DOI: 10.3897/jucs.160204

Authors: Abdulhadi Altherwi, Md. Mottahir Alam, Mastoor M. Abushaega, Ahmed Hamzi, Abdulmajeed Azyabi, Shabbir Hassan, Asif Irshad Khan

Abstract: The integration of hybrid renewable energy systems (HRES) has introduced both opportunities and challenges in managing multisource power systems such as wind and solar. Accurate forecasting of HRES performance is critical to efficient planning and grid stability. This paper proposes a data efficient hybrid framework that combines Grey Wolf Optimization (GWO) for feature selection with Deep Belief Networks (DBN) for predictive modeling. GWO effectively selects relevant features from high dimensional environmental and system parameters, reducing computational burden and enhancing learning performance. The DBN is then trained on the optimized input set to forecast system performance. Two public datasets capturing wind and solar power production across distinct geographic conditions were used for validation. The proposed model significantly outperforms conventional methods, achieving a mean square error of 0.0207, RMSE of 0.144, and an energy efficiency of 98.32%. These results demonstrate the framework’s potential for deployment in smart grid forecasting environments.

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DOI: 10.3897/jucs.148927

Authors: İbrahim Çakırlar, Sevcan Emek, Şebnem Bora, Oğuz Dikenelli

Abstract: This study introduces a novel methodology and framework for the verification, validation, and testing of agent-based simulation models: RatKit. Building on repeatable automated testing in ABMS, the present contribution significantly extends the foundation by proposing an integrated metamodel and systematic development methodology that embeds these activities throughout the simulation lifecycle. The RatKit methodology is both general, in that it applies to a wide range of agent-based simulation models using a well-defined metamodel, and comprehensive, in that it addresses the macro-level (societal), the meso-level (interaction) and the micro-level (agent) aspects of simulations. It also provides a generic infrastructure to be able to support various VV&T techniques. RatKit is designed as a general VV&T framework for all ABM frameworks. The methodology comes with a dedicated implemented framework. It is implemented by selecting the Repast ABM development framework. RatKit is demonstrated through a detailed case study of the Boids model, where the dynamics of alignment, cohesion, and separation are examined. Results from the case study show that a test-driven approach can enhance model reliability and ensure that individual agent behaviors coalesce into realistic emergent phenomena. Experiences and feedback obtained during the case studies show that developing ABM with a test-driven method based on VV&T facilitates the creation of desired models.

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DOI: 10.3897/jucs.106543

Authors: Melih Kuncan, Kaplan Kaplan, Yılmaz Kaya, Mehmet Recep Minaz, H. Metin Ertunç

Abstract: In the contemporary landscape of industrial manufacturing, the concept of computer numerical control (CNC) has emerged due to the optimization of conventional machinery, distinguished by its remarkable precision and expeditious processing capabilities. These inherent advantages have seamlessly paved the way for the pervasive integration of CNC machines across a myriad of industrial manufacturing sectors. The present study embarks upon a comprehensive inquiry, delving into the intricate analysis of a specialized prototype CNC molding machine, encompassing a meticulous assessment of its structural rigidity, robustness, and propensity for vibrational occurrences. Moreover, an insightful exploration is undertaken to discern the intricate interplay between vibrational signals and intricate machining processes, particularly under diverse conditions such as the presence or absence of the cutting tool, and at varying rotational speeds denoted in revolutions per minute (RPM). The trajectory of this research voyage encompasses an extensive array of empirical experiments meticulously conducted on the prototype CNC machine, with synchronous real-time acquisition of vibrational data. This empirical journey starts by generating two distinct datasets, each meticulously designed to encompass an assemblage of seven distinct rotational speeds, spanning the spectrum from 18000 to 30000 RPM, thereby facilitating enhanced diversity within the dataset. In parallel, a secondary dataset is meticulously derived from the CNC machine operating in the absence of the cutting tool, thereby encapsulating an exhaustive range of 20 discrete RPM values. The extraction of pivotal features aimed at discerning between the vibrational signals arising from distinct conditions (i.e., those emanating from situations involving the presence or absence of the cutting tool) and the associated variance in CNC machine speeds is facilitated through an innovative framework grounded in co-occurrence matrices. The culmination of this methodological framework is the identification of discernible co-occurrence matrices, thereby facilitating the subsequent computation of Heralick features. The classification effort was performed systematically using 10-fold cross-validation analysis, covering a number of different machine learning models. The outcomes emanating from this intricate sequence of systematic methodologies underscore remarkable achievements. Specifically, the classification of vibrational signals corresponding to varying CNC machine speeds, contingent upon the presence or absence of the cutting tool, yields commendable accuracy rates of 94.27% and 94.16%, respectively. Notably, an exemplary accuracy rate of 100% is attained when classifying differing conditions (i.e., situations involving the presence or absence of the cutting tool) across specific RPM settings, prominently at 22000 24000 26000 28000 and 30000 RPM.

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DOI: 10.3897/jucs.104901

Authors: Tien Quang Dam, Nghia Thinh Nguyen, Trung Viet Le, Tran Duc Le, Sylvestre Uwizeyemungu, Thang Le-Dinh

Abstract: In recent years, the rapid evolution of ransomware has led to the development of numerous techniques designed to evade traditional malware detection methods. To address this issue, a novel approach is proposed in this study, leveraging machine learning to encode critical information from Portable Executable (PE) headers into visual representations of ransomware samples. The proposed method selects highly impactful features for data sample classification and encodes them as images based on predefined color rules. A deep learning model named peIRCECon (PE Header-Image-based Ransomware Classification Ensemble with Concatenating) is also developed by integrating prominent architectures, such as VGG16 and ResNet50, and incorporating the concatenating method to enhance ransomware detection and classification performance. Experimental results using self-collected datasets demonstrate the efficacy of this approach, achieving high accuracy of 99.85% in distinguishing between ransomware and benign samples. This promising approach holds the potential to significantly improve the effectiveness of ransomware detection and classification, thereby contributing to more robust cybersecurity defense systems.

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DOI: 10.3897/jucs.112797

Authors: Lorenz Gillner, Ekaterina Auer

Abstract: In this paper, we describe the requirements for traceable open-source data retrieval in the context of computation of BRCA1/2 mutation probabilities (mutations in two tumor-suppressor genes responsible for hereditary BReast or/and ovarian CAncer). We show how such data can be used to develop a Dempster-Shafer model for computing the probability of BRCA1/2 mutations enhanced by taking into account the actual age of a patient or a family member in an appropriate way even if it is not known exactly. The model is compared with PENN II and BOADICEA (based on undisclosed data), two established platforms for this purpose accessible online, as well as with our own previous models. A proof-of-concept implementation shows that set-based techniques are able to provide better information about mutation probabilities, simultaneously highlighting the necessity for ground truth data of high quality.

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DOI: 10.3897/jucs.97170

Authors: Layse Santos Souza, Michel S. Soares

Abstract: A smart city is an urban centre that integrates a variety of solutions to improve infrastructure performance and achieve sustainable urban development. Urban roads are a crucial infrastructure highly demanded by citizens and organisations interested in their deployment, performance, and safety. Urban traffic signal control is an important and challenging real-world problem that aims to monitor and improve traffic congestion. The deployment of traffic signals for vehicles or pedestrians at an intersection is a complex activity that changes constantly, so it is necessary to establish rules to control the flow of vehicles and pedestrians. Thus, this article describes the joint use of the SmartCitySysML, a profile proposed by the authors, with TCPN (Timed Coloured Petri Nets) to refine and formally model SysML diagrams specifying the internal behaviour, and then verify the developed model to prove behavioural properties of an urban traffic signal control system.

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DOI: 10.3897/jucs.89254

Authors: Fahimeh Ramazankhani, Mahdi Yazdian-Dehkord, Mehdi Rezaeian

Abstract: Features extracted from facial images are used in various fields such as kinship verification. The kinship verification system determines the kin or non-kin relation between a pair of facial images by analysing their facial features. In this research, different texture and color features have been used along with the metric learning method, to verify the kinship for the four kinship relations of father-son, father-daughter, mother-son and mother-daughter. First, by fusing effective features, NRML metric learning used to generate the discriminative feature vector, then SVM classifier used to verify to kinship relations. To measure the accuracy of the proposed method, KinFaceW-I and KinFaceW-II databases have been used. The results of the evaluations show that the feature fusion and NRML metric learning methods have been able to improve the performance of the kinship verification system. In addition to the proposed approach, the effect of feature extraction from the image blocks or the whole image is investigated and the results are presented. The results indicate that feature extraction in block form, can be effective in improving the final accuracy of kinship verification.

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DOI: 10.3897/jucs.94162

Authors: Zhiling Yang

Abstract: Natural Language Processing (NLP) is an efficient method for enhancing educational outcomes. In educational settings, implementing NLP entails starting the learning process through natural acquisition. English teaching and learning have received increased attention from the relevant education departments as an integral aspect of the new curriculum reform. The environment of English teaching and learning is undergoing extraordinary changes as a result of the constant improvement and extension of teaching level and scale, as well as the growth of Internet information technology. As a result, the current research aims to look into techniques for efficiently using AI (artificial intelligence) apps to teach and learn English from the perspective of university students. This research can measure the levels as well as effectiveness of the employment of AI applications for teaching English based on deep learning techniques. There, the NLP based language enhancement has been carried out using Character-level recurrent neural network with back Propagation neural network (Cha_RNN_BPNN) based classification. With the help of this DL (deep learning) technique, it is possible to use AI methods to assist teachers in analysing and diagnosing students' English learning behaviour, replacing teachers in part to answer students' questions in a timely manner, and automatically grading assignments during the English teaching process. Experimental analysis shows Word Perplexity, Flesch-Kincaid (F-K) Grade Level for Readability, Cosine Similarity for Semantic Coherence, gradient change of NN, validation accuracy, and training accuracy of the proposed technique.

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DOI: 10.3897/jucs.94160

Authors: Xin Ma

Abstract: Higher vocational education is a self-contained method of higher education that is aligned with global productivity and economic development. Its goal is to develop talented workers who contribute significantly to the economy and industry. Teaching analysis, teaching strategy, teaching practice, and assessment are all part of the course design process in high vocational education. Teaching assessment is one of the most effective methods for improving the quality of course teaching among teaching processes. This research proposes novel techniques in English teaching based on artificial intelligence for course selection based on students' feedback. Here, the dataset has been collected based on the students’ feedback on courses for Higher Vocational Education in English teaching. This dataset has been processed to remove invalid data, missing values, and noise. The processed data features have been dimensionality reduction integrated with K-means neural network. And the extracted features have been classified with higher accuracy using recursive elimination-based convolutional neural network. Based on this feedback data classification, recommendation for courses in Higher Vocational Education in English teaching has been suggested. The experimental analysis shows various students' feedback dataset validation and training in terms of accuracy of 96%, precision of 92%, recall of 93%, RMSE of 68%, and computational time of 65%.

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DOI: 10.3897/jucs.68692

Authors: Riri Fitri Sari, Asri Ilmananda, Daniela Romano

Abstract: In the current digital era, information exchanges can be done easily through the Internet and social media. However, the actual truth of the news on social media platforms is hard to prove, and social media platforms are susceptible to the spreading of hoaxes. As a remedy, Blockchain technology can be used to ensure the reliability of shared information and can create a trusted communications environment. In this study, we propose a social media news spreading model by adapting an epidemic methodology and a scale-free network. A Blockchain-based news verification system is implemented to identify the credibility of the news and its sources. The effectiveness of the model is investigated by utilizing agent-based modelling using NetLogo software. In the simulations, fake news with a truth level of 20% are assigned a low News Credibility Indicator (NCI ± -0.637) value for all of the different network dimensions. Moreover, the Producer Reputation Credit is also decreased (PRC ± 0.213) so that the trust factor value is reduced. Our epidemic approach for news verification has also been implemented using Ethereum Smart Contract and several tools such as React with Solidity, IPFS, Web3.js, and Metamask. By showing the measurements of the credibility indicator and reputation credit to the user during the news dissemination process, this proposed smart contract can effectively limit user behaviour in spreading fake news and improve the content quality on social media.

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DOI: 10.3897/jucs.2020.015

Authors: Yannick Pencolé, Audine Subias

Abstract: The overall context of this paper is the event-based behavior analysis and focuses on modeling and analyzing behaviors of interest involving time information. Any behavior of interest from any time event system is concisely defined as a set of time constrained events that must occur (positive behavior) and a set of time constrained events that must not occur (negative behavior). This article proposes a formal extension of the chronicle formalism that allows for the concise description of positive and negative behaviors. Based on this new formalism, several criteria are introduced, they formally characterize and compare a set of chronicles. A fully proved implementation of the proposed criteria is then described; it relies on the use of polyhedron techniques to solve systems of linear inequalities.

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DOI: 10.3217/jucs-025-08-0967

Authors: Benjamin Weyers, Ekaterina Auer, Wolfram Luther

Abstract: We suggest to widen the focus of the scientific computations community from an isolated consideration of reliable numerical algorithms using standardized arithmetic to a broad user-centered system modeling and simulation approach relying on an appropriate verification and validation (V&V) design. Most V&V works rarely consider human-related issues specifically. However, modern applications generate and employ huge amounts of heterogeneous data and usually exhibit high complexity - challenges that are best tackled by augmenting human reasoning with automated techniques. That is, novel visual and collaborative approaches are needed to interpret the results, which has to be accounted for in the general V&V procedure. This should include an assessment of (meta-) data and code/outcome quality, selection of methods to propagate and bound uncertainty and, lastly, formally rigorous validation efforts. We present an approach to reliable visual analytics (i.e., analytics subjected to this V&V assessment), which can in turn contribute to the overall V&V procedure after that. Two use cases illustrate the potential of the introduced framework for reliable visual analytics.

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DOI: 10.3217/jucs-025-03-0203

Authors: Xiaoling Tao, Liyan Liu, Feng Zhao, Yan Huang, Saide Zhu

Abstract: With the rapid development of high-speed and large-scale complex network, network vulnerability data presents the characteristics of massive, multi-source and heterogeneous, which makes data fusion become more complex. Although existing data fusion methods can fuse multi-source data, they do not consider that the multisource data may affect the accuracy of fusion result. To solve this problem, we propose an ontology and weighted D-S evidence theory-based vulnerability data fusion method. In our method, we utilize ontology to describe the network vulnerability semantically and construct the network vulnerability ontology hierarchically. Then we use weighted D-S evidence theory to perform the operation of probability distribution and fusion processing. Besides, we simulate our method on MapReduce parallel computing platform. The experiment results show that our method is more effective and accurate compared with existing fusion approaches using single detection tool and traditional D-S evidence theory.

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DOI: 10.3217/jucs-020-05-0640

Authors: Mauricio Alférez, Roberto Lopez-Herrejón, Ana Moreira, Vasco Amaral, Alexander Egyed

Abstract: Software Product Line Engineering (SPLE) is a successful paradigm to produce a family of products for a specific domain. A challenge in SPLE is to check that different models used in early SPL specification do not contain inconsistent information that may be propagated and generate inconsistent products that do not conform to its requirements. This challenge is difficult to address due to the high number of possible combinations of product features and model fragments specifying those features. Variability Consistency Checking (VCC) offers automatic means to address that challenge. VCC relates information inferred from the relationships between features and from base models related to those features. Validating if all the products in an SPL satisfy user-defined consistency constraints is based on searching for a satisfying assignment of each formula generated by VCC. We validated VCC and its supporting tool on two case studies from different application domains, the results were encouraging as we did not observed significant performance penalties.

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DOI: 10.3217/jucs-019-01-0053

Authors: Hyun-Seok Min, Sang-Mun Chung, Jin-Young Choi

Abstract: Traditionally, System Analysis and Software Design are treated as separate processes. Software Design is based on System Analysis but they have little direct relationship to each other. UML (Unified Modeling Language) is widely accepted by industry as the de facto standard for System Analysis and Software Design. The primary tool for System Analysis is the Use Case Diagram and its Scenarios, while the primary tools for Software Design are the Class Diagram and Sequence Diagram. State Machine Diagram is also very useful for behavioral modeling. Our aim is to derive system behavior from software design, so that separate processes can work together. This paper suggests how to make a system-wide State Machine Diagram by gathering State Machine Diagrams in the system using predefined Stereotypes and Synchronization/Externalization. The resulting system-wide State Machine Diagram can be used for various things, such as automatic test case generation for the system, deadlock detection, and Use Case scenario consistency checking. The proposed method is applied to the Missile project of ADD (Agency for Defense Development) in South Korea. The result is very promising. It is expected to be applied to more projects.

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DOI: 10.3217/jucs-016-20-3102

Authors: Bernhard Thalheim

Abstract: Conceptual modelling is a widely applied practice and has led to a large body of knowledge on constructs that might be used for modelling and on methods that might be useful for modelling. It is commonly accepted that database application development is based on conceptual modelling. It is however surprising that only very few publications have been published on a theory of conceptual modelling. Modelling is typically supported by languages that are well-founded and easy to apply for the description of the application domain, the requirements and the system solution. It is thus based on a theory of modelling constructs. At the same time, modelling incorporates a description of the application domain and a prescription of requirements for supporting systems. It is thus based on methods of application domain gathering. Modelling is also an engineering activity with engineering steps and engineering results. It is thus engineering. The first facet of modelling has led to a huge body of knowledge. The second facet is considered from time to time in the scientific literature. The third facet is underexposed in the scientific literature. This paper aims in developing principles of conceptual modelling. They cover modelling constructs as well as modelling activities as well as modelling properties. We first clarify the notion of conceptual modelling. Principles of modelling may be applied and accepted or not by the modeler. Based on these principles we can derive a theory of conceptual modelling that combines foundations of modelling constructs, application capture and engineering. A general theory of conceptual modelling is far too comprehensive and far too complex. It is not yet visible how such a theory can be developed. This paper therefore aims in introducing a framework and an approach to a general theory of conceptual modelling. We are however in urgent need of such a theory. We are sure that this theory can be developed and use this paper for the introduction of the main ingredients of this theory.

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DOI: 10.3217/jucs-016-20-2904

Authors: Alessander Benevides, Giancarlo Guizzardi, Bernardo Ferreira Bastos Braga, Joao Paulo Andrade Almeida

Abstract: Assessing the quality of conceptual models is key to ensure that conceptual models can be used effectively as a basis for understanding, agreement and construction of information systems. This paper proposes an approach to assess conceptual models defined in OntoUML by transforming these models into specifications in the logic-based language Alloy. These Alloy specifications include the modal axioms of the theory underlying OntoUML, allowing us to validate the modal meta-properties representing ontological commitments of the OntoUML types and relations.

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DOI: 10.3217/jucs-016-09-1152

Authors: James Decraene, Thomas Hinze

Abstract: All processes of life are controlled by networks of interacting biochemical components. The purpose of modelling these networks is manifold. From a theoretical point of view it allows the exploration of network structures and dynamics, to find emergent properties or to explain the organisation and evolution of networks. From a practical point of view, in silico experiments can be performed that would be very expensive or impossible to achieve in the laboratory, such as hypothesis-testing with regards to knock-out experiments or overexpression, or checking the validity of a proposed molecular mechanism. The literature on modelling biochemical networks is growing rapidly and the motivations behind different modelling techniques are sometimes quite distant from each other. To clarify the current context, we review several of the most popular methods and outline the strengths and weaknesses of deterministic, stochastic, probabilistic, algebraic and agent-based approaches. We then present a comparison table which allows one to identify easily key attributes for each approach such as: the granularity of representation or formulation of temporal and spatial behaviour. We describe how through the use of heterogeneous and bridging tools, it is possible to unify and exploit desirable features found in differing modelling techniques. This paper provides a comprehensive survey of the multidisciplinary area of biochemical networks modelling. By increasing the awareness of multiple complementary modelling approaches, we aim at offering a more comprehensive understanding of biochemical networks.

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DOI: 10.3217/jucs-015-17-3246

Authors: Yannick Pencolé, Audine Subias

Abstract: This paper addresses the problem of diagnosability analysis in Web Services. In particular, it focuses on the analysis of the impact of time to the diagnostic capabilities in Web Service workflows. The diagnosability analysis that is proposed in this paper aims at determining the diagnostic capabilities of a previously developped algorithm for the diagnosis of Web Services. This diagnostic algorithm is based on chronicle recognitions. Faults that can occur during the execution of service workflows are described by means of chronicles. To perform this diagnosability analysis, the problem is firstly defined as a languagebased analysis which leads to the definition of exclusiveness tests between the languages represented by the chronicles. To deal with the time aspects inherent to the chronicles, we then propose to perform the automatic analysis by the use of time Petri nets. Exclusiveness tests are then defined on reachability graphs of time Petri nets which implicitly represent chronicle languages.

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DOI: 10.3217/jucs-014-13-2212

Authors: Miguel Pérez-Toledano, Amparo Navasa, Juan-Manuel Murillo-Rodríguez, Carlos Canal

Abstract: One focus of current software development is the re-use of components in the construction of systems. Software Adaptation facilitates the consequent need to adapt these components to the new environment by employing adaptors which are obtained automatically and hence with a certain guarantee of suitability, from formal descriptions of the interface behaviour. One appropriate technique for Software Adaptation is Aspect-Oriented Programming (AOP) which makes use of aspects to facilitate the dynamic adaptation of components transparently and non-intrusively. However, owing to the way that aspects are integrated, these can unexpectedly modify the functionality of the system, and consequently completely alter its semantics. It is hence necessary to study the final behaviour of the system to ensure its correctness after adding aspects for its adaptation. This study must go beyond just detecting problems at the protocol level, to analyze the potential semantic problems. This is the main focus of the present communication. We start from the Unified Modeling Language (UML 2.0) specification of both the initial system and the aspects. This specification is validated by generating an algebraic Calculus of Communicating Systems (CCS) description of the system. Next, extended (finite) state machines are automatically generated to verify, simulate, and test the modeled system's behaviour. The result of that process can also be compared with the behaviour of the new running system. To facilitate this task, we propose grouping components so as to centre the study on the points actually affected by the behaviour of the aspects.

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DOI: 10.3217/jucs-013-04-0572

Authors: Milena Vujošević-Janičić, Jelena Tomašević, Predrag Janičić

Abstract: We present a new type of sat problem called the k-GD-SAT, which generalizes k-sat and GD-sat. In k-GD-SAT, clause lengths have geometric distribution, controlled by a probability parameter p; for p = 1, a k-GD-SAT problem is a k-SAT problem. We report on the phase transition between satisfiability and unsatisfiability for randomly generated instances of k-GD-SAT. We provide theoretical analysis and experimental results suggesting that there is an intriguing relationship (linear in the parameter 1/p) between crossover points for different parameters of k-GD-SAT. We also consider a relationship between crossover points for k-SAT and k-GD-SAT and provide links between these values.

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DOI: 10.3217/jucs-012-06-0608

Authors: Kazuyuki Nakamura, Tomoyuki Higuchi, Naoki Hirose

Abstract: Data assimilation is a method of combining an imperfect simulation model and a number of incomplete observation data. Sequential data assimilation is a data assimilation in which simulation variables are corrected at every time step of observation. The ensemble Kalman filter is developed for a sequential data assimilation and frequently used in geophysics. On the other hand, the particle filter developed and used in statistics is similar in view of ensemble-based method, but it has different properties. In this paper, these two ensemble based filters are compared and characterized through matrix representation. An application of sequential data assimilation to tsunami simulation model with a numerical experiment is also shown. The particle filter is employed for this application. An erroneous bottom topography is corrected in the numerical experiment, which demonstrates that the particle filter is useful tool as the sequential data assimilation method.

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DOI: 10.3217/jucs-011-04-0452

Authors: Norbert Gronau, Claudia Müller, Roman Korf

Abstract: Existing approaches in the area of knowledge-intensive processes focus on integrated knowledge and process management systems, the support of processes with KM systems, or the analysis of knowledge-intensive activities. For capturing knowledge-intensive business processes well known and established methods do not meet the requirements of a comprehensive and integrated approach of process-oriented knowledge management. These approaches are not able to visualise the decisions, actions and measures which are causing the sequence of the processes in an adequate manner. Parallel to conventional processes knowledge-intensive processes exist. These processes are based on conversions of knowledge within these processes. To fill these gaps in modelling knowledge-intensive business processes the Knowledge Modelling and Description Language (KMDL) got developed. The KMDL is able to represent the development, use, offer and demand of knowledge along business processes. Further it is possible to show the existing knowledge conversions which take place additionally to the normal business processes. The KMDL can be used to formalise knowledge-intensive processes with a focus on certain knowledge-specific characteristics and to identify process improvements in these processes. The KMDL modelling tool K-Modeler is introduced for a computer-aided modelling and analysing. The technical framework and the most important functionalities to support the analysis of the captured processes are introduced in the following contribution.

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DOI: 10.3217/jucs-011-02-0303

Authors: Frantisek Capkovic

Abstract: An application of the method suitable for modelling and control of general discrete event dynamic systems (DEDS) to special kinds of communication systems is presented in this paper. The approach is based on Petri nets (PN) defined in [Peterson 1981] and directed graphs (DG) described e.g. in [Diestel 1997]. It is supported by the previous author s works, especially [Capkovic 2003], [Capkovic 1998], [Tzafestas and Capkovic 1997].

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DOI: 10.3217/jucs-010-12-1693

Authors: Gianpiero Cabodi, Sergio Nocco, Stefano Quer

Abstract: Binary Decision Diagrams (BDDs) have been widely used in synthesis and verification. Boolean Satisfiability (SAT) Solvers, on the other hand, have been gaining ground only recently, with the introduction of efficient implementation procedures. Specifically, while BDDs have been mainly adopted to formally verify the correctness of hardware devices, SAT-based Bounded Model Checking (BMC) has been widely used for debugging. In this paper, we combine BDD and SAT-based methods to increase the efficiency of BMC. We first exploit affordable BDD-based symbolic approximate reachability analysis to gather information on the state space. Then, we use the collected overestimated reachable state sets to restrict the search space of a SAT-based BMC. This is possible by feeding the SAT solver with a description that is the combination of the original BMC problem with the extra information coming from BDD-based symbolic analysis. We develop specific strategies to appropriately mix BDD and SAT efforts, and to efficiently convert BDD-based symbolic state set representations into SAT-oriented ones.Experimental results prove the validity of our strategy to reduce the amount of variable assignments and variable conflicts generated by SAT solvers, with a subsequent significant performance gain. We gather results with four among the most used SAT solvers, namely Chaff, Limmat, BerkMin, and Siege. We could reduce the number of conflicts up to more than 100x, and the verification time up to 30x.

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DOI: 10.3217/jucs-009-11-1277

Authors: Frantisek Scuglik, Miroslav Sveda

Abstract: Two possibilities of automated CSP (Communicating Sequential Processes) support are introduced in [11] and [10] using either behavioral diagrams or application source code. While in the first approach a tool generates CSP specification from behavioral diagrams, based on UML Composite States diagram, in the second approach an application source code is translated directly into CSP specification using a compiler. This paper reviews tools related to both techniques.

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DOI: 10.3217/jucs-009-03-0223

Authors: Lenore Zuck, Amir Pnueli, Yi Fang, Benjamin Goldberg

Abstract: There is a growing awareness, both in industry and academia, of the crucial role of formally verifying the translation from high-level source-code into low-level object code that is typically performed by an optimizing compiler. Formally verifying an optimizing compiler, as one would verify any other large program, is not feasible due to its size, ongoing evolution and modification, and, possibly, proprietary considerations. Translation validation is a novel approach that offers an alternative to the verification of translators in general and compilers in particular: Rather than verifying the compiler itself, one constructs a validation tool which, after every run of the compiler, formally confirms that the target code produced in the run is a correct translation of the source program. The paper presents voc, a methodology for the translation validation of optimizing compilers. We distinguish between structure preserving optimizations, for which we establish a simulation relation between the source and target code based on computational induction, and structure modifying optimizations, for which we develop specialized permutation rules. The paper also describes voc-64 - a prototype translation validator tool that automatically produces verification conditions for the global optimizations of the SGI Pro-64 compiler.

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DOI: 10.3217/jucs-009-02-0106

Authors: Hans-Dieter Ehrich, Maik Kollmann, Ralf Pinger

Abstract: We present a method for checking global conditions for object systems in a way that avoids state space explosion. The objects referred to in a global condition are checked step by step against local conditions and communication requirements derived from the global condition. The derivation is automatic, based on information about the system structure contained in the global condition. The approach is demonstrated using model checking, but the idea works for other approaches to verification or testing as well. In our current investigation, a multi-object variant of CTL is used for expressing global conditions. The local conditions and communication requirements can be verified independently using standard model checkers. The method is illustrated by a large example (about 10 24 states) where our method shows a spectacular speedup over global model checking.

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DOI: 10.3217/jucs-007-01-0089

Authors: Jürgen Ruf

Abstract: In this paper we present the real-time verification and analysis tool RAVEN. RAVEN is developed for verifying timed systems on various levels of abstraction. It integrates a real-time model checker for real-time specifications, it offers algorithms for analyzing critical delay times, for inspecting data values and event occurrences and for detecting dead_locks and live-locks. The counter example generator provides helpful information for error recovering by printing system execution paths (failing a given specification) to the integrated wave_form browser. All included algorithms are based on a common data structure enabling a compact representation and possibilities for acceleration. By some examples we show that our approach outperforms some state-of-the-art verification tools.

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DOI: 10.3217/jucs-002-02-0058

Authors: Svetoslav Markov, Y. Akyildiz

Abstract: This paper is devoted to the software implementation of two mathematical methods which are often used in biological applications: interpolation and curve fitting in the presence of uncertainties in the input data given in the form of intervals. The methods involve model functions linear in their parameters and are formulated by means of simple expressions in terms of interval arithmetic allowing the computation of verified bounds for the interpolating/approximating functions. The methods are demonstrated for certain classes of nonlinear modelling functions finding applications in biology. A case study involving enzyme-catalysed reaction is considered. The numerical results are performed in the computer algebra system Mathematica, which supports interval-arithmetic computations.

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