Recent advances in diffusion-based models have significantly broadened their scope, extending well beyond image generation to encompass zero-shot segmentation tasks. In this work, we introduce a novel, training-free approach that harnesses both self- and cross-attention maps to achieve highly detailed hair segmentation. Our method demonstrates remarkable efficacy in producing fine-grained results without the need for additional training.

Knowledge tracing (KT) in programming education presents unique challenges due to the complexity of coding tasks and the diverse methods students use to solve problems.Although students' questions often contain valuable signals about their understanding and misconceptions, traditional KT models often neglect to incorporate these questions as inputs to address these challenges.This paper introduces SQKT (Students' Question-based Knowledge Tracing), a knowledge tracing model that leverages students' questions and automatically extracted skill information to enhance the accuracy of predicting students' performance on subsequent problems in programming education.Our method creates semantically rich embeddings that capture not only the surfacelevel content of the questions but also the student's mastery level and conceptual understanding.Experimental results demonstrate SQKT's superior performance in predicting student completion across various Python programming courses of differing difficulty levels.In in-domain experiments, SQKT achieved a 33.1% absolute improvement in AUC compared to baseline models.The model also exhibited robust generalization capabilities in cross-domain settings, effectively addressing data scarcity issues in advanced programming courses.SQKT can be used to tailor educational content to individual learning needs and design adaptive learning systems in computer science education.Our code is available at

In designing multiple-choice questions (MCQs) in education, creating plausible distractors is crucial for identifying students’ misconceptions and gaps in knowledge and accurately assessing their understanding. However, prior studies on distractor generation have not paid sufficient attention to enhancing the difficulty of distractors, resulting in reduced effectiveness of MCQs. This study presents a pipeline for training a model to generate distractors that are more likely to be selected by students. First, we train a pairwise ranker to reason about students’ misconceptions and assess the relative plausibility of two distractors. Using this model, we create a dataset of pairwise distractor ranks and then train a distractor generator via Direct Preference Optimization (DPO) to generate more plausible distractors. Experiments on computer science subjects (Python, DB, MLDL) demonstrate that our pairwise ranker effectively identifies students’ potential misunderstandings and achieves ranking accuracy comparable to human experts. Furthermore, our distractor generator outperforms several baselines in generating plausible distractors and produces questions with a higher item discrimination index (DI).

A sensing glove is an attractive application of wearable devices. Soft sensors are emerging to replace rigid sensing units, especially for wearable sensor systems, due to its inherent softness, flexibility, and stretchability. However, the fabrication process for the soft sensors is usually complex, time-consuming, labor-intensive, and has low production rate. To integrate a sensor system, an assembly process is essential, which may make the system bulky. Moreover, a solution for the electrode parts has rarely been suggested, although a bulky electrode part may obstruct the user's movement and degrade performance of the sensor. Thus, in this study, a novel fabrication process is suggested based on direct ink writing (DIW) of eutectic gallium-indium (EGaIn), which forms all the items in the sensor system from the sensing units, wiring, and the electrode part. A sensing glove for 2D finger motions was fabricated, and its performance was verified in terms of linearity, dynamic response, and accuracy. The sensing glove can be used as an easily-wearable and an intuitive interface to the virtual reality environment.

In programming education, instructors often supplement lectures with active learning experiences by offering programming lab sessions where learners themselves practice writing code. However, widely accessed instructional programming screencasts are not equipped with assessment format that encourages such hands-on programming activities. We introduce Elicast, a screencast tool for recording and viewing programming lectures with embedded programming exercises, to provide hands-on programming experiences in the screen-cast. In Elicast, instructors embed multiple programming exercises while creating a screencast, and learners engage in the exercises by writing code within the screencast, receiving auto-graded results immediately. We conducted an exploratory study of Elicast with five experienced instructors and 63 undergraduate students. We found that instructors structured the lectures into small learning units using embedded exercises as checkpoints. Also, learners more actively engaged in the screencast lectures, checked their understanding of the content through the embedded exercises, and more frequently modified and executed the code during the lectures.

In this paper, we investigate the effectiveness of visualization of code history on peer assessment in computer science education. Peer assessment is found to be an effective learning tool for programming education. While many systems are proposed to support peer assessment in programming education, little effort has been devoted to finding ways to improve the peer assessment by assisting the students to understand the programs they are assessing. We introduce Eliph, a web-based peer assessment system for programming education with code history visualization. Eliph incorporates the visualization of character-level code history, selection-based history tracking and the integration of execution events to assist students in understanding programs written by peers, thereby leading to more effective peer assessment. We evaluate Eliph with an experiment in an undergraduate CS course. We show that visualization of code history has positive effects on promoting higher quality of peer feedback by understanding the intention and thought process.

For physical human-robot interaction (pHRI), it has been an important issue to control the output force of actuators. The aim of this study was to apply a new control strategy, named model-inverse time delay control (MiTDC), to series elastic actuators (SEA) in a lower extremity exoskeleton, even in the presence of uncertainties from pHRI. The law for time delay control (TDC) is derived and implementation issues are discussed, including the design of the state observer and the selection of the nominal value of the control distribution coefficient. Additionally, a new concept, a new reference position using the inverse of model dynamics, is introduced to realize satisfactory tracking performance without delay. Experimental results showed that the suggested controller achieved satisfactory performance without accurate information on system parameters, requiring only a nominal value of the control distribution coefficient.

We present Elice, an online CS (computer science) education platform, and Elivate, a system for taking student learning data from Elice and infers their progress through an educational taxonomy tailored for programming education. Elice captures detailed student learning activities, such as the intermediate revisions of code as students make progress toward completing their programming exercises. With those data, Elivate recognizes each student's progression through an education taxonomy which organizes intermediate stages of learning such that the taxonomy can be used to evaluate student progress as well as to design and improve course materials and structure. With more than 240,000 intermediate source codes generated by 1,000 students, we demonstrate the practicality of the Elice and Elivate. We present case studies that confirm that categorizing student actions into the different steps of the taxonomy results in better understanding of the effect of TA's assist and student's performance.

We present Elice, an online CS (computer science) education platform, and Elivate, a system for (i) taking student learning data from Elice, (ii) inferring their progress through an educational taxonomy tailored for programming education, and (iii) generating the real-time assistance for students and lecturers. Online courses suffer from high average attrition rates, and early prediction can enable early personalized feedback to motivate and assist students who may be having difficulties. Elice captures detailed student learning activities including intermediate revisions of code as students make progress toward completing their programming exercises and timestamps of student logins and submissions. Elivate then takes those data to analyze each student's progress and estimate the time to completion. In doing so, Elivate uses a learning taxonomy and automatic clustering of source code revisions. Using more than 240,000 code revisions generated by 1,000 students, we demonstrate how Elivate processes large-scale student data and generates appropriate real-time feedback for students.

Sporting championships and other media events can induce very strong feelings of co-presence that can change communication patterns within large communities. Live tweeting reactions to media events provide high-resolution data with time-stamps to understand these behavioral dynamics. We employ a computational focus group method to identify a population of 790,744 international Twitter users, and we track their behavior before, during, and after the 2014 FIFA World Cup. We pick, in particular, a set of Twitter users who specified the teams that they are supporting, such that we can identify communities of fans of the teams, as well as the entire community of World Cup fans. The structure, dynamics, and content of communication of these communities of users are analyzed to compare behavior outside of the matches to behavior during the event and to examine behavioral responses across languages. Specifically, the temporal patterns of the tweeting volume, topics, retweet- ing, and mentioning behaviors are analyzed. We find there are similarities in the responses to media events, characteristic changes in activity patterns of users, and substantial differences in linguistic features. These findings have implications for designing more resilient socio-technical systems during crises and developing better models of complex social behavior.

To help users quickly understand the major opinions from massive online reviews, it is important to automatically reveal the latent structure of the aspects, sentiment polarities, and the association between them. However, there is little work available to do this effectively. In this paper, we propose a hierarchical aspect sentiment model (HASM) to discover a hierarchical structure of aspect-based sentiments from unlabeled online reviews. In HASM, the whole structure is a tree. Each node itself is a two-level tree, whose root represents an aspect and the children represent the sentiment polarities associated with it. Each aspect or sentiment polarity is modeled as a distribution of words. To automatically extract both the structure and parameters of the tree, we use a Bayesian nonparametric model, recursive Chinese Restaurant Process (rCRP), as the prior and jointly infer the aspect-sentiment tree from the review texts. Experiments on two real datasets show that our model is comparable to two other hierarchical topic models in terms of quantitative measures of topic trees. It is also shown that our model achieves better sentence-level classification accuracy than previously proposed aspect-sentiment joint models.

Topic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes (HDP) are simple solutions to discover topics from a set of unannotated documents. While they are simple and popular, a major shortcoming of LDA and HDP is that they do not organize the topics into a hierarchical structure which is naturally found in many datasets. We introduce the recursive Chinese restaurant process (rCRP) and a nonparametric topic model with rCRP as a prior for discovering a hierarchical topic structure with unbounded depth and width. Unlike previous models for discovering topic hierarchies, rCRP allows the documents to be generated from a mixture over the entire set of topics in the hierarchy. We apply rCRP to a corpus of New York Times articles, a dataset of MovieLens ratings, and a set of Wikipedia articles and show the discovered topic hierarchies. We compare the predictive power of rCRP with LDA, HDP, and nested Chinese restaurant process (nCRP) using heldout likelihood to show that rCRP outperforms the others. We suggest two metrics that quantify the characteristics of a topic hierarchy to compare the discovered topic hierarchies of rCRP and nCRP. The results show that rCRP discovers a hierarchy in which the topics become more specialized toward the leaves, and topics in the immediate family exhibit more affinity than topics beyond the immediate family.