Augmented Human Research最新文献

This article addresses a renowned issue of allocating periodic tasks to a network of heterogeneous processors in distributed computing systems (DCS) subject to timing constraints, tasks precedence, and arbitrary communication among them, in order to lessen the overall busy time whereas guaranteeing the tasks deadlines. A new hybrid optimization (NHO) technique is introduced, a fusion of k-mean clustering (KMC) and Branch-and-Bound (B&B) method for reducing overall normalized busy time (NSBT) of the system. This technique is stationed on B&B method in which each branch grants scheduling solution. K-mean clustering (KMC) technique has been utilized to reduce the complexity of B&B technique by pruning the branches those do not lead feasible solution. A specialized case of non-periodic tasks allocation issue is also studied in this work. The problem is intractable in nature. Finally, a demonstrative example and comparison with some computational experiences are presented. Experimental results reveal that proposed technique achieves better proficiency than other existing techniques in literature. This model is advisable for arbitrary number of processors and tasks.

The nation has already had to contend with such a major public health issue when this COVID-19 disease was propagated in March 2020. The corona's propagation does have an effect on all areas of people's lives. The hostile effect on education systems throughout all locations is indeed a diverse expansion impact. A complete shutdown keeps adding fresh problems for students to learn and also for educators to effectively manage the class, likely to result in the conversion of such an offline education system into a virtual class. This study investigated and described various online learning platforms, as well as teaching resource distribution technology and modern technologies used to ensure that students can study. Overall, this relates to channels that can be used to create a confidential environment in order to prevent copying in online tests. This also outlines so many difficulties in managing online teaching methods, like learners' perceptions of e-learning being more traumatic and having a negative impact on general wellbeing and social relationships. So, a technique has been proposed to provide learners with a superior online classroom teaching experience, allowing the online classroom to be as good as, if not better than, a single online classroom. This study focused on daily teaching methods that employ online learning supported by a machine teaching approach to provide an individual with a relevant stress-free solution.

The professional capacity of preschool teachers is a decisive factor for the quality of child care and early education for children. However, many preschool teachers teaching in disadvantaged areas have not yet responded to the increasing demands of society and educational innovation. Therefore, developing educators’ competencies in disadvantaged areas is an important task in education renovation. This article investigates the status of the professional competencies of preschool teachers in disadvantaged and mountainous areas to find out the advantages and disadvantages in implementing educational innovation. The main method is the quantitative research method entailing observation, questionnaire investigation and in-depth interviews with 250 early childhood educators and 30 administrators. Research results have revealed that the professional capacity of teachers in mountainous areas has many limitations, especially the ability to apply information technology and develop their own professional skills. Besides, research also pointed out a number of factors affecting the professional capacity development of preschool teachers. This research result is the foundation for improving professional competencies for preschool teachers in educational innovation.

The variability that occurs in spontaneous network communication has brought about increased attention in the area of study that is centered on analytical approaches and models aimed at addressing the shorter timescales conceivable with dynamic functional networks. As the shifts in functional connectivity have been immense in the quantification of task performance in the cognitive domain so has the usefulness in the clinical setting been predicted. More so, the analysis of dynamic functional connections can be of considerable clinical relevance as had been observed in the studies of pathologies such as schizophrenia, Alzheimer's disease mild cognitive impairment. The evaluation of dynamic functional connectivity is however far from being perfect. Though functional magnetic resonance, imaging which has been vastly employed in evaluating neural communication in the human brain, does not appear to be efficient in measuring neuronal dynamics, and this could be down to the variability in sampling, physiological, noise, and head motion that usually accompany fMRI. This is where EEG, despite its limited spatial resolution, has found significance owing to the delivery of temporal resolution which is higher in measuring the time-varying relationships feasible in the rhythmic patterns of neural activity.

In this paper, we shall aim at reviewing the strides that have been made in the efforts to develop an effective technique for quantifying the transitions in functional connectivity that take place over specific timescales.

Team games with a ball provide a great workout and are social activities, making them a great way to spend free time. Unfortunately, it can be challenging to play when the participants' level is not more or less equal. We use digital augmentation technology to promote collaboration during the game and soften the focus that is usually put exclusively on the physical skill levels. Using computer game elements (attack points, defense points, life points) enables us to create a unique mixed reality playing experience that is approachable and enjoyable to various people who have different playing skills. Therefore, it works for promoting physical activity among such players. When designing a fast-paced game with a real ball, taking place in the physical world and the virtual world, special considerations on tracking the game progress, information display, and player experience have to be taken. We describe the development of the augmented dodgeball game, which includes the systems required to play the game and player status display systems. We also present a user study, which consists of questionnaires, interviews, and player-observations. We found that players had a positive experience playing the developed game; they liked the game concept and saw it as a fun activity to do with friends. The players were also communicating and collaborating more during the augmented game. This research contributes to providing design insights specific to designing for augmented reality team games with the ball.

Coronavirus disease 2019 (COVID-19) is a public health emergency and is of international concern. Till now, there is no effective pharmaceutical treatment available for this disease. This paper presents a multilayer neural network algorithm to calculate the risk factor of getting COVID-19 to the individual based on the symptoms described by World Health Organization. The aim of this study is to provide an approximate risk factor of getting COVID-19 to an individual that helps for further treatment.

Today, technology has advanced tremendously that it is now being incorporated into the education sector for academic enhancement. Certain technologies like Artificial Intelligence, Machine Learning, Blockchain, Big data, Internet of Things, Augmented Reality, Cloud computing, etcetera changed the conventional education system making it a better platform for the growth of students. In this paper, we dissect the importance of two blooming technologies, Blockchain and Machine Learning, in the education field. Blockchain technology, having data immutability as one of its advantages, has been used in miscellaneous fields for security aspects. It can be used to securely store the degree or other achievement certificates. Such information would be added by the college or university to the blockchain, which can be accessed or shared by the student through the online CV with employers. This approach is secure as there is no need to worry about changes to the institution or the loss of data. Also, Machine learning with its fully capable learning algorithms is the breakthrough technology for future perspectives because it can accurately predict the future based on experience; hence, the incorporation of this technology in the educational field helps the student to make a strategy with the help of various algorithms. By doing such things, better outcomes should be made from present conditions. When the benefits of blockchain are combined with Machine Learning algorithms, we can get certain predictions beforehand and we can securely store the actual results, which is the proposed idea of this study. In this study, the emphasis is made on the impacts created by recent technologies in the educational field and review of various systems proposed by blockchain and machine learning technology and assumption is made for combining two technologies for the betterment of the educational field.

In this paper, a one-dimensional ECG signal is decomposed as symmetry tree structure at each level using discrete wavelet transforms which outcomes from a larger quantity of insignificant coefficients. They are measured as zero amplitude value and represented as sparse datasets that improve the compression rate, and Huffman coding helps to represent the signal with low bit rate data. These results compressed data codes of large ECG time-series datasets of the signal. Here, different wavelet filters are evaluated for compression based on sparse data from wavelet decomposition. The performance of an algorithm in terms of compression is 43.52% and 42.8% with a 99.9% correlation between original and recovered signals from compressed ECG data for the MIT-BIH arrhythmia and compression dataset, respectively. Further, heart rate variability (HRV) analysis with correlation of R-R intervals in between original and reconstructed ECG signals validates the reconstruction as well as sensitivity of compression technique toward data accuracy.

As the number of patients with impaired nerves is continuously increasing day by day around the globe, rehabilitation training by the physical therapist is more time-consuming and less effective. From last two decades, many robot-based upper limb rehabilitation devices have been developed for physical therapy of the human upper limb by employing state-of-the-art technologies. Hence, there is a need for a comprehensive systematic analysis to understand the basic principle and working of rehabilitation devices. The devices are primarily classified as exoskeleton- and end-effector-oriented robotic devices depending on the alignment of the upper limb joints. The objective of the review is to investigate the functionality developments of the robot-based upper limb rehabilitation devices. In this work, a systematic analysis is being carried out depending on the factors such as compatible designs, control aspects, training modes, actuation methods, and clinical developments of the devices. Furthermore, a tabular comparison is presented for the above factors considering different types of robotic devices and the status of the developments. Finally, the scope of improvement is discussed by minimizing the potential gaps between design and prototyping establishments. This review will help the therapists, researchers, and manufacturers to augment the safety and cost-effective concerns for patients with upper limb impairments.

The film industry has begun to increase the frame rate of movies in order to enhance viewer's perception of visual smoothness. This decision is causing controversy, and it is exacerbated by the development of high frame rate technology for television. To address this issue, we investigated if higher (60 frames per second or fps) versus conventional lower frame rates (24 fps) influence viewing behaviour and preference. Observers (N = 30) were eye-tracked while they viewed pairs of identical movie clips that differed only in their frame rate. Results showed that individuals looked more frequently at the videos they preferred; however, many could not discriminate between the high and low rate clips. However, those individuals who could reliably discriminate between the two frames rates preferred the lower 24 fps clips. Our results provide empirical support to those who argue that the viewing quality of films at higher frame rates is compromised on 2D displays.