Digital Game-Based Learning in high school Computer Science education: Impact on educational effectiveness and student motivation

The aim of this study was to assess the learning effectiveness and motivational appeal of a computer game for learning computer memory concepts, which was designed according to the curricular objectives and the subject matter of the Greek high school Computer Science (CS) curriculum, as compared to a similar application, encompassing identical learning objectives and content but lacking the gaming aspect. The study also investigated potential gender differences in the game’s learning effectiveness and motivational appeal. The sample was 88 students, who were randomly assigned to two groups, one of which used the gaming application (Group A, N = 47) and the other one the non-gaming one (Group B, N = 41). A Computer Memory Knowledge Test (CMKT) was used as the pretest and posttest. Students were also observed during the interventions. Furthermore, after the interventions, students’ views on the application they had used were elicited through a feedback questionnaire. Data analyses showed that the gaming approach was both more effective in promoting students’ knowledge of computer memory concepts and more motivational than the non-gaming approach. Despite boys’ greater involvement with, liking of and experience in computer gaming, and their greater initial computer memory knowledge, the learning gains that boys and girls achieved through the use of the game did not differ significantly, and the game was found to be equally motivational for boys and girls. The results suggest that within high school CS, educational computer games can be exploited as effective and motivational learning environments, regardless of students’ gender.     

Automated analysis of security-design models

We have previously proposed SecureUML, an expressive UML-based language for constructing security-design models, which are models that combine design specifications for distributed systems with specifications of their security policies. Here, we show how to automate the analysis of such models in a semantically precise and meaningful way. In our approach, models are formalized together with scenarios that represent possible run-time instances. Queries about properties of the security policy modeled are expressed as formulas in UML’s Object Constraint Language. The policy may include both declarative aspects, i.e., static access-control information such as the assignment of users and permissions to roles, and programmatic aspects, which depend on dynamic information, namely the satisfaction of authorization constraints in a given scenario. We show how such properties can be evaluated, completely automatically, in the context of the metamodel of the security-design language. We demonstrate, through examples, that this approach can be used to formalize and check non-trivial security properties. The approach has been implemented in the SecureMOVA tool and all of the examples presented have been checked using this tool.     

Determinization and minimization of finite acyclic automata by incremental techniques

The determinization of a nondeterministic finite automaton (FA) is the process of generating a deterministic FA (DFA) equivalent to (sharing the same regular language of) . The minimization of is the process of generating the minimal DFA equivalent to . Classical algorithms for determinization and minimization are available in the literature for several decades. However, they operate monolithically, assuming that the FA to be either determinized or minimized is given once and for all. By contrast, we consider determinization and minimization in a dynamic context, where augments over time: after each augmentation, determinization and minimization of into is required. Using classical monolithic algorithms to solve this problem is bound to poor performance. An algorithm for incremental determinization and minimization of acyclic finite automata, called IDMA, is proposed. Despite being conceived within the narrow domain of model‐based diagnosis and monitoring of active systems, the algorithm is general‐purpose in nature. Experimental evidence indicates that IDMA is far more efficient than classical algorithms in solving incremental determinization and minimization problems. Copyright © 2015 John Wiley & Sons, Ltd.     

Data Refinement of Remote Procedures

Recently the action systems formalism for parallel and distributed systems has been extended with the procedure mechanism. This gives us a very general framework for describing different communication paradigms for action systems, e.g. remote procedure calls. Action systems come with a design methodology based on the refinement calculus. Data refinement is a powerful technique for refining action systems. In this paper we will develop a theory and proof rules for the refinement of action systems that communicate via remote procedures based on the data refinement approach. The proof rules we develop are compositional so that modular refinement of action systems is supported. As an example we will especially study the atomicity refinement of actions. This is an important refinement strategy, as it potentially increases the degree of parallelism in an action system. Received February 1999 / Accepted in revised form July 2000     

Molecular basis of agonicity and antagonicity in the androgen receptor studied by molecular dynamics simulations

Treatment of prostate cancer patients with antiandrogens is initially successful, though the therapy often becomes refractory over the time. This mechanism is not fully understood, but the presence of androgen receptor (AR) mutant forms which are activated by antiandrogens and other endogenous ligands, and overexpression of the receptor have been suggested. In an attempt to explain the molecular basis for agonicity and antagonicity in the androgen receptor, and the changes on biological activity of subtle modifications at the ligand and receptor (mutations) level, molecular dynamics simulations were performed on the androgen receptor wild type (WT), and T877A and W741 mutant forms, complexed with several non-steroidal androgens. The stabilizing role of residues from helices 3, 5, 11 and 12 was observed in non-steroidal androgens R-3, S-1, and R-bicalutamide and hydroxyflutamide in resistant mutations. In the AR WT antiandrogen R-bicalutamide complex, destabilization of M895 by both W741 and the sulfonyl linkage of the ligand may be responsible for reported antagonism. Changes in the ligand or mutations alleviating this effect were observed to stabilize the receptor in the active conformation, thus developing resistance to R-bicalutamide. The results presented provide a plausible explanation for the molecular basis of agonicity and antagonicity in the androgen receptor, and complement previous studies using static crystal structures, incorporating for the first time protein dynamics into the analysis. Thus, our results provide a valuable framework for the structure-based design of improved antiandrogens.     

Ab initio determination of heavy oxide perovskite related structures from precession electron diffraction data

Two complex perovskite-related structures were solved by ab initio from precession electron diffraction intensities. Structure models were firstly derived from HREM images and than have been confirmed independently using two and three-dimensional sets of precession intensities. Patterson techniques prove to be effective for ab initio structure resolution, specially in case of projections with no overlapping atoms. Quality of precession intensity data may be suitable enough to resolve unknown heavy oxide structures.     

SnapPaste: an interactive technique for easy mesh composition

Editing and manipulation of existing 3D geometric objects are a means to extend their repertoire and promote their availability. Traditionally, tools to compose or manipulate objects defined by 3D meshes are in the realm of artists and experts. In this paper, we introduce a simple and effective user interface for easy composition of 3D mesh-parts for non-professionals. Our technique borrows from the cut-and-paste paradigm where a user can cut parts out of existing objects and paste them onto others to create new designs. To assist the user attach objects to each other in a quick and simple manner, many applications in computer graphics support the notion of “snapping”. Similarly, our tool allows the user to loosely drag one mesh part onto another with an overlap, and lets the system snap them together in a graceful manner. Snapping is accomplished using our Soft-ICP algorithm which replaces the global transformation in the ICP algorithm with a set of point-wise locally supported transformations. The technique enhances registration with a set of rigid to elastic transformations that account for simultaneous global positioning and local blending of the objects. For completeness of our framework, we present an additional simple mesh-cutting tool, adapting the graph-cut algorithm to meshes.     

Optimizations of the naïve-Bayes classifier for the prognosis of B-Chronic Lymphocytic Leukemia incorporating flow cytometry data

Prognosis of B-Chronic Lymphocytic Leukemia (B-CLL) remains a challenging problem in medical research and practice. While the parameters obtained by flow cytometry analysis form the basis of the diagnosis of the disease, the question whether these parameters offer additional prognostic information still remains open. In this work, we attempt to provide computer-assisted support to the clinical experts of the field, by deploying a classification system for B-CLL multiparametric prognosis that combines various heterogeneous (clinical, laboratory and flow cytometry) parameters associated with the disease. For this purpose, we employ the na?ve-Bayes classifier and propose an algorithm that improves its performance. The algorithm discretizes the continuous classification attributes (candidate prognostic parameters) and selects the most useful subset of them to optimize the classification accuracy. Thus, in addition to the high classification accuracy achieved, the proposed approach also suggests the most informative parameters for the prognosis. The experimental results demonstrate that the inclusion of flow cytometry parameters in our system improves prognosis.     

Behavioral adaptation of information systems through goal models

Customizing software to perfectly fit individual needs is becoming increasingly important in information systems engineering. Users want to be able to customize software behavior through reference to terms familiar to their diverse needs and experience. We present a requirements-driven approach to behavioral customization of software systems. Goal models are constructed to represent alternative behaviors that users can exhibit to achieve their goals. Customization information is then added to restrict the space of possibilities to those that fit specific users, contexts, or situations. Meanwhile, elements of the goal models are mapped to units of source code. This way, customization preferences posed at the requirements level are directly translated into system customizations. Our approach, which we apply to an on-line shopping cart system and an automated teller machine simulator, does not assume adoption of a particular development methodology, platform, or variability implementation technique and keeps the reasoning computation overhead from interfering with the execution of the configured application.     

EDEN: An Intelligent Software Environment for Diagnosis of Discrete-Event Systems

A software environment, called EDEN, that prototypes a recent approach to model-based diagnosis of discrete-event systems, is presented. The environment integrates a specification language, called SMILE, a model base, and a diagnostic engine. SMILE enables the user to create libraries of models and systems, which are permanently stored in the model base, wherein both final and intermediate results of the diagnostic sessions are hosted as well. Given the observation of a physical system gathered during its reaction to an external event, the diagnostic engine performs the a posteriori reconstruction of all the possible evolutions of the system over time and, then, draws candidate diagnoses out of them. The diagnostic method is described using a simplified example within the domain of power transmission networks. Strong points of the method include compositional modeling, support for model update, ability to focus on any sub-system, amenability to parallel execution, management of multiple faults, and broad notions of system and observation.