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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 27094

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Prof. Dr. Teen-Hang Meen

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Department of Electronic Engineering, National Formosa University, Yunlin City 632, Taiwan
Interests: IoT devices; photovoltaic devices; STEM education
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Prof. Dr. Charles Tijus

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Director of the Cognitions Humaine et Artificielle Laboratory, University Paris 8, 93526 Saint-Denis, France
Interests: internet of objects; data mining; brain–computer interaction
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Prof. Dr. Chun-Yen Chang

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The Graduate Institute of Science Education and the Department of Earth Sciences, National Taiwan Normal University (NTNU), Taipei, Taiwan
Interests: science education; E-learning; interdisciplinary science learning; science communication
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Dear Colleagues,

Human–computer interaction (HCI) involves research on the design and use of computer technology, focusing in particular on the interfaces between people (users) and computers. HCI researchers observe the ways in which humans interact with computers and design technologies that allow them to interact in novel ways. HCI research encompasses the design, evaluation and implementation of interactive computing and computer-based systems for the benefit of human use. Yet, while driven by recent technological advances and the increasing transformation of computing devices into radically new future forms of interaction, HCI is a field in need of significant innovation and breakthroughs. For example, an understanding of user experience (UX) is integral to the development and success of digital systems that deliver appropriate user experiences in different contexts; as HCI evolves into HCI 2.0, user experiences and feedback become ever more relevant.

The 4^thIEEE Eurasia Conference on IOT, Communication and Engineering 2022 (IEEE ECICE 2022, http://www.ecice.asia) will be held in Yunlin, Taiwan, on October 28-30, 2022, providing a communication platform for the scientific exchange of IOT and advanced manufacturing research among scholars. The booming economic development in Asia, driven by the leading manufacturing industries, with production ranging from automobiles, machinery, computers, communication devices, consumer products and flat panel displays to semiconductor and micro/nano areas, has stimulated significant interest among universities, research institutions and numerous industrial corporations. This conference aims to provide a broad international forum for world researchers, engineers and professionals in IOT and manufacturing fields for the discussion and exchange of various scientific, technical and management discoveries across the world. This Special Issue, “Human Computer Interactions 2.0”, will present a selection of excellent papers presented in IEEE ECICE 2022 and other high-quality papers considering human–computer interactions. Potential topics include, but are not limited to:

HCI 2.0 in smart manufacturing;
HCI 2.0 in emerging technologies;
HCI 2.0 in IOT;
Human–robot interaction;
Interaction in virtual/augmented reality;
Multilingual speech processing;
Multimodal HCI 2.0;
Deep learning in HCI/IS;
EEG in HCI 2.0;
Biometrics in HCI 2.0;
Human factors of HCI 2.0;
Speech recognition and synthesis;
Natural language processing;
Emotion and mood analysis;
Prosodic and phonetics;
Accessible computing.

Prof. Dr. Teen-Hang Meen
Prof. Dr. Charles Tijus
Prof. Dr. Chun-Yen Chang
Guest Editors

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Related Special Issue

Human–Computer Interactions and Web 3.0 in Applied Sciences

Published Papers (12 papers)

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Editorial

Jump to: Research

5 pages, 176 KiB

Open AccessEditorial

Special Issue on Human–Computer Interactions 2.0

by Teen-Hang Meen, Charles Tijus and Chun-Yen Chang

Appl. Sci. 2023, 13(7), 4260; https://doi.org/10.3390/app13074260 - 27 Mar 2023

Viewed by 1140

Abstract

Human–computer interaction (HCI) research involves the design and use of computer technology, focusing in particular on the interfaces between people (users) and computers. HCI researchers observe the ways in which humans interact with computers and design technologies that allow them to interact in novel ways. As HCI evolves into HCI 2.0, user experiences and feedback become ever more relevant. This Special Issue, “Human Computer Interactions 2.0”, presents 11 excellent papers about topics related to human–computer interactions. It aims to provide a broad international forum for world researchers, engineers and professionals in human–computer interaction research for the discussion and exchange of various scientific, technical and management discoveries across the world. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

Research

Jump to: Editorial

24 pages, 5888 KiB

Open AccessArticle

Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes

by Tiago Cerqueira, Francisco M. Ribeiro, Vítor H. Pinto, José Lima and Gil Gonçalves

Appl. Sci. 2022, 12(21), 10752; https://doi.org/10.3390/app122110752 - 24 Oct 2022

Cited by 3 | Viewed by 1455

Abstract

This article focuses on 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 the algebraic 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. Considering that the calibration is a very important process in gyroscope performance, the online and offline calibration data was analyzed, pointing out its challenges and improvements. To better visualize the model and sensors a simulation was conducted in Unity. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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22 pages, 5807 KiB

Open AccessArticle

MinerGuard: A Solution to Detect Browser-Based Cryptocurrency Mining through Machine Learning

by Min-Hao Wu, Yen-Jung Lai, Yan-Ling Hwang, Ting-Cheng Chang and Fu-Hau Hsu

Appl. Sci. 2022, 12(19), 9838; https://doi.org/10.3390/app12199838 - 29 Sep 2022

Cited by 5 | Viewed by 4223

Abstract

Coinhive released its browser-based cryptocurrency mining code in September 2017, and vicious web page writers, called vicious miners hereafter, began to embed mining JavaScript code into their web pages, called mining pages hereafter. As a result, browser users surfing these web pages will benefit mine cryptocurrencies unwittingly for the vicious miners using the CPU resources of their devices. The above activity, called Cryptojacking, has become one of the most common threats to web browser users. As mining pages influence the execution efficiency of regular programs and increase the electricity bills of victims, security specialists start to provide methods to block mining pages. Nowadays, using a blocklist to filter out mining scripts is the most common solution to this problem. However, when the number of new mining pages increases quickly, and vicious miners apply obfuscation and encryption to bypass detection, the detection accuracy of blacklist-based or feature-based solutions decreases significantly. 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. 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 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. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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13 pages, 1195 KiB

Open AccessArticle

Requesting Help Module Interface Design on Key Partial Video with Action and Augmented Reality for Children with Autism Spectrum Disorder

by Chuan-Po Wang, Cheng-Hui Tsai and Yann-Long Lee

Appl. Sci. 2022, 12(17), 8527; https://doi.org/10.3390/app12178527 - 26 Aug 2022

Cited by 4 | Viewed by 2050

Abstract

Children with autism spectrum disorder (ASD) have marked difficulty with vocabulary, lack of language, or shortcomings with their ability to organize their oral expression; thus, they cannot effectively communicate with others. In particular, people with moderate or severe disabilities cannot systematically narrate an incident and cannot follow pragmatic rules provided by others. Their attempts at standard everyday conversation lead to cognitive problems. When children with ASD are faced with difficult circumstances, they are usually unable to seek help from others, which in turn can result in their being unable to communicate effectively. 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. We conclude that the interface of our requesting help modules is efficacious enough to assist children with ASD at different levels. The proposed AR sentence intervention helps them build scenarios by themselves, helps them organize communication with their peers, and assists them to request help. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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14 pages, 1658 KiB

Open AccessArticle

WPFD: Active User-Side Detection of Evil Twins

by Fu-Hau Hsu, Min-Hao Wu, Yan-Ling Hwang, Chia-Hao Lee, Chuan-Sheng Wang and Ting-Cheng Chang

Appl. Sci. 2022, 12(16), 8088; https://doi.org/10.3390/app12168088 - 12 Aug 2022

Cited by 3 | Viewed by 2042

Abstract

The bothersome evil twin problem has an active user-side remedy in the form of the Wireless Packet Forwarding Detector (WPFD). The evil twin issue can lead to further security problems, including man-in-the-middle (MITM) attacks. Open public Wi-Fi connections have provided potential answers to this issue, although they often need more data that people either cannot get or are too pricey for regular users. The solution that we created does not require these standards. It allows users’ notebooks to be used to check for evil twins. We have 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 we 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. We 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 Received Signal Strength Index (RSSI) is 45%. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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16 pages, 5951 KiB

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Using Convolutional Neural Networks in the Development of a Water Pipe Leakage and Location Identification System

by Yao-Long Tsai, Hung-Chih Chang, Shih-Neng Lin, Ai-Huei Chiou and Tin-Lai Lee

Appl. Sci. 2022, 12(16), 8034; https://doi.org/10.3390/app12168034 - 11 Aug 2022

Cited by 5 | Viewed by 2147

Abstract

To overcome the challenges brought about by abnormal weather and the growing industrial water consumption in Taiwan, the Taiwanese government is transporting water from the northern to the southern part of the country to help with droughts occurring in Taoyuan and Hsinchu. In addition, the government invested NTD 2.78 billion to build the backup water pipelines necessary in Taiyuan and Hsinchu, which help ensure a stable and safe water supply required for regional economic development. The construction adheres to the four major strategic goals of “open source, throttling, dispatch, and backup”. However, the leakage rate of water pipelines remains high. To help with large-scale right-of-way applications and the timeliness of emergency repairs, establishing a system that can detect the locations of leakages is vital. This study intended to apply artificial intelligence (AI) deep learning to develop a water pipe leakage and location identification system. 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. Actual excavation verification results show that the accuracy of the convolutional neural network (CNN) after training is greater than 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%. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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12 pages, 2125 KiB

Open AccessArticle

Data Imbalance Immunity Bone Age Assessment System Using Independent Autoencoders

by Ching-Tung Peng, Yung-Kuan Chan and Shyr-Shen Yu

Appl. Sci. 2022, 12(16), 7974; https://doi.org/10.3390/app12167974 - 09 Aug 2022

Cited by 1 | Viewed by 1275

Abstract

Bone age assessment (BAA) is an important indicator of child maturity. Generally, a person is evaluated for bone age mostly during puberty stage; compared to toddlers and post-puberty stages, the data of bone age at puberty stage are much easier to obtain. As a result, the amount of bone age data collected at the toddler and post-puberty stages are often much fewer than the amount of bone age data collected at the puberty stage. This so-called data imbalance problem affects the prediction accuracy. To deal with this problem, in this paper, a data imbalance immunity bone age assessment (DIIBAA) 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, respectively. 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 which 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, we 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. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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18 pages, 1657 KiB

Open AccessArticle

A Robust Countermeasures for Poisoning Attacks on Deep Neural Networks of Computer Interaction Systems

by I-Hsien Liu, Jung-Shian Li, Yen-Chu Peng and Chuan-Gang Liu

Appl. Sci. 2022, 12(15), 7753; https://doi.org/10.3390/app12157753 - 01 Aug 2022

Cited by 1 | Viewed by 1658

Abstract

In recent years, human–computer interactions have begun to apply deep neural networks (DNNs), known as deep learning, to make them work more friendly. Nowadays, adversarial example attacks, poisoning attacks, and backdoor attacks are the typical attack examples for DNNs. In this paper, we focus on poisoning attacks and analyze three poisoning attacks on DNNs. We develop a countermeasure for poisoning attacks, which is Data Washing, an algorithm based on a denoising autoencoder. It can effectively alleviate the damages inflicted upon datasets caused by poisoning attacks. Furthermore, we also propose the Integrated Detection Algorithm (IDA) to detect various types of attacks. In our experiments, for Paralysis Attacks, Data Washing represents 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%. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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19 pages, 8463 KiB

Open AccessArticle

Machine Learning Prediction of Turning Precision Using Optimized XGBoost Model

by Cheng-Chi Wang, Ping-Huan Kuo and Guan-Ying Chen

Appl. Sci. 2022, 12(15), 7739; https://doi.org/10.3390/app12157739 - 01 Aug 2022

Cited by 7 | Viewed by 2450

Abstract

The present 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 which 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 are evaluated and compared using the leave-one-out cross-validation technique. The experimental results show that the XGBoost model, combined with SMOGN and CPSO, provides the best performance, and is a useful tool for predicting the machining error of turning. The method can also reduce the cost of obtaining the optimized turning parameters corresponding with the predicted machining error. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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14 pages, 18929 KiB

Open AccessArticle

Application of Virtual Reality Method in Aircraft Maintenance Service—Taking Dornier 228 as an Example

by Wen-Chung Wu and Van-Hoan Vu

Appl. Sci. 2022, 12(14), 7283; https://doi.org/10.3390/app12147283 - 20 Jul 2022

Cited by 8 | Viewed by 3350

Abstract

Flight safety and airlines operation have been at the center of research since aircraft were first invented, as even slight errors in aircraft maintenance may cause serious accidents. Thus, aircraft maintenance is critical to the aviation industry all the time. To prevent maintenance errors, it is important to train for aviation maintenance. Therefore, an aircraft maintenance virtual reality (AMVR) system was developed in this study. For a Dornier-228 aircraft, a walk-around visual inspection of its fuel system was designed and tested in a virtual environment. For the system, CATIA V5 and Unity 3D software were used for designing the 3D model of the aircraft and developing the visual environment, respectively. With the software, the visual environment of the aircraft hangar was created for the system. The developed system was tested by students to validate the effectiveness of using the AMVR system in training. The students acknowledged that the system was beneficial to their learning, which proved that the developed system is highly effective for training students to improve aircraft maintenance skills. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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25 pages, 5126 KiB

Open AccessArticle

Hardware Development and Safety Control Strategy Design for a Mobile Rehabilitation Robot

by Lian-Wang Lee, I-Hsum Li, Liang-Yu Lu, Yu-Bin Hsu, Shean-Juinn Chiou and Te-Jen Su

Appl. Sci. 2022, 12(12), 5979; https://doi.org/10.3390/app12125979 - 12 Jun 2022

Cited by 9 | Viewed by 2089

Abstract

The use of bodyweight unloading force control on a treadmill with therapist manual assistance for gait training imposes constraints on natural walking. It influences the patient’s training effect for a full range of natural walks. This study presents a prototype and a safety controller for a mobile rehabilitation robot (MRR). The prototype integrates an autonomous mobile bodyweight support system (AMBSS) with a lower-limb exoskeleton system (LES) to simultaneously achieve natural over-ground gait training and motion relearning. Human-centered rehabilitation robots must guarantee the safety of patients in the presence of significant tracking errors. It is difficult for traditional stiff controllers to ensure safety and excellent tracking accuracy concurrently, because they cannot explicitly guarantee smooth, safe, and overdamped motions without overshoot. This paper integrated a linear extended state observer (LESO) into proxy-based sliding mode control (ILESO-PSMC) to overcome this problem. The LESO was used to observe the system’s unknown states and total disturbance simultaneously, ensuring that the “proxy” tracks the reference target accurately and avoids the unsafe control of the MRR. Based on the Lyapunov theorem to prove the closed-loop system stability, the proposed safety control strategy has three advantages: (1) it provides an accurate and safe control without worsening tracking performance during regular operation, (2) it guarantees safe recoveries and overdamped properties after abnormal events, and (3) it need not identify the system model and measure unknown system states as well as external disturbance, which is quite difficult for human–robot interaction (HRI) systems. The results demonstrate the feasibility of the proposed ILESO-PSMC for MRR. The experimental comparison also indicates better safety performance for the ILESO-PSMC than for the conventional proportional–integral–derivative (PID) control. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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21 pages, 7500 KiB

Open AccessArticle

Visual Management and Gamification: An Innovation for Disseminating Information about Production to Construction Professionals

by Regina Maria Cunha Leite, Ingrid Winkler and Lynn Rosalina Gama Alves

Appl. Sci. 2022, 12(11), 5682; https://doi.org/10.3390/app12115682 - 03 Jun 2022

Cited by 3 | Viewed by 2230

Abstract

The construction industry is undergoing a digital transformation, with the goal of developing long-term solutions that promote construction companies’ alignment with market demands and that empower them to reduce production losses as much as possible. The purpose of this paper was to evaluate a gamified model for disseminating production information in the construction industry using visual management. This was a qualitative exploratory study that employed the Design Science methodology and the Design Science Research method. The model was designed, developed, and evaluated by 35 people, including 10 off-site users who focused on usability, user experience, and model promotability, 15 engineers, and 10 workers who considered user experience and promotability. Employees and managers thought the model was excellent, while outside users thought it was good. Furthermore, the evaluators made suggestions for improvements aimed at achieving excellence. We conclude that the proposed model improves production information dissemination in construction by considering the target audience’s digital inclusion and knowledge diffusion within work teams. Full article

(This article belongs to the Special Issue Human-Computer Interactions 2.0)

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