Constructed model for micro-content recognition in lip reading based deep learning

Communication between human beings has several ways, one of the most known and used is speech, both visual and acoustic perceptions sensory are involved, because of that, the speech is considered as a multi-sensory process. Micro contents are a small pieces of information that can be used to boost the learning process. Deep learning is an approach that dives into deep texture layers to learn fine grained details. The convolution neural network (CNN) is a deep learning technique that can be employed as a complementary model with micro learning to hold micro contents to achieve special process. In This paper a proposed model for lip reading system is presented with proposed video dataset. The proposed model receives micro contents (the English alphabet) in video as input and recognize them, the role of CNN deep learning is clearly appeared to perform two tasks, the first one is feature extraction and the second one is the recognition process. The implementation results show an efficient accuracy recognition rate for various video dataset that contains variety lip reader for many persons with age range from 11 to 63 years old, the proposed model gives high recognition rate reach to 98%.

Multimedia Filtering Analysis of Massive Information Combined with Data Mining Algorithms

With the advent of the big data era, information presentation has exploded. For example, rich methods such as audio and video have integrated more information, but with it, a lot of bad information has been brought. In view of this situation, this paper relies on data mining algorithms, builds a multimedia filtering system model for massive information, and integrates content recognition, packet filtering, and other technologies to match the two to ensure the integrity and real time of filtering. Practice results prove that the method is effective.

Experiences of Pathology Course among Hospital Management Graduates

The purpose of this study was to explore hospital management graduates’ experience in pathology courses. Data were gathered through four focus group interviews by 16 hospital management graduates who attended pathology courses. Data were collected from June to August, 2020. Conventional content analysis was used for data analysis. Six categories were extracted that described hospital management graduates’ experience in pathology courses, as follows: “Suggestions for the curriculum,” “Students’ preference for pathology professor,” “Demands for various teaching methods,” “Broad and difficult class content,” “Recognition of pathology courses during college years,” and “The importance of studying the pathology course realized after graduation.” The findings suggest that it is important to identify hospital management graduates’ perspectives to improve pathology curriculum in the educational process. Additionally, it is necessary to continuously connect educational and practical environments for the effective management of pathology courses.

Multitask Learning with Local Attention for Tibetan Speech Recognition

In this paper, we propose to incorporate the local attention in WaveNet-CTC to improve the performance of Tibetan speech recognition in multitask learning. With an increase in task number, such as simultaneous Tibetan speech content recognition, dialect identification, and speaker recognition, the accuracy rate of a single WaveNet-CTC decreases on speech recognition. Inspired by the attention mechanism, we introduce the local attention to automatically tune the weights of feature frames in a window and pay different attention on context information for multitask learning. The experimental results show that our method improves the accuracies of speech recognition for all Tibetan dialects in three-task learning, compared with the baseline model. Furthermore, our method significantly improves the accuracy for low-resource dialect by 5.11% against the specific-dialect model.

Minor Politics, Major Consequences

Metadata is part of our knowledge systems and, so, represents and perpetuates political hierarchies and perceptions of relevance. While some of these have come up for scrutiny in the discourses on digitization, some ‘minor’ issues have gone unnoticed and a few new mechanisms of imbalance have escaped attention as well. Yet, all of these, too, influence the usability of digital image collections. This paper traces three fields of ‘minor politics’ and their epistemic consequences, both in general and in particular, with respect to the study of architecture and its visual representation: first, the intrinsic logic of the original collections and their digital representation; second, the role of support staff in the course of digitization and data transfer; and, third, keywording as a matter of disciplinary habitus. It underlines the ‘political’ role of metadata within the context of knowledge production, even on the local level of a single database, and connects to the implementation of contemporary technologies like computer vision and artificial intelligence for image content classification and the creation of metadata. Given the abundance of digitally available (historical) images, image content recognition and the creation of metadata by artificial intelligence are sheer necessities in order to make millions of hitherto unexplored images available for research. At the same time, the challenge to overcome existing colonial and other biases in the training of AI remains. Hence, we are once again tasked to reflect on the delicate criterion of objectivity. The second part of this paper focuses on research done in the ArchiMediaL project (archimedial.eu); it demonstrates both the potentials and the risks of applying artificial intelligence for metadata creation by addressing the three fields mentioned above through the magnifying glass of programming.

High precision epidermal radio frequency antenna via nanofiber network for wireless stretchable multifunction electronics

Recently, stretchable electronics combined with wireless technology have been crucial for realizing efficient human-machine interaction. Here, we demonstrate highly stretchable transparent wireless electronics composed of Ag nanofibers coils and functional electronic components for power transfer and information communication. Inspired by natural systems, various patterned Ag nanofibers electrodes with a net structure are fabricated via using lithography and wet etching. The device design is optimized by analyzing the quality factor and radio frequency properties of the coil, considering the effects of strain. Particularly, the wireless transmission efficiency of a five-turn coil drops by approximately only 50% at 10 MHz with the strain of 100%. Moreover, various complex functional wireless electronics are developed using near-field communication and frequency modulation technology for applications in content recognition and long-distance transmission (>1 m), respectively. In summary, the proposed device has considerable potential for applications in artificial electronic skins, human healthcare monitoring and soft robotics.