Informatica

Effective movement of materials plays an important role in successful operation of any organization. Proper methods adopted for material movement are also crucial for the overall safety of the personnel involved in the manufacturing processes. Selection of the appropriate material handling equipment (MHE) is a vital task for improving productivity of an organization. In today’s technological era, varieties of MHEs are available to carry out a desired task. Depending on the type of material to be moved, there are many quantitative and qualitative factors influencing the selection decision of a suitable MHE. The problem of selecting the right type of MHE for a given purpose can be solved using multi-criteria decision-making (MCDM) methods which are capable of dealing with the combination of crisp and fuzzy data. In this paper, an MCDM method employing fuzzy axiomatic design principles is applied for selecting the most appropriate MHE for the given task. As a measure of suitability, the total information content is calculated for each MHE and the MHE alternative with the least total information content is regarded as the best choice. Two real time problems from the literature, i.e. selection of an automated guided vehicle, and selection of loading and hauling equipments in surface mines, are solved to validate the applicability, flexibility and potentiality of the adopted approach.

We propose a normalized parameter for characterization of similarity/dissimilarity of two sequences providing a smoothly varying measure for varying symmetry score. Such a parameter can be used for analysis of experimental data and fitting to a theoretical model, mirror symmetry estimation with respect to a selected or presumed symmetry axis, in particular, in symmetry detection applications where the selected symmetry parameters must be evaluated multiple times. We compare the proposed parameter, as well as several of the well-known distance and similarity measures, on an ensemble of template functions morphing continuously from symmetric to antisymmetric shape. This comparison allows to evaluate different similarity and symmetry measures in a more controlled and systematic setting than a simple visual estimation in sample images.

Nowadays the web can serve as a perfect technological environment for personalized learning which is suggested by educators and based on interactive learning objects. While a range of technological solutions for the development of integrated e-learning environments already exists, the most appropriate solutions require further improvement of the implementation of novel learning objects, unification of standardization and integration of learning environments, based on semantic web services that are still in the early stages of development. The aim of the research is to create a model for the development of semantic learning objects, connect it with the architecture of the educational system for semantic learning object design and to present the experimental part of the model’s impact on the course design process. The paper presents the research with the main question on how to improve the e-learning course with semantic learning objects by exploring the application of learning object design approaches, usability, performing modern training facilities and the learning object design model based on semantic web technologies.

This paper investigates an approach which is called structural legal visualization (SLV). It is about diagrammatical views which facilitate comprehension of the meaning of legal contents. Complexity reduction is a motive. An issue is the complexity of the entire legal system and the layman’s limited ability to understand legal institutions and the millions of documents. A sequence of views in SLV can be compared with a narrative. SLV differs from information visualization and knowledge visualization. SLV relates to a scenario-centered graphical narrative rather than information display or user interfaces. SLV is about the generation (synthesis) of diagrams. The sequence of images depends on the user’s goals. Different pathways through the informational space are concerned. With respect to an object’s change or non-change, two variations of SLV are identified: dynamic SLV and static SLV. The latter is divided into two: incremental SLV and alternate focuses SLV.

This paper proposes an access control mechanism of verifiable cloud computing services using chameleon hashing and Diffie–Hellman key exchange protocol. By this mechanism, an entity can apply for cloud computing services and he can authorize other users to access granted data or services. When an authorized user or entity wants to access cloud computing services, he can authenticate the cloud computing service provider. Moreover, no entity secret will be revealed by data kept by cloud servers such that security and cost saving can be both ensured. Security proof under the simulation paradigm is also given.

Traditional researches on scheduling of cloud workflow applications were mainly focused on time and cost. However, security and reliability have become the key factors of cloud workflow scheduling. Taking time, cost, security and reliability into account, we present a trust-based scheduling strategy. We firstly formulate the cloud workflow scheduling and then propose the corresponding algorithm, in which the trustful computation service and storage service are selected according to the set-based particle swarm optimization (S-PSO) method and set covering problem (SCP) tree search heuristic method, respectively. Finally, experimental results show that, compared with traditional methods, the proposed algorithm has better performance.

P2P systems enable decentralised applications for supporting collaborating groups and communities, where the collaboration may involve both sharing of data and sharing of group processes among group members. In such applications, monitoring and awareness are critical functionalities required for an effective collaboration. However, to date there has been little research into providing generic, application-independent awareness in P2P groupware systems. We present a distributed event-based awareness approach for such systems that provides different forms of awareness through a set of interoperating, low-level awareness services. The user and technical requirements for the approach are motivated with reference to Project-Based Learning in a P2P environment. We describe the implementation of a superpeer P2P network on a Cloud platform and the provision of reliable awareness services (AaaS – Awareness as a Service) from the Cloud. We report on the outcomes of an empirical evaluation of the performance and scalability of the approach.

Mobile cloud computing has emerged aiming at assisting mobile devices in processing computationally or data intensive tasks using cloud resources. This paper presents an optimization approach for utilizing cloud services for mobile client in mobile cloud, which considers the benefit of both mobile device users and cloud datacenters. The mobile cloud service provisioning optimization is conducted in parallel under the deadline, budget and energy expenditure constraint. Mobile cloud provider runs multiple VMs to execute the jobs for mobile device users, the cloud providers want to maximize the revenue and minimize theelectrical cost. The mobile device user gives the suitable payment to the cloud datacenter provider for available cloud resources for optimize the benefit. The paper proposes a distributed optimization algorithm for utilizing cloud services for mobile devices. The experiment is to test convergence of the proposed algorithm and also compare it with other related work. The experiments study the impacts of job arrival rate, deadline and mobility speeds on energy consumption ratio, execution success ratio, resource allocation efficiency and cost. The experiment shows that the proposed algorithm outperforms other related work in terms of some performance metrics such as allocation efficiency.

Poisson conditional autoregressive model of spatio-temporal data is proposed. Markov property and probabilistic characteristics of this model are presented. Algorithms for maximum likelihood estimation of the model parameters are constructed. Optimal forecasting statistic minimizing probability of forecast error is given. The “plug-in” principle based on ML-estimators is used for forecasting in the case of unknown parameters. The results of computer experiments on simulated and real medical data are presented.

A nonlinear substitution operation of bytes is the main strength factor of the Advanced Encryption Standard (AES) and other modern cipher systems. In this paper we have presented a new simple algorithm to generate key-dependent S-boxes and inverse S-boxes for block cipher systems. The quality of this algorithm was tested by using NIST tests, and changing only one bit of the secret key to generate new key-dependent S-boxes. The fact that the S-boxes are key-dependent and unknown is the main strength of the algorithm, since the linear and differential cryptanalysis require known S-boxes. In the second section of the paper, we analyze S-boxes. In the third section we describe the key-dependent S-boxes and inverse S-boxes generation algorithm. Afterwards, we experimentally investigate the quality of the generated key-dependent S-boxes. Comparison results suggest that the key-dependent S-boxes have good performance and can be applied to AES.