Modeling has an important role in systems biology because it can provide a better understanding of biological systems. Advances in biological systems have introduced a series of challenges, such as the repetition of components (cells), their variation and hierarchical organization. There is a wide variety of modeling approaches, including Petri nets, Brane calculi, and P systems. In this paper we propose an approach based on an object-oriented Petri net formalism, which encodes a category of P systems, denoted by dynamical P systems. In addition, we show the validity of the modeling method by modeling a viral infection process with the influenza virus.

An approach to the Colored Petri Nets (CPN)-based control is proposed in this paper. CPN are used for modeling the dynamics of both the controller and the controlled process in the control system structure. The mathematical model of the controlled process is discretized in order to use CPN in modeling the controlled process and the control system as well. The proposed controller implements a Moore automaton. The theoretical aspects of the controller design are presented in the paper, as well as aspects of the system controllability. The level control of three-tank systems is considered to validate the proposed controller. Extensive simulations are performed using the CPN Tools tool and experimental results on laboratory equipment are included.

The evolution of the systems' complexity grew exponentially in the last years. The security and safety topics became more important than ever in the critical systems, and currently no end-user accepts any product without clear traceability for ensuring robustness to errors and external attacks. To be able to offer this kind of products, a high amount of effort must be invested in testing topics. Even that much part of the testing can be done automatically using automated test sequences, it is critical from the timing point of view to find as many errors as possible in the first hours/days of the testing time slot. The current paper presents a solution based on machine learning which decides the order of the tests, based on learned patterns: it analyses which functionalities are more prone to errors, and it generates the test sequence which needs to be executed at each step, in a recursive manner.

The car-2-car communication is one functionality already accepted by most important car manufacturers as a “must have” for the next generation of cars. Such a functionality will increase the road safety by enhancing drivers awareness about road conditions and eventual obstacles. It can be also used to inform the cars behind about risky situations in order to trigger early avoidance actions (e.g. breaking). In order to standardize this type of communication, it was created the Car2Car Communication Consortium who’s task is to choose between multiple technologies and design a common architecture which allows cars communication between different OEMs. The current paper presents an analysis of the LiFI technology applied to car-2-car communication: it is proposed a new solution for the implementation which is an extension of a functionality already available in new cars: automatic high beam controller.

This paper presents a system for smart attendance and facial feedback recording, based on a face recognition mechanism. The system is designed to help professors to mark the attendance at the laboratories and to have an overview on how their development is reflected in students' facial expressions - based feedback. It is a mobile application that uses the Microsoft Azure Cognitive Services Face API, highlighting some of its amazing functionalities. The UI of the application allows professors to create laboratory groups, consisting of a list of students' names and face(s), and to select an image that contains the faces of those who are present during the laboratory. Their attendance is marked within the group by performing face detection on that image, followed by face identification, two API specific operations that will be discussed in this paper. The detected faces are checked among the faces associated to the students enrolled in the laboratory and the attendance is marked for the ones that are recognized. The detection also provides information about facial expressions, relevant for a satisfaction feedback of each student. A detailed explanation of the whole process will be presented in order to give a better understanding of how Face API work and how its features are integrated into this system.

The paper presents a relational multimedia database management system for managing visual and alphanumerical information from the medical domain. The MMDBMS offers numerical and char data types for alphanumerical information, and Image data type used for storing in an original manner the visual information. An Image data type stores the image in a binary manner, its type, its dimensions and information about color and texture that are automatically extracted. This information will be used for content-based visual query process. The color information is represented by the color histogram quantified to 166 colors in the HSV color space. The texture information is represented by a vector with 12 values resulted from the method that uses Gabor filters for texture detection. This DBMS brings up as an element of originality the visual interface for building content-based image query using color and texture characteristics and a modified Select command. This MMDBMS, implemented using Java technologies is platform independent and can be easily used by the medical personnel.

The weather monitoring is currently performed by very expensive professional weather stations. They collect information and they are able to process the data, catalogue weather statistics and even provide predictions about the expected conditions in the near future. However, these warnings have a series of disadvantages: they are very general, they cannot be detailed on small geographical areas, and it is hard for the information to reach the drivers if they are provided in short time. The solution proposed in this paper takes advantage of the new intelligent cars and the inter-connection between them: a low-cost weather sensor can be mounted in each car and will record periodically the weather status, GPS position and time. The car2car communication performed via LI-FI technology is used to send the information to all cars within an area and inform drivers of risky situation.

This chapter presents a couple of original e-assessment methods included in the non-commercial e-learning platform developed by the computers and information technology department. The platform has been in use for over 10 years in both University of Craiova and University of Medicine and Pharmacy of Craiova. Thus, two original e-assessment methods specially created for medical e-learning that use a medical imagistic database acquired in patient diagnosis process are presented. These two methods use content-based image query and content-based region query. Furthermore, the chapter aims to present two methods for question generating: a semi-automated method that uses tags and templates defined by professors, while the second one, automated, is based on domain ontologies developed for course content available in the database of the e-learning platform. The next discussed topic refers to an automatic assessment of narrative answers using the space vector model, a technique coming from information retrieval domain.

The aim of this paper is to present multimedia data for efficient graph-based method to detect visual objects from colour digital images and to extract their colour and geometric features, in order to determine later the contours of the visual objects and to perform syntactic analysis of the determined shapes. The presented method is a general-purpose segmentation algorithm and it produces good results from two different perspectives: (a) from the perspective of perceptual grouping of regions from the natural images, and also (b) from the perspective of determining regions if the input images contain visual objects. We present a unified framework for planar image segmentation and contour extraction that uses a virtual hexagonal structure defined on the set of the image pixels. This method may be extended for volumetric digital images. Despite of the majority of multimedia data for the segmentation methods our method does not require any parameter to be chosen in order to produce a better segmentation and thus our method it is totally adaptive. To investigate the performance of the proposed method, we conduct experiments by comparing our segmentation results with the results produced by other six well known segmentation algorithms.

Graph-based segmentation is gaining popularity among the many approaches in performing image segmentation, primarily due to its ability in reflecting global image properties. The most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. We developed a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The most important characteristic of using a virtual tree-hexagonal network over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

Due to their increasing complexity, the research in the area of Flexible Manufacturing Systems (FMS) is still is in a continuous increase. In this paper we consider the design and the modeling aspects of FMS from an object-oriented perspective. We use the formalism of High-Level Petri Nets with Object-Orientation (HLPNOO) to model an FMS by considering a number of basic FMS components. Each component of a FMS is modeled as a class from the HLPNOO formalism, as well a the entire FMS and its controller. We will strengthen the proposed method with a modeling example.

A weakness of using Petri nets for dynamic systems modelling refers to the difference between the dynamic evolution of systems, and the fixed structure of Petri nets. This is one of the main reasons for the emergence of various proposals aiming to merging Petri net formalisms and the object-oriented paradigm by preserving the advantages of both frameworks. Unfortunately most object-oriented Petri net proposals do not address all issues of these frameworks: Petri nets and object-orientation. In this paper we purpose the formalism of High-Level Petri Nets with Object-Orientation (HLPNOO), whose aim is twofold: (a) to develop a formalism based on the ISO standard of the High-Level Petri Nets (HLPN), which represent an unification of the main high-level Petri net formalisms, and (b) to integrate in this formalism the main concepts of Object-Oriented paradigm, including polymorphism, inheritance and subtyping, which are appropriate for modeling dynamic systems. An example of modelling a dynamic system by using the HLPNOO formalism is presented.

Among the agent-oriented methodologies that use goals for specication of multi-agent systems, the Goal Model for Dynamic Systems (GMoDS) method allows to specify goals during requirements engineering process and then to use them throughout the system development and at runtime. Because the semantics of the GMoDS models involves the use of object-oriented concepts we choose to express a GMoDS model in an object-oriented specication. We use Event-B as a method for both specifying the GMoDS models and implementing the semantics of the runtime model of GMoDS. Because Event-B is not an object-oriented language, the goal of our research is to add support to Event-B for object-oriented modeling by using the modularization plug-in of the Rodin framework. This aim of paper is twofold: (a) to describe an object-oriented specication in Event-B, and (b) to express a GMoDS model into an object-oriented Event-B specication.

In this paper we propose a framework based on BDI software agents for the modeling and simulation of dynamic systems. The target system is broken down into a number of interacting components. Each component is then mapped to a BDI agent that captures its behavioral aspects. The system model is described as a multi-agent program that is specified using the Jason agent-oriented programming language.

This paper presents a social learning application meant to bring students, teachers, courses and study groups all in one safe place for a university. It is focused on academic communication, successful support-based relationships between students and teachers, meeting colleagues, active and efficient participation in study groups and ease of information retrieval. This social learning environment is based on the social learning graphs formalism. For the implementation of the social network Neo4j has been used, the leader in graph databases management. The implementation of the WEB-based application is done in Java with Spring framework. This social learning environment has been created in order to complete the e-learning process in the University of Craiova that uses the private e-learning platforms: Tesys and MedLearn.

Image segmentation plays a crucial role in effective understanding of digital images, planar or volumetric images. The current research in graph based methods is oriented towards producing approximate solution (or sub-optimal solution) for such graph matching problem to reduce processing time. We are introducing an algorithm for volumetric segmentation based on virtual tree-hexagonal structure (prisms) constructed on the image voxels to improve the speed of segmentation. Here, a graph-based theoretic framework is considered by modeling image segmentation as a graph partitioning problem using input spatial graph. Then we can use the graph facilities and their related algorithms and computational complexity can be viewed as slow as the fundamental graph algorithms. The key to the whole algorithms of volumetric segmentation method is the prism cells as vertices. Volumetric segmentation algorithm contains many other algorithms but only segmentation algorithm is presented based on the limited space of paper.

The aim in this paper is to present a graph-based method for image processing from color digital images and to extract their color and geometric features, in order to determine later the contours of the visual objects and to perform syntactic analysis of the determined shapes. This method may be extended for volumetric digital images. The proposed graph-based segmentation method is divided into two different steps: (a) a step that produces a maximum spanning tree of the connected components of the virtual triangular grid graph constructed on the hexagonal structure of the input image, and (b) the final segmentation step that produces a minimum spanning tree of the connected components, representing the visual objects, by using dynamic weights based on the geometric features of the regions. The first step uses only color information extracted from RGB input image, whereas the final segmentation step uses both color and geometric and configuration of image regions.

Swarm computing emerged as a computing paradigm for solving complex optimization problems using a nature-inspired approach. A swarm of particles populates a virtual space that mimics the physical environment. Virtual particles modeled as computational objects are behaving in the virtual space according to the laws of nature, seeking to solve a mathematical optimization problem. In this paper we propose a formal model of a generic distributed framework for swarm computing based on Finite State Process algebra. The model is simple, clear and technology-independent, and it can serve as a basis for concurrent or distributed implementation using available software technologies.

The paper presents a framework that implements our original algorithm of automatic mapping a MySQL relational database to a MongoDB NoSQL database.The algorithm uses the metadata stored in the MySQL system tables.It takes into consideration the concepts from Entity-Relationship (ER) model: entity type represented by a relation in the Relational Model (RM), 1:1 and 1:M relationship type represented with Foreign Keys (FK) in the RM and N:M relationship type represented in RM with a join table that contains the Primary Keys (PK) from the original tables, each representing a FK and two 1:M relationships between the original tables and the join table.The initial results of our algorithm that was tested on small size databases (10-15 tables with many relationships and 100 records/ table) are presented in this paper.