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The application of artificial intelligence in designing the future aircraft cockpit may lead to conflicts especially during the human-computer decision-making process. To harmonize the human-computer relationship, the evolutionary game and collaboration mechanism based on system dynamics (SDs) is proposed in this article. The evolutionary game model is established containing the flight crew and the intelligent decision-making system as players. The profit matrix of different game strategies is constructed, and the time-varying dynamic characteristics of the human-computer evolution game is analyzed. Based on the SDs theory, the human-computer collaboration model is constructed. Its functional component modules are respectively defined by the behavioral characteristics of pilots, decision-making performance, and human-computer evolutionary game. The coupling relationship between them is also presented. With the background of typical task scenarios, a dynamic simulation case of the human-computer collaboration is given. The impacts of task load change rate, task load level, and decision-making performance related parameters are analyzed. The evolutionary laws of human-computer game strategies are thereby revealed. This work can provide modeling methods for human-computer interaction in the aircraft cockpit.

Since the hard of hearing cannot communicate effectively with the non-disabled, which may cause various inconveniences. As an essential member of a harmonious society, it is particularly urgent to solve their communication problems with non-disabled people. Effective communication between the hard of hearing and the non-disabled has become possible with the continuous development of artificial intelligence. In this paper, an intelligent human-computer interaction system is designed to solve communication inconvenience between the hard of hearing and the non-disabled. This system combines artificial intelligence with wearable devices and classifies gestures with BP neural network, effectively solving the communication problem between the hard of hearing and the non-disabled.

Voice assistants (VA) are finding a place in many households, with increasing numbers. Nevertheless, for every interaction user invokes VA using a key or wake-up word, which is too common and hinders natural conversation. To solve this, we propose an On-Device solution that listens to the user continuously for only predefined period and classifies the utterance into device-directed or non-device-directed using a deep learning-based model. Since our solution is On-Device, the privacy of the user is maintained. We tried to solve false acceptance of utterance as a command and incorrect rejection of the command as background noise to improve user experience. We calculated the False Acceptance Rate (FAR) and False Rejection Rate (FRR) for different thresholds to evaluate our model. Then we calculated Equal Error Rate (EER) results with the FRR and FAR data. We achieved 3.6% of EER on our best optimized Stage-1 model and EER of 5.1% on Stage-2 model. We have also used accuracy as evaluation metrics during training and testing. We achieved an accuracy of 96.8% on testing data with our Stage-1 model and 95.3% on Stage-2 model.

Over the years, network monitoring tools have evolved to keep pace with the advances in networking technologies and paradigms, such as virtualization and softwarization, and more recently, data plane programmability. However, there have been few advances regarding user interfaces and interactions in network management systems. In this paper, an investigation based on the human-computer interaction perspective is presented and improvements on the usability aspects of network monitoring tools are proposed. First, we conducted a survey to capture general information from network administrator professionals. On the basis of these initial data, we identified and specified two personas to represent the groups of users. Thereafter, a study focused on observing users’ interaction and experience with a well-known network monitoring tool was conducted. Finally, our qualitative analysis revealed several findings that were used to develop recommendations to aid developers in the (re)design of these tools to enhance end-user interaction.

The green Internet of Things (IoT) technology has increased the need for a more emotion-connected human-computer interaction. The lower network latency and longer battery life ensure the continuous and real-time work of human-computer interaction systems in embedded environments. However, the lack of emotional dynamics limits the empathy capability of human-computer interactions. In this paper, we propose a novel emotion control system for embedded human-computer interaction in green IoT. The Van Der Pol emotional dynamics with nonlinear resilience is proposed for the modeling of human emotional mechanisms. Moreover, respectively for the autonomous and stochastic-excitation situation, we analyze the stability of the solution, based on which four kinds of the emotional health status are defined. Finally, we obtain the steady-state solution and design its feedback controller. Results of the numerical simulation and prototype demonstrate the validity and practical operability of the theory.

Modern consumer electronics (CE) devices have evolved such that human interaction with machines is ever more natural and effective. The forthcoming deployment of the Internet of Things (IoT) over the existing Internet infrastructure promises to expand this interaction by providing applications and services that allow the human-computer interaction to integrate an automated customization of everyday life [1]. The vision is very optimistic on the amount of smart objects (SOs) deployed around us as the building blocks of the IoT. Experts forecast that 28 billion "things" will be connected to the Internet by 2020 [2].

Contribution: This work presents a teaching and learning process for human–computer interaction (HCI) courses, combining traditional lecture-based classroom (TLBC), active learning (AL) and project-based learning elements, such that students can increase their understanding over HCI. Background: In undergraduate science computing, the academic curriculum is composed of theoretical and practical courses. The theoretical courses address more abstract content. This process intends to increase the involving of students over the theoretical discipline of HCI. Research Questions: This work shows a different approach to improve student retention for a better understanding of the theoretical aspects over HCI. Methodology: This proposed process has three mainstages and, as newness, uses the $t$ -learning concept to simulate interactive educational videos. In the first stage, the teacher presents concepts of HCI using TLBC. The second stage is AL, where students make oral presentations or make of learning objects based on interactive videos (LOBIVs) to reinforce their self-comprehension over theoretical content. The third stage is the PBL activity of the production of new LOBIVs. Findings: The results show higher statistical scores using the proposed method (PM) in comparison with the TLBC used in HCI course. A total of 131 students concepts were compared in this work, that uses TLBC, with 113 students concepts that use the PM. The results indicate that there are advantages in the PM, because it helps to reduce the number of reproved students and increases the average students grade.

This article proposes a novel smart car demonstration platform, with a focus on the intuitive paradigm of human?computer interaction (HCI). The main objective here is to circumvent driver distraction when manipulating built-in car-appropriate applications (apps) for future smart car development. Two major development directions for a smart car's HCI are investigated and discussed in detail: 1) an in-vehicle user interface, which focuses on utilizing the user interface of a transparent windshield display to keep the driver's head up and eyes focused on the roadway, and 2) an in-vehicle interaction design, which emphasizes an intuitive interaction framework to minimize distractions while driving. This article addresses the major difficulties of each direction and discusses related challenges and solutions to achieve safety and comfort during smart car operation.

The author of a 1979 Computer article reflects on the remarkable changes that human–computer interaction research has brought to the computing and information technology field.

In this article, a pen-type device that enables three-dimensional (3D) human-computer interaction is presented. This device is based on a high-precision 3D ultrasonic (US) positioning method, which can achieve millimeter-level accurate 3D positioning and six degrees of freedom (6DoF) within a working range of 4 m × 1.5 m × 1.5 m. The 3D positioning of the electronic pen is realized by a highprecision time-of-flight (TOF) detection method based on dynamic thresholding and zero-crossing detection as well as a multigroup positioning coordinate averaging method. During the 3D positioning process, correlation filtering is adopted to process the received US signals and suppress any interference signals below 20 kHz and above 60 kHz. The positioning method can achieve a high positioning accuracy without any complex calculations. The attitude of the electronic pen is measured using an inertial measurement units (IMUs). To demonstrate the positioning accuracy of the proposed device, a total of 108 test positions are uniformly set in the workspace, and static and dynamic positioning accuracy experiments are carried out at all test positions. The experimental results show static and dynamic positioning errors of less than 0.35 mm and less than 1.0 mm, respectively, which indicate a high positioning accuracy. The proposed device can provide 6DoF and enable writing in 3D. Therefore, the device can meet the common requirements of a 3D human-computer interaction interface.

This essay examines the state of the history of human–computer interaction (HCI) from multiple fields of study. In many ways, there is a lack of consensus about what we mean by not only HCI but also computer interfaces. I find that the different definitions and approaches found primarily in history and media studies offer many different avenues for scholars to continue exploring how humans and computers relate to each other. A theoretical background on the topic is provided and then historical case studies concerning both computer interfaces and the embodied use of computers are discussed to understand what conversations are held in common and what areas are still left to be explored.

In this work, we present a novel method to intention recognition, based on electroencephalogram (EEG) and eye movement in human-computer interaction(HCI). The fusion of EEG and eye movement will allow the utmost of the advantages of the two physiological signals. Signals of EEG and eye movement were collected for feature extraction, recognition network of machine learning pattern was input for intent recognition, final recognition result was attained by decision-level fusion. We compare the results of the Intention Recognition Algorithms to those of an experiment involving the intention recognition of the operator in a simulated flight mission. In most every case, results show that the intention recognition algorithms performed better than solely rely on single signal.

Human-computer interaction (HCI) is a growing area. With the advent of new technologies and a continually expanding user population, there is a need for HCI professionals in both academia and software companies. The authors examine the state of the HCI discipline in Ibero-American countries for both academia and industry.

The process of learning correct pronunciation can greatly benefit from the twin disciplines of information visualization (InfoVis) and human–computer interaction (HCI). HCI concerns with design, evaluation, and implementation for the ease of use of users, while InfoVis is responsible for representing multidimensional data in a specific information context. The two disciplines can, therefore, complement each other with the purpose of providing a visualization model in the information context of pronunciation. Such a model could provide an aid to pronunciation learning for different types of users. The proposed model integrates graphic aspects to be able to represent information in such a way that the user can visually capture a greater quantity of diverse voice characteristics and instantly recognize the pronunciation quality.