they wish to study. We also built an application to help non-native speakers learn English and confirmed that it can improve users' speech intelligibility.