In this study, we propose quantifying students' learning depth in video programming courses by analyzing their video annotations using natural language processing techniques and Bloom's Digital Taxonomy (BDT). Utilizing a video-based Learning Management System that allows direct annotation of video frames, we collected 3,014 annotations from 42 students enrolled in a “Data Management” course. We optimized Japanese morphological analysis tools by integrating a custom IT terminology dictionary to enhance the recognition of domain-specific terms. A specialized corpus was constructed, and a domain-specific Word2vec model was trained to expand the BDT classification table through word embeddings. This expansion enabled the comprehensive classification of keywords extracted from student annotations into cognitive process categories. The results revealed a clear shift from lower-order to higher-order cognitive processes as the course progressed, indicating significant changes in students' learning depth influenced by instructional content and teaching strategies. The method effectively captured dynamic trajectories of cognitive development, and these findings provide educators with valuable insights to conduct nuanced assessments and enhance personalized learning experiences. This study provides a practical method for quantifying the depth of learning in online programming education, advancing the understanding of students' cognitive processes, and supporting the improvement of teaching practices through data-driven adjustments to instructional design.

This research full paper describes a study on Video Position-Based Notes (VPB-Notes). Video-based learning environments have become increasingly prevalent in programming education, offering flexibility and personalized pacing. The VPB-Notes which allow students to capture thoughts, questions, and observations at specific video timestamps, have emerged as a promising tool to promote active learning and engagement. However, the lack of standardization in VPB-Notes and the presence of mixed code and natural language poses significant challenges for learning analysis using traditional natural language processing techniques. This study aims to propose a novel analytical framework for understanding the cognitive processes and engagement levels reflected in VPB-Notes taken by students in asynchronous video-based programming courses. The research questions focus on uncovering the key characteristics and patterns of VPB-Notes and investigating the relationships between VPB-Notes features and learners' performance outcomes. Our study combines qualitative analysis of VPB-Notes from four programming courses with quantitative analysis using correlation and cluster analysis. The qualitative analysis reveals that VPB- Notes exhibit contextual relevance, conciseness, dynamic structure, and code integration with commentary. It also proposes an analytical framework for categorizing notes across four dimensions. The quantitative analysis suggests a hierarchy of note features, types, and initiative associated with academic performance. In contrast, cluster analysis highlights the complexity of note portfolios and the influence of personal learning styles. Despite limitations in the automation of note analysis and the need for further validation, this study contributes new insights to the growing body of knowledge on note-taking in online programming education, offering valuable guidance for instructional design and personalized learning support.

Shyness have been noted as a problem of the minority, which should be overcome on one's own. However, surveys have shown that nearly half of the society are introverts even in cultures believed to be extroversive. We can see the possibility of more contribution from the introverts if the social space is properly designed. As an initial exploration to this design space, we report here a field study where we held a conference banquet with a seating arrangement system developed with introversive participants in mind.

The RCA cleaning method is the industry standard way to clean silicon wafers, where temperature control is important for a stable cleaning performance. However, it is difficult mainly because the RCA solutions cause nonlinear and time-varying exothermic chemical reactions. So far, the MSPC (model switching predictive controller) using the CAN2 (competitive associative net 2) has been developed and the effectiveness has been validated. However, we have observed that the control performance, such as the settling time and the overshoot, does not always improve with the increase of the number of learning iterations of the CAN2. To solve this problem, we introduce the bagging method for the CAN2 and first-difference signals for the MSPC. The effectiveness of the present method is shown by means of computer simulation.

An application of CAN2 (competitive associative net 2) to plane extraction from 3D range images obtained by a LRF (laser range finder) is presented. The CAN2 basically is a neural net which learns efficient piecewise linear approximation of nonlinear functions, and in this application it is utilized for learning piecewise planner surfaces from the range image. As a result of the learning, the obtained piecewise planner surfaces are much smaller and much more than the actual planner surfaces, so that we introduce a method to gather piecewise planner surfaces for reconstructing the actual planner surfaces. We apply this method to real range images, and examine the performance and the comparative advantage to other methods.

In this article, we apply the competitive associative net called CAN2 to the processing of the range data of indoor environment acquired by a mobile robot, where the CAN2 is a neural net or a learning machine which performs piecewise linear approximation. After introducing several methods for dealing with range data by the CAN2, we show the following results; (1) an original range image involving lack of data or jump edges can be learned to be recalled as a natural range image by means of modifying the learning and recalling procedure of the CAN2, (2) high data compression ratio can be achieved the CAN2 although the quality of the range image is not reduced so much.

Fracture reduction of carpal bones is rather difficult, because of their tight formation. Therefore, it is difficult to recognize their posture and position as well as their segmentation. This paper deals with a novel robotic system for a K-wire guiding using CT and fluoroscopic images for scaphoid fracture reduction. The robot is designed to consider issues such as sterilization, X-ray halation and easy setup. Template matching in registration is investigated focusing on shape and structure of the carpal bones as well as scaphoid itself. Experiments show validity of the developed system in terms of insertion accuracy and operation time

A unified modeling framework for end-to-end performance evaluation in a network management system is presented. A network management application, e.g., alarm surveillance, can be mostly accomplished by several processes distributed over a network. In order to predict the completion time for such an application, components, whether software or hardware, incurring a delay must be identified. A proposed model, which is referred to as the end-to-end performance model, specifies: (1) components which incur a significant delay in a management application, (2) their characteristics, and (3) their interactions. In determining the major delay components, management application processing, protocol processing, transmission delay, and protocol conversion, are incorporated. A particular modeling procedure is explained using a SONET (synchronous optical network) management network.>

An analytical model for predicting the performance of protocol software implementation in a layered communication architecture is proposed. Each layer is modeled by a queuing network. The model is constructed by focusing the behavior of the protocol entity in a layer. A first come, first served (FCFS) server queue with preemptive priority is employed for an entity and an infinite-server queue for response time of acknowledge packets. Respective layer models are piled up by embedding the lower services in the infinite-server queue. From the perspective of end-to-end performance, the model provides a solution for the following problems, which arise in implementing a layered communication protocol: assigning each layer's process to a processor; giving priority to respective processes in each processor; and providing an error recovery service. The authors present an approximate analysis to obtain an average end-to-end transmission delay and discuss the possible solutions for the problems mentioned.>

A design method for packet-switched networks is presented that enhances the reliability and prevents the performance degradation caused by network failures. The method is based on a design technique wherein the redundant capacity is assigned to appropriate links in advance to be capable of accommodating the detoured traffic caused by failed links. Using this technique, it is crucial to accurately evaluate to how large an amount the capacity for each link must be augmented. An augmented capacity function is introduced, which determines the capacity of a link based on the probability of a particular network state and a parameter which represents how important the link is in the state. Numerical examples are given to demonstrate the feasibility of the method, in terms of cost/performance effectiveness. The relation between the reliability level and the network cost is discussed.>