Guest Editorial: Hybrid intelligent fusion systems

This special issue covers topics related to information fusion in the context of hybrid intelligent systems which are becoming popular due to their capabilities in handling many real world complex problems, involving imprecision, uncertainty, vagueness and high-dimensionality. They provide us with the opportunity to use both, our knowledge and raw data to solve problems in a more interesting and promising way. This multidisciplinary research field is in continuous expansion in the artificial intelligence research community. One of the most promising areas of classifier systems is that of combined classifiers, which is currently the focus of intense research. Information fusion helps to overcome limitations of traditional approaches based on single classifiers thereby opening new areas of research. Accordingly, the current issue presents a survey and five research papers dealing with recent aspects of the hybrid systems where information fusion plays a relevant role. The issue includes comments on one of the articles and the author response to the comments. The special issue starts with the survey article by Wozniak et al., in which the authors introduce along with a short overview about the recent history, the state-of-the-art, and some key research areas of hybrid intelligent systems related to pattern recognition and optimization. Three of the five research papers focus on hybrid classifiers, while the other two are related to data fusion for hybrid modeling. More specifically, the first paper, by Lin and Jiang, presents two new hybrid weighted averaging operators for aggregating crisp and fuzzy information, whose desirable formal properties are studied in depth. Additionally, three special types of preferred centroid of triangular fuzzy number are defined with respect to the proposed operators and two novel decision algorithms are developed. The paper content, thanks to prior-to-print online access, has been the subject of a fruitful discussion which is reflected in the comments by Wang arguing on the non-monotonicity of the aggregation operators, and the reply by Lin providing additional proofs of their monotonic properties. The next paper, by Olatunji et al., also follows a fuzzy approach proposing a combination of type-2 fuzzy logic systems and extreme learning machine to model permeability of carbonate reservoir. The comparative computer experiments show that the hybrid classifier outperforms conventional artificial neural networks and support vector machines for the problem under consideration. Appearing third is the study, by Tsai, which describes a novel hybrid financial distress model based on combining the clustering technique and classifier ensembles. The author uses both techniques to develop different types of bankruptcy prediction models. Up to 21 different models are produced as combination of unsupervised and supervised classification techniques. Tests evaluating prediction accuracy show that the SOM and the traditional MLP provide the best results. In the next article, by Hernández et al., an innovative information fusion process of ecological and remote sensing data is proposed. It is based on spatial interpolation methods to provide high resolution accurate estimations of the Leaf Area Index (LAI) which is a critical input variable for dynamical models of the biomass, based on the modelling of the interactions between the soil, the atmosphere and the vegetation models. The information sources used are the in situ field measurements, the remote sensing images and the altitude data obtained from digital elevation maps. The last research paper, by Kaburlasos and Pachidis, elaborates on the properties of a lattice computing approach based on intervals numbers to deal with disparate data types in a unified framework. Fusion of heterogeneous information sources is achieved on the basis of lattice theoretical formalization. Besides, authors propose an ensemble of fuzzy lattice reasoning classifiers, involving information fusion at the classifier output level in a lattice computing framework. The approach is successfully tested in a real industrial application of beverage brewing control. To conclude we would like to thank Belur V. Dasarathy, Editor-in-Chief of Information Fusion journal, for giving us the opportunity of preparing this special issue. We would also like to thank the reviewers for contributing to this issue with their work and time, and all the authors who submitted papers to the issue

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System

Ontology, Matter and Emergence

“Ontological emergence” of inherent high-level properties with causal powers is witnessed nowhere. A non-substantialist conception of emergence works much better. It allows downward causation, provided our concept of causality is transformed accordingly

Energy efficient wireless sensor network communications based on computational intelligent data fusion for environmental monitoring

The study presents a novel computational intelligence algorithm designed to optimise energy consumption in an environmental monitoring process: specifically, water level measurements in flooded areas. This algorithm aims to obtain a tradeoff between accuracy and power consumption. The implementation constitutes a data aggregation and fusion in itself. A harsh environment can make the direct measurement of flood levels a difficult task. This study proposes a flood level estimation, inferred through the measurement of other common environmental variables. The benefit of this algorithm is tested both with simulations and real experiments conducted in Donñana, a national park in southern Spain where flood level measurements have traditionally been done manually.Junta de Andalucía P07-TIC-0247

TRECVid 2007 experiments at Dublin City University

In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2007. We submitted the following six automatic runs: • F A 1 DCU-TextOnly6: Baseline run using only ASR/MT text features. • F A 1 DCU-ImgBaseline4: Baseline visual expert only run, no ASR/MT used. Made use of query-time generation of retrieval expert coefficients for fusion. • F A 2 DCU-ImgOnlyEnt5: Automatic generation of retrieval expert coefficients for fusion at index time. • F A 2 DCU-imgOnlyEntHigh3: Combination of coefficient generation which combined the coefficients generated by the query-time approach, and the index-time approach, with greater weight given to the index-time coefficient. • F A 2 DCU-imgOnlyEntAuto2: As above, except that greater weight is given to the query-time coefficient that was generated. • F A 2 DCU-autoMixed1: Query-time expert coefficient generation that used both visual and text experts

A minimalistic approach to appearance-based visual SLAM

This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses

Breaking the Mexican Cartels: A Key Homeland Security Challenge for the Next Four Years

Although accurate statistics are hard to come by, it is quite possible that 60,000 people have died in the last six-plus years as a result of armed conflict between the Mexican cartels and the Mexican government, amongst cartels fighting each other, and as a result of cartels targeting citizens. And this figure does not even include the nearly 40,000 Americans who die each year from using illegal drugs, much of which is trafficked through the U.S.-Mexican border. The death toll is only part of the story. The rest includes the terrorist tactics used by cartels to intimidate the Mexican people and government, an emerging point of view that the cartels resemble an insurgency, the threat—both feared and realized—of danger to Americans, and the understated policy approach currently employed by the U.S. government. This short article only scratches the surface by identifying the Mexican Situation as a pressing U.S. homeland security issue requiring a renewed strategic effort by the United States over the next four years. Involving a complex web of foreign policy, law enforcement, intelligence, military, border security, drug consumption and public policy considerations, breaking the Mexican cartels is no easy feat. But it is a necessary one to secure our southern border, eliminate the presence of dangerous cartels in our cities, reduce Americans’ contribution to the drug trade and resulting violence, and play our role in restoring the Mexican citizenry to a society free from daily terror

Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixel Measurement Models

This paper proposes a method for fusing data acquired by a ToF camera and a stereo pair based on a model for depth measurement by ToF cameras which accounts also for depth discontinuity artifacts due to the mixed pixel effect. Such model is exploited within both a ML and a MAP-MRF frameworks for ToF and stereo data fusion. The proposed MAP-MRF framework is characterized by site-dependent range values, a rather important feature since it can be used both to improve the accuracy and to decrease the computational complexity of standard MAP-MRF approaches. This paper, in order to optimize the site dependent global cost function characteristic of the proposed MAP-MRF approach, also introduces an extension to Loopy Belief Propagation which can be used in other contexts. Experimental data validate the proposed ToF measurements model and the effectiveness of the proposed fusion techniques