## 4 New Computing Architectures
### Online Learning Experiment  
In order to verify the effectiveness of the computing architecture and algorithms, taking online learning as a scenario, collecting physiological data of students during online learning, and running with the new computing architecture proposed in this paper, considerable results were obtained, which is of great significance to the future online education and medical fields. We invited 30 subjects as shown in Figure 5(b) (age range 22-26 years), 17 males and 13 females, all of whom had received more than six years of formal EFL education. The experimental equipment is placed as in Figure 5(a) (Shimmer3 ECG device, E4 wristband, windows core i5, ASHU 603, Hi3559A TPU, ubuntu 32 G/4 T Server), before participating in the experiment, sign the required process description and give informed consent, and the acquisition process complies with the ethical requirements of the Human Biobanking Educational Exam. Establish the context as offline. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Exponential/Time(s)** & **CNN (200 epoch)** & \multicolumn{3}{c}{**SVC**} & \multicolumn{3}{c}{**XGBoost**} & \multicolumn{3}{c}{**Naive Bays**} \\ \hline Running on the cloud-edge-device system Runtime(s) & Yes & No & Yes & No & Yes & No & Yes & No \\ Runtime & 18.74 & 21.58 & 11.31 & 14.36 & 17.65 & 19.36 & 10.53 & 13.24 \\ Determine Response Latency Rate & 80.4\% & 82.6\% & 60.7\% & 66.5\% & 75.6\% & 79.3\% & 53.1\% & 56.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The response times of our two calculation modes are compared, and the comparison results are as follows. \begin{table}
\begin{tabular}{c c} \hline \hline Terminal side & Inter(R)Core(TM) i7-4790 CPU, 3.60 GHz, 4 core, 8 GB RAM, NVIDIA \\  & GeForce GTX 1050 Ti (4 GB) \\ Edge side & ARM Cortex A73 1.6 GHz, 2 core, ARM Mail G71@900 MHz (256 K) \\ Cloud side & Inter(R)Core(TM) i7-4790 CPU, 3.60 GHz, 8 core, 16 GB RAM, NVIDIA \\  & GeForce GTX 1050 Ti (4 GB) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The experimental equipment is as follows. The experimental flow is shown in Figure 6, and the detailed procedure is as follows:  
1. Make sure the subjects remain calm, take a five-minute baseline test, and fill in the familiarity of the test questions before the experiment, and evaluate the difficulty level of the test according to the familiarity;
2. Show multiple-choice questions to the subjects. After each answer, the participants self-assess their arousal level and valence, and the background selects the difficulty level of the next question according to the subject's emotional score;
3. The test paper contains 30 questions, and 30 min of ECG (Shimmer3 ECG equipment) and GSR (E4 wristband) data are collected;
4. After the experiment, annotators performed annotations based on video clips, first for valence and then for arousal. The collection frequency of the Shimmer3 ECG device is 256 Hz, the amount of ECG data collected is (13,824,000) per subject, the E4 wristband is used to collect GSR frequency of 4 Hz, and the quantity is (216,000) per subject. The emotion annotation includes user self-assessment Valence and Arousal external annotation. We performed variable statistical analysis on the collected data. The degree of influence between these variables was measured using the Pearson correlation coefficient, defined as:  
\[\rho_{X,Y}=\frac{\text{cov}(X,Y)}{\sigma_{X}\sigma_{Y}}=\frac{E[(X-\mu_{X})(Y- \mu_{Y})]}{\sigma_{X}\sigma_{Y}}, \tag{9}\]  
Figure 5: (a)Equipment distribution; (b) 30 subjects. Figure 6: Experimental flow chart. \(X\) carries the ECG or GSR physiological data vector and \(Y\) represents emotional decision making. The correlation between ECG and affective state is usually lower than the correlation between GSR and affective state, which proves that different subjects stimulates different control factors for affective state. The system can adjust the difficulty of the questions according to the emotions fed by different subjects.