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This article presents an online application developed to improve vocabulary learning through games, quizzes and interaction with a chatbot. The application was tested experimentally on a sample of twenty proficiency-level students divided into two groups and tested on English vocabulary learning in two stages. In the first stage, the first group of students was the control group, who studied a vocabulary section from the book. The second group was the experimental group, which studied using the proposed application. In the second stage, the two groups were reversed and asked to learn the vocabulary of the second unit. According to experiment results, using the particular application improved students' performance, as, in both tests, students of the experimental group outperformed those of the control group. In addition, the analysis of a questionnaire answered by all students confirmed that most prefer this application over traditional learning methods as they consider it quite enjoyable and easy while agreeing that it helped them learn vocabulary. The application is available on the website: http://users.sch.gr/polizipan.

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.

The use of chatbots evolved rapidly in numerous fields in recent years, including Marketing, Supporting Systems, Education, Health Care, Cultural Heritage, and Entertainment. In this paper, we first present a historical overview of the evolution of the international community’s interest in chatbots. Next, we discuss the motivations that drive the use of chatbots, and we clarify chatbots’ usefulness in a variety of areas. Moreover, we highlight the impact of social stereotypes on chatbots design. After clarifying necessary technological concepts, we move on to a chatbot classification based on various criteria, such as the area of knowledge they refer to, the need they serve and others. Furthermore, we present the general architecture of modern chatbots while also mentioning the main platforms for their creation. Our engagement with the subject so far, reassures us of the prospects of chatbots and encourages us to study them in greater extent and depth.

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.