Simultaneous Localization and Mapping Using Rao-Blackwellized Particle Filters in Multi Robot Systems

In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests.     

Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method

A hybrid forecasting method is proposed which leverages from statistical and neural network techniques to perform multi-step ahead forecasting. The proposed method is based on the disaggregation of time series components, the prediction of each component individually and the reassembling of the extrapolations to obtain an estimation for the global data. The STL decomposition procedure from the literature [5] is implemented to obtain the seasonal, trend and irregular components of the time series, whilst Generalized Regression Neural Networks (GRNN) [12] are used to perform out-of sample extrapolations of the seasonal and residual components. The univariate Theta model is employed for the estimation of the directional component. The application of the GRNN is based on the dynamic calibration of the training process for each of the seasonal and irregular components individually. The proposed hybrid forecasting method is applied to 60 time series from the NN3 competition and 227 time series from the M1 Competition dataset, to obtain 18 out-of sample predictions. The results from the application demonstrate that the proposed method can outperform standard statistical techniques in the literature. One of the main contributions of the current research lies in the investigation of the strengths and weaknesses of the GRNN in extrapolating structural components of time series.     

Requirements Engineering f��r Product Service Systems

In recent years, manufacturing companies and service providers have moved towards offering customer-specific problem solutions. These integrated bundles usually consist of hardware, software, and service components and are called product service systems (PSS) or hybrid products. Since the success of the resulting solution depends on the understanding of all requirements, requirements engineering (RE) has become a key factor. The article analyzes the state of the art of RE for PSS based on an extensive literature review in the domains of product-, software-, and service engineering. For this, criteria are derived from the characteristics of PSS and from the task area of RE in the life cycle of PSS. Based on these criteria we analyze the most established RE approaches for their suitability for PSS. An important finding is that integrated/interdisciplinary approaches for RE are missing. Moreover, the maturity of RE approaches in the three domains varies significantly. All analyzed approaches heavily rely on concepts and solution characteristics of their own domain so that a transfer to other domains is hardly possible. This literature review lays the foundation for successful RE for PSS and especially for future research aiming at combining and integrating RE approaches and models of product-, software-, and service engineering. Such requirement models could connect concepts of single domains and enable an integrated and seamless RE for PSS.     

Reliability and validity of an ergonomics-related Job Factors Questionnaire

Musculoskeletal disorders are an important cause of work disability and absenteeism. Specific job factors or tasks may increase the likelihood of developing work-related musculoskeletal disorders. The purpose of the present study was to translate and adapt a Job Factors Questionnaire for the Brazilian–Portuguese language and evaluate its psychometric properties. A cross-cultural adaptation was performed according to an internationally recommended methodology involving translation, synthesis, back-translation, revision by an expert committee, and pre-testing. The psychometric properties were evaluated by administering the questionnaire to 148 employees at two companies involved in the assembly and fabrication of metal products. The reliability of the questionnaire was assessed by measuring the temporal stability through a test–retest design. Construct validity was assessed using the known-groups technique and by comparing workers responses with findings from the Nordic Questionnaire. Reliability was determined to be satisfactory based on Intraclass Correlation Coefficients ranging from 0.54 to 0.73. Construct validity analysis demonstrated a significant difference (p ≤ 0.02) between production and office workers on answers to 10 questionnaire items, and a significant difference (p ≤ 0.01) on the scores of the adapted questionnaire between subjects that indicated presence or absence of musculoskeletal symptoms. The results indicated that the adapted version of the questionnaire had psychometric properties that were acceptable for use in occupational studies involving workers that speak Brazilian–Portuguese.

Relevance to industry

The present study provides results of a cross-cultural adaptation process and a psychometric properties analysis of an instrument that verifies workers' perception of job factors that may lead to work-related pain and discomfort.     

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.     

Secure computation of hidden Markov models and secure floating-point arithmetic in the malicious model

Hidden Markov model (HMM) is a popular statistical tool with a large number of applications in pattern recognition. In some of these applications, such as speaker recognition, the computation involves personal data that can identify individuals and must be protected. We thus treat the problem of designing privacy-preserving techniques for HMM and companion Gaussian mixture model computation suitable for use in speaker recognition and other applications. We provide secure solutions for both two-party and multi-party computation models and both semi-honest and malicious settings. In the two-party setting, the server does not have access in the clear to either the user-based HMM or user input (i.e., current observations) and thus the computation is based on threshold homomorphic encryption, while the multi-party setting uses threshold linear secret sharing as the underlying data protection mechanism. All solutions use floating-point arithmetic, which allows us to achieve high accuracy and provable security guarantees, while maintaining reasonable performance. A substantial part of this work is dedicated to building secure protocols for floating-point operations in the two-party setting, which are of independent interest.     

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     

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.