## 2 Materials and Methods
### The Proposed Method
#### 2.2.2 DAB-minimize Contrasting Learning
When dropping the total RFS, the framework can still learn temporal and contextual information. However, the classification performance is not as good as using \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**SSC**} & \multicolumn{2}{c}{**HAR**} & \multicolumn{2}{c}{**ESP**} \\ \cline{2-7}  & **Avg. Acc** & **MF1** & **Avg. Acc** & **MF1** & **Avg. Acc** & **MF1** \\ \hline supervised & **81.14 \(\pm\) 0.36** & **74.07 \(\pm\) 0.33** & 91.47 \(\pm\) 0.75 & 91.09 \(\pm\) 0.83 & 96.63 \(\pm\) 0.31 & 94.56 \(\pm\) 0.54 \\ \hline simCLR & 56.58 \(\pm\) 0.44 & 48.81 \(\pm\) 0.13 & 86.99 \(\pm\) 0.18 & 86.05 \(\pm\) 0.20 & 96.00 \(\pm\) 0.20 & 93.33 \(\pm\) 0.35 \\ TS-TCC & 79.34 \(\pm\) 0.33 & 72.92 \(\pm\) 0.49 & 89.08 \(\pm\) 0.19 & 88.45 \(\pm\) 0.19 & 95.59 \(\pm\) 0.13 & 92.62 \(\pm\) 0.21 \\ TS2 Vec & 73.50 \(\pm\) 1.48 & 65.93 \(\pm\) 0.25 & 87.91 \(\pm\) 0.02 & 86.55 \(\pm\) 0.22 & 97.05 \(\pm\) 0.36 & 94.63 \(\pm\) 0.21 \\ DABaCLT (ours) & 79.53 \(\pm\) 0.11 & 73.25 \(\pm\) 0.15 & **92.04 \(\pm\) 0.24** & **91.52 \(\pm\) 0.26** & **98.05 \(\pm\) 0.10** & **96.93 \(\pm\) 0.18** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between DABaCLT model against baselines using linear classifier evaluation experiments. or calculating \(L_{CC}^{s}\) and \(L_{CC}^{s}\). Moreover, the complete DABaCLT achieves the best average accuracy, which means the RFS is greatly meritorious in terms of DAB awareness and minimization. ### Impact of Parameter Selection

The sensitivity analysis was performed on the HAR dataset to study the influence of parameters \(\lambda 1\), \(\lambda 2\), \(\lambda 3\), and \(\lambda 4\) on the contrasting learning process. Figure 5 shows the evaluation results. When evaluating \(\lambda_{i}\), the others are fixed to 1. It is observed that the model is not sensitive to \(\lambda 1\), and it is easier for \(\lambda 2\), \(\lambda 3\), and \(\lambda 4\) to influence the representation. Commonly, the model achieves the best performance when \(0.1<\lambda s<10\). ### The Visualization of DAB Problem

The distribution of raw signals and augmented signals from the three datasets is shown in Figure 6. It can be observed that the HAR and ESR datasets exhibit a more chaotic distribution after applying data augmentation methods. However, there is no significant change in the distribution of the Sleep-EDF data. The spatial distribution of different categories in the Sleep-EDF dataset appears to be more uniform compared to the other two datasets. This can be mainly attributed to the sample length of the data. For the HAR and ESR datasets, the number of sampling points in their individual samples

Figure 4: Classification performance on HAR dataset with (**A**) different augmentation strategy, # represents that the two views are transformed with the same augmentation methods, and (**B**) dropping different raw features. Figure 5: Classification performance on HAR dataset of different \(\lambda_{1}\), \(\lambda 2\), \(\lambda 3\), and \(\lambda 4\) with different values. is 128 and 178, respectively, making the tempo-scale augmentation method significantly alter the data distribution. On the other hand, the Sleep-EDF dataset has a sample length of 3000, resulting in a less-pronounced disruption of its distribution by the tempo-scale augmentation method. The accuracy results further support this observation, as DABaCLT demonstrates improved prediction accuracy for the HAR and Epilepsy datasets compared to supervised methods, while showing no improvement for SSC. Figure 6: Spatial distribution visualization of signals with T-SNE method. Spatial distribution of (**A1**) the raw, (**A2**) scale-augmented, and (**A3**) tempo-augmented signals in HAR dataset; spatial distribution of (**B1**) the raw, (**B2**) scale-augmented and (**B3**) tempo-augmented signals in ESR dataset; spatial distribution of (**C1**) the raw, (**C2**) scale-augmented, and (**C3**) tempo-augmented signals in SleepEDF dataset.