A multi-objective genetic algorithm applied to autonomous underwater vehicles for sewage outfall plume dispersion observations

This work presents a multi-objective genetic algorithm to solve route planning problem for multiple autonomous underwater vehicles (AUVs) for interdisciplinary coastal research. AUVs are mobile unmanned platforms that carry their own energy and are able to move themselves in the water without intervention from an external operator. Using AUVs one can provide high-quality measurements of physical properties of effluent plumes in a very effective manner under real oceanic conditions. The AUV's route planning problem is a combinatorial optimization problem, where the vehicles must travel through a three-dimensional irregular space with all dimensions known. Therefore, minimization of the total travel distance while considering the maximum number of water samples is the main objective. Besides the AUV kinematics restrictions other considerations must be taken into account to the problem, like the ocean currents. The practical applications of this approach are the environmental monitoring missions which typically require the sampling of a volume of water with non-trivial geometry for which parallel line sweeping might be a costly solution. Some real-life test problems and related solutions are presented.     

AH-questionnaire: An adaptive hierarchical questionnaire for learning styles

One of the main concerns when providing learning style adaptation in Adaptive Educational Hypermedia Systems is the number of questions the students have to answer. Most of the times, adaptive material available will discriminate among a few categories for each learning style dimension. Consequently, it is only needed to take into account the general tendency of the student and not the specific score obtained in each dimension. In this context, we present AH-questionnaire, a new approach to minimize the number of questions needed to classify student Learning Styles. Based on the Felder-Silverman’s Learning Style Model, it aims at classifying students into categories in spite of providing precise scores. The results obtained in a case study with 330 students are very promising. It was possible to predict students’ learning style preference with high accuracy and only a few questions.     

Iterated local search and simulated annealing algorithms for the inventory routing problem

This paper addresses the inventory routing problem (IRP), which consists in defining the customer visit schedule, the delivery quantities, and the vehicle routing plan to meet the demands of a set of customers over a given time horizon. We consider the variant with a single item, a single supplier, multiple vehicles, and a finite multiperiod planning horizon, minimizing the sum of inventory and travel costs. In addition, we address an alternative objective function that minimizes the logistic ratio, defined as the total travel cost divided by the total quantity delivered to customers. This second objective function, while more realistic in some logistics settings, poses a challenge for integer programming models and exact methods because of its nonlinearity. To our knowledge, no heuristic method has been proposed to address this objective in the IRP variant addressed in this paper. To solve this problem with each of these objective functions, we propose effective metaheuristic algorithms based on iterated local search and simulated annealing. Computational experiments show that these algorithms provide reasonably high‐quality solutions in relatively short running times for both objective functions when compared to other methods for well‐known instances from the literature. Moreover, the algorithms produce new best solutions for some of these instances.     

A metamodeling approach for the identification of organizational smells in multi-agent systems: application to ASPECS

Software Quality is one of the most important subjects in the Process Development Software, especially in large and complex systems. Much effort has been devoted to the development of techniques and concepts to improve software quality over the years. We are especially interested on smells, which represent anomalies or flaws in the design/code that can have serious consequences in maintenance or future development of the systems. These techniques have a strong development in the Object Oriented paradigm, however, very few studies were conducted in the agent oriented paradigm. In this paper we focus on the detection of design smells applied to multi-agent systems models based on the organizational approach, named Organizational Design Smells (ODS). Early and automatic detection of these ODS allows reducing the costs and development times, while increasing the final product’s quality. To achieve this objective, validation rules were defined based in the EVL language. The approach is illustrated with two examples, their validation rules, and the refactoring solutions proposed.     

Preprocessing techniques for context recognition from accelerometer data

The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers, it is possible to determine whether a user is walking or running at a specific time of day. By correlating this knowledge with GPS data, it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.     

FTSET-a software tool for fault tolerance evaluation and improvement

It is commonly assumed that neural networks have a built-in fault tolerance property mainly due to their parallel structure. The international community of neural networks discussed these properties until 1994 and afterward the subject has been mostly ignored. Recently, the subject was again brought to discussion due to the possibility of using neural networks in areas where fault tolerance and graceful degradation properties would be an added value, like medical applications of nano-electronics or space missions. Nevertheless, the evaluation of fault tolerance and graceful degradation characteristics remained difficult because there were no systematic methods or tools that could be easily applied to a given Artificial Neural Networks application. The discussion of models is the first step for sorting ways of developing the fault tolerance capability and for building a tool that can evaluate and improve this characteristic. The present work proposes a fault tolerance model, presents solutions for improving it and introduces the Fault Tolerance Simulation and Evaluation Tool for Artificial Neural Networks that evaluates and improves fault tolerance.     

Orthogonal persistence in nonvolatile memory architectures: A persistent heap design and its implementation for a Java Virtual Machine

Current computer systems separate main memory from storage, and programming languages typically reflect this distinction using different representations for data in memory and storage. However, moving data back and forth between these different layers and representations compromise both programming and execution efficiency. To remedy this, the concept of orthogonal persistence (OP) was proposed in the early 1980s advocating that, from a programmer's standpoint, there should be no differences in the way that short-term and long-term data are manipulated. However, at that time, the underlying implementations still had to cope with the complexity of moving data across memory and storage. Today, recent nonvolatile memory (NVM) technologies, such as resistive RAM and phase-change memory, allow main memory and storage to be collapsed into a single layer of persistent memory, opening the way for more efficient programming abstractions for handling persistence. In this work, we revisit OP concepts in the context of NVM architectures and propose a persistent heap design for languages with automatic memory management. We demonstrate how it can significantly increase programmer and execution efficiency, removing the impedance mismatch of crossing semantic boundaries. To validate and demonstrate the presented concepts, we present JaphaVM, an implementation of the proposed design based on JamVM, an open-source Java Virtual Machine. Our results show that JaphaVM, in most cases, executes the same operations between one and two orders of magnitude faster than regular database-based and file-based implementations, while requiring significantly less lines of code.     

Cross-linguistic Alignment of Wordnets with an Inter-Lingual-Index

This paper discusses the design of the EuroWordNet database, in which semantic databases like WordNet1.5 for several languages are combined via a so-called inter-lingual-index. In this database, language-independent data is shared whilst language-specific properties are maintained. A special interface has been developed to compare the semantic configurations across languages and to track down differences.     

Workforce planning and financing on a production/capital discrete‐time model

Traditionally, companies have considered production planning and financial commitments separately. Production planning involves planning when to produce how much of a product, while the financial commitment considers which loans to take and how to repay them. In the current difficult financial environment with new challenges and with different opportunities, such as short‐term flexible loans for paying salaries, these related problems must be considered together. In this paper, we model the two processes (production and cash flows) in a single framework, using a mixed‐integer programming discrete‐time formulation. When taken individually, each of the problems has been thoroughly discussed in the literature, while the combined version that also incorporates labor financial costs and workforce sizing is more scarce. The main contribution of the paper involves new strategies for financing labor costs in strong agreement with the company's production plan and financial commitments. The new strategy relates credit ceiling with employment funding, using a sequence of flexible short‐term loans. We consider applications and propose mathematical programming based tools that can be used by companies’ managers for conducting their own solutions analysis, following their own findings and discussion of alternative scenarios.     

Real-time camera motion tracking in planar view scenarios

We propose a novel method for real-time camera motion tracking in planar view scenarios. This method relies on the geometry of a tripod, an initial estimation of camera pose for the first video frame and a primitive tracking procedure. This process uses lines and circles as primitives, which are extracted applying classification and regression tree. We have applied the proposed method to high-definition videos of soccer matches. Experimental results prove that our proposal can be applied to processing high-definition video in real time. We validate the procedure by inserting virtual content in the video sequence.