top of page


Открытая·18 пользователей

Download File Niru Class Teacher [TOP]

A podcast is a free digital audio service that allows online users to download online audio files from a podcasting website or app, like Spotify, Apple Music, and more, to edit and listen to them anytime and anywhere. In a simple sense, a podcast is the modern version of an audio commentary that people used to hear from their favorite radio shows.

Download File Niru Class Teacher

As most ICT classes are tailored by activities that require the utilization of ICT tools, ICT teachers can integrate some learning podcast episodes into the activities by simply placing the podcast QR code in the activity sheets. Some activities that they can incorporate with the podcast QR codes are listening activities and more.

For their students to easily recognize the podcast for the lecture reviews, the teacher can incorporate the use of a QR code generator with logo online to create customized podcast QR codes for its ICT classes.

Online learning is rapidly becoming one of the most effective ways to impart education. The impact of the virus was so strong that online education became a seemingly ubiquitous part of our growing world, which resulted in the closure of schools and no further physical interaction of teachers with students. Fortunately, soon enough most of the schools and educational institutions moved to online mode to resume their studies. As a result, education has changed dramatically, with the distinctive rise of e-learning, whereby teaching is undertaken remotely on digital platforms instead of physical classrooms.

For students, online classes have become an imminent trend in the education sector around the globe. Digital learning has provided easy access to the files and folders that can now be organised and saved without any physical damage. With one click, students can access their notes and assignments without the fear of misplacing or spoiling them. With advanced technology, this mode of learning has not only been simpler but fun and engaging as well. Technology-enabled learning is beneficial and has proven to be more engaging as it helps in making those subjects interactive and fun which are traditionally considered dull by students. It became very convenient for the students to attend classes from anywhere in the world as both classes and learning content was easily accessible at home. Integration of the learning platforms with new-age interactive applications has made online classes more convenient for both students and teachers as more students are able to express their views at the same time using certain online applications. Students have been more particular with their online submission as they are notified on a regular basis and it is an effortless task for the teachers to track down the students who have failed to submit their assignments on time. Online learning has helped students to become independent learners before they make their way into the real world. Students got opportunities to explore new learning applications and platforms during the class, which helped them to develop new skills and capabilities accelerating their growth trajectory. Some of the students have been responding well to the active learning environment created online by the teachers whereas others need a push in fits and starts.

One of the biggest challenges of online learning that many students face is the struggle to focus for long durations on screen. Not to mention, there is a plethora of distracting content available online which attracts and distracts the students more often than not. To avoid this and help the students stay focused on the class, the teachers have made strenuous efforts and designed their online classes to be crisp, engaging, and interactive. Online classes are not completely reliable as internet connectivity plays a vital role. While access to the internet has drastically improved over the past few years, in some parts of the country, people still lack access to decent internet speed and connectivity. Inconsistent internet connectivity has emerged as one of the top excuses for the students to dodge some important requirements such as an active visual presence which is imperative for due vigilance. With the cameras turned off there is a disconnection between the teachers and students. It is observed widely that students would log into the class and then get distracted with other activities. Given that students are free from the regulations and boundaries of an appropriate classroom environment, it is perceived that the curriculum is not given importance by the students. The notebook work may have been taken lightly. There are high chances of the students distracting themselves while learning online. Traditional classroom education offers the benefit of face-to-face interactions with peers which are typically moderated by a teacher. Physical classroom interaction provides children, especially those in their early developmental years, with a stable environment for social interactions, helping them develop skills like empathy and cooperation. It helps them in their overall development and real-life situations.

In this paper, we propose the use of knowledge distillation to overcome these limitations [54]. Knowledge distillation [24] is a technique used to train small, efficient convolutional neural networks with reduced need of resources (i.e., processing time, memory, and so on) transferring the knowledge learned by a more complex model. The method, in its general form, consists in the extraction of the class probability vectors produced by a large model, also called teacher, and the adoption of these vectors as a target for training the smaller model, known as student. An alternative, naive approach, would be to train the small network directly on the same dataset that was used to train the large model; however, it has been demonstrated that for complex problems the student network can achieve higher accuracy when trained with knowledge distillation than if it is directly trained with the labels of the original dataset [3]. The intuition behind distillation, i.e., the supposed advantage, is that the large teacher model is able to better fit the dataset and encode its peculiarity due to its higher representative power, in a way that the smaller model just could not; the student model may be, however, able to leverage the knowledge that has been pre-digested and encoded into a simpler annotation, namely the output probability vectors of the teacher.

The age estimation methods based on deep learning are extensively described in a recent survey on the topic [6]. We notice that all the approaches achieving the best ranking within the ChaLearn LAP competitions are indeed deep learning based [13, 14], and so are most of the new methods obtaining the best results that were proposed after the competitions. Since the datasets available for training were quite small (4691 images in 2015, 7591 in 2016, but only \(50\%\) of the samples for training), the biggest challenge was the preparation of a dataset sufficiently large and representative for age estimation pre-training. Rothe et al. [49, 50] won the competition in 2015, proposing for the first time the IMDB-Wiki dataset and adopting a cleaned version to pre-train a VGG-16 network for age estimation. Then, the authors applied a bagging procedure to fine-tune 20 versions of the pre-trained one on different splits of the LAP 2015 training set, augmented 10 times with random rotations and translations. The final age prediction is the average of the outputs of 20 CNNs. An even more complex method based on the use of 10 structured output SVMs reached the third place in the competition of 2016 [58]. The method which achieved the second top rank in 2015 [38] is an ensemble of 4 classifiers and 4 regressors based on GoogLeNet, pre-trained for face recognition by using the CASIA-WebFace dataset and fine-tuned for real age estimation over MORPH-II, CACD and WebFace. Then, starting from the learned weights, 4 classifiers and 4 regressors are trained with different versions of the training samples of LAP 2015. The approach holding the best performance over this dataset is the one proposed by Tan et al. [56], which also claims the second top result over the dataset used in the competition of 2016. The method is based on a version of VGG-16, modified with an output layer including K+7 neurons, where K is the number of age labels; each neuron acts as a binary classifier which decides whether the sample belongs to an age group of 7 years. The predictions of the classifiers are then analyzed by an age decoding algorithm which provides the final estimation. The network is pre-trained on a cleaned version of IMDB-Wiki and fine-tuned over a 36 times augmented (random flip, rotation and noise addition) training set of LAP 2016. Dehghan et al. [9] achieve a similar performance with a private CNN pre-trained for face recognition with a private dataset composed of 4 million of images and 40,000 identities and for age estimation with around 600,000 samples manually labeled by human annotators. Another ensemble, composed of 4 VGG-Face models trained on different folds of IMDB-Wiki and LAP 2015 datasets, achieved the second top rank in 2016. The winner of the competition of 2016 is the approach that we chose as teacher network [2], which we will describe in detail in the following. It won the challenge with a substantial gap (0.069 \(\epsilon \)-error) and holds also the best performance over MORPH-II, thanks to an ensemble of 14 CNNs (3 dedicated to individuals under 12 years old) but especially to the contribution of 26 people which carefully cleaned the samples and the age annotations of IMDB-Wiki and extended the training set with a private dataset of children.

The teacher method achieved an impressive 0.2433 \(\epsilon \)-error in the ChaLearn LAP 2016 competition, winning by a large margin on the second classified. However, its accuracy is paid in terms of processing time, which is 6.3 seconds for each face image. 041b061a72

  • О группе

    Bem-vindo ao grupo! Você pode se conectar com outros membros...


    Página do Grupo: Groups_SingleGroup
    bottom of page