Intelligent Interaction Research Articles

Multilayer Bionic Tunable Strain Sensor with Mutually Non‐Interfering Conductive Networks for Machine Learning‐Assisted Gesture Recognition

AbstractFlexible strain sensors capable of acquiring tiny mechanical signals and attaching to various irregular surfaces are becoming prevalent in physiological measurement, soft robotics, and human‐machine interaction. However, the advancement of flexible strain sensors is substantially impeded by the intrinsic compromise between sensitivity and the range of detectable signals. Inspired by the multilayer structure of nacre in nature, a carbon nanotubes (CNTs)/graphene (GR)/graphene (GR)/thermoplastic polyurethane (TPU) mat (CGGTM) is prepared by electrospinning and high‐pressure spraying technology. By designing a multilayer structure with mutually non‐interfering conductive networks, the resulting CGGTM possesses a low detection limit (0.05% strain), high sensitivity (gauge factor, GF > 152537), large detection range (up to 364% strain), fast response/recovery time (80 ms/100 ms), and excellent cyclic durability (up to 1000 cycles). Furthermore, the CGGTM also exhibits satisfactory triboelectric performances when assembled into a triboelectric nanogenerator (TENG, 3 × 3 cm2), including high triboelectric output (open‐circuit voltage Voc = 135.4 V, short‐circuit current Isc = 1.25 µA) and power density (88 mW m−2), enabling reliable power supply, self‐powered sensing, and pulse monitoring capability. Finally, CGGTM is successfully applied to the collection of biological signals and multi‐gesture motion recognition assisted by machine learning algorithms, which holds promise for intelligent interaction with gestures in the future.

Assessing Performance of Cloud-based Heterogeneous Chatbot Systems.

Cloud-based heterogeneous chatbot systems represent a significant advancement in automating diverse tasks across various platforms, including customer service, healthcare, and education. These systems are designed with multi-modal capabilities, allowing them to interact via text, voice, and visual interfaces, and are built on a cloud infrastructure that ensures scalability and reliability. The cloud-based architecture allows dynamic resource allocation, enabling the system to handle high volumes of traffic without compromising performance. These chatbots also benefit from global availability, as cloud regions distributed across the world ensure low latency and high availability.The integration of advanced AI and NLP models, such as GPT and BERT, enhances the chatbot's response accuracy and contextual understanding, making interactions more precise and human-like. Moreover, the cloud infrastructure supports continuous learning through machine learning models that refine chatbot performance over time. With multitenant capabilities, these chatbots can cater to different users or organizations, providing tailored services while ensuring that performance are not affected. Additionally, their ability to integrate with external enterprise systems, like CRMs or ERPs, allows for real-time data exchange and task automation.In terms of security, cloud-based chatbots benefit from encryption protocols and compliance with industry regulations, ensuring safe handling of sensitive data, particularly in sectors like healthcare and finance. Despite the advantages, challenges such as network dependency and cost management need careful consideration, as performance can be affected by poor connectivity or high processing costs, especially when using complex AI models. Nonetheless, cloud-based heterogeneous chatbots excel in delivering scalable, reliable, and intelligent interactions, adapting to growing user demands and a wide range of applications.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.
In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

Large language model-based optical network log analysis using LLaMA2 with instruction tuning

The optical network encompasses numerous devices and links, generating a significant volume of logs. Analyzing these logs is significant for network optimization, failure diagnosis, and health monitoring. However, the large-scale and diverse formats of optical network logs present several challenges, including the high cost and difficulty of manual processing, insufficient semantic understanding in existing analysis methods, and the strict requirements for data security and privacy. Generative artificial intelligence (GAI) with powerful language understanding and generation capabilities has the potential to address these challenges. Large language models (LLMs) as a concrete realization of GAI are well-suited for analyzing DCI logs, replacing human experts and enhancing accuracy. Additionally, LLMs enable intelligent interactions with network administrators, automating tasks and improving operational efficiency. Moreover, fine-tuning with open-source LLMs protects data privacy and enhances log analysis accuracy. Therefore, we introduce LLMs and propose a log analysis method with instruction tuning using LLaMA2 for log parsing, anomaly detection and classification, anomaly analysis, and report generation. Real log data extracted from the field-deployed network was used to design and construct instruction tuning datasets. We utilized the dataset for instruction tuning and demonstrated and evaluated the effectiveness of the proposed scheme. The results indicate that this scheme improves the performance of log analysis tasks, especially a 14% improvement in exact match rate for log parsing, a 13% improvement in F1-score for anomaly detection and classification, and a 23% improvement in usability for anomaly analysis, compared with the best baselines.

Wearable Biosensor Smart Glasses Based on Augmented Reality and Eye Tracking.

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user's intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system's real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system's ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses' algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.

Graded Nanotexturing Architectural Wearable Triboelectric Sensor for Programmable Haptic Exploration.

Emulating biological perception mechanisms to construct intelligent sensing devices and systems represents a paradigm for promoting human-computer interaction in the Internet of Everything era. Nonetheless, developing highly sensitive, real-time sensing and rapidly integrated intelligent interaction units remains a challenging and time-consuming endeavor. This study employs a low-temperature glow discharge technique to rapidly fabricate graded nanotexturing architectural triboelectric nanopaper, upon which wearable triboelectric sensors for real-time tactile detection are designed. The structure enhances the contact area under an external force. Additionally, the Z-stacking structure design enables the sensor to achieve a remarkable sensitivity of 10.3 kPa-1 and a rapid response time of 52 ms. Furthermore, a tactile sensor array was designed to demonstrate the triboelectric sensor's ability to recognize characteristic pressures. With programmable machine learning techniques, the object recognition rate reached 97%. This study supports material structural design across disciplines, laying a solid foundation for the rapid fabrication and integration of transient wearable electronics.

A Blockchain‐Based Proxy Re‐Encryption Scheme With Cryptographic Reverse Firewall for IoV

ABSTRACTAs the internet of vehicles (IoV) technology develops, it promotes the intelligent interaction among vehicles, roadside units, and the environment. Nevertheless, it also brings vehicle information security challenges. In recent years, vehicle data sharing is suffering to algorithm substitution attacks (ASA), which means backdoor adversaries can carry out filtering attacks through data sharing. Therefore, this paper designs a blockchain‐based proxy re‐encryption (PRE) scheme with cryptographic reverse firewall (BIBPR‐CRF) for IoV. In our proposal, CRF can promise the internal safety of vehicle units. More specifically, it can prevent ASA attacks while ensuring chosen plaintext attack (CPA)‐security. Meanwhile, the PRE algorithm can provide the confidential sharing and secure operation of data. Moreover, we use a consortium blockchain service center (CBSC) to store the first ciphertext and re‐encrypt it with smart contracts on the blockchain, which can avoid single point of failure and achieve higher efficiency compared to proxy servers. Finally, we evaluate the performance of BIBPR‐CRF with regard to communication cost, computational cost, and energy consumption. Our proposal is the most fitting for IoV application, in contrast with the other three schemes.

Icon similarity model based on cognition and deep learning

Human-computer cooperation guided by natural interaction, intelligent interaction, and human–computer integration is gradually becoming a new trend in human–computer interfaces. An icon is an indispensable pictographic symbol in an interface that can convey pivotal semantics between humans and computers. Research on similar icons’ cognition in humans and the discrimination of computers can reduce misunderstandings and facilitate transparent cooperation. Therefore, this research focuses on images of icons, extracted contours, and four features, including the curvature, proportion, orientation, and line of the contour, step by step. By manipulating the feature value change to obtain 360 similar icons, a cognitive experiment was conducted with 25 participants to explore the boundary values of the feature dimensions that cause different levels of similarity. Its boundary values were applied to deep learning to train a discrimination algorithm model that included 1500 similar icons. This dataset was used to train a Siamese neural network using a 16-layer network branch of a visual geometry group. The training process used stochastic gradient descent. This method of combining human cognition and deep learning technology is meaningful for establishing a consensus on icon semantics, including content and emotions, by outputting similarity levels and values. Taking icon similarity discrimination as an example, this study explored the analysis and simulation methods of computer vision for human visual cognition. The accuracy evaluated is 90.82%. The precision was evaluated as 90% for high, 80.65% for medium, and 97.30% for low. Recall was evaluated as 100% for high, 89.29% for medium, and 83.72% for low. It has been verified that it can compensate for fuzzy cognition in humans and enable computers to cooperate efficiently.

Role of Artificial Intelligence in the Computer Network: Review

A combination Artificial Intelligence (AI) with networks systems increases from efficiency and performance of the networks. Today routine life needs huge of data and applications shared between people that lead to a traffic load on networks, therefore intelligence networks produces are important abilities of AI in controlling and avoiding troubleshooting. Also AI improves from security and management of databases .Effective high-intelligent networks operate networks in automation systems .It is one of the most hopeful computer networks future. The development of artificial intelligence in the computer network relies on the importance of applications security and cloud computing. In recent years, a big development has occurred in Artificial Intelligence and majority of computer networks that leads to the of AI in the applications of network techniques. This paper reviews a role of combination of Artificial Intelligence with Computer Networks. Discussed two topics combination of AI in the computer network (Cloud Computing and Security). AI in cloud computing increases efficiency and significant networking. In addition, reduced the cost and minimized base of clouding. Artificial Intelligence controls huge amounts of databases in the cloud avoiding problems in heavy traffic networks. AI is a good solution in cyber-attacks, also solving most problems of security. Today more intelligent interaction with computer networks. Automate solutions in more application applying successful training and testing data sets. Artificial intelligence contributes to all fields of life and encourages the development of new technologies and uses. More robots, data automation, and many applications in the Internet of Things are expected.

Digital Human Intelligent Interaction System Based on Multimodal Pre-training Mode

ABSTRACT As the forefront of human–computer intelligent interaction, digital humans have increasingly diverse application scenarios, but also face many challenges. In order to enhance the intelligence level and interaction capability of digital human, the study first optimizes the traditional UniLM model, then introduces the mechanism of multi-head attention, and mitigates the exposure bias by using adversarial training and random replacement of decoder to design an improved UniLM model, and finally applies the improved model to the digital human management system. The results show that the precision rate of the improved UniLM model is improved by 7.68%, 6.4%, and 4.96%, the recall rate is improved by 11.94%, 9.69%, and 8.83%, and the F1-score is improved by 8.34%, 6.41%, and 7.68% compared with the other three models, which proves that it has a better precision rate, robustness, and generalization ability. The perplexity of the improved UniLM model is 135, 95, 76, 71, and 55 under five text lengths, which is significantly lower than the other models, proving that its text generation ability is better. The above results demonstrate the performance of the research-designed digital human system based on the Improved UniLM model, which provides a direction for the further development of digital human technology.

Can we trust LLMs to help us? An examination of the potential use of GPT-4 in generating quality literature reviews

Purpose This study aims to examine the ability of Generative Pre-trained Transformer 4 (GPT-4), one of the most powerful large language models, to generate a literature review for peer-reviewed journal publications. The objective is to determine whether business scholars can rely on GPT-4’s assistance with literature reviews and how the nature of human–artificial intelligence (AI) interaction may affect the quality of the reviews generated by GPT-4. Design/methodology/approach A survey of 30 experienced researchers was conducted to assess the quality of the literature reviews generated by GPT-4 in comparison with a human-authored literature review published in a Social Science Citation Index (SSCI) journal. The data collected were then analyzed with analysis of variance to ascertain whether we may trust GPT-4’s assistance in writing literature reviews. Findings The statistical analysis reveals that when a highly structured approach being used, GPT-4 can generate a high-quality review comparable to that found in an SSCI journal publication. However, when a less structured approach is used, the generated review lacks comprehensive understating and critical analysis, and is unable to identify literature gaps for future research, although it performed well in adequate synthesis and quality writing. The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction. Research limitations/implications The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction. Nonetheless, cautions should be taken due to the limitations of this study discussed in the text. Originality/value By breaking down the specific tasks of a literature review and using a quantitative rather than qualitative assessment method, this study provides robust and more objective findings about the ability of GPT-4 to assist us with a very important research task. The findings of this study should enhance our understanding of how GPT-4 may change our research endeavor and how we may take a full advantage of the advancement in AI technology in the future research.

A Study of the Impact of Artificial Intelligence on Consumer Decision Making

The article considers not only the obvious benefits of using AI, but also potential risks associated with changing consumer behavior. The study introduces the User Engagement Index (UEI), which allows measuring and comparing user activity and interaction with marketplaces, considering the influence of AI. The proposed methodology can become the basis for further research in the field of human-artificial intelligence interaction in the digital environment. The UEI formula allows for a comprehensive assessment of user engagement, considering several key factors that influence user behavior on the site.

Cognitive evaluation based on regression and eye-tracking for layout on human–computer multi-interface

ABSTRACT The human–computer cooperation process guided by natural interaction, intelligent interaction, and human–computer integration is gradually becoming a new trend in human–computer interaction. Cooperative scenarios of human–computer interaction systems often contain multi-interface and multi-device results in edges often interrupt the cognitive ergonomics of interface layout. This research takes typical areas as an example to establish a stepwise regression model to predict reaction time at an arbitrary position on the left interface. It uses a foveal region to position the starting point of attention and a parafoveal region to calculate the radius of each objective area, and design 10 similar tasks to analyze eye-tracking indexes through physiological assessment. Unlike fixed thinking such as spatial proximity on multi-interfaces, this research summarises cognitive features of layout based on the positive and negative effects of edge impact through eye-tracking analysis. It analyzes cognition including input, process, and output in human–computer cooperation from human intelligence and artificial intelligence respectively, and visualises the mapping relationship between these indexes and specific stages of cognition. Besides, the quantitative evaluation of the regression equation and qualitative analysis of the eye-tracking indexes provide a reference for other interfaces around the front interface.

Construction and Practice of an Interactive Virtual English Learning Community on Campus Information Platforms

In the context of the information age, campus information platforms have gradually become essential tools for enhancing educational quality. This paper explores the construction and practice pathways of an interactive virtual English learning community based on a campus information platform, aiming to provide students with a more flexible and efficient English learning environment. By analyzing the current development of campus information platforms and interactive learning theories, this paper proposes the design principles and functional modules of the virtual learning community and elaborates on the technical architecture and practical construction pathways of the community. The research results indicate that the interactive virtual English learning community not only effectively enhances students' learning motivation and outcomes but also strongly supports the innovative practice of English education. In the future, with the development of artificial intelligence and big data technologies, virtual learning communities are expected to achieve greater breakthroughs in personalized learning recommendations and intelligent interaction.

Artistic sense of interior design and space planning based on human machine intelligent interaction

The rapid development of artificial intelligence technology is gradually penetrating into multiple fields such as interior design and spatial planning. The aim of this study is to integrate artificial intelligence with interior design, enhance design artistry and user experience, and address the interactive needs of interior space design choices. A set of indoor space design recognition system has been designed by introducing artificial intelligence networks and attention mechanisms. This study first optimizes the CenterNet algorithm based on attention mechanism and feature fusion to improve its accuracy in identifying complex components. Afterwards, the long short-term memory network and convolutional neural network are trained to complete the task of spatial layout feature recognition and design. The performance test results showed that after testing 100 images, the software could recognize indoor design space images and create corresponding vector format space maps in about 5 minutes, providing them to the 3D modeling interface to generate 3D scenes. Compared to the approximately 25 minutes required by manual methods, the design efficiency has been significantly improved. The research and design method has a fast convergence speed and low loss during the retraining process. In simulation testing, its mAP value reached 91.0%, higher than similar models. It performs better in detecting walls, doors and windows, bay windows, double doors, and two-way doors. Moreover, it has outstanding ability when facing structures such as short walls and door corners, and can recognize and create vector format spatial maps within 5 minutes, which is accurate and efficient. The system designed in this project has optimized the interaction between designers and clients in interior design, accurately capturing user intentions and assisting designers in improving work efficiency.

Immersive artificial intelligence technology based on entertainment game experience in simulation of psychological health testing for university students

At present, the mental health problems of college students are becoming more and more prominent, but the existing mental health detection methods have certain limitations. Therefore, the combination of entertainment and game experience with artificial intelligence technology in this paper can provide a more comprehensive and accurate mental health detection scheme. The research designed an immersive entertainment game experience platform, in which artificial intelligence technology was used to detect mental health. The platform is based on emotion recognition algorithms and artificial intelligence interaction technology to assess the level of mental health of users by analyzing their behavior and emotional expression in the game. The results show that immersive artificial intelligence technology based on entertainment and game experience can effectively simulate the mental health state of college students. Compared with traditional mental health assessment methods, this technique has obvious advantages in terms of accuracy and comprehensiveness. Users also showed a high degree of acceptance and participation for this entertaining mental health detection method. This technology can provide students with a more relaxed and interesting mental health assessment experience, and can also provide accurate and comprehensive assessment results, and provide scientific and effective guidance for mental health management and intervention of college students.

Bridging the Digital Divide: The Collision and Integration of Media Intelligence and Age-friendliness in the Context of Aging

With the aging of the global population, the integration between media intelligence and age-friendliness becomes particularly important. Media intelligence, through technological means such as big data and cloud computing, enables personalized contents delivery and intelligent interaction. However, these advanced technologies often deviate from the usage habits and needs of the elderly. Older people may be unskilled in using smart devices, feel overwhelmed by information overload, and have concerns about privacy protection. Therefore, it is necessary to find a balance point in the collision between media intelligence and through technological means such as big data and cloud computing so that media intelligence can better serve the elderly. This paper aims to explore the collision and integration of media intelligence and age-friendliness in the context of aging and proposes corresponding strategies to bridge the digital divide.

Unsupervised deep domain adaptation algorithm for video based human activity recognition via recurrent neural networks

The integration of cyber-physical systems and artificial intelligent human activity recognition (HAR) applications enables intelligent interactions within a physical environment. In real-world HAR applications, the domain shift between training (source) and testing (target) images captured in different scenarios leads to low classification accuracy. Existing unsupervised domain adaptation (UDA) methods often require some labeled target samples for model adaptation, which limits their practicality. This study proposes a novel unsupervised deep domain adaptation algorithm (UDDAA) for HAR using recurrent neural networks. UDDAA introduces a maximum mean class discrepancy (MMCD) metric, accounting for both inter-class and intra-class differences within each domain. MMCD extends the maximum mean discrepancy to measure the class-level distribution discrepancy across source and target domains, aligning these distributions to enhance domain adaptation performance. Without relying on labeled target data, UDDAA predicts pseudo-labels for the unlabeled target dataset, combining these with labeled source data to train the model on domain-invariant representations. This approach makes UDDAA highly practical for scenarios where labeled target data is difficult or expensive to obtain, enabling human-computer interaction (HCI) systems to function effectively across varied environments and user behaviors. Extensive experiments on benchmark datasets demonstrate UDDAA's superior classification accuracy over existing baselines. Notably, UDDAA achieved 92% and 99% accuracy for University of Central Florida database (UCF) to Human Motion Database (HMDB) and HMDB to UCF transfers, respectively. Additionally, on personal recorded videos with complex backgrounds, it achieved high classification accuracies of 95% for basketball and 90% for football activities, underscoring its generalization ability, robustness, and effectiveness.

A human activity recognition method based on Vision Transformer

Human activity recognition has a wide range of applications in various fields, such as video surveillance, virtual reality and human–computer intelligent interaction. It has emerged as a significant research area in computer vision. GCN (Graph Convolutional networks) have recently been widely used in these fields and have made great performance. However, there are still some challenges including over-smoothing problem caused by stack graph convolutions and deficient semantics correlation to capture the large movements between time sequences. Vision Transformer (ViT) is utilized in many 2D and 3D image fields and has surprised results. In our work, we propose a novel human activity recognition method based on ViT (HAR-ViT). We integrate enhanced AGCL (eAGCL) in 2s-AGCN to ViT to make it process spatio-temporal data (3D skeleton) and make full use of spatial features. The position encoder module orders the non-sequenced information while the transformer encoder efficiently compresses sequence data features to enhance calculation speed. Human activity recognition is accomplished through multi-layer perceptron (MLP) classifier. Experimental results demonstrate that the proposed method achieves SOTA performance on three extensively used datasets, NTU RGB+D 60, NTU RGB+D 120 and Kinetics-Skeleton 400.

상황인식 기반 AR-HUD(헤드업 디스플레이) 인터페이스 디자인에 관한 연구

With the development of intelligentization of the vehicle industry, cars are no longer just means of transportation, and various sensors and virtual entertainment systems have begun to be applied for autonomous vehicles and smart cars. Rise of in-vehicle information required by the new intelligent interaction method for information processing theory, this study has see car - human interaction interface based on AR technology introducing, driving experience and can improve the driving safety is studied interface design. To this end, literature research, case comparative analysis, and surveys were conducted, and comparative analysis was conducted on Tesla HUD and Huawei AR-HUD. In addition, through in-depth investigation of contextual user demands, it analyzed existing problems and proposed optimization of AR-HUD interface design based on situational awareness from three aspects of function, interaction and vision. Study driver's driving experience, improve driving stability, AR - propulsion, smart and personalization of HUD interface design intelligent vehicles and vehicles of the self-driving car and future improvement and theoretical foundation for design of an interface by providing drivers contributing to meet demand.