# Special Issue on Human-Computer Interactions 2.0
## 2 The Topics of the Special Issue \"Human Computer Interactions 2.0\"  
This Special Issue showcases 11 excellent papers on topics related to human-computer interactions. [PERSON] et al. wrote the article \"Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes\" [1]. This paper presents a sensorial glove prototype capable of acquiring hand motion and estimating its pose. The presented solution features twelve inertial measurement units (IMUs) to track hand orientation. The sensors are attached to a glove to decrease the project cost. The system also focuses on sensor fusion algorithms for the IMUs and further implementations, presenting ahealgebraic quaternion algorithm (AQUA), used because of its modularity and intuitive implementation. An adaptation of a human hand model is proposed, explaining its advantages and its limitations. [PERSON] et al. present \"MinerGuard: A Solution to Detect Browser-Based Cryptocurrency Mining through Machine Learning\" [2]. This paper proposes a solution, called MinerGuard, to detect mining pages. MinerGuard was designed based on the observation that mining JavaScript code consumes a lot of CPU resources because it needs to execute plenty of computation. MinerGuard does not need to update data used for detection frequently. On the contrary, blacklist-based or feature-based solutions must update their blocklists frequently. The experimental results show that MinerGuard is more accurate than blacklist-based or feature-based solutions in mining page detection. MinerGuard's detection rate for mining pages is 96%, but that of MinerBlock, a blacklist-based solution, is 42.85%. Moreover, MinerGuard can detect 0-day mining pages and scripts, but the blacklist-based and feature-based solutions cannot. [PERSON] et al. present the paper \"Requesting Help Module Interface Design on Key Partial Video with Action and Augmented Reality for Children with Autism Spectrum Disorder\" [3]. This research focused on three child participants with ASD and language disorders. The goal was to strengthen the effectiveness of their requesting help and to organize their oral expression, to use requesting help modules, to remove static key images, and to use augmented reality (AR) combined with the dynamic video clips in key partial video with action (KPV). This study developed request-assistance training in conjunction with an auto organizational menu (AOM), multiple case studies and withdrawal designs, training-response methods, and a comparison of outcomes. The proposed AR sentence intervention effectively increased the children's desire to communicate with others and the accuracy rate of their help requests and increased their level of communication. [PERSON] et al. present \"WPFD: Active User-Side Detection of Evil Twins\" [4]. This study succeeded in developing a user-side detection system that can successfully identify the presence of an evil twin. The packet forwarding behavior generated by the evil twin and the TCP/IP (Transmission Control Protocol/Internet Protocol) protocol are both used by the WPFD. It can identify evil twins without a hitch when the authors utilize accessible Wi-Fi settings in public spaces or IoT smart homes with unencrypted WLANs (Wireless Local Area Network). However, neither additional data nor a wireless network administrator's assistance is needed. The authors compare our work to various publications on popular Rogue Access Points (APs) or IoT (Internet of Things) smart homes. The WPFD does not require any extra setup to install on the host of any end user. According to experimental findings, the WPFD true positive and true negative rates are 100% even when the Received Signal Strength Index (RSSI) is 45%. [PERSON] et al. present the paper \"Using Convolutional Neural Networks in the Development of a Water Pipe Leakage and Location Identification System\" [5]. This study intended to apply artificial intelligence (AI) deep learning to develop a water pipe leakage and location identification system.