Showing papers in "Informatica (lithuanian Academy of Sciences) in 2016"

Some Single Valued Neutrosophic Number Heronian Mean Operators and Their Application in Multiple Attribute Group Decision Making

Abstract: Heronian mean (HM) has the characteristic of capturing the correlations of the aggregated arguments and the neutrosophic set can express the incomplete, indeterminate and inconsistent in- formation, in this paper, we applied the Heronian mean to the neutrosophic set, and proposed some Heronian mean operators. Firstly, we presented some operational laws and their properties of single valued neutrosophic numbers (SVNNs), and analyzed the shortcomings of the existing weighted HM operators which have not idempotency, then we propose the improved generalized weighted Heronian mean (IGWHM) operator and improved generalized weighted geometric Heronian mean (IGWGHM) operator based on crisp numbers, and prove that they can satisfy some desirable proper- ties, such as reducibility, idempotency, monotonicity and boundedness Further, we proposed the sin- gle valued neutrosophic number improved generalized weighted Heronian mean (NNIGWHM) op- erator and single valued the neutrosophic number improved generalized weighted geometric Hero- nian mean (NNIGWGHM) operator, and some desirable properties and special cases of them are discussed. Moreover, with respect to multiple attribute group decision making (MAGDM) prob- lems in which attribute values take the form of SVNNs, the decision making approaches based on the proposed operators are developed. Finally, an application example has been given to show the decision making steps and to discuss the influence of different parameter values on the decision- making results.

The Framework for the Selection of Personnel Based on the SWARA and ARAS Methods Under Uncertainties

Abstract: In the hiring process at companies, decision makers have underused the methods of the multi-criteria decision-making processes of selection of personnel. Therefore, this paper aims to establish a framework for the selection of candidates during the process of the recruitment and selection of personnel based on the SWARA and ARAS methods under uncertainties. The usability and efficiency of the proposed framework is considered in the conducted case study of the selection of candidate for the position of the sales manager.

A Distributed Security Mechanism for Resource-Constrained IoT Devices

Abstract: Internet of Things (IoT) devices have grown up to comprise embedded systems and sensors with the ability to connect, collect, and transmit data over the Internet. Although, solutions to secure IoT systems exist, Class-0 IoT devices with insufficient resources to support such solutions are considered too resource constrained for a secure communication. This paper provides a distributed security mechanism that targets Class-0 IoT devices. The research goal is to secure the entire data path in two segments; device-to-gateway and gateway-to-server data communications. The main concern in the provided solution is that lighter security operations with minimal resource requirements are performed in the device, while heavier tasks are performed in the gateway side. The proposed mechanism utilizes a symmetric encryption for data objects combined with the native wireless security to offer a layered security mechanism between the device and the gateway. In the offered solution, the IoT gateways provide additional protection by securing data using Transport Layer Security (TLS). The real-time experimental evaluations have proven the applicability of the proposed mechanism pertaining to the security assurance and the consumed resources of the target IoT devices.

Advances in the Field of Automated Essay Evaluation

Abstract: Automated essay evaluation represents a practical solution to a time-consuming, labor-intensive and expensive activity of manual grading of students' essays. During the last 50 years, many challenges have arised in the field, including seeking ways to evaluate the semantic content, providing automated feedback, determining validity and reliability of grades and others. In this paper we provide comparison of 21 state-of-the-art approaches for automated essay evaluation and highlight their weaknesses and open challenges in the field. We conclude with the findings that the field has developed to the point where the systems represent a useful complement (not replacement) to human scoring.

Entropy Measures for Interval-Valued Intuitionistic Fuzzy Information from a Comparative Perspective and Their Application to Decision Making

Abstract: This paper reviews the existing definitions and formulas of entropy for interval-valued intuitionistic fuzzy sets (IVIFSs) and demonstrates that they cannot fully capture the uncertainty of IVIFSs. Then considering both fuzziness and intuitionism of IVIFSs, we introduce a novel axiomatic definition of entropy for IVIFSs and develop several entropy formulas. Example analyses show that the developed entropy formulas can fully reflect both fuzziness and intuitionism of IVIFSs. Furthermore, based on the entropy formulas of IVIFSs, a method is proposed to solve multi-attribute decision making problems with IVIFSs. Additionally, an investment alternative selection example is provided to validate the practicality and effectiveness of the method.

Robust Optimization for Strategic Energy Planning

Abstract: Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favour of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on energy planning decisions. A classification of uncertainty in energy systems decision-making is performed. Robust optimization is then applied to a Mixed-Integer Linear Programming problem, representing the typical trade-offs in energy planning. It is shown that in the uncertain domain investing in more efficient and cleaner technologies can be economically optimal.

A New Model for Interactive Group Decision Making with Intuitionistic Fuzzy Preference Relations

Abstract: The authors are very grateful to the anonymous reviewers and the editor for their valuable comments and constructive suggestions that improve the previous versions of this paper. This paper is supported by National Social Science Foundation of China (No. 15TBJ004), the MOE Project of Humanities and Social Sciences No. 14YJC910006), Zhejiang Province Natural Science Foundation (No. LQ14G010002), K.C. Wong Magna Fund in Ningbo University, Zhejiang Province Soft Science Fund (No. 2015C35007), Ningbo Natural Science Foundation (No. 2015A610161), Key Research Center of Philosophy and Social Science of Zhejiang Province Modern Port Service Industry and Creative Culture Research Center.

A Hesitant Fuzzy Programming Method for Hybrid MADM with Incomplete Attribute Weight Information

Abstract: Gai-Li XU, Shu-Ping WAN, Jiu-Ying DONG, College of Information Technology, Jiangxi University of Finance and Economics Nanchang, Jiangxi 330013, PR China College of Science, Guilin University of Technology Guilin Guangxi 541002, PR China College of Statistics, Jiangxi University of Finance and Economics Nanchang, Jiangxi 330013, PR China Research Center of Applied Statistics, Jiangxi University of Finance and Economics Nanchang, Jiangxi 330013, PR China e-mail: jiali0706@126.com, shupingwan@163.com

Recommender System for Virtual Assistant Supported Museum Tours

Abstract: Personalization technology and services have been widely adopted in many domains, especially on various web platforms. This kind of technology is also finding its way to cultural heritage applications since it can broaden the interest for visiting cultural sights. Recommendation systems suggest to users some specific domains that they might be interested in but were unfamiliar with due to their lesser popularity. In this paper we propose a web service for virtual museum tours that is based on intelligent virtual assistants that can learn user preferences and provide recommendations regarding museum exhibitions.

Towards Internal Modelling of the Information Systems Application Domain

Abstract: Model-driven IS engineering methods invoke the IS application domain modelling met- hods to acquire essential characteristics of organizational systems (enterprises). Business modelling for value creation is a relatively separate area, meanwhile it correlates with the IS application domain modelling methodologies and gives new insights for enhancement of enterprise modelling, business process modelling and BP management modelling approaches. The IS application domain and the business domain modelling are not isolated and could be investigated using the same paradigm of modelling. Yet there is some uncertainty in model-driven approaches towards the understanding of the enterprise management activities. A problematic consistency of modelling approaches indicate a need for a systemic analysis of IS application domain modelling concepts. The internal modelling paradigm is used for analysis of an enterprise management activity as a self-managed system, and hereby the lack of the conceptual basis for domain modelling in IS engineering is determined. This approach is aimed to reveal hidden information transactions of the business management activities. The understanding of the IS application domain as a self-managed system allowed to redefine such concepts as management transaction, management function and enterprise process. The metastruc- ture of management transaction is defined and illustrated for business management layer and IS development layer.

PCARD Platform for mHealth Monitoring

Abstract: The introduction of information and communication technologies (ICT) into the integrated healthcare system could increase the self-management of health and therefore increase the efficacy and decrease the costs of overall health management. A personal mobile health monitoring system (PCARD) has been developed, which uses moderately-priced and user-friendly technological solutions, e.g. wireless body sensors for data acquisition, advanced algorithms for data analysis, widely available smart phones for visualization of measurements, and the existing communication infrastructure for data transfer. The solution is unobtrusive, works with existing devices, and provides useful information to both direct users and to the health care system. The PCARD system starts with measurement of ECG signal that incorporates significant information about the global health state. It then continues with display of the signal and its analysis on a personal terminal, such as smartphone and on Cloud-based storage, processing, and visualization software. Four pilot studies have been designed to validate to which extent the continuous measured ECG data could contribute to improved quality and efficiency of the healthcare. Also, the level of safety and reliability, the acceptance from users, and the potential for commercialization will be validated in the scope of the pilots.

A Mixed Noise Removal Method Based on Total Variation

Abstract: Due to the technology limits, digital images always include some defects, such as noise. Noise reduces image quality and affects the result of image processing. While in most cases, noise has Gaussian distribution, in biomedical images, noise is usually a combination of Poisson and Gaussian noises. This combination is naturally considered as a superposition of Gaussian noise over Poisson noise. In this paper, we propose a method to remove such a type of mixed noise based on a novel approach: we consider the superposition of noises like a linear combination. We use the idea of the total variation of an image intensity (brightness) function to remove this combination of noises.

A Revocable Certificateless Short Signature Scheme and Its Authentication Application

Abstract: Certificateless short signature (CLSS) possesses the advantages of both certificateless signature and short signature. CLSS eliminates the certificate management in conventional signatures and solves the key escrow problem in ID-based signatures. In the meantime, due to its short signature length, CLSS reduces the bandwidth for communication so that it is suitable for some specific authentication applications requiring bandwidth-constrained communication environments. However, up to now, there is no work on studying the revocation problem in existing CLSS schemes. In this article, we address the revocation problem and propose the first revocable certificateless short signature (RCLSS) scheme. Based on the computational Diffie–Hellman (CDH) assumption, we demonstrate that our RCLSS scheme possesses strong unforgeability against adaptive chosenmessage attacks under an accredited security model. It turns out that our scheme has the shortest signature length while retaining computational efficiency. Thus, the proposed RCLSS scheme is well suited for low-bandwidth communication environments. Finally, we combine the proposed RCLSS scheme with cloud revocation authority (CRA) to present a CRA-aided authentication scheme with period-limited privileges for mobile multi-server environment.

Semi-Automatic Service Provision Based on Interaction of Data Warehouses for Evaluation of Water Resources

Abstract: Our research is devoted to development of information infrastructure for e-service semiautomatic provision by using distributed data warehouses (DWs) of water protection domain. Development of software for semi-automatic service provision is based on artificial planner and structure of goals adapted for specialized needs of end users. Such e-service preparation mechanism can work under the unified coherent framework for solving the environment protection problems by evaluation of the processes of water consumption and contamination. The possibilities of integration of distributed DWs of water management sector into web portal meeting the requirements of conceptual interoperability are presented. Design approach is based on development of decision support system (DSS) that is designed as multilayered system with multi–componential, interoperable structure of databases (DBs), which are under responsibility of different public administration institutions such as European Environment Information and Observation Network (EIONET) and national environment protection agencies. The infrastructure of EIONET is used for supporting and improving data and information flows. The Water resource management information system (WRMIS) became the kernel component of DSS. WRMIS prototype facilitates data flows between the institutions and gives access to data for relevant institutions and the public providing e-services using proposed DSS. The research investigations are made according to the requirements of European Union Water Framework Directive, Sustainable Development Strategy and ReportNet as the EIONET infrastructure for supporting and improving data and information flows. Additional means are integrated in the structures of DSS as knowledge representation techniques based on conceptual schemas, data flow diagrams, and decision-making rules. The on-line management techniques are based on assurance of interoperability by using Open Web Platform W3C standards for web service development, such as XML, SOAP, HTTP, etc.

Fractal-Based Methods as a Technique for Estimating the Intrinsic Dimensionality of High-Dimensional Data: A Survey

Abstract: The estimation of intrinsic dimensionality of high-dimensional data still remains a challenging issue. Various approaches to interpret and estimate the intrinsic dimensionality are developed. Referring to the following two classifications of estimators of the intrinsic dimensionality – local/global estimators and projection techniques/geometric approaches – we focus on the fractalbased methods that are assigned to the global estimators and geometric approaches. The computational aspects of estimating the intrinsic dimensionality of high-dimensional data are the core issue in this paper. The advantages and disadvantages of the fractal-based methods are disclosed and applications of these methods are presented briefly.

Performance Comparison of Featured Neural Network Trained with Backpropagation and Delta Rule Techniques for Movie Rating Prediction in Multi-criteria Recommender Systems

Abstract: Recommender systems are software tools that have been widely used to recommend valuable items to users. They have the capacity to support and enhance the quality of decisions people make when nding and selecting items online. Such systems work based on which techniques are used to estimate users' preferences on potentially new items that might useful to them. Traditionally, the most common techniques used by many existing recommendation systems are collaborative ltering, content-based, knowledge-based and hybrid-based which combines two or more techniques in different ways. The multi-criteria recommendation technique is a new technique used to recommend items to users based on ratings given to multiple attributes of items. This technique has been used and proven by researchers in industries and academic institutions to provide more accurate predictions than traditional techniques. What is still not yet clear is the role of some machine learning algorithms such as the articial neural network to improve its prediction accuracy. This paper proposed using a feedforward neural network to model user preferences in multi-criteria recommender systems. The operational results of experiments for training and testing the network using two training algorithms and Yahoo!Movie dataset are also presented.

Agile methodologies in software maintenance:a systematic review

Abstract: Agile Methodologies has been gaining popularity since 2000. The Software Maintenance phase of software lifecycle is the most expensive and tedious in nature and use of Agile methodologies helps in maintaining software over time in flexible and iterative manner. This study reviews several papers with different case studies to evaluate the performance and quality of software using agile methodologies. In this study, more than 30 research studies are investigated which are conducted between 2001 and 2015 and have been categorized according to the publication year, datasets, tools, type of techniques etc. This will be the first review paper on the use of the Agile in software maintenance which will help the researchers and encourages companies and beginners to adopt these methodologies to gain software quality. This study would also be helpful to professional academicians to identify the current trends and future gaps in the field of agile methodologies.

A Hybrid Computational Method Based on Convex Optimization for Outlier Problems: Application to Earthquake Ground Motion Prediction

Abstract: Statistical modelling plays a central role for any prediction problem of interest. However, predictive models may give misleading results when the data contain outliers. In many real-world applications, it is important to identify and treat the outliers without direct elimination. To handle such issues, a hybrid computational method based on conic quadratic programming is introduced and employed on earthquake ground motion dataset. This method aims to minimize the impact of the outliers on regression estimators as well as handling the nonlinearity in the dataset. Results are compared against widely used parametric and nonparametric ground motion prediction models.

e-Turist: An Intelligent Personalised Trip Guide

Abstract: We present e-Turist, an intelligent system that helps tourists plan a personalised itinerary to a tourist area, taking into account individual’s preferences and limitations. After creating the route, e -Turist also offers real-time GPS guidance and audio description of points of interest visited. Here we focus on two main components, the recommender system and the route planning algorithm. We also present some use cases to highlight e-Turist functionalities in different configurations.

Parameter Tuning of PI-controller with Bat Algorithm

Abstract: Correct input controller parameter settings are vital and in constant connection with output functions - e.g. robotic positioning. Optimal positioning of robotic arm auto-matically provides a high level of safety and functionality. The rst prevents robot from hurting any people around or even itself, while the second ensures robot advantage. In order to improve both safety and functionality, we propose two nature-inspired algorithms for parameter tuning of PI-controller and test them on the laboratory robotic manipulator. However the manipulator is not designed to perform a real robotic work, it offers a detailed approach of positioning control. Our goal is to access the positioning control unit and combinatorially set the input controller parameters with the help of two implemented algorithms. This principle is called automatic parameter tuning, which rstly tests the corresponding setting, then evaluates it and nally tries to improve former result with new one.

A VIKOR-Based Approach to Group Decision Making With Uncertain Preference Ordinals and Incomplete Weight Information

Abstract: In this paper, we focus on group decision making problems with uncertain preference ordinals, in which the weight information of decision makers is completely unknown or partly unknown. First of all, the consistency and deviation measures between two uncertain preference ordinals are defined. Based on the two measures, a multi-objective optimization model which aims to maximize the deviation of each decision maker’s judgements and the consistency among different decision makers’ judgements is established to obtain the weights of decision makers. The compromise solution method, i.e. the VIKOR method is then extended to derive the compromise solution of alternatives for group decision making problems with uncertain preference ordinals. Finally, three examples are utilized to illustrate the feasibility and effectiveness of the proposed approach.

A maximization problem in tropical mathematics: a complete solution and application examples

Abstract: A multidimensional optimization problem is formulated in the tropical mathematics setting as to maximize a nonlinear objective function, which is defined through a multiplicative conjugate transposition operator on vectors in a finite-dimensional semimodule over a general idempotent semifield. The study is motivated by problems drawn from project scheduling, where the deviation between initiation or completion times of activities in a project is to be maximized subject to various precedence constraints among the activities. To solve the unconstrained problem, we first establish an upper bound for the objective function, and then solve a system of vector equations to find all vectors that yield the bound. As a corollary, an extension of the solution to handle constrained problems is discussed. The results obtained are applied to give complete direct solutions to the motivating problems from project scheduling. Numerical examples of the development of optimal schedules are also presented.

An Approach to Hesitant Fuzzy Group Decision Making with Multi-Granularity Linguistic Information

Abstract: Abstract. The 2-tuple linguistic computational model is an important tool to deal with linguistic information. To extend the application of hesitant fuzzy linguistic term sets and avoid information loss, this paper introduces hesitant fuzzy 2-tuple linguistic term sets that are expressed by using several symbolic numbers in [0,1]. Considering the order relationship between hesitant fuzzy 2tuple linguistic term sets, measures of expected value and variance are defined. Meanwhile, several induced generalized hesitant fuzzy 2-tuple linguistic aggregation operators are defined, by which the comprehensive attribute values of alternatives can be obtained. Then, models for the optimal weight vector on a decision maker set, on an attribute set and on their ordered sets are constructed, respectively. Furthermore, an approach to multi-granularity group decision making with hesitant fuzzy linguistic information is developed. Finally, an example is selected to illustrate the feasibility and practicality of the proposed procedure.

Some Generalized Interval-Valued 2-Tuple Linguistic Correlated Aggregation Operators and Their Application in Decision Making

Abstract: With respect to multi-attribute decision making under uncertain linguistic environment, a new interval-valued 2-tuple linguistic representation model is introduced. To deal with the sit- uation where the elements in a set are interdependent, several generalized interval-valued 2-tuple linguistic correlated aggregation operators are defined. It is worth pointing out that some interval- valued 2-tuple linguistic operators based on additive measures are special cases of our operators. Meanwhile, several special cases and desirable properties are discussed. Furthermore, models based on the correlation coefficient are constructed, by which the optimal weight vector can be obtained. Moreover, an approach to multi-attribute group decision making with uncertain linguistic infor- mation is developed. Finally, an example is selected to show the effectivity and feasibility of the developed procedure.

On Efficiency Analysis of the OpenFOAM-Based Parallel Solver for Simulation of Heat Transfer in and Around the Electrical Power Cables

Abstract: In this work, we study the efficiency of developed OpenFOAM-based parallel solver for the simulation of heat transfer in and around the electrical power cables. First benchmark problem considers three cables directly buried in the soil. We study and compare the efficiency of conjugate gradient solver with diagonal incomplete Cholesky (DIC) preconditioner, generalized geometricalgebraic multigrid GAMG solver from OpenFOAM and conjugate gradient solver with GAMG multigrid solver used as preconditioner. The convergence and parallel scalability of the solvers are presented and analyzed on quadrilateral and acute triangle meshes. Second benchmark problem considers a more complicated case, when cables are placed into plastic pipes, which are buried in the soil. Then a coupled multi-physics problem is solved, which describes the heat transfer in cables, air and soil. Non-standard parallelization approach is presented for multi-physics solver. We show the robustness of selected parallel preconditioners. Parallel numerical tests are performed on the cluster of multicore computers.

Software Features Extraction From Object-Oriented Source Code Using an Overlapping Clustering Approach

Abstract: For many decades, numerous organizations have launched software reuse initiatives to improve their productivity. Software product lines (SPL) addressed this problem by organizing software development around a set of features that are shared by a set of products. In order to exploit existing software products for building a new SPL, features composing each of the used products must be specified in the first place. In this paper we analyze the effectiveness of overlapping clustering based technique to mine functional features from object-oriented (OO) source code of existing systems. The evaluation of the proposed approach using two different Java open-source applications, i.e. “Mobile media” and “Drawing Shapes”, has revealed encouraging results.

HTML5-based mobile agents for Web-of-Things

Abstract: Systems and services utilizing Internet-of-Things can benefit from dynamically updated software in a significant way. In this paper we show how the most advanced variant of moving code, mobile agents, can be used for operating and managing Internet-connected systems composed of gadgets, sensors and actuators. We believe that the use of mobile agents brings several benefits, for example, mobile agents help to reduce the network load, overcome network latency, and encapsulate protocols. In addition, they can perform autonomous tasks that would otherwise require extensive configuration. The need for moving agents is even more significant if the applications and other factors of the over experience should follow the user to new contexts. When multiple agents are used to provide the user with services, some mechanisms to manage the agents are needed. In the context of Internet-of-Things such management should reflect the physical spaces and other relevant contexts. In this paper we describe the technical solutions used in implementation of the mobile agents, describe two proof concepts and we also compare our solution to related work. We also describe our visions of the future work.