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Nowadays, chatbots are helpful in many areas, including education. This article presents a chatbot that aims to help first-grade students get acquainted with arithmetic operations. The architecture we propose has a general character, scalability and can build other closed-domain chatbots. The results of an initial pilot study are satisfactory and encourage us to continue the research.

FreeBSD (an open source Unix-like OS) and Apple’s Mac OS use similar BSD functionality but take different approaches. FreeBSD implements a traditional compact monolithic Unix kernel, whereas Mac OS builds the BSD Unix functionality on top of the Mach microkernel. The authors provide an in-depth technical investigation of both approaches.

Video has been widely used as an effective media for delivering varied educational content. The enormous expansion of educational video is due to its effectiveness and to the spectacular evolution of video construction technology. Modern technology allows the rapid and economic development of educational videos as Software Systems. Such videos may be fully developed using software tools without the need for cameras or other expensive resources, e.g. actors. In this paper, we propose a framework for the effective development of Educational Video. The proposed framework consists of a methodology and a set of design guidelines, both oriented towards the achievement of the learning objectives related to an Educational Video. Experimentally, we compare videos produced following the proposed framework with videos produced following a well-known alternative methodology. Experimental results give rise to the success of our approach and encourage further exploration.

The last few decades, crowd simulation for crisis management is highlighted as an important topic of interest for many scientific fields. As the continues evolution of computational resources increases, along with the capabilities of Artificial Intelligence, the demand for better and more realistic simulation has become more attractive and popular to scientists. Along those years, there have been published hundreds of research articles and have been created numerous different systems that aim to simulate crowd behaviors, crisis cases and emergency evacuation scenarios. For better outcomes, recent research has focused on the separation of the problem of crisis management, to multiple research sub-fields (categories), such as the navigation of the simulated pedestrians, their psychology, the group dynamics etc. There have been extended research works suggesting new methods and techniques for those categories of problems. In this paper, we propose three main research categories, each one consist of several sub-categories, relying on crowd simulation for crisis management aspects and we present the outcomes of the last decade, focusing mostly on works exploiting multi-agent technologies. We analyze a number of technologies, methodologies, techniques, tools and systems introduced throughout the last years. A comparative review and discussion of the proposed categories is presented towards the identification of the most efficient aspects of the proposed categories. A general framework, towards the future crowd simulation for crisis management is presented based on the most efficient to yield the most realistic outcomes of the last decades. The paper is concluded with some highlights and open questions for future directions., Comment: Submitted to Expert Systems with Applications

Plagiarism in students’ source codes constitutes an important drawback for the educational process. In addition, plagiarism detection in source codes is time consuming and tiresome task. Therefore, many approaches for plagiarism detection have been proposed. Most of the aforementioned approaches receive as input a set of source files and calculate a similarity between each pair of the input set. However, the tutor often needs to detect the clusters of plagiarism, i.e. clusters of students’ assignments such as all assignments in a cluster derive from a common original. In this paper, we propose a novel plagiarism detection algorithm that receives as input a set of source codes and calculates the clusters of plagiarism. Experimental results show the efficiency of our approach and encourage us to further research.

ScalaLab is a MATLAB-like environment for the Java Virtual Machine (JVM). ScalaLab is based on the Scala programming language. It utilizes an extensive set of Java and Scala scientific libraries and also has access to many native C/C[Formula: see text] scientific libraries by using mainly the Java Native Interface (JNI).The performance of the JVM platform is continuously improved at a fast pace. Today JVM can effectively support demanding high-performance computing and scales well on multicore platforms. However, sometimes optimized native C/[Formula: see text] code can yield even better performance, by exploiting low-level programming issues, such as optimization of caches and architecture-dependent instruction sets.The present work reports some of the experiences that we gained with experiments with both Just in Time (JIT) JVM code and native code. We compare some aspects of Scala and C[Formula: see text] that concern the requirements of scientific computing and highlight some strong features of the Scala language that facilitate the implementation of scientific scripting. This paper describes how ScalaLab tries to combine the best features of the JVM with those of the C/C[Formula: see text] technology, in order to implement an effective scientific computing environment.

Artificial graphs are commonly used for the evaluation of community mining and clustering algorithms. Each artificial graph is assigned a pre-specified clustering, which is compared to clustering solutions obtained by the algorithms under evaluation. Hence, the pre-specified clustering should comply with specifications that are assumed to delimit a good clustering. However, existing construction processes for artificial graphs do not set explicit specifications for the pre-specified clustering. We call these graphs, randomly clustered graphs. Here, we introduce a new class of benchmark graphs which are clustered according to explicit specifications. We call them optimally clustered graphs. We present the basic properties of optimally clustered graphs and propose algorithms for their construction. Experimentally, we compare two community mining algorithms using both randomly and optimally clustered graphs. Results of this evaluation reveal interesting insights both for the algorithms and the artificial graphs.

Most approaches for mining the community structure of a graph are based on the assumption that each member of a community has more links within than outside its community. We argue that this delimitation of a community is not appropriate for graphs representing web sites; hence, we propose a more detailed delimitation for the community structure of web sites. Moreover, we propose a novel graph clustering algorithm for mining communities from web sites. Experimentation on real web sites shows that our approach compares favourably to alternative, well known, community mining algorithms.