Project-based learning in human–computer interaction: a service‐dominant logic approach

Purpose This study aims to propose a service-dominant logic (S-DL)-informed framework for teaching innovation in the context of human–computer interaction (HCI) education involving large industrial projects. Design/methodology/approach This study combines S-DL from the field of marketing with experiential and constructivist learning to enable value co-creation as the primary method of connecting diverse actors within the service ecology. The approach aligns with the current conceptualization of central university activities as a triad of research, education and innovation. Findings The teaching framework based on the S-DL enabled ongoing improvements to the course (a project-based, bachelor’s-level HCI course in the computer science department), easier management of stakeholders and learning experiences through students’ participation in real-life projects. The framework also helped to provide an understanding of how value co-creation works and brought a new dimension to HCI education. Practical implications The proposed framework and the authors’ experience described herein, along with examples of projects, can be helpful to educators designing and improving project-based HCI courses. It can also be useful for partner companies and organizations to realize the potential benefits of collaboration with universities. Decision-makers in industry and academia can benefit from these findings when discussing approaches to addressing sustainability issues. Originality/value While HCI has successfully contributed to innovation, HCI education has made only moderate efforts to include innovation as part of the curriculum. The proposed framework considers multiple service ecosystem actors and covers a broader set of co-created values for the involved partners and society than just learning benefits.

Recommender Systems: Past, Present, Future

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender sys-tem requires more than a clever general-purpose algorithm. It requires an in-depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the fore-front. It features articles on the latest learning technology, reflects on the human-computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.

Research on the Construction of Human-Computer Interaction System Based on a Machine Learning Algorithm

In this paper, we use machine learning algorithms to conduct in-depth research and analysis on the construction of human-computer interaction systems and propose a simple and effective method for extracting salient features based on contextual information. The method can retain the dynamic and static information of gestures intact, which results in a richer and more robust feature representation. Secondly, this paper proposes a dynamic planning algorithm based on feature matching, which uses the consistency and accuracy of feature matching to measure the similarity of two frames and then uses a dynamic planning algorithm to find the optimal matching distance between two gesture sequences. The algorithm ensures the continuity and accuracy of the gesture description and makes full use of the spatiotemporal location information of the features. The features and limitations of common motion target detection methods in motion gesture detection and common machine learning tracking methods in gesture tracking are first analyzed, and then, the kernel correlation filter method is improved by designing a confidence model and introducing a scale filter, and finally, comparison experiments are conducted on a self-built gesture dataset to verify the effectiveness of the improved method. During the training and validation of the model by the corpus, the complementary feature extraction methods are ablated and learned, and the corresponding results obtained are compared with the three baseline methods. But due to this feature, GMMs are not suitable when users want to model the time structure. It has been widely used in classification tasks. By using the kernel function, the support vector machine can transform the original input set into a high-dimensional feature space. After experiments, the speech emotion recognition method proposed in this paper outperforms the baseline methods, proving the effectiveness of complementary feature extraction and the superiority of the deep learning model. The speech is used as the input of the system, and the emotion recognition is performed on the input speech, and the corresponding emotion obtained is successfully applied to the human-computer dialogue system in combination with the online speech recognition method, which proves that the speech emotion recognition applied to the human-computer dialogue system has application research value.

Human–Computer Interaction-Oriented African Literature and African Philosophy Appreciation

African literature has played a major role in changing and shaping perceptions about African people and their way of life for the longest time. Unlike western cultures that are associated with advanced forms of writing, African literature is oral in nature, meaning it has to be recited and even performed. Although Africa has an old tribal culture, African philosophy is a new and strange idea among us. Although the problem of “universality” of African philosophy actually refers to the question of whether Africa has heckling of philosophy in the Western sense, obviously, the philosophy bred by Africa’s native culture must be acknowledged. Therefore, the human–computer interaction-oriented (HCI-oriented) method is proposed to appreciate African literature and African philosophy. To begin with, a physical object of tablet-aid is designed, and a depth camera is used to track the user’s hand and tablet-aid and then map them to the virtual scene, respectively. Then, a tactile redirection method is proposed to meet the user’s requirement of tactile consistency in head-mounted display virtual reality environment. Finally, electroencephalogram (EEG) emotion recognition, based on multiscale convolution kernel convolutional neural networks, is proposed to appreciate the reflection of African philosophy in African literature. The experimental results show that the proposed method has a strong immersion and a good interactive experience in navigation, selection, and manipulation. The proposed HCI method is not only easy to use, but also improves the interaction efficiency and accuracy during appreciation. In addition, the simulation of EEG emotion recognition reveals that the accuracy of emotion classification in 33-channel is 90.63%, almost close to the accuracy of the whole channel, and the proposed algorithm outperforms three baselines with respect to classification accuracy.

Wearable devices in diving: A systematic review (Preprint)

BACKGROUND Wearable devices have grown enormously in importance in recent years. While wearables have generally been well studied, they have not yet been discussed in the underwater environment. OBJECTIVE The reason for this systematic review was to systematically search for the wearables for underwater operation used in the scientific literature, to make a comprehensive map of their capabilities and features, and to discuss the general direction of development. METHODS In September 2021, we conducted an extensively search of existing literature in the largest databases using keywords. For this purpose, only articles were used that contained a wearable or device that can be used in diving. Only articles in English were considered, as well as peer-reviewed articles. RESULTS In the 36 relevant studies that were found, four device categories could be identified: safety devices, underwater communication devices, head-up displays and underwater human-computer interaction devices. CONCLUSIONS The possibilities and challenges of the respective technologies were considered and evaluated separately. Underwater communication has the most significant influence on future developments. Another topic that has not received enough attention is human-computer interaction.