Enhancing information-based spaces using IoT and multimedia visualization - a case study

O principal objetivo desta pesquisa é fazer uma exploração em torno das estruturas conceituais, estado da arte e aplicações plausíveis da Internet de Coisas Multimédia em serviços distribuídos com para a criação de ambientes aumentados que contribuam a melhorar a experiência coletiva e participação das pessoas assistentes a conferências profissionais, reuniões grupais e espaços públicos em geral. Assim, a metodologia será baseada em uma revisão do estado da arte das tecnologias de IoT aplicáveis a coisas de multimédia e visualização de informação, especialmente no contexto de espaços públicos aumentados, onde o acesso a informação de alta qualidade possa ser possível sem influenciar negativamente a interação no mundo real entre os participantes, assim como melhorar a experiência global dos mesmos, considerando também soluções tecnológicas projetadas para os eventos de prazos limitados.The main objective of this research is to make an exploration around conceptual frameworks, state of the art and plausible applications of the Internet of Multimedia Things in distributed services for creating augmented environments that contribute to enhance the collective experience and participation of people attending professional conferences, group meetings and public spaces in general. Thus, the methodology will be based on a review of the state of the art of IoT technologies applicable for multimedia things and information visualization, specially in the context of augmented public spaces, where the access of high-quality data can be possible without preventing real-world interaction among attendants, as well as improving the overall experience of participants, considering also technological solutions designed for the events of limited time-frames

Assessment Framework for Deepfake Detection in Real-world Situations

Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and have exhibited remarkable performance. However, the performance of such detectors is often assessed on related benchmarks that hardly reflect real-world situations. For example, the impact of various image and video processing operations and typical workflow distortions on detection accuracy has not been systematically measured. In this paper, a more reliable assessment framework is proposed to evaluate the performance of learning-based deepfake detectors in more realistic settings. To the best of our acknowledgment, it is the first systematic assessment approach for deepfake detectors that not only reports the general performance under real-world conditions but also quantitatively measures their robustness toward different processing operations. To demonstrate the effectiveness and usage of the framework, extensive experiments and detailed analysis of three popular deepfake detection methods are further presented in this paper. In addition, a stochastic degradation-based data augmentation method driven by realistic processing operations is designed, which significantly improves the robustness of deepfake detectors

Educators’ Perceptions of the Substitution, Augmentation, Modification, Redefinition Model for Technology Integration

The Substitution, Augmentation, Modification, Redefinition (SAMR) model has been introduced (Puentedura, 2006) claims that use of technology could predict student outcomes. School districts and educational institutions have been adopting this model in hopes to enhance the educational experience and outcomes for their students (SAMR Model, n.d.). This study explored six teachers’ and three administrators’ perception of the SAMR model in integrating technology into the classroom environment. This qualitative research, used surveys and interviews for indicative analysis using the constructivist approach. Data analysis found that educators using the SAMR model were and had a common level used for technology integration as well as a favorite level. This study also found the SAMR model changed teacher practices by encouraging them to integrate technology at a higher level. With regard to integrating technology, this study found three areas of agreement between teachers and administrators: teachers require increased planning time; the use of technology in the classroom can lead to off-task behavior; and when implemented correctly, digital tools increase student achievement. Furthermore, three new issues were found. First, educators suggested the SAMR model puts too much emphasis on higher-level integration. Second, educators mentioned an increase in off-task behavior when using technology. Third, educators believed the SAMR model is best used as a secondary consideration during lesson development. This study suggested three changes for the SAMR model. My first suggestion is to transform the SAMR model into a box-shaped diagram, opposed to its current hierarchical arrangement, to place equal significance on each level of technology integration. Second, it is recommended that the SAMR model be integrated into existing instructional design models. Third, new language added to digital citizenship standards to include behavior with technology

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Faculty\u27s Knowledge, Pedagogy, and Integration Levels in the Implementation of Ipads as an Instructional Tool

Current literature showed there is a need to help faculty improve their iPad integration practices. Using a sequential mixed-methods design, the researcher explored the relationship among faculty’s iPad integration levels, their teachers’ knowledge (TPACK), and pedagogy among faculty members who had integrated iPads into their teaching for at least two semesters. The data were collected with a cross-section questionnaire, follow-up interviews and artifacts. Responses were collected respectively with the three sections of the questionnaire: iPad Usage (N=160), TPACK (N=151), and demographics (N=147). Eight participants were interviewed after the survey. The results indicated TPACK and learning-centered pedagogy were necessary but insufficient conditions for the transformation levels of iPad integration. Technology itself might not bring a pedagogical shift. Learning to teach with technology could be a catalyst that triggers changes in teaching practices. However, the teacher must act as the agent for these changes. The results of this study could be informative to faculty who hope to improve their own iPad integration levels, or faculty developers and administrators to determine more effective ways to support iPad integration in their institutions