Model Of Human-computer Interaction Research Articles

Automated English Teaching System Through Deep Belief Network for Human-Computer Interaction Experience

This paper presented the integration of Human-Computer Interaction (HCI) with the Automated Teaching Belief Network (ATBN) to enhance automated English teaching experiences. The proposed ATBN model implemented the Deep Belief Network for the estimation of the factors related to the HCI to promote the experience of the users. The ATBN model uses the deep learning model for the classification in English Teaching. Through the capabilities of deep learning and HCI principles, the ATBN system offers personalized and adaptive learning experiences tailored to individual student needs. The proposed ATBN model estimates the features in English teaching to improve the performance of the Students through HCI model. Simulation analysis expressed that proposed ATBN model improves the pre-test and post-test score by +15 for the English Teaching. The classification values are achieved with accuracy value of 94.8% with minimal loss of 0.12. The assessment of student performance through pre-test and post-test score is improved by 15 for the beginner, intermediate and advanced level. The findings expressed that proposed ATBN model achieves the higher teaching test performance for the HCI language level through the belief network those significantly improves the user experience.

Integrating advanced human-computer interaction and machine learning models for optimizing VR systems in educational and business applications

This paper presents the development of an advanced Human-Computer Interaction (HCI) model and algorithm integrating Natural Language Processing (NLP) to optimize Virtual Reality (VR) systems for educational and business applications. The proposed model enhances user experience and operational efficiency by addressing interface design, user engagement, real-time data processing, and accessibility. Continuous learning and contextual data integration ensure adaptive and personalized interactions, improving the functionality and applicability of VR environments. Fig.: 2. Refs.: 11 titles.

Probing perfection: The relentless art of meddling for pulmonary airway segmentation from HRCT via a human-AI collaboration based active learning method

In the realm of pulmonary tracheal segmentation, the scarcity of annotated data stands as a prevalent pain point in most medical segmentation endeavors. Concurrently, most Deep Learning (DL) methodologies employed in this domain invariably grapple with other dual challenges: the inherent opacity of ‘black box’ models and the ongoing pursuit of performance enhancement. In response to these intertwined challenges, the core concept of our Human-Computer Interaction (HCI) based learning models (RS_UNet, LC_UNet, UUNet and WD_UNet) hinge on the versatile combination of diverse query strategies and an array of deep learning models. We train four HCI models based on the initial training dataset and sequentially repeat the following steps 1–4: (1) Query Strategy: Our proposed HCI models selects those samples which contribute the most additional representative information when labeled in each iteration of the query strategy (showing the names and sequence numbers of the samples to be annotated). Additionally, in this phase, the model selects the unlabeled samples with the greatest predictive disparity by calculating the Wasserstein Distance, Least Confidence, Entropy Sampling, and Random Sampling. (2) Central line correction: The selected samples in previous stage are then used for domain expert correction of the system-generated tracheal central lines in each training round. (3) Update training dataset: When domain experts are involved in each epoch of the DL model's training iterations, they update the training dataset with greater precision after each epoch, thereby enhancing the trustworthiness of the ‘black box’ DL model and improving the performance of models. (4) Model training: Proposed HCI model is trained using the updated training dataset and an enhanced version of existing UNet.Experimental results validate the effectiveness of this Human-Computer Interaction-based approaches, demonstrating that our proposed WD-UNet, LC-UNet, UUNet, RS-UNet achieve comparable or even superior performance than the state-of-the-art DL models, such as WD-UNet with only 15 %–35 % of the training data, leading to substantial reductions (65 %–85 % reduction of annotation effort) in physician annotation time.

HCI-modelling for improving the clinical usability of digital health technologies

IntroductionDigital Health Technologies (DHTs) have been shown to have variable usability as measured by efficiency, effectiveness and user satisfaction despite large-scale government projects to regulate and standardise user interface (UI) design. We hypothesised that Human-Computer Interaction (HCI) modelling could improve the methodology for DHT design and regulation, and support the creation of future evidence-based UI standards and guidelines for DHTs. MethodologyUsing a Design Science Research (DSR) framework, we developed novel UI components that adhered to existing standards and guidelines (combining the NHS Common User Interface (CUI) standard and the NHS Design System). We firstly evaluated the Patient Banner UI component for compliance with the two guidelines and then used HCI-modelling to evaluate the “Add New Patient” workflow to measure time to task completion and cognitive load. ResultsCombining the two guidelines to produce new UI elements is technically feasible for the Patient Banner and the Patient Name Input components. There are some inconsistencies between the NHS Design System and the NHS CUI when implementing the Patient Banner. HCI-modelling successfully quantified challenges adhering to the NHS CUI and the NHS Design system for the “Add New Patient” workflow. DiscussionWe successfully developed new design artefacts combing two major design guidelines for DHTs. By quantifying usability issues using HCI-modelling, we have demonstrated the feasibility of a methodology that combines HCI-modelling into a human-centred design (HCD) process could enable the development of standardised UI elements for DHTs that is more scientifically robust than HCD alone. ConclusionCombining HCI-modelling and Human-Centred Design could improve scientific progress towards developing safer and more user-friendly DHTs.

Comparing perception of L1 and L2 English by human listeners and machines: Effect of interlocutor adaptationsa).

Speakers tailor their speech to different types of interlocutors. For example, speech directed to voice technology has different acoustic-phonetic characteristics than speech directed to a human. The present study investigates the perceptual consequences of human- and device-directed registers in English. We compare two groups of speakers: participants whose first language is English (L1) and bilingual L1 Mandarin-L2 English talkers. Participants produced short sentences in several conditions: an initial production and a repeat production after a human or device guise indicated either understanding or misunderstanding. In experiment 1, a separate group of L1 English listeners heard these sentences and transcribed the target words. In experiment 2, the same productions were transcribed by an automatic speech recognition (ASR) system. Results show that transcription accuracy was highest for L1 talkers for both human and ASR transcribers. Furthermore, there were no overall differences in transcription accuracy between human- and device-directed speech. Finally, while human listeners showed an intelligibility benefit for coda repair productions, the ASR transcriber did not benefit from these enhancements. Findings are discussed in terms of models of register adaptation, phonetic variation, and human-computer interaction.

Linguistic patterning of laughter in human-socialbot interactions

Laughter is a social behavior that conveys a variety of emotional states and is also intricately intertwined with linguistic communication. As people increasingly engage with voice-activated artificially intelligent (voice-AI) systems, an open question is how laughter patterns during spoken language interactions with technology. In Experiment 1, we collected a corpus of recorded short conversations (~10 min in length) between users (n = 76) and Amazon Alexa socialbots (a voice-AI interface designed to mimic human conversational interactions) and analyzed the interactional and pragmatic contexts in which laughter occurred. Laughter was coded for placement in the interaction relative to various speech acts, as well as for phonetic patterning such as duration and voicing. Our analyses reveal that laughter is most commonly found when the content of Alexa’s speech is considered inappropriate for the discourse context. Laughter in the corpus was also largely short in length and unvoiced– characteristics which are commonly associated with negative social valence. In Experiment 2, we found that a separate group of listeners did not distinguish between positive and negative laughter from our dataset, though we find that laughs rated as more positive are also rated as more excited and authentic. Overall, we discuss our findings for models of human-computer interaction and applications for the use of laughter in socialbot conversations.

Retraction Note: Computer vision for facial analysis using human–computer interaction models

Retraction Note: Computer vision for facial analysis using human–computer interaction models

An Integration Model on Brainstorming and Extenics for Intelligent Innovation in Big Data Environment

Brainstorming is a widely used problem-solving method that generates a large number of innovative ideas by guiding and stimulating intuitive and divergent thinking. However, in practice, the method is limited by the human brain's capacity or special capabilities, especially by the experience and knowledge they possess. How does our brain create ideas like storming? Based on the new discipline of Extenics, the authors propose a new model that explores the process of how ideas are created in our brain, with the goal of helping people think multi-dimensionally and getting more ideas. With the support of information technology and artificial intelligence, we can systematically collect more information and knowledge than ever before to form a basic-element information base and build human-computer interaction models, to make up for the lack of information and knowledge in the human brain. In addition, the authors provide a methodology to help people think positively in a multidimensional way based on the guidance of Extenics in the brainstorming process.

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

Gesture recognition, as a core technology of human–computer interaction, has broad application prospects and brings new technical possibilities for smart homes, medical care, sports training, and other fields. Compared with the traditional human–computer interaction models based on PC use with keyboards and mice, gesture recognition-based human–computer interaction modes can transmit information more naturally, flexibly, and intuitively, which has become a research hotspot in the field of human–computer interaction in recent years. This paper described the current status of gesture recognition technology, summarized the principles and development history of electromagnetic wave sensor recognition, stress sensor recognition, electromyographic sensor recognition, and visual sensor recognition, and summarized the improvement of this technology by researchers in recent years through the direction of sensor structure, selection of characteristic signals, the algorithm of signal processing, etc. By sorting out and comparing the typical cases of the four implementations, the advantages and disadvantages of each implementation and the application scenarios were discussed from the two aspects of dataset size and accuracy. Based on the abovementioned discussion, the problems and challenges of current gesture recognition technology were discussed in terms of the biocompatibility of sensor structures, wearability and adaptability, stability, robustness, and crossover of signal acquisition and analysis algorithms, and the future development directions in this field were proposed.

A robust hybrid decision making model for Human-Computer interaction in the environment of Bipolar complex picture fuzzy soft Sets

The goal of this paper is to present novel concepts and effective theories for the ability to accurately represent difficult and ambiguous information in real-life problems. In order to accomplish this goal, this paper investigated the original idea of bipolar complex picture fuzzy soft set (BCPFSS), which combination of two distinct theories known as the bipolar complex picture fuzzy set (BCPFS) and the soft set (SS). The capabilities of this new model are to achieve a larger range with the aid of the membership degree (MD), non-membership (NMD), and abstinence degree (AD) with complex integers. The BCPFSS represented two different degrees, known as positive degrees examine positive aspects of the problem and negative degrees examine negative aspects of the problem. Moreover, human–computer interaction (HCI) is an effective human–computer interface that presents a realistic world. HCI is a field that recognizes the significant role of educational applications and platforms in visualizing content, providing students with easy access to education in their homes, and paving the way for the development of alternative and advanced methods for implementing the concept of e-learning. With the technological advancements of HCI, it has become a fundamental tool in every area of activity, revolutionizing work environments and expanding their scope. In order to resolve the challenge of analyzing complex and unreliable data in real-world contexts, we proposed the idea of bipolar complex picture fuzzy soft sets (BCPFSSs). The innovative concept of BCPFSS defines many operations such as complement, extended intersection, and restricted intersection. The idea of BCPFSSs possesses a broad structure as they explore all values of MD, AD, and NMD with both positive (+ve) and negative (−ve) perspectives, making them adept at handling multivariable challenges. In this paper, modeling approaches based on BCPFSSs are proposed for choosing the HCI for effective implementation by formulating and analyzing score functions. Finally, a comparative analysis section illuminates a comprehensive and organized comparison of recent works to the earlier work.

Human-computer interaction based on background knowledge and emotion certainty.

Aiming at the problems of lack of background knowledge and the inconsistent response of robots in the current human-computer interaction system, we proposed a human-computer interaction model based on a knowledge graph ripple network. The model simulated the natural human communication process to realize a more natural and intelligent human-computer interaction system. This study had three contributions: first, the affective friendliness of human-computer interaction was obtained by calculating the affective evaluation value and the emotional measurement of human-computer interaction. Then, the external knowledge graph was introduced as the background knowledge of the robot, and the conversation entity was embedded into the ripple network of the knowledge graph to obtain the potential entity content of interest of the participant. Finally, the robot replies based on emotional friendliness and content friendliness. The experimental results showed that, compared with the comparison models, the emotional friendliness and coherence of robots with background knowledge and emotional measurement effectively improve the response accuracy by 5.5% at least during human-computer interaction.

Prototype of MyoWare (Electromyography Muscle Sensor) for Measuring People’s Muscle Strengths

Human-Computer Interaction (HCI) becomes a solution to help humans connect with computers. Research and tools related to HCI have been developed by many researchers. HCI is able to help humans connect between humans and computers and humans with humans at a considerable distance. One of HCI model is applied to the MyoWare tool that can capture hand muscle movements using an electromyograph (EMG) sensor. This article describes how to assemble and identify the raw data generated from the MyoWare tool. Using MyoWare on the hand could produce EMG data output. MyoWare only used the EMG sensor and generated data in the form of Envelope EMG and Raw EMG which differed in scale and size. This required a extraction features process to make the data uniform. This study uses the Moment Invariant method to extract features and min-max to normalize each data generated on the MyoWare sensor. Testing was done by doing simple hand movements. The test results showed that the differences in gestures were recognized well even though they were performed in different positions.

A new method for constructing roads map in forest area using UAV images

High definition map is a map used for automatic driving. Its absolute precision is sub meter level, that is, the error between the position of an element on the map and the position of the element in the real world is less than 100 cm. In addition, compared with ordinary maps, high definition maps contain more geographic information, such as road width, slope, curvature, etc. At present, high definition roads map construction is largely for the main traffic road. However, there are few studies on the construction of high definition forest roads map for forestry production and management. Thus, it is imperative and helpful to explore an efficient and low-cost method for constructing high definition forest roads map that can provide accurate basic map services for forestry management activities. In this study, firstly, the DJI Phantom 4 RTK UAV was used for aerial photography to obtain the forest oblique images. Then, combined with close range photogrammetry, the high-resolution images under the forest are acquired and generated into dense point cloud images. Next, the two images are fused using iterative closest point (ICP) algorithm combined with manual registration; Finally, the fine-grained 3D scene model is generated by using the Context Capture 3D modeling software, based on which a high definition forest roads map was constructed using human-computer interaction model. Taking Menggongshan Forest Farm as an example, the construction of the fine 3D scene model was carried out to solve the problem of loopholes and ambiguities of the road model under forest, based on which the high definition forest roads map was constructed. To evaluate the accuracy of the forest roads map, the accuracy check was conducted. The results showed that the point position on the forest roads map, compared to the actual point, had a medium, minimum and maximum error of 0.076 m, 0.020 m and 0.170 m in plane, respectively, a medium, minimum and maximum error of 0.122 m, 0.005 m and -0.214 m in elevation, respectively. The accuracy of the high definition forest roads map meets the need of forestry production and management activities. It is feasible to adopt the method of UAV oblique photogrammetry combined with close-range photogrammetry, fine 3D scene model construction and human-computer interactive for constructing high definition forest road map. And this method can provide a low-cost and high-efficiency way for constructing high definition forest roads map, and also can better provide basic map data for forest management activities.

Reminiscences on Influential Papers

This issue's contributors chose influential works that create bridges from other research communities (human-computer interaction, computer architecture, and programming models) to the data management community. Their write-ups highlight the importance of reaching across fields without forgetting the core. Enjoy reading! While I will keep inviting members of the data management community, and neighboring communities, to contribute to this column, I also welcome unsolicited contributions. Please contact me if you are interested.

Extended Human Computer Interaction (HCI) Model for People with Visual Impairment in Education System; A Case of HVP Gatagara, Rwanda

Extended Human Computer Interaction (HCI) Model for People with Visual Impairment in Education System; A Case of HVP Gatagara, Rwanda

Alcohol corporations and the metaverse: Threats to public health?

Alcohol corporations and the metaverse: Threats to public health?

A Study on the Construction of Translation Curriculum System for English Majors from the Perspective of Human-Computer Interaction

This paper proposes a collaborative human-computer interaction recommendation model for English major translation courses to address the problems of poor course recommendation and lack of robustness to noisy data in traditional recommendation models for English major translation courses. First, a new data enhancement method is proposed for the bipartite graph structure. Then, the enhanced data is fed into a graph convolutional neural network for node feature extraction to obtain node representations of users and items. A recommendation supervision task and an auxiliary task for contrast learning are constructed for joint optimization. The human-computer interaction model of the knowledge graph is designed, and the dialogue entities are embedded in the knowledge graph ripple network to obtain potentially interesting content for students. Finally, the student interaction content and node representations are combined to obtain the optimal translation course recommendation. The experimental results indicate that the proposed model in this work is capable of producing higher-quality English major course recommendations and beats other current models. This model is suitable for English primary translation course content recommendation and helps to improve students’ translation ability.

Hakka culture brand image design based on the human-computer interaction model.

In order to better disseminate Hakka culture, this thesis focuses on the cultural dissection of Hakka culture in Gannan and the development and reflection of Hakka culture in Gannan. This article aims to design the brand image of Hakka culture through human–computer interaction model. And based on this, this article discusses the brand concept and the elements of cultural and creative products and discusses the connection between Hakka cultural and creative and tourism brands. At the same time, this article conducts an in-depth study on the creative design of the Hakka cultural tourism brand in Gannan. First, this article analyzes the brand connotation and analyzes the connotation and constituent elements of brand cultural and creative products. Second, this article analyzes the relationship between Hakka culture and tourism brand cultural and creative product design. It discusses how to inherit and develop Hakka culture from the perspective of how Hakka culture is integrated into brand cultural and creative products. Third, this article analyzes the design practice of cultural and creative products of the Hakka cultural tourism brand in Gannan. This article first analyzes the visual image design of the cultural and creative products of the Hakka cultural tourism brand in Gannan. Then it analyzes the design of a series of cultural and creative products of the Hakka cultural tourism brand in Gannan. The experimental results show that the popularity of different products is more than 50%, which shows the effectiveness of this design.

Quantum Water Strider Algorithm with Hybrid-Deep-Learning-Based Activity Recognition for Human–Computer Interaction

Human action and activity recognition are clues that alleviate human behavior analysis. Human action recognition (HAR) becomes a significant challenge in various applications involving human computer interaction (HCI) and intellectual video surveillance for enhancing security in distinct fields. Precise action recognition is highly challenging because of the variations in clutter, backgrounds, and viewpoint. The evaluation method depends on the proper extraction and learning of data. The achievement of deep learning (DL) models results in effectual performance in several image-related tasks. In this view, this paper presents a new quantum water strider algorithm with hybrid-deep-learning-based activity recognition (QWSA-HDLAR) model for HCI. The proposed QWSA-HDLAR technique mainly aims to recognize the different types of activities. To recognize activities, the QWSA-HDLAR model employs a deep-transfer-learning-based, neural-architectural-search-network (NASNet)-based feature extractor to generate feature vectors. In addition, the presented QWSA-HDLAR model exploits a QWSA-based hyperparameter tuning process to choose the hyperparameter values of the NASNet model optimally. Finally, the classification of human activities is carried out by the use of a hybrid convolutional neural network with a bidirectional recurrent neural network (HCNN-BiRNN) model. The experimental validation of the QWSA-HDLAR model is tested using two datasets, namely KTH and UCF Sports datasets. The experimental values reported the supremacy of the QWSA-HDLAR model over recent DL approaches.

Construction of the Human-Computer Interaction Model of Hybrid Course Based on Machine Learning

The massive open online course (MOOC) is a large-scale open online course teaching method with many advantages such as abundant resources, open access, social interaction, and learning freedom. However, there are also flaws such as low completion rate, lack of personalized consultation, mechanical inspection, and unity. In order to resolve these shortcomings of MOOC, this study proposes a hybrid course model consisting of MOOC and small private online courses (SPOC) based on machine learning. This integrates deterministic rules into our machine learning pipeline in a variety of ways, gradually adding rules as data preprocessing steps and then using object-oriented programming (OOP) to generate novel ML model classes. Finally, it includes data in all deterministic rules through a hybrid model so that we can train it like any other machine learning model. Through experimental analysis, it can be observed that the MOOC + SPOC hybrid teaching mode can effectively integrate MOOC, SPOC, and physical classrooms and make full use of the advantages of the three to provide learners with a unified interactive interface for implementation.