Search

Purpose: The purpose of this paper is to articulate how the user experience (UX) approach was initiated and integrated into the centre's scope of operations with the objective of improving the e-learning layout on the D2L learning management system (LMS). One of the most effective ways to collect user feedback has historically been to evaluate user interfaces using strategies from user testing. The integration of a UX approach by the Centre for ODL Experiences (COLE) at Wawasan Open University has led to a more user-oriented design of FlexLearn by conducting user testing on students as the target users of the platform and gathering course leaders' (CLs) feedback after the presentation of the new template. Design/methodology/approach: Since the process of design and development is a looping process, the first user testing methods employed were observation and interviews, which were conducted over the course of numerous sessions. The data collection used a mixed-methods approach, combining quantitative demographic and background data with qualitative feedback from open-ended questions and real-time interview responses. A standardized questionnaire gathered demographic information, while questions for feedback forms and interviews were adjusted based on specific tasks to explore usability and user interactions comprehensively. Findings: The findings revealed overall positive feedback, with some concerns highlighted by the students who claimed to have trouble navigating the courses during the initial prototype. In addition to the qualitative data from the user testing session, a quantitative method based on an online questionnaire was also utilised for the CLs after the presentation of the final layout. Positive responses were received from the CLs, and constructive suggestions were considered for FlexLearn 3.0. Research limitations/implications: This paper is among the first that articulates the process of initiating and integrating user-centred design in an effort to improve the user experience of online and ODL platforms and LMSs. It will contribute to a dialogue on investigating and prioritising learners' ODL experiences to ensure education equity across all levels or categories of students, which aligns with the United Nations Sustainable Development Goals. Practical implications: The integration of UX and user testing allows us to better identify what users like, their concerns and their needs. We gain important input on how easy or difficult it is to use the system, move around it and how much they enjoy using it. This feedback helps us make changes to the design so that the final product is more in line with what users want. It also allows us to discover problems before they become major, saving time and effort later on. Finally, integrating user input improves the LMS, delivering a more fun and successful learning experience for everyone. Social implications: User-friendly systems arise as institutions prioritise user-centred design, breaking down barriers for various learners. This develops an innovative culture, improving present learning experiences and setting a precedent for future generations. The emphasis on user demands helps to create a more accessible, adaptive and egalitarian educational landscape by connecting education with current technological trends. As education becomes more inclusive, the broader community benefits, emphasising the beneficial social impact of LMS user testing. Originality/value: By articulating the process of integrating user testing on an LMS/e-learning prototype, helps us understand what users like, where they face problems and what needs improvement. By involving users in testing, we get valuable feedback on how easy it is to use the system, navigate around and overall, how much they enjoy using it. Case studies like this also offer universities concrete examples of real-world challenges and successes.

Purpose: Inclusive growth in the education sector is still a major challenge in some countries because of limited access to technologies and internet connectivity, among other reasons. However, as a technology, augmented reality (AR) is expected to be widely used in the field of education in the future. The main purpose of this paper is to explore the use of AR for inclusive growth in education as well as identify the challenges, particularly in countries like India and Tanzania. Design/methodology/approach: This paper applies a systematic literature review by analysing and synthesising relevant documents, mainly journal articles, books and conference papers. Descriptive statistics and cross-tabulation were used for the analysis. Content analysis was used to evaluate the contents of the reviewed literature. Findings: For the use of AR, it is important to have adequate digital infrastructure, access to universal internet or broadband facilities and the digital empowerment of citizens. Major challenges to inclusive growth in education include the lack of trained teacher educators and students' preference for practical or project-based curriculum. Practical implications: Practitioners in both countries may consider the use of AR for inclusive growth in education. Originality/value: This paper specifically examines the use of AR in higher education and the related challenges based on a review of two countries, namely India and Tanzania.

Purpose: This paper evaluated the effectiveness of blended learning of business courses in higher learning institutions (HEIs) in developing economies. Design/methodology/approach: A survey, involving 215 learners, was used to collect data. A stratified sampling technique was used in this study. The data were analyzed using the PROCESS macro in SPSS. Findings: In the blended learning approach, student attitudes, social presence, IT infrastructure and flexible learning are all favorable predictors of learner satisfaction. The impact of blended learning on learner satisfaction is further mediated by IT infrastructure, social presence and learner attitude. Practical implications: HEIs need to invest in planning and resource mobilization in order to realize several benefits derived from the use of blended learning. For optimal learning outcomes, this should be combined with training on IT infrastructure usage for both facilitators and learners. In order to assist learners in developing competencies through consistent use, institutions should also invest in tailored blended learning technologies. In addition, emphasis should be placed on training all actors in order to better manage change. Originality/value: This paper presents and ranks several dimensions for blended learning success in low-budget universities. In addition, the study contributes to the understanding of intervening variables necessary for enhancing the potential of pedagogy in maximizing learner satisfaction.

Many studies have recently focused on the importance of the effective integration of Information and Communication Technology (ICT) tools in science education and the need for science teachers to receive adequate training and support to use them effectively. This paper aims to explore the Albanian science teachers' perceptions and use of ICTs in teaching processes. The study provides an interpretative analysis of the opinions of science teachers teaching in the middle and high schools of several Albanian regions expressed in an online survey. Overall, the results suggest that the use of ICT by science teachers in Albania is limited by a lack of proper infrastructure, limited access to technology, and training of teachers on integrating technology. However, there are indications that the adoption of ICT in science education may increase in the future, particularly after the boost from the COVID-19 pandemic emergency.

Though a computer professional, Meredith Broussard feels "that the way people talk about technology is out of touch with what digital technology can actually do", and that seeing the latter as a panacea has "resulted in a tremendous amount of poorly designed technology" (6), which needlessly complicates instead of improving or making life easier. Artificial Unintelligence: How Computers Misunderstand the World: by Meredith Broussard, Cambridge, MA, MIT Press, 2019, 248 pp., $15.95T/£12.99 (paper) Supported by extensive research, I Artificial Unintelligence i cogently challenges the prevailing technophile hype extolling the unlimited ways in which technology supposedly can "change the world for the better" and create a digital utopia with infinite benefits in every area of life. [Extracted from the article]

Learning Management Systems (LMSs) in education are necessary, especially since the COVID-19 pandemic. A good user experience (UX) in these systems has become increasingly important. However, there are limited resources that could guide implementing both the hedonic and pragmatic qualities of UX. The objective of the present study is to identify emotional design factors and apply an innovative perspective from a rhetorical perspective that could serve to improve the user experience. Utilizing the User Experience Questionnaire, a rhetorical evaluation, and four open-ended questions in three control groups, we evaluate Moodle at the Faculty of Sciences of the Universidad Autónoma de San Luis Potosí (UASLP). The findings show an improvement in UX that generated better user satisfaction. We concluded that ethos, pathos, and logos serve to cover the pragmatic and hedonic aspects of UX. This paper serves as a guide for other educational institutions that want to improve their LMS.

Collaborative design approaches have been increasingly adopted in the design of learning technologies since they contribute to develop pedagogically inclusive and appropriate learning designs. Despite the positive reception of collaborative design strategies in technology-enhanced learning, little attention has been dedicated to analyzing the challenges faced in design processes using a collaborative approach. In this paper, we disclose the collaborative design of a chatbot for self-regulated learning in higher education using an action research approach. We analyze the design process of EDUguia chatbot, which includes diverse evidence from questionnaires and workshops with students and lecturers, as well as intermediary design objects. Based on the qualitative analysis, we identify several challenges that are transversal to the co-design work, as well as specific to the design phases. We critically reflect on the strategies deployed to overcome these challenges and how they relate to decision-making processes, highlighting the need to make stakeholders' tacit knowledge explicit, cultivate trust-building and support democratic decision-making in technology design processes. We believe that the recommendations we present in this paper contribute to developing best practices in the collaborative design of chatbots for the self-regulation of learning, as well as learning technology in general.

Montessori considers as an effective method that is commonly used in nurseries to improve the mental performance and develop the cognitive skills toward children. Tangible user interfaces (TUI) is an effective tool that allows interaction with physical objects in a way that makes this interaction augmented through embedded computation. This paper proposed a new concept of Montessori, which is Interactive Technological Montessori (ITM) using TUI. It aims to measure the impact of using TUI on enhancing the effectiveness of Montessori and make a new futuristic design vision for Montessori activities to motivate children positively. The findings of this paper revealed that Merging TUI with ITM has a great potential to increase the efficiency of Montessori. In addition to considering the appropriate design principles and Multi aging group work help children to be motivated positively to interact with the Montessori activities.

Project-based learning is an effective method for students' learning design. For the "Graphic Design for Interactive Entertainment" elective course, the author uses project-based learning to teach UI and UX design to Universitas Pelita Harapan's Visual Communication Design Department students. For the project, the author designed a game mechanic. The students of the course later designed visuals for the game. One game that resulted from this project was "Pro Pickpockets", a game about a group of pickpockets competing to collect valuable prizes for the best pickpockets. This paper elaborates on the design case of Pro Pickpockets as a design project and project-based learning for students in learning UI and UX.

Purpose: The massive expansion of digital platform has been responsible for the widespread progressive engagement created amongst learners and educators. The practice of requiring student feedback on online learning services ensures that teacher education continues to advance its strategic approach to online learning. This paper aims to examine the level of accessibility and adaptability of digital technology with particular focus on Malaysia, by elaborating the value of superior learning service and practical adaptability of online learning during the pandemic era. Design/methodology/approach: This study was conducted using qualitative approach of data collection, namely via structured interview. The listed respondents included 30 higher learners who participated in the study by providing feedback on the issues encountered during the research process. Findings: The findings revealed that the strategic enhancement of digital accessibility continued with digital adaptability to sources of learning services would contribute to advancing achievement of digital learning pathway. Practical implications: Increasing accessibility to digital platforms in digital learning system can help to shape the digital environment. Digital expansion can create unlimited boundaries for online knowledge acquisition. Social implications: The social implication refers to acquiring the abilities developed through online engagement with peers by actualising and exploring information together with continuous inter-connectedness of sharing pathway in online platform. The instructor would need to give a proportional gateway to make learners experience the digital environment for future education. Originality/value: This study aims to assess the value of developing accessibility of digital technology for students' online learning services during the pandemic and beyond. A well-structured plan would enable digital learning capabilities and mutual accessibility amongst learners. This can allow digital abilities to be transformed into collaborative teamwork amongst learners.

Debugging (or troubleshooting) provides a rich context to foster problem-solving. Yet, while we know much about some problems and strategies that novices face in programming on-screen, we know far less about debugging and troubleshooting in the context of physical computing, where coding issues may overlap with materially embedded problems. In this paper, we study the thought processes novice students employed and the challenges they faced in debugging an electronic textile project with multiple overlapping problems that crossed physical, electronic, and computational domains. We employed a think-aloud protocol to develop an instrumental case study by video-recording 45 minutes of one pair of 9th-grade students debugging and fixing a buggy e-textile project. The problem space included the computational system's programmatic, electronic, and physical spatial aspects, which are more generally reflective of physical computing systems. We found that (1) students' troubleshooting was more recursive and less linear than traditional approaches that usually propose linear, procedural, step-wise activities, and (2) students coordinated their approach across multiple modalities, taking advantage of a distributed set of tools and people in order to tackle a complex set of problems. In the discussion, we address various pedagogical implications for improving teaching about troubleshooting.

This paper outlines the development of a Hardware Development Kit (HDK) for a remote training platform on FPGA Devices designed to provide university students pursuing degrees in electronic and informatics engineering (at the bachelor's, master's, and PhD levels) with the tools they need to learn and develop systems related to artificial intelligence, digital signal processing, image processing, digital systems, integrated and embedded systems using an FPGA chip. Through the internet or a local network, students can engage with the platform to conduct engineering experiments. The HDK is equipped with a Raspberry Pi, a screen, a camera, and LEDs to facilitate the transfer of experiment results and to aid in assessing the validity of lab experiment execution. The Raspberry Pi plays a crucial role in the HDK, providing control and monitoring, remote access, data processing, user interface, and integration with other possible systems.

Humanities scholars can today engage in research inquiry using data from a range of varied collections which are often characterised by poor subject access, often resulting in systems that underperform and even effectively prevent access to data, information and knowledge. In spite of the availability of professional standards and guidelines to provide quality-controlled subject access through knowledge organisation systems (KOS), subject access in such collections is rarely based on KOS. At the same time, KOS themselves may come with problems such as being slow to update, being rigidly structured and not incorporating end-users' vocabulary. It may therefore be useful to consider methods for remediating these deficiencies in KOSs, such as collecting user-generated metadata via social tagging or complementing automated indexing techniques with manual ones. To help address the above problems, the paper discusses these challenges and points to possible solutions in different contexts. It does so by reflecting on an open educational resource (OER) devoted to this theme, titled Introduction to Knowledge Organisation Systems for Digital Humanities. It was developed as part of an EU project called DiMPAH (Digital Methods Platform for the Arts and Humanities), 2021-2023, creating seven OERs for inclusion in DARIAH Teach.

This paper addresses the existing gap in the literature on Metaverse applications in education by providing a comprehensive analysis of associated paradigms, platforms, hardware, and software, which have not been systematically reviewed yet. Notably, this study is among the first, to the best of our knowledge, to introduce the pioneering concept of Meta-AI in education and propose its potential to revolutionize the educational landscape by synergistically combining generative AI technologies, such as ChatGPT, with Metaverse education. Through this in-depth examination, our research makes a significant contribution to the field, establishing a solid foundation for the development of effective Metaverse teaching applications and guiding future scholarly endeavours.

The human brain functions through electrical signals. By measuring these signals, one can monitor brain activity and gain insights into the brain function of the subject. An electroencephalogram (EEG) allows one to monitor brain activity by having the subject wear an array of sensors on their head. This process is frequently used to diagnose medical conditions such as epilepsy. In recent years, there have been efforts to use EEG signals in concert with deep learning to create a brain computer interface (BCI). Such a device would enable the wearer to communicate to a system via brain signals. While such a system would not be so advanced as to enable the translation of complex thoughts, it would enable a user to command a machine to perform a small number of functions. The objective of this paper was to develop and optimize recurrent neural network architectures for use with a brain computer interface. Using EEG data collected from subjects, a variety of neural network models were created to learn from the data. The models that were used were simple recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU). This paper proposes a novel approach to EEG signal classification, demonstrating the capabilities of recurrent networks which are seldom explored for this purpose. This study produced promising results for recurrent models, obtaining a 91% accuracy with the 4-layer LSTM architecture. This presents a solid foundation for the argument that LSTM and similar architectures are feasible for BCI applications. [ABSTRACT FROM AUTHOR]

This paper presents MATLAB user interfaces for two multiphase kinetic models: the kinetic double-layer model of aerosol surface chemistry and gas--particle interactions (K2-SURF) and the kinetic multilayer model of aerosol surface and bulk chemistry (KM-SUB). Each interface has simple and user-friendly features that allow undergraduate and graduate students in physical, environmental, and atmospheric chemistry classes to learn about multiphase chemistry modeling without prior computer programming or modeling experience. It is easy to input parameters, and the simulation results are promptly displayed in the interface; thus, these model interfaces are particularly suitable for in-classroom and homework teaching applications. The model input parameters include surface and bulk reaction rate coefficients, surface accommodation coefficient, and gas and bulk diffusivities, while model outputs include gas uptake coefficient and surface and bulk concentrations. Students can use the K2-SURF interface to simulate surface processes and the KM-SUB interface to simulate surface and bulk processes. Example simulations were performed for each interface to present atmospherically relevant applications and to demonstrate its versatility for exploring model sensitivity on various kinetic parameters. The K2-SURF interface was used to show how the rate of ozone uptake by an organic surface and temporal evolution of surface concentrations are affected by the surface accommodation coefficient, desorption lifetime, and surface reaction rate coefficient. Additionally, the KM-SUB interface was applied to demonstrate how bulk diffusivities impact the degradation kinetics of oleic acid particles, so that students can learn how the phase state (liquid vs semisolid vs glassy solid) impacts multiphase chemical kinetics. The developed K2-SURF and KM-SUB interfaces are effective tools for modeling surface and bulk reactions in college-level educational settings, helping students to obtain a deeper understanding of the complex behaviors of heterogeneous and multiphase systems.

This paper reports on a study which designed and developed a multi-fingered haptic interface in conjunction with a three-dimensional (3D) virtual model of a section of the cell membrane in order to enable students to work collaboratively to learn cell biology. Furthermore, the study investigated whether the addition of haptic feedback to the 3D virtual reality (VR) simulation affected learning of key concepts in nanoscale cell biology for students aged 12 to 13. The haptic interface was designed so that the haptic feedback could be turned on or switched off. Students (N = 64), in two secondary schools, worked in pairs, on activities designed to support learning of specific difficult concepts. Findings from observation of the activities and interviews revealed that students believed that being immersed in the 3D VR environment and being able to feel structures and movements within the model and work collaboratively assisted their learning. More specifically, the pilot/co-pilot model that we developed was successful for enabling collaborative learning and reducing the isolating effects of immersion with a 3D headset. Results of pre and post-tests of conceptual knowledge showed significant knowledge gains but addition of haptic feedback did not affect the knowledge gains significantly. The study enabled identification of important issues to consider when designing and using haptic-enabled 3D VR environments for collaborative learning.

Automatically describing the content of a picture with meaningful and contextually appropriate textual descriptions is a difficult challenge at the interface of computer vision and natural language processing. This review paper provides a thorough summary of recent developments in picture caption generating methods, including both conventional strategies and cutting-edge deep learning-based solutions. We examine the changes in datasets, evaluation criteria, and model designs that have influenced this field's advancement. In order to improve the standard and variety of generated captions, we also integrate visual attention processes, transformer-based models, and reinforcement learning techniques. The focus of this study is on the algorithmic overlap between text to speech and visual. [ABSTRACT FROM AUTHOR]

A new type of interface using a conduction hot spot reflecting the user's intention is presented. Conventional methods using fingertips to generate conduction hot points cannot be applied to those who have difficulty using their hands or cold hands. In order to overcome this problem, an exhaling interaction using a hollow rod is proposed and extensively analyzed in this paper. A preliminary study on exhaling interaction demonstrated the possibility of the method. This paper is an attempt to develop and extend the concept and provide the necessary information for properly implementing the interaction method. We have repeatedly performed conduction hot-point-generation experiments on various materials that can replace walls or screens to make wide use of the proposed interfaces. Furthermore, a lot of experiments have been conducted in different seasons, considering that the surface temperature of objects also changes depending on the season. Based on the results of an extensive amount of experiments, we provide key observations on important factors such as material, season, and user condition, which should be considered for realizing contactless exhaling interfaces. [ABSTRACT FROM AUTHOR]

This paper presents Multi-view Leap2 Hand Pose Dataset (ML2HP Dataset), a new dataset for hand pose recognition, captured using a multi-view recording setup with two Leap Motion Controller 2 devices. This dataset encompasses a diverse range of hand poses, recorded from different angles to ensure comprehensive coverage. The dataset includes real images with the associated precise and automatic hand properties, such as landmark coordinates, velocities, orientations, and finger widths. This dataset has been meticulously designed and curated to maintain a balance in terms of subjects, hand poses, and the usage of right or left hand, ensuring fairness and parity. The content includes 714,000 instances from 21 subjects of 17 different hand poses (including real images and 247 associated hand properties). The multi-view setup is necessary to mitigate hand occlusion phenomena, ensuring continuous tracking and pose estimation required in real human-computer interaction applications. This dataset contributes to advancing the field of multimodal hand pose recognition by providing a valuable resource for developing advanced artificial intelligence human computer interfaces. [ABSTRACT FROM AUTHOR]

This paper presents an interpretable, spiking neural classifier (IpT-SNC) with time-varying weights. IpT-SNC uses a two-layered spiking neural network (SNN) architecture in which weights of synapses are modeled using amplitude-modulated, time-varying Gaussian functions. Self-regulated particle swarm optimization (SRPSO) is used to update the amplitude, width, and centers of the Gaussian functions and thresholds of neurons in the output layer. IpT-SNC has been developed to improve the interpretability of spiking neural networks. The time-varying weights in IpT-SNC allow us to describe the rationale behind predictions in terms of specific input spikes. The performance of IpT-SNC is evaluated on ten benchmark datasets in the UCI machine learning repository and compared with the performance of other learning algorithms. According to the performance results, IpT-SNC enhances classification performance on testing datasets from a minimum of 0.5% to a maximum of 7.7%. The significance level of IpT-SNC with other learning algorithms is evaluated using statistical tests like the Friedman test and the paired t-test. Furthermore, on the challenging real-world BCI (Brain Computer Interface) competition IV dataset, IpT-SNC outperforms current classifiers by about 8% in terms of classification accuracy. The results indicate that IpT-SNC has better generalization performance than other algorithms. [ABSTRACT FROM AUTHOR]

Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system. [ABSTRACT FROM AUTHOR]

Introduction: Brain computer interfaces (BCI), which establish a direct interaction between the brain and the external device bypassing peripheral nerves, is one of the hot research areas. How to effectively convert brain intentions into instructions for controlling external devices in real-time remains a key issue that needs to be addressed in brain computer interfaces. The Riemannian geometry-based methods have achieved competitive results in decoding EEG signals. However, current Riemannian classifiers tend to overlook changes in data distribution, resulting in degenerated classification performance in cross-session and/or cross subject scenarios. Methods: This paper proposes a brain signal decoding method based on Riemannian transfer learning, fully considering the drift of the data distribution. Two Riemannian transfer learning methods based log-Euclidean metric are developed, such that historical data (source domain) can be used to aid the training of the Riemannian decoder for the current task, or data from other subjects can be used to boost the training of the decoder for the target subject. Results: The proposed methods were verified on BCI competition III, IIIa, and IV 2a datasets. Compared with the baseline that without transfer learning, the proposed algorithm demonstrates superior classification performance. In contrast to the Riemann transfer learning method based on the affine invariant Riemannian metric, the proposed method obtained comparable classification performance, but is much more computationally efficient. Discussion: With the help of proposed transfer learningmethod, the Riemannian classifier obtained competitive performance to existingmethods in the literature. More importantly, the transfer learning process is unsupervised and time-efficient, possessing potential for online learning scenarios. [ABSTRACT FROM AUTHOR]

Introduction: Binocular color fusion and rivalry are two specific phenomena in binocular vision, which could be used as experimental tools to study how the brain processes conflicting information. There is a lack of objective evaluation indexes to distinguish the fusion or rivalry for dichoptic color. Methods: This paper introduced EEGNet to construct an EEG-based model for binocular color fusion and rivalry classification. We developed an EEG dataset from 10 subjects. Results: By dividing the EEG data from five different brain areas to train the corresponding models, experimental results showed that: (1) the brain area represented by the back area had a large difference on EEG signals, the accuracy of model reached the highest of 81.98%, and more channels decreased the model performance; (2) there was a large effect of inter-subject variability, and the EEG-based recognition is still a very challenge across subjects; and (3) the statistics of EEG data are relatively stationary at different time for the same individual, the EEG-based recognition is highly reproducible for an individual. Discussion: The critical channels for EEG-based binocular color fusion and rivalry could be meaningful for developing the brain computer interfaces (BCIs) based on color-related visual evoked potential (CVEP). [ABSTRACT FROM AUTHOR]

The paper deals with a short review regarding neurorehabilitation technologies for regaining human hand mobility functions after a cerebrovascular accident or stroke. The aim of this paper is to form a general understanding of the current technologies used in the field of neurorehabilitation and highlight key characteristics, advantages and disadvantages. Technologies that are studies include robot exoskeletons, electro stimulation, brain computer interfaces (BCI), EEG and limb mounted sensors. After a presenting a summary of current existing technologies, a brief conclusion proposing the future direction of this study is proposed. [ABSTRACT FROM AUTHOR]

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems. [ABSTRACT FROM AUTHOR]

Gesture recognition is a mechanism by which a system recognizes an expressive and purposeful action made by a user's body. Hand-gesture recognition (HGR) is a staple piece of gesture-recognition literature and has been keenly researched over the past 40 years. Over this time, HGR solutions have varied in medium, method, and application. Modern developments in the areas of machine perception have seen the rise of single-camera, skeletal model, hand-gesture identification algorithms, such as media pipe hands (MPH). This paper evaluates the applicability of these modern HGR algorithms within the context of alternative control. Specifically, this is achieved through the development of an HGR-based alternative-control system capable of controlling of a quad-rotor drone. The technical importance of this paper stems from the results produced during the novel and clinically sound evaluation of MPH, alongside the investigatory framework used to develop the final HGR algorithm. The evaluation of MPH highlighted the Z-axis instability of its modelling system which reduced the landmark accuracy of its output from 86.7% to 41.5%. The selection of an appropriate classifier complimented the computationally lightweight nature of MPH whilst compensating for its instability, achieving a classification accuracy of 96.25% for eight single-hand static gestures. The success of the developed HGR algorithm ensured that the proposed alternative-control system could facilitate intuitive, computationally inexpensive, and repeatable drone control without requiring specialised equipment. [ABSTRACT FROM AUTHOR]

As new methods for interacting with systems are being developed for use within augmented or virtual reality, their impact on the quality of the user's experience needs to be assessed. Although many instruments exist for evaluating the overall user experience or the computer interface used to complete tasks, few provide measures that can be used to evaluate the specific forms of interaction typically used in these environments. This paper describes the development of a customizable questionnaire for measuring the subjective user experience that focuses on the quality of the interactions with objects in augmented reality/virtual reality (AR/VR) worlds, which we are calling the Customizable Interactions Questionnaire, or (CIQ). The final questionnaire measures five factors that are related to user satisfaction while using the system: quality of interactions, assessment of task performance, comfort, quality of sensory enhancements, and consistency with expectations. [ABSTRACT FROM AUTHOR]

Emotion is an important factor that decides the the state of the mind of an individual. However, there are many people who cannot express their emotions explicitly due to various psychological or physiological issues. The recent technology development in interdisciplinary techniques has made the emotion recognition easy. The main objective of this paper is to uncover the contribution of hybrid deep learning model in classifying the emotions. This novel hybrid Bi-LSTM model is applied to three dimensional VAD model to classify 16 emotions. In addition, this paper addresses their performance comparison with respect to other models. Deep learning models like CNN, LSTM, Hybrid Bi-LSTM, and Hybrid Bi-GRU were used for experimentation. FFT is used to convert from time domain to frequency domain in all the models. The performance measure of these models is estimated in terms of accuracy, precision, recall, and F1score. The estimated accuracy of different deep learning models are approximated to be 87.5% for CNN, 88.7 % for LSTM, 93.9% for Hybrid Bi-LSTM, and 92.2% for Hybrid GRU based on the number of subjects. These comparisons have helped to find the suitable deep learning model for emotion recognition. [ABSTRACT FROM AUTHOR]

Introduction: Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insu cient movement execution features in EEG signals. Methods: This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement. Results: A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%. Discussion: This approach can be used to develop a hybrid EEG-EMG brain- computer interface to provide patients with more accurate neural feedback to aid their recovery. [ABSTRACT FROM AUTHOR]

Based on the brain signals, decoding and analyzing the gait features to make a reliable prediction of action intention are the core issues in the brain computer interface (BCI)-based hybrid rehabilitation and intelligent walking aid robot system. In order to realize the classification and recognition of the most basic gait processes such as standing, sitting, and quiet, this paper proposes a feature representation method based on the signal complexity and entropy of each brain region. Through the statistical analysis of these parameters between different conditions, these characteristics which sensitive to different actions are determined as a feature vector, and the classification and recognition of these actions are completed by combing support vector machine, linear discriminant analysis, and logistic regression. Experimental results show the proposed method can better realize the recognition of the aforementioned action intention. The recognition accuracy of standing, sitting, and quiet of 13 subjects is higher than 80.9%, and the highest one can reach 86.8%. Directed dynamic brain network analysis of the 8 brain regions shows that the occurrence of lower limb movement will weaken the dependence between brain regions, resulting in the weakening of network topological connection. The result has significant value for understanding human's brain cognitive characteristics in the process of lower limb movement and carrying out the study of BCI based strategy and system for lower limb rehabilitation. [ABSTRACT FROM AUTHOR]

According to recent legislative initiatives, non-consensual pornography is a crime in several countries and social media providers have a duty to provide their users easy to use mechanisms to report abuses. In this paper, we analyse the state of the art of the interfaces for reporting non-consensual pornography from the victim's perspective. Firstly, we analysed 45 content sharing platforms where aggressors might post non-consensual pornography. The analysis identified three distinct interaction styles for reporting the crime: Scriptum (a text-field where the user verbally describes the abuse), Bonam (a multilayered menu that includes a correct option), and Malam (a multilayered menu that does not include a correct option). Secondly, we conducted a within-subject study to evaluate the experience elicited by these interaction styles. Participants (N = 39) were given a scenario and asked to report six blurred images as non-consensual pornography using a medium-fidelity prototype. The results exposed complex trade-offs between clarity, efficiency, and distress among the different interaction styles. These trade-offs open foundational research directions transcending boundaries between human-computer interaction and multimedia studies and interfacing computer science research with the law. [ABSTRACT FROM AUTHOR]

To find relevant values with characteristic parameters of the system, there is a need to create a structure that enables continuous communication within the system, and the ability to communicate with external devices considered objects that can be controlled. This requires the structuring of a stable environment, and the ordering of elements according to priority in command. If a system is set to regulate the flow of information then this system needs different resources, which represent the requirement to achieve the stabilization process through the inclusion of various electronic devices, auxiliary modules, etc. This paper deals with the analysis of the system which sets the structured parameters of the system based on the help of certain electronic modules. Also, the system is described in analytical and tabular form, the definition of constructive parameters which follow in detail the activation of the system according to certain conditions of control elements and software programs that control the application of the system through the so-called programmatic approach. The tabular presentation enables the construction of the working logic of the system. This enables the control in the physical aspect, of all the elements intertwined in the system which, however, integrates devices of different formats, and is interconnected through the computer platform. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study on the effect of the pump energy and flow rate of the dye gain medium and one amplifier stage on the linewidth in a dye laser pumped by a frequency-doubled Q-switched Nd:YAG laser. A Fabry-Pérot etalon and imaging camera-based arrangement interfaced with a personal computer (PC) is used to measure the linewidth of the 565 nm laser line. [ABSTRACT FROM AUTHOR]

Continuous progress in disruptive technologies such as artificial intelligence (AI) and control theory has prompted considerable improvements in BCI applications. Brain-Machine Interface, also called Brain-Computer Interface (BCI), is an emerging research field with diverse applications ranging from rehabilitation to human augmentation. Practically speaking, control algorithms for critical components of the BCI algorithm have a high level of important and are often called a decoder. [Extracted from the article]

The ZX Spectrum was a popular 8-bit home computer by Sinclair Research in the 1980s. Even though some of these computers may still work, the audio tapes, the TV with an analog tuner, and the micro-switch joystick that were used with the original ZX Spectrum are outdated and hard to find in good working order or to replicate. As many other old closed systems are also very difficult to update to support modern peripherals there is a necessity to provide a methodology to adapt such systems to support new peripherals while being compatible with existing software. This implementation is a means by which to validate the methodology before applying it to a physical system. The work proposed in this paper focused on recreating a ZX Spectrum+/48K computer and interfacing it with modern peripherals on an FPGA. This was accomplished by adding a co-processor to assist with the control of the more complex peripherals. Otherwise, the original system would require complex architectural changes and would perform poorly due to the low performance of the Z80 CPU. This work distanced itself from previous works on emulating a ZX Spectrum, as it focused on the use of different upgraded peripherals and the use of a NIOS II soft processor as a co-processor to manage the SD card accesses and save-state functionality. A demonstration of the proposed modernized architecture was made by successfully running a diagnostics ROM and playing original ZX Spectrum games from an SD card for games with a PS/2 keyboard and a pair of joysticks. [ABSTRACT FROM AUTHOR]

Tongue-drive system (TDS) is an advanced assistive technology (AT) that enables people with severe disabilities to independently control the electronic devices and machines using their tongue. Through the use of a small magnetic tracer, it can be translated into user-defined commands. Real time system accuracy is the major parameter of effectiveness of such AT's. So, a projected model needs a hardware based real time implementations or optimum model development with reconfigurable architectures before design of application specific ICs. This paper reviews various tongue drive systems and their features and approaches to the brain, tongue, and computer interface. In this paper, the data rate of serial peripheral interfaces compared and discussed the attenuation techniques and their signal-to-noise ratio for processing the data. The projected machine learning classifiers mainly decision tree, k-Nearest Neighbor, logistic regression, Naïve Bayes, random forest, and support vector machine are compared in terms of accuracy, F1 score, and power consumption. The performance comparison of TDS and implementation methods on field programmable logic arrays are discussed. The design and simulation work done so far on MATLAB and Xilinx environment are mainly analyzed to identify future scope of work. Also, the fortes and drawbacks of these systems and approaches are included. [ABSTRACT FROM AUTHOR]

Optical orthogonal frequency-division multiplexing (OFDM) has proven to be an effective technique for the visible light communication (VLC) system in which data is sent and received through the intensity-modulated/direct-detected optical channel. However, OFDM exhibits high peak-to-average power ratio (PAPR) and for the generation of real-valued signal employs Hermitian symmetry (HS) of the frequency symbols which in turn boost the computational complexity (CC) and deteriorates the performance of the system. Besides, OFDM also included cyclic prefix (CP) as redundancy that reduces the spectral efficiency. This paper aims to target the aforesaid OFDM issues and introduce a novel technique in the form of wavelet transform (WT) based on non-HS OFDM (NHS-OFDM). In the proposed technique, an N-point complex OFDM signal is generated by utilizing wavelets as basis function, from which real and imaginary components are extracted and then concatenated to form a 2N-point real signal. The work also confirmed that the CC of the proposed technique is about 83% less than the conventional Fourier transform (FT) based NHS-OFDM technique due to which reduction in PAPR and bit error rate (BER) improvement is also noticed from the simulated results. The superior performance of WT based NHS-OFDM technique is further verified via experiments conducted by interfacing the computer terminal with Raspberry Pi (RPi) 3b+ modules. In the designed VLC prototype, the NHS-OFDM modem work as the software-defined radio (SDR) on a stand-alone computer. The experiment results reveal that real-time text message is transmitted and received without error at the communication range of 1.75 m while the data rate attains from the prototype is 3.921 Mbps. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The onset of the COVID-19 pandemic has led to an increased demand for mental health interventions, with a special focus on digitally assisted ones. Self-regulation describes a set of meta-skills that enable one to take control over his/her mental health and it is recognized as a vital indicator of well-being. Mindfulness training is a promising training strategy for promoting self-regulation, behavioral change, and mental well-being. A growing body of research outlines that smart technologies are ready to revolutionize the way mental health training programs take place. Artificial intelligence (AI); extended reality (XR) including virtual reality (VR), augmented reality (AR), and mixed reality (MR); as well as the advancements in brain computer interfaces (BCIs) are ready to transform these mental health training programs. Mindfulness-based interventions assisted by smart technologies for mental, emotional, and behavioral regulation seem to be a crucial yet under-investigated issue. The current systematic review paper aims to explore whether and how smart technologies can assist mindfulness training for the development of self-regulation skills among people at risk of mental health issues as well as populations with various clinical characteristics. The PRISMA 2020 methodology was utilized to respond to the objectives and research questions using a total of sixty-six experimental studies that met the inclusion criteria. The results showed that digitally assisted mindfulness interventions supported by smart technologies, including AI-based applications, chatbots, virtual coaches, immersive technologies, and brain-sensing headbands, can effectively assist trainees in developing a wide range of cognitive, emotional, and behavioral self-regulation skills, leading to a greater satisfaction of their psychological needs, and thus mental wellness. These results may provide positive feedback for developing smarter and more inclusive training environments, with a special focus on people with special training needs or disabilities. [ABSTRACT FROM AUTHOR]

In the field of neuroscience, a significant challenge lies in extracting essential features from biological signals like Electroencephalography (EEG). Utilized as a non-invasive method, EEG records brain activities through metal electrodes on the scalp. The analysis of EEG data finds applications in various domains, including concealed information tests, aimed at detecting deception. This paper introduces the Spatial Spiking Neural Network, a supervised approach for classifying EEG data collected during concealed information tests. Temporal EEG data undergoes filtration using a Finite Impulse Response (FIR) filter, while Common Spatial Pattern (CSP) is employed to extract spatial components. Binary classification is achieved through an integrate-and-fire neuron model, where the frequency of spike generation determines the classification. Spiking Neural Networks (SNNs) offers advantages in terms of temporal precision, event-driven processing, and low power consumption. Their spike-based communication allows for efficient handling of sparse data and recognition of temporal patterns, contributing to robustness and energy efficiency. The proposed model is applied separately to each subject's EEG data, and the results are compared with traditional classification algorithms. The proposed approach attains a peak accuracy of 90.15%, showcasing superior performance compared to alternative methods. [ABSTRACT FROM AUTHOR]

A brain-computer interface (BCI) based on an electroencephalograph (EEG) establishes a new channel of communication between the human brain and a computer. Redundant, noisy, and irrelevant channels lead to high computational costs and poor classification accuracy. Therefore, an effective feature selection technique for determining the optimal number of channels can improve BCI's performance. However, existing meta-heuristic algorithms are prone to get trapped in local optimum due to high dimensional dataset. Thus, to reduce dimension, solve inter subject variation and choose an optimal subset of channels, a novel framework called Component Loading followed by Clustering and Classification (CLCC) is proposed in this paper. This novel framework is further divided into two experiment configurations-CLCC with Feature Selection (CLCC-FS) and CLCC without Feature Selection (CLCC-WFS). All these frameworks have been implemented on a motor imagery (MI) EEG dataset of 10 subjects in order to choose the best subset of channels. Further, seven different classifiers have been employed to assess the performance. Experimental outcomes show that on comparing various feature selection techniques, our proposed algorithm i.e., CLCC-FS Opposition-Based Whale Optimization Algorithm (CLCC-FS(OBWOA)) performed substantially better than the other feature selection techniques. We demonstrate that the proposed algorithm is able to achieve 99.6% accuracy by using only few channels and can improve the practicality of the BCI system by reducing the computation cost. [ABSTRACT FROM AUTHOR]

AbstractMotor imagery brain computer interface (BCI) systems are considered one of the most crucial paradigms and have received extensive attention from researchers worldwide. However, the non-stationary from subject-to-subject transfer is a substantial challenge for robust BCI operations. To address this issue, this paper proposes a novel approach that integrates joint multi-feature extraction, specifically combining common spatial patterns (CSP) and wavelet packet transforms (WPT), along with transfer learning (TL) in motor imagery BCI systems. This approach leverages the time-frequency characteristics of WPT and the spatial characteristics of CSP while utilizing transfer learning to facilitate EEG identification for target subjects based on knowledge acquired from non-target subjects. Using dataset IVa from BCI Competition III, our proposed approach achieves an impressive average classification accuracy of 93.4%, outperforming five kinds of state-of-the-art approaches. Furthermore, it offers the advantage of enabling the design of various auxiliary problems to learn different aspects of the target problem from unlabeled data through transfer learning, thereby facilitating the implementation of innovative ideas within our proposed approach. Simultaneously, it demonstrates that integrating CSP and WPT while transferring knowledge from other subjects is highly effective in enhancing the average classification accuracy of EEG signals and it provides a novel solution to address subject-to-subject transfer challenges in motor imagery BCI systems. [ABSTRACT FROM AUTHOR]