Is Internal State Feedback in an E-learning Environment Acceptable to People?

In on-demand e-learning environments, the lack of direct intervention can lead to a decline in learners’ engagement. To address this issue, systems that estimate the learners’ attitudes and provide feedback have been proposed. However, the acceptability of such systems has not been sufficiently researched. In this study, we investigated the acceptability by people to an e-learning system with internal state feedback, for future personalized learning support. To this end, we developed a system that estimates and visualizes the learner’s internal state in real-time. The system was exhibited in a public space for free use, and users’ impressions were analyzed. To estimate the learners’ internal state, we developed a machine-learning model that recognizes learners’ alertness from facial videos. The system was deployed in an exhibition space, and 131 responses were collected. These responses were coded and analyzed using a co-occurrence network. The result indicated that learners tend to dislike the system due to feelings of being observed by supervisors. In contrast, instructors expressed favorable options toward the introduction of the system.conference pape

R&D of the EM Calorimeter Energy Calibration with Machine Learning based on the low-level features of the Cluster

We have developed an energy calibration method using machine learning for the ILC electromagnetic (EM) calorimeter (ECAL), a sampling calorimeter consisting of Silicon-Tungsten layers. In this method, we use a deep neural network (DNN) for a regression to determine the energy of incident EM particles, improving the energy calibration resolution of the ECAL. The DNN architecture takes cluster hit data as low-level features of the cluster. In this paper, we report the status of our R&D and present results on energy calibration accuracy.journal articl

Volunteer Support Network for Elderly Foreigners : A New Movement of Korean Residents in Kyoto

departmental bulletin pape

Observation of B+ → χc0K+

journal articl

Dead time compensation for three-level flying capacitor inverter with phase shift PWM

Multilevel inverter can obtain low distortion output voltage and current wave. However, dead time causes an output voltage error in the phase of inverter. Dead time error causes nonlinearity of output voltage and phase currents ripples with 5th and 7th order ripples of fundamental frequency. Current ripple decreases motor control performance. This paper presents dead time compensation for three-level flying capacitor inverter which is operated by phase shift pulse width modulation. This method is focused on the fact that power switching devices, which cause voltage error by dead time, depend on current polarities. The algorithm is simple, and the dead time is inserted at the instant of turning-on and turning-off of switching devices so as not to affect output voltage. The simulation result shows that high order harmonics which caused by dead time effect are eliminated using this method.journal articl

監査・調査・指導

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Same as Figure 3, but with the influence of noise, as described in the main text

<p><b>Copyright information:</b></p><p>Taken from "Reconstruction of cellular variability from spatiotemporal patterns of "</p><p>http://www.nonlinearbiomedphys.com/content/1/1/10</p><p>Nonlinear Biomedical Physics 2007;1():10-10.</p><p>Published online 30 Aug 2007</p><p>PMCID:PMC2034575.</p><p></p> Parameter values are the same as in Figure 3

Snapshots of experimental data sets analyzed on their spatial distribution in cell-cell differences (bar size 2 mm)

<p><b>Copyright information:</b></p><p>Taken from "Reconstruction of cellular variability from spatiotemporal patterns of "</p><p>http://www.nonlinearbiomedphys.com/content/1/1/10</p><p>Nonlinear Biomedical Physics 2007;1():10-10.</p><p>Published online 30 Aug 2007</p><p>PMCID:PMC2034575.</p><p></p> Time points are indicated above the array of snapshots. In addition the spatial size the experimental data differ in their resolution: (A) and (B) 22.6 pixels/mm, (C) 68.2 pixels/mm, (D) 68.0 pixels/mm, (E) 38.6 pixels/mm, (F) 53.3 pixels/mm. As a rule of thumb at a cell density of 6.172·10cells/one can expect that 1 pixel contains approximately 12 cells in (A) and (B), 1 cell in (C) and (D), 4 cells in (E) and 2 cells in (F)

Same as Figure 4, but for the spatiotemporal patterns under the influence of noise, as shown in Figure 5

<p><b>Copyright information:</b></p><p>Taken from "Reconstruction of cellular variability from spatiotemporal patterns of "</p><p>http://www.nonlinearbiomedphys.com/content/1/1/10</p><p>Nonlinear Biomedical Physics 2007;1():10-10.</p><p>Published online 30 Aug 2007</p><p>PMCID:PMC2034575.</p><p></p

Correlation coefficients between the matrices (Ωand , respectively) and (and , respectively) for the array of patterns shown in Figure 3

<p><b>Copyright information:</b></p><p>Taken from "Reconstruction of cellular variability from spatiotemporal patterns of "</p><p>http://www.nonlinearbiomedphys.com/content/1/1/10</p><p>Nonlinear Biomedical Physics 2007;1():10-10.</p><p>Published online 30 Aug 2007</p><p>PMCID:PMC2034575.</p><p></p> Here, and have been varied in the same ranges as in Figure 3, but with half the step size