# Special Issue on Human-Computer Interactions 2.0
## 2 The Topics of the Special Issue \"Human Computer Interactions 2.0\"
This research established an intelligent sound-assisted water leak identification system, developed and used a localized AI water leak diagnostic instrument to capture on-site dynamic audio, and integrated Internet of Things (IoT) technology to simultaneously identify and locate the leakage. The actual excavation verification results show that the accuracy of the convolutional neural network (CNN) after training is greaterthan 95%, and the average absolute error calculated between the output data and the input data of the encoder is 0.1021, confirming that the system has high reliability and can reduce the cost of excavation by 26%. [PERSON] et al. wrote the article \"Data Imbalance Immunity Bone Age Assessment System Using Independent Autoencoders\" [6]. In this paper, a data imbalance immunity bone age assessment (DII BAA) system is proposed. It consists of two branches, the first branch consists of a CNN-based autoencoder and a CNN-based scoring network. This branch builds three autoencoders for the bone age data of toddlers, puberty, and post-puberty stages. Since the three types of autoencoders do not interfere with each other, there is no data imbalance problem in the first branch. After that, the outputs of the three autoencoders are input into the scoring network, and the autoencoder that produces the image with the highest score is regarded as the final prediction result. In the experiments, imbalanced training data with a positive and negative sample ratio of 1:2 are used, which has been alleviated compared to the original highly imbalanced data. In addition, since the scoring network converts the classification problem into an image quality scoring problem, it does not use the classification features of the image. Therefore, in the second branch, the authors also add the classification features to the DIIBAA system. At this time, DIIBAA considers both image quality features and classification features. Finally, the DenseNet169-based autoencoders are employed in the experiments, and the obtained evaluation accuracies are improved compared to the baseline network. [PERSON] et al. present \"A Robust Countermeasures for Poisoning Attacks on Deep Neural Networks of Computer Interaction Systems\" [7]. In this paper, the authors focus on poisoning attacks and analyze three poisoning attacks on DNNs. They develop a countermeasure for poisoning attacks, Data Washing, an algorithm based on a denoising autoencoder. It can effectively alleviate the damages inflicted upon datasets caused by poisoning attacks. Furthermore, the authors also propose the Integrated Detection Algorithm (IDA) to detect various types of attacks. In our experiments, for Paralysis Attacks, Data Washing shows a significant improvement (0.5384) over accuracy increment and can help IDA detect those attacks, while for Target Attacks, Data Washing makes it so that the false positive rate is reduced to just 1% and IDA can have a high accuracy detection rate of greater than 99%. [PERSON] et al. present \"Machine Learning Prediction of Turning Precision Using Optimized XGBoost Model\" [8]. This study proposes a machine learning approach for optimizing turning parameters in such a way as to maximize the turning precision. The Taguchi method is first employed to optimize the turning parameters, and the experimental results are then used to train three machine learning models to predict the turning precision for any given values of the input parameters. The model that shows the best prediction performance (XGBoost) is further improved through the use of a synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN) and four different optimization algorithms, including center particle swarm optimization (CPSO). Finally, the performances of the various models were evaluated and compared using the leave-one-out cross-validation technique. The experimental results show that the XGBoost model, combined with SMOGN and CPSO, exhibits the best performance and is a useful tool for predicting the machining error of turning.