Find research datasets worth reusing

BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 1. Semantria result

In Figure 1 the Semantria output is presented, having a positive polarity, with a score of 0.218. What is interesting to note here are the keywords extracted from students’ feedback. They noticed the integration of MOOCs in the Embedded Systems course as positive due to the fact that the new information is perceived as easier and the gained knowledge seems to be valuable. Students are affected by too many concepts and also by the idea of paying for the course.

BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 3. Representation of the grammar G1 in the labelled graph G0 1

If we take the labeled graph G0 1 given in Figure 3 and construct the stratified graph structure over (99) such that (100) we obtain (101), (102).   In this paper, we proposed a new system for formal language generation by means of stratified graphs structures. This mechanism can generate languages of the first type and of the second type. More precisely, we propose a new system for formal language generation by means of a system of knowledge based on stratified graphs. We exemplified that, using an interpretation  system specially defined for stratified graphs representations, a particular formal language can be obtained by means of the resulted accepted structured paths.

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 8. Virtual social space on Google+

For micro-blogging we hashtagged the main topic as #maps_of_time and created keywords related to three ancient technologies specific for the studied contexts (textiles, glass, ceramics) to facilitate a categorization of the topics and their retrieval. To achieve a unified and coherent platform, the personal spaces of the social networks were customized with logos and landing pages, designed by Associate Professor Marina Theodorescu (NUA).

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 14. The survey as a public posting on Google+ Time Maps page

During this experimentation phase a symposium was organized at Vădastra School with the purpose to present our learning experiment to a group of 30 teachers from the Olt County. An open history lesson on the Time Maps web site was held by a history teacher, and a school girl described the Facebook page of the Vădastra School (Figure13), maintained by both teachers and children. The invited teachers gave a feedback on the effectiveness and utility of the Time Maps learning system by responding to a questionnaire-based survey, which was posted on the Google+ page (Figure 14).

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 10. The educational blog on Google+ Time Maps page – the glass manufacturing techniques

For this subject two video films were posted on Google+ (a performance and a 3D reconstruction), slightly different from those available on the Time Maps web site, but containing the same information. The children had to make a little effort to relate this information with the one presented on the site, to make a connection between the questions, the fragments from videos at which the answers referred to and the information from the site. The set of questionnaires lead the school children through the majority of data offered by the web site regarding to the two historical periods (Figures 9, 10).

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 5. Fiber artist Alexandra Rusu (NUA) working at a Roman vertical loom (video movie)

The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea Hasnaş. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 11. Children applying weaving techniques under the control of the staff from NUA Bucharest

The information from a) and b) has been used by other colleagues in the local school and in the future will be used by other schools in the country or abroad. This represented the second educational level of the project, i.e. the analysis of the collected data. Information from c) was further analyzed by university teachers, filtered and added as an enhancement to the content of the AR platform. An example of user-created content are the movies made with smartphone cameras, recording the children2 while performing traditional crafts (Figures 11, 12).

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 11.Weka Results for Images

The dataset of the real images is also treated the same way as the dataset of the sketches. Total tree images used = 86 Total car images used = 54 Learning rate = 0.3 Momentum = 0.2 Number of epochs = 500 70% of data is used to train the neural network and the remaining 30% is used for testing the trained neural network. Figure 10 shows the neural network for images. The results are shown in Figure 11 and are as follows: Total Correct Recognition = 83.7838% Total Incorrect Recognition = 16.2162% Error Per epoch = 0.0235851

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 8. Training Neural Network for Sketches

It is described in the previous section that a mobile application is used to collect data about the sketches and the data is converted to 20x20=400 integer numbers to give it as input to Weka. Our experimentation includes only two objects for recognition i.e. trees and cars. Total tree sketches used = 175 Total car sketches used = 72 Learning rate = 0.3 Momentum = 0.2 Number of epochs = 500 70% of data is used to train the neural network and the remaining 30% is used for testing the trained neural network. Figure 8 shows the neural network for sketches. The results are shown in Figure 9 and are as follows: Total Correct Recognition = 100%

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 5. Cropped Sketch

Figure 5 shows the interface of the mobile application that is created to collect the data of the sketches. It contains a drawing canvas; where drawings are made and it also contains few text boxes which are filled with the information of the drawing. For example, if the drawing is a tree, ‘1’ is written in the tree text box. Furthermore, the undo and redo buttons help modify the drawing and the clear button clears everything drawn onto the drawing canvas.  The drawing of a tree is shown on the drawing canvas in Figure 4. It is clear from the figure that there is some empty area on top, right, left and bottom of the drawing sketch, which can cause problems while using this image for training the neural network. Therefore, there is implemented a crop function which crops the image very minutely and removes the empty space as shown in Figure 5.

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 4. Mobile Application to collect Sketch data

The drawing of a tree is shown on the drawing canvas in Figure 4. It is clear from the figure that there is some empty area on top, right, left and bottom of the drawing sketch, which can cause problems while using this image for training the neural network.

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 3. The used methodology

The training process we use in both trainings is based on neural networks, using the backpropgation algorithm. By using this algorithm, we are going to finally obtain the weights that will be used in our model to make the system recognize the input given by the user.  Once the training for the hand drawn sketches is over, we are going to get weights that will be used to recognize any inputted hand drawn sketches. For example, if the user draws a tree, the system will be able to recognize what has been drawn as a tree, using the obtained weights from the neural networks. On the other hand, once the training for the real images is over, it means that if we provide our system with a real image, for example a tree, the system will be able to recognize it using the obtained weights from the training phase.

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 6. Reducing size of Sketch

As previously described, there are a total of 1600 integers, which can be the input to the dataset for neural networks. But it is a huge number, so in order to minimize the size of inputs to the neural network the 40x40 matrix is reduced to 20x20 by skipping odd rows and columns of the original matrix. Figure 6 shows a matrix containing green and red rows and columns. If this was the 40x40 matrix, then the red part of this matrix would be skipped to convert it into a 20x20 sized matrix. Now there are only 20x20=400 values, which is a reasonable input size for the neural network.  The other part of the developed approach is to collect the data about the same two objects of the real pictures taken by the camera. These images are converted into black and white pictures and then treated the same way as the sketches, i.e. black and white pictures are also converted into integers based on the color of each pixel.

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 1. Methodology used in the research by (Egmont-Petersen et al., 2002)

Figure 1 shows the methodology used by Egmont-Petersen et al. (2002) to come up with an answer to their research question. Their study says that image recognition using neural networks goes through the stages shown in Figure 1. In our research we shall use this general proposed approach.

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 5. Weekly session stats for 2016

On following figures, statistic usage is given as Monthly and Weekly statistic for individual users and sessions. The number of the individual users and of the sessions is far better for the 2015 period, especially for the extent of time until the end of June. This is reasonable because it was during the project and the site is frequently put forward in promotional conferences, in press, in direct contacts with schools, and companies. After that period the site was not promoted additionally, and the only pointer to the site is a number of links found on our institutional websites as one of the services we are offering to the students. Considering all that, the figures for 2015 and even 2016, seems to be quite satisfactory. (Figure 1, Figure 4, and Figure 5).

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 3. Monthly user/session stats for 2016

It is also interesting to observe that in Figure 3, where the usage for 2016 is presented, that in the non-promotional period, the site is mostly used in January (before January exam term), in April, May, and June (before the June exam session and during colloquial exams) or in July and August (before the September exam session).

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 2. Monthly user/session stats for 2015

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 10. Training Neural Network for Images

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 2. The used approach

In this section we present in details the methodology used for this research. Figure 2 depicts the used methodology. The adopted approach is based on first training the system to be able to recognize certain sketches by providing it with various hand-drawn examples such as trees, cars and mountains. Secondly, the system has to be trained again to recognize real images by providing it with real images such as trees, cars and mountains.