Zero-Shot Edge Detection with SCESAME: Spectral Clustering-based Ensemble for Segment Anything Model Estimation

This paper proposes a novel zero-shot edge detection with SCESAME, which stands for Spectral Clustering-based Ensemble for Segment Anything Model Estimation, based on the recently proposed Segment Anything Model (SAM). SAM is a foundation model for segmentation tasks, and one of the interesting applications of SAM is Automatic Mask Generation (AMG), which generates zero-shot segmentation masks of an entire image. AMG can be applied to edge detection, but suffers from the problem of overdetecting edges. Edge detection with SCESAME overcomes this problem by three steps: (1) eliminating small generated masks, (2) combining masks by spectral clustering, taking into account mask positions and overlaps, and (3) removing artifacts after edge detection. We performed edge detection experiments on two datasets, BSDS500 and NYUDv2. Although our zero-shot approach is simple, the experimental results on BSDS500 showed almost identical performance to human performance and CNN-based methods from seven years ago. In the NYUDv2 experiments, it performed almost as well as recent CNN-based methods. These results indicate that our method effectively enhances the utility of SAM and can be a new direction in zero-shot edge detection methods.Comment: 11 pages, accepted to WACV 2024 Worksho

An electron-microscopic study on lipogenesis

With the purpose to elucidate morphologically the site where fat synthesis takes place in the cell, electron-microscopic observation has been conducted on the interscapular brown fat tissue of mice at various periods of carbohydrate introduction after starvation. By starving mice, the depot lipids in the brown fat have been discharged almost completely, and the carbohydrate introduction has caused the biosynthesis of lipids from carbohydrtates in the same tissue. Observations on the tissues proved that the lipogenesis in the brown fat tissue cells takes place in the ground substance keeping the intimate correlation with the endoplasmic reticulum but not in the mitochondria.</p

An Automatic Self-explanation Sample Answer Generation with Knowledge Components in a Math Quiz

Part of the Lecture Notes in Computer Science book series (LNCS, volume 13356)Little research has addressed how systems can use the learning process of self-explanation to provide scaffolding or feedback. Here, we propose a model automatically generating sample self-explanations with knowledge components required to solve a math quiz. The proposed model contains three steps: vectorization, clustering, and extraction. In an experiment using 1434 self-explanation answers from 25 quizzes, we found 72% of the quizzes generated sample answers with all necessary knowledge components. The similarity between human-created and machine-generated sentences was 0.719, with a significant correlation of R = 0.48 for the best performing generation model by BERTScore. These results suggest that our model can generate sample answers with the necessary key knowledge components and be further improved by using the BERTScore

Unsupervised techniques for generating a standard sample self-explanation answer with knowledge components in a math quiz

Self-explanation is a widely recognized and effective pedagogical method. Previous research has indicated that self-explanation can be used to evaluate students’ comprehension and identify their areas of difficulty on mathematical quizzes. However, most analytical techniques necessitate pre-labeled materials, which limits the potential for large-scale study. Conversely, utilizing collected self-explanations without supervision is challenging because there is little research on this topic. Therefore, this study aims to investigate the feasibility of automatically generating a standardized self-explanation sample answer from unsupervised collected self-explanations. The proposed model involves preprocessing and three machine learning steps: vectorization, clustering, and extraction. Experiments involving 1, 434 self-explanation answers from 25 quizzes indicate that 72% of the quizzes generate sample answers containing all the necessary knowledge components. The similarity between human-generated and machine-generated sentences was significant with moderate positive correlation, r(23) = .48, p < .05.The best-performing generative model also achieved a high BERTScore of 0.715. Regarding the readability of the generated sample answers, the average score of the human-generated sentences was superior to that of the machine-generated ones. These results suggest that the proposed model can generate sample answers that contain critical knowledge components and can be further improved with BERTScore. This study is expected to have numerous applications, including identifying students’ areas of difficulty, scoring self-explanations, presenting students with reference materials for learning, and automatically generating scaffolding templates to train self-explanation skills

Automated labeling of PDF mathematical exercises with word N-grams VSM classification

In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and difficult to scale. It recognizes that automated labeling eases the workload on experts, as seen in previous studies using automatic classification algorithms for research papers and Japanese mathematical exercises. However, these studies didn’t delve into fine-grained labeling. In addition to that, as the use of materials in the system becomes more widespread, paper materials are transformed into PDF formats, which can lead to incomplete extraction. However, there is less emphasis on labeling incomplete mathematical sentences to tackle this problem in the previous research. This study aims to achieve precise automated classification even from incomplete text inputs. To tackle these challenges, we propose a mathematical exercise labeling algorithm that can handle detailed labels, even for incomplete sentences, using word n-grams, compared to the state-of-the-art word embedding method. The results of the experiment show that mono-gram features with Random Forest models achieved the best performance with a macro F-measure of 92.50%, 61.28% for 24-class labeling and 297-class labeling tasks, respectively. The contribution of this research is showing that the proposed method based on traditional simple n-grams has the ability to find context-independent similarities in incomplete sentences and outperforms state-of-the-art word embedding methods in specific tasks like classifying short and incomplete texts

EXAIT: Educational eXplainable Artificial Intelligent Tools for personalized learning

As artificial intelligence systems increasingly make high-stakes recommendations and decisions automatically in many facets of our lives, the use of explainable artificial intelligence to inform stakeholders about the reasons behind such systems has been gaining much attention in a wide range of fields, including education. Also, in the field of education there has been a long history of research into self-explanation, where students explain the process of their answers. This has been recognized as a beneficial intervention to promote metacognitive skills, however, there is also unexplored potential to gain insight into the problems that learners experience due to inadequate prerequisite knowledge and skills that are required, or in the process of their application to the task at hand. While this aspect of self-explanation has been of interest to teachers, there is little research into the use of such information to inform educational AI systems. In this paper, we propose a system in which both students and the AI system explain to each other their reasons behind decisions that were made, such as: self-explanation of student cognition during the answering process, and explanation of recommendations based on internal mechanizes and other abstract representations of model algorithms

Oxygen Isotope Exchange Between Molten Silicate Spherules and Ambient Water Vapor with Nonzero Relative Velocity: Implication for Chondrule Formation Environment

Oxygen isotope compositions of chondrules reflect the environment of chondrule formation and its spatial and temporal variations. Here, we present a theoretical model of oxygen isotope exchange reaction between molten silicate spherules and ambient water vapor with finite relative velocity. We found a new phenomenon, that is, mass-dependent fractionation caused by isotope exchange with ambient vapor moving with nonzero relative velocity. We also discussed the plausible condition for chondrule formation from the point of view of oxygen isotope compositions. Our findings indicate that the relative velocity between chondrules and ambient vapor would be lower than several 100 m/s when chondrules crystallized.Comment: 15 pages, 8 figures. Accepted for publication in Icaru

ACCURACY IN DETERMINING KINETIC PARAMETERS WITH FORCE PLATES EMBEDDED UNDER SOIL-FILLED BASEBALL MOUND

We developed a force measurement system embedded in a soil-filled mound for measuring ground reaction forces (GRF) acting on baseball pitchers and examined the accuracy of determining the point of force application (PFA) and kinetic parameters computed from GRF. Three 1.0 x 0.9 m2 force platforms were placed on the concrete foundation of an indoor sports facility and three bays were fixed onto the aluminum plates of the force plateorms. In each tray, clay-blocks were laid tightly and a mixture of red sand and volcanic-ash was used to make a smooth surface layer. The mean absolute error was 6.0 f 4.0 mm in determining PFA, less than 15.5 Ns (5% of the true value) in determining linear impulse. These results suggest that the present method is valid for measuring the PFA and GRF acting on the pitcher's legs for analyzing kinetics of pitching performances

Studies on the Effect of Radio Frequency Field in a Cusp-Type Charge Separation Device for Direct Energy Conversion

In D-3He fusion power generation, an application of direct energy conversion is expected in which separation of charged particles is necessary. A cusp-type direct energy converter (CuspDEC) was proposed as a charge separation device, but its performance was degraded for a high density plasma. The goal of the present study is to establish an additional method to assist charge separation by using a nonlinear effect of a radio frequency (rf) electric field. Following to the previous study, we experimentally examine the effect of an rf field to electron motion in a CuspDEC device. Two ring electrodes were newly installed in a CuspDEC simulator and the current flowing into the electron collector located in the line cusp region was measured on an rf field application. The significant variation in the current was found, and an improvement of the charge separation can be expected by using the phenomenon appropriately