7,419 research outputs found
The Effects of the Digitally Supported Multimodal Print Texts on Students’ Summarization Skills
The aim of this study is to analyse the effects of the multimodal texts created from print texts through addition of digital mode on the students’s summarizing skills. Through the ROAR the digital modes were integrated into the print texts and the multimodal texts were produced. There are two such texts, one of them is an informative text, and the other one is a narrative text. The participants of the study were 128 seventh grade secondary school students from Antalya province (Türkiye) whose ages range between 12 and 13. They were randomly assigned to the experimental and control groups. At the pre-test step both groups read and summarized the print texts. At the post-test step the experimental group read and summarized the multimodal texts created by adding a digital mode whereas the control group the print texts. The results showed that there was a significant difference in favor of the experimental group in the total scores and content scores concerning the informative and narrative texts. On the other hand, it is found that the form and style scores from the informative and narrative texts did not differ significantly between the groups. In addition, in the post-test results of the experimental group, there was a significant difference in favor of the narrative text. The results suggest that the use of the multimodal texts have positive effects on the participants’ summarizing skills
Influence of hand tracking in immersive virtual reality for memory assessment
Few works analyze the parameters inherent to immersive virtual reality (IVR) in applications for memory evaluation. Specifically, hand tracking adds to the immersion of the system, placing the user in the first person with full awareness of the position of their hands. Thus, this work addresses the influence of hand tracking in memory assessment with IVR systems. For this, an application based on activities of daily living was developed, where the user must remember the location of the elements. The data collected by the application are the accuracy of the answers and the response time; the participants are 20 healthy subjects who pass the MoCA test with an age range between 18 to 60 years of age; the application was evaluated with classic controllers and with the hand tracking of the Oculus Quest 2. After the experimentation, the participants carried out presence (PQ), usability (UMUX), and satisfaction (USEQ) tests. The results indicate no difference with statistical significance between both experiments; controller experiments have 7.08% higher accuracy and 0.27 ys. faster response time. Contrary to expectations, presence was 1.3% lower for hand tracking, and usability (0.18%) and satisfaction (1.43%) had similar results. The findings indicate no evidence to determine better conditions in the evaluation of memory in this case of IVR with hand tracking
AQ-GT: a Temporally Aligned and Quantized GRU-Transformer for Co-Speech Gesture Synthesis
The generation of realistic and contextually relevant co-speech gestures is a
challenging yet increasingly important task in the creation of multimodal
artificial agents. Prior methods focused on learning a direct correspondence
between co-speech gesture representations and produced motions, which created
seemingly natural but often unconvincing gestures during human assessment. We
present an approach to pre-train partial gesture sequences using a generative
adversarial network with a quantization pipeline. The resulting codebook
vectors serve as both input and output in our framework, forming the basis for
the generation and reconstruction of gestures. By learning the mapping of a
latent space representation as opposed to directly mapping it to a vector
representation, this framework facilitates the generation of highly realistic
and expressive gestures that closely replicate human movement and behavior,
while simultaneously avoiding artifacts in the generation process. We evaluate
our approach by comparing it with established methods for generating co-speech
gestures as well as with existing datasets of human behavior. We also perform
an ablation study to assess our findings. The results show that our approach
outperforms the current state of the art by a clear margin and is partially
indistinguishable from human gesturing. We make our data pipeline and the
generation framework publicly available
The Media Inequality, Uncanny Mountain, and the Singularity is Far from Near: Iwaa and Sophia Robot versus a Real Human Being
Design of Artificial Intelligence and robotics habitually assumes that adding
more humanlike features improves the user experience, mainly kept in check by
suspicion of uncanny effects. Three strands of theorizing are brought together
for the first time and empirically put to the test: Media Equation (and in its
wake, Computers Are Social Actors), Uncanny Valley theory, and as an extreme of
human-likeness assumptions, the Singularity. We measured the user experience of
real-life visitors of a number of seminars who were checked in either by Smart
Dynamics' Iwaa, Hanson's Sophia robot, Sophia's on-screen avatar, or a human
assistant. Results showed that human-likeness was not in appearance or behavior
but in attributed qualities of being alive. Media Equation, Singularity, and
Uncanny hypotheses were not confirmed. We discuss the imprecision in theorizing
about human-likeness and rather opt for machines that 'function adequately.
Cultivating Collaboration: Optimizing Communication Between Designers and Non-Designers
Clients and designers, having different tacit knowledge, fail to effectively communicate with each other during the design process. These inadequacies risk relationships, reputations, and project success. This issue has long been recognized in the field of design, often as a concern with client involvement. This research aims to identify these complications in the design process and inform how they might be amended. Specifically, it investigates how the relationship between designers and clients can be improved in order to garner better communication and greater project success. In this context, clients are defined as non-designers that commission design professionals. A literature review as well as several case studies and visual analyses conclude that collaboration, empathy, and project structure improve communication and project outcomes. These findings will inform a proposed visual solution that aims to provide knowledge and structure to client-designer partnerships in order to facilitate these benefits
Enhancing primary care psychological therapy for clients with comorbid physical health conditions: A Critical Discourse Analysis investigation into interprofessional identity
Background / Aim: Improving Access to Psychological Therapies (IAPT) services are the largest provider in England of primary care psychological therapy for depression and anxiety disorders. Over recent years there has been increased recognition of the importance of therapists and their physical health colleagues (e.g. nurses, physiotherapists or other allied health professionals) integrating care for patients with comorbid long-term health conditions and common psychological disorders. Specialist teams have been creating differentiating Psychological Therapists as Core and Integrated. The aim is to investigate the implications of this shift for Therapists’ professional identity.
Method: A Critical Discourse Analysis was conducted based on five focus groups with eighteen professionals from Core IAPT, Integrated IAPT and physical healthcare backgrounds.
Key Findings: Discourses related to expertise, responsibility and innovation / creativity emerged from the corpora. The research highlights the niche set of behaviours, skills, values and attitudes under construction by Integrated Therapists and the way in which their role shapes and is shaped by their interactions with their counterparts.
Implications: The research makes recommendations for Integrated Therapists’ professional identity including to showcase niche skills and effective collaborative therapy. Future research recommendations are made regarding unheard voices and silenced discourses in professional identity reconstruction.
Key Terms: Professional Identity; Integrated Therapy; Cognitive Behaviour Therapy; Long-Term Conditions and Medically Unexplained Symptoms (LTC/MUS
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Audio-Visual Automatic Speech Recognition Towards Education for Disabilities
Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition
Evaluating 3D human face reconstruction from a frontal 2D image, focusing on facial regions associated with foetal alcohol syndrome
Foetal alcohol syndrome (FAS) is a preventable condition caused by maternal alcohol consumption during pregnancy. The FAS facial phenotype is an important factor for diagnosis, alongside central nervous system impairments and growth abnormalities. Current methods for analysing the FAS facial phenotype rely on 3D facial image data, obtained from costly and complex surface scanning devices. An alternative is to use 2D images, which are easy to acquire with a digital camera or smart phone. However, 2D images lack the geometric accuracy required for accurate facial shape analysis. Our research offers a solution through the reconstruction of 3D human faces from single or multiple 2D images. We have developed a framework for evaluating 3D human face reconstruction from a single-input 2D image using a 3D face model for potential use in FAS assessment. We first built a generative morphable model of the face from a database of registered 3D face scans with diverse skin tones. Then we applied this model to reconstruct 3D face surfaces from single frontal images using a model-driven sampling algorithm. The accuracy of the predicted 3D face shapes was evaluated in terms of surface reconstruction error and the accuracy of FAS-relevant landmark locations and distances. Results show an average root mean square error of 2.62 mm. Our framework has the potential to estimate 3D landmark positions for parts of the face associated with the FAS facial phenotype. Future work aims to improve on the accuracy and adapt the approach for use in clinical settings.
Significance:
Our study presents a framework for constructing and evaluating a 3D face model from 2D face scans and evaluating the accuracy of 3D face shape predictions from single images. The results indicate low generalisation error and comparability to other studies. The reconstructions also provide insight into specific regions of the face relevant to FAS diagnosis. The proposed approach presents a potential cost-effective and easily accessible imaging tool for FAS screening, yet its clinical application needs further research
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