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BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 7. Cropping image

This function takes five parameters, a WriteableBitmap type of object, the location of the starting point on x coordinate, the location of the starting point on y coordinate, width and height. It crops the image according to these parameters as shown in Figure 7.

BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition

In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation.

BRAIN Journal-Developing Distance Learning Environments in the Context of Cross-Border Cooperation-Figure 1. Components of EduWebCast System

The aim of this partnership would be to implement an infrastructure for live and on-demand video streaming of learning material for the targeted groups and, to this purpose, to establish a long and fruitful cooperation between teachers, pupils, and students on both sides of the border. The joint creation and administration of the webcast project is the ground stone of the partnership between the two universities and will result in more common projects based on the materials obtained through the project, contests between pupils and students, possible periodic educational exchanges.

BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 1. The AGLO online assessment approach

In Figure 1, we present the lifetime of AGLOs in the context of online student assessment following a set of steps. In the backend, the tutor develops an AGLO model respecting a predefined meta-model. The model is intuitive, it has a few sections where symbols are defined using formulas and random numbers and then used in a section of a presentation for the student. When such models are created they are stored in a storage facility like a database to be selected by the student through the web application frontend. In the frontend, the students access the web application using a web browser from a workstation, tablet or smartphone. In the assessment process, the student will access several AGLOs. At this step, the accessed AGLOs are instantiated with random numbers, formulas are evaluated to fulfill the designed learning or testing scenario and to create the presentation content for the student. Nevertheless, the instantiated symbols will be used for the automatic assessment of the answers correctness

BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 5. Sample Image data 2

It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images.   After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation.

BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 3. The methodology flowchart

The methodology of our experimental method is described in Figure 3 below. It helps to write our code in C# and to make an application in dot net framework, which collects facial images using a webcam/or other video grabbing tools. Then it implements Haar detection to extract facial features and to draw image pattern for matching both images.

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BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 2. The representation of the rule

In order to model these derivations in the stratified graph G, each production of the grammar will be represented in the labeled graph G0 by a direct arc of the form given in Figure 2.

BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 1. The graphical representation of the morphism

A morphism of partial algebras such that (30) and if (31), then (32) (see Figure 1). We obtain f(L) = T which means that “for every element of L the associated element of T is computed by the morphism f” (Ţăndăreanu, 2000).

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BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 4. Online test assessment example

Thus, applying these restrictions the computed solution is C, E, G, J, L, H, I and is unique. Node C is the starting node since it is the first from the lexicographical point of view. The first step CE is the only choice coping with the restrictions from the [CE, CG, and CJ] edges. Next, EG is the first edge in the list of [EG, EJ]. The next step is GJ which is the only choice. Edge JL is another unique choice. Edge LH is the next step from the list [LH, LI]. Finally, the last edge is obtained by backtracking to node L and then taking edge LI. These restrictions allow us to drive the student to build only one solution from the possible set of solutions. This will determine an easier way of comparing the student’s answer with the answer of the computer. Another more general solution is to use validation functions which require implementation in domain libraries written in JavaScript.

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 9. Weka Results for Sketches

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-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 4. Sample image data 1

After image matching, we got a positive result at 93% times, for 1000 random sample images tested on the nine criteria of orientation.  After matching the images with the reference image, it gets the nearest orientation matches and they could be Font left, Font right, Down-left, Down Right, Up left, Upright, Font Straight, Up Straight, Down Straight. Initially, some constraints must be satisfied to realize a successful correct matching. The facial regions, concerned on eyes and nose points, have the following characteristic: if there is almost one missing point for the region of the same type then the comparison will be performed. There must be the same number of feature points for both eyes and nose separately. If this condition is satisfied then a new comparison will be performed.

BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature

Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features.

BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 1. Face area detection

The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The result is shown in Figure 1. Since each portion of the image used to detect a feature is much smaller than that of the whole image, detection of all three facial features takes less time on average than detecting the face itself. Using a 1.2GHz AMD processor to analyze a 320 by 240 image, a frame rate of 3 frames per second was achieved. Since a frame rate of 5 frames per second was achieved in facial detection only by using a much faster processor, regionalization provides a tremendous increase in efficiency in facial feature detection.

BRAIN Journal-About the Design of QUIC Firefox Transport Protocol-Figure 3. An example of a network switch

Seamless network transition: While switching networks, QUIC can adapt to a network or a subnet switch (illustrated in Figure 3). This means that if the IP address of the device is changed, then the QUIC connection is not broken or lost. Unlike the TCP protocol where the connection is defined by the IP address and a port number, QUIC connections are defined by a connection ID. Whenever a network switch occurs, QUIC detects it and sends a piggybacked notification to the other party by indicating the new IP address and connection ID. In this way, the communication can resume normally. In contrast, TCP a new connection has to be established.

BRAIN Journal-A New Challenge for Information Mining-Figure 4. Results of Weka's Clustering

In the Figure 4 we show that, in a particular cluster, attributes are grouped in the "good" attribute of "Expertise with technology” with the "Low" attribute of "Student's performance" together. Thus, we can deduce that the level of Student's Performance is influenced by other factors over "Expertise with technology" of teacher. These factors can be searched inside the cluster, providing useful information to a significant exploration. These aspects are not deducible only by exploration through the portal and, for this reason, the clustering technique allows to user to navigate better during the search.

BRAIN Journal-A New Challenge for Information Mining-Figure 5: New Approach Rich Data Exploration

In the following figure (see Figure 5) it has represented a scheme of the new approach proposed to Rich Data Set's Exploration. Other experiments are running in order to validate our idea, both in order to optimize this clustering model by applying new algorithms and distance measures to the datasets presented here, and both applying these techniques to a different domain from the didactic one. Other experiments are also conducted to improve user exploration by skillfully combining multiple methods and exploration techniques through the application of a variety of models such as the Association Rule to extract hidden relationships and association rules between data and Artificial Neural Network mechanisms of learning applicable to classification and forecasting problems.

BRAIN Journal-A New Challenge for Information Mining-Figure 3: Clustered Instances

We tested the algorithm with different values of K, to find the optimal centroids. In general, as you know, there is no method for determining the exact value of K, but an accurate estimate can be obtained, for example, monitoring the value of the sum of squared error (SSE) for some values of k (for example 2, 4, 6, 8, etc.). The SSE is defined as the sum of the squared distance between each member of the cluster and its centroid. Mathematically, we can write (1): ( , ) (1) 1 2     K i c x i i SSE dist x c In our case we estimated in k = 8 the best number of cluster. We obtained the following clustered instances.

BRAIN Journal-A New Challenge for Information Mining-Figure 2. A ScreenShot of L4All Portal

Simple selection or complex selection operations, with boolean operators, are possible. Each widget shows the value of the attributes for the current state of the dataset with different visualization. The current set of objects is shown on a “canvas” (see Figure 2 - right side of the interface). Thanks to advanced Human-Computer Interaction mechanisms, the portal can support sophisticated exploration activities in the cycle . Based on L4All, a number of scientific investigations by different research groups took place: on the relation between different forms of group-work and inclusion, on digital storytelling and related benefits, etc. (Di Blas, Paolini, 2013; Falcinelli, 2012; Falcinelli, Laici, 2012).

BRAIN Journal-A New Challenge for Information Mining-Figure 1. kinds of information needs (Wildemuth and Freund,2012)

The taxonomy of tasks, related to the two different kinds of information needs is illustrated in Figure 1.  In the precision-oriented information needs category, the task’s goal is to locate one resource and get information about its attributes or metadata while in the recall-oriented information needs category the task’s goal is to locate (and get information about) a set of resources. In this category we can distinguish goals that require accessing sets of resources just in groups, or in groups accompanied by count information for getting an overview of a set of resources, e.g. as in Faceted Dynamic Taxonomies (FDT). Furthermore, we may have goals that require more complex aggregated results like those provided by data warehouses. For instance, aggregations of arithmetic (min, max, average) and Boolean functions over the numeric attributes of the documents in the answers of free-text queries. Moreover, counts are computed and displayed over combinations (pairs, triples, quadruplets, etc.) of attributes (of grouping criteria in general). In comparison to OnLine Analytical Processing (OLAP) queries, in exploratory search the information demand in unknown a priori (in OLAP it is known and the schema is fixed) and the objective is not only to compute and see various aggregate values (e.g. sales per month and department), but also to support a flexible process for finding the desired individual resources (Tzitzikas et al., 2016).

BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 4. The solution of C6 found by CEACO

Furthermore, the GA has not been able to find the best solutions in thirteen of the fourteen examples. Therefore, it is the weakest algorithm among the five presented algorithms. However, SS_ACO has been able to find better solutions than the GA and has come up with the best solution in 12 examples. Among remaining five algorithms, PSO has failed in improving the solutions in 10 examples and has come up with solutions similar to the ones found by GA. From the comparison between GAPSO and CEACO, it can be seen that GAPSO in six examples has been able to find better solutions than the proposed algorithm. However, the CEACO has found better solutions than this algorithm for one example. For example, the solution of C6 is shown in Figure 4 which is the best found solution until now by other algorithms.