Data and knowledge visualization with virtual reality spaces,neural networks and rough sets: Application to cancer and geophysical prospecting data

Visual data mining with virtual reality spaces is used for the representation of data and symbolic knowledge. High quality structure-preserving and maximally discriminative visual representations can be obtained using a combination of neural networks (SAMANN and NDA) and rough sets techniques, so that a proper subsequent analysis can be made. The approach is illustrated with two types of data: for gene expression cancer data, an improvement in classification performance with respect to the original spaces was obtained; for geophysical prospecting data for cave detection, a cavity was successfully predicted.     

Nonlinear PI control of fed-batch processes for growth rate regulation

This paper deals with the regulation of the biomass specific growth rate, which is an important goal in many fed-batch fermentation processes. The proposed control system is based on the minimal model paradigm, requiring only biomass and volume measurement along with some bounds on the reaction rate. The controller has the structure of a partial state feed-back with adjustable gain. An integral-proportional control algorithm is designed to adjust this gain. It is inspired in concepts of invariant control and system immersion. First, a nonlinear integral action that makes invariant a goal manifold defined by a reference model dynamics is developed. Then, a proportional output error feed-back is incorporated to the control law with the aim of fastening convergence. Stability is investigated in detail using Lyapunov functions. To implement the control law, an estimation of the growth rate is required like any other PI-like controller. Because of its strong convergence properties, a sliding observer that requires the same process information as the controller is used for this task, although conventional continuous observers can alternatively be used provided they are fast enough to preserve stability. Simulation results showing the transient response and robustness features of the controller under nominal and perturbed scenarios are presented.     

Optimal configuration of Energy-Efficient Ethernet

The IEEE 802.3az standard provides a new low power mode that Ethernet network interfaces can use to save energy when there is no traffic to transmit. Simultaneously with the final standard approval, several algorithms were proposed to govern the physical interface state transition between the normal active mode and the new low power mode. In fact, the standard leaves this sleeping algorithm unspecified to spur competition among different vendors and achieve the greatest energy savings. In this paper, we try to bring some light to the most well known sleeping algorithms, providing mathematical models for the expected energy savings and the average packet delay inflicted on outgoing traffic. We will then use the models to derive optimum configuration parameters for them under given efficiency constraints.     

Solving differential equations with Fourier series and Evolution Strategies

A novel mesh-free approach for solving differential equations based on Evolution Strategies (ESs) is presented. Any structure is assumed in the equations making the process general and suitable for linear and nonlinear ordinary and partial differential equations (ODEs and PDEs), as well as systems of ordinary differential equations (SODEs). Candidate solutions are expressed as partial sums of Fourier series. Taking advantage of the decreasing absolute value of the harmonic coefficients with the harmonic order, several ES steps are performed. Harmonic coefficients are taken into account one by one starting with the lower order ones. Experimental results are reported on several problems extracted from the literature to illustrate the potential of the proposed approach. Two cases (an initial value problem and a boundary condition problem) have been solved using numerical methods and a quantitative comparative is performed. In terms of accuracy and storing requirements the proposed approach outperforms the numerical algorithm.     

Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA

Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The few methods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a new evolutionary algorithm, based on a hybrid model of global and local heuristic search, is proposed for the same task, and some experimentation is done with different datasets and indexes. Due to its design, independent of any clustering procedure, it is applicable to virtually any clustering method like the widely used \(k\)-means algorithm. Moreover, the use of non-parametric statistical tests over the experimental results, clearly show the proposed algorithm to be more efficient than other evolutionary algorithms currently used for the same task.     

The ALTER-NATIVA knowledge management approach

Nowadays, it is commonly known that information systems need an agile capability of handling knowledge. To accomplish this, systems have to have a formal knowledge representation ability supported by specific and advanced reasoning features. This research work proposes a knowledge management approach with the purpose to gather, model and consume community knowledge for specific recommendation commitments. Such approach is accomplished by a semantic lexicon alignment between the various community knowledge assets, to facilitate collaborations establishment between people and systems in an interoperable fashion. Thus, a knowledge base supported by a thesaurus able to represent all the metadata needed to represent and characterize the various community stakeholders’ resources is proposed. The thesaurus represents the lexicon in the domain, which in the ALTER-NATIVA systems is mostly used to support the various e-Learning elements (e.g. courses) and users categorization, sustained by synchronization features to facilitate a constant update of its information. A set of services designed to recommend specific resources in relation to a determined profile of user is provided. Additionally, a discussion about how the ALTER-NATIVA knowledge management approach can be applied to industrial environments is presented.     

Scientometric Study of the Progress and Development of e-Government Research During the Period 2000–2012

Many countries have implemented changes in public-sector management models, based on the strategic and intensive use of new information and communication technologies. From a critical standpoint, this paper analyzes and characterizes the contributions made by research in the field of e-government, identifying future areas of interest and potentially valuable methodologies. In addition, it compares research efforts focused on developing countries with those concerning developed economies, in order to identify research gaps and possibilities for improvement in the context of e-government research in developing countries. Diverse scientometric approaches are employed in this analysis of papers published by international journals listed in the SSCI index in the fields of Public Administration and of Information Science & Library Science. Our findings reveal the existence of various research gaps and highlight areas that should be addressed in future research, especially in developing countries. Indeed, the research approach to e-government remains immature, focusing on particular cases or dimensions, while little has been done to produce theories or models to clarify and explain the political processes of e-government. In addition, significant differences are found between the impact of scientific output and patterns of scientific production as regards developing and developed countries.     

A Comparison of Head Pose and Deictic Pointing Interaction Methods for Smart Environments

Pointing is one of the more meaningful gestures in human expression. In this article, deictic and head pose pointing methods are evaluated as interaction means, with the objective of comparing their efficiency and delivered comfort, usability, and user experience to apply them to the deployment of services in smart spaces. Standard tools and questionnaires are applied in a user study with 20 individuals who have tried a Kinect V2-enabled pointing system to a) complete a multidirectional International Organization for Standardization (ISO) 9241-9 pointing task and b) test a service to command smart objects. The results show that both deictic and head pose interaction perform in a similar way, providing a throughput of 2.14 and 2.04 bits/s, respectively (comparable to joysticks or touchpads in equivalent ISO tasks). The system being quite robust to the user position in the Kinect coverage area, target size influences performance, but also learnability and the overall response when cursor visual cues are not available. From comfort, usability, and user experience responses, it can be said that deictic is perceived to perform better than head pose in terms of accuracy, efficiency, and organization, although head pose is preferred for its speed.     

Randomness on Computable Probability Spaces—A Dynamical Point of View

We extend the notion of randomness (in the version introduced by Schnorr) to computable probability spaces and compare it to a dynamical notion of randomness: typicality. Roughly, a point is typical for some dynamic, if it follows the statistical behavior of the system (Birkhoff’s pointwise ergodic theorem). We prove that a point is Schnorr random if and only if it is typical for every mixing computable dynamics. To prove the result we develop some tools for the theory of computable probability spaces (for example, morphisms) that are expected to have other applications.