Image Compression and Video Segmentation Using Hierarchical Self-Organization

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model.     

WHAD: Wikipedia historical attributes data

This paper describes the generation of temporally anchored infobox attribute data from the Wikipedia history of revisions. By mining (attribute, value) pairs from the revision history of the English Wikipedia we are able to collect a comprehensive knowledge base that contains data on how attributes change over time. When dealing with the Wikipedia edit history, vandalic and erroneous edits are a concern for data quality. We present a study of vandalism identification in Wikipedia edits that uses only features from the infoboxes, and show that we can obtain, on this dataset, an accuracy comparable to a state-of-the-art vandalism identification method that is based on the whole article. Finally, we discuss different characteristics of the extracted dataset, which we make available for further study.     

Semi-parametric nonlinear regression and transformation using functional networks

Functional networks are used to solve some nonlinear regression problems. One particular problem is how to find the optimal transformations of the response and/or the explanatory variables and obtain the best possible functional relation between the response and predictor variables. After a brief introduction to functional networks, two specific transformation models based on functional networks are proposed. Unlike in neural networks, where the selection of the network topology is arbitrary, the selection of the initial topology of a functional network is problem driven. This important feature of functional networks is illustrated for each of the two proposed models. An equivalent, but simpler network may be obtained from the initial topology using functional equations. The resultant model is then checked for uniqueness of representation. When the functions specified by the transformations are unknown in form, families of linear independent functions are used as approximations. Two different parametric criteria are used for learning these functions: the constrained least squares and the maximum canonical correlation. Model selection criteria are used to avoid the problem of overfitting. Finally, performance of the proposed method are assessed and compared to other methods using a simulation study as well as several real-life data.     

On the simulation,economic analysis,and life cycle assessment of batch-mode organic solvent recovery alternatives for the pharmaceutical industry

This study explores design alternatives for the purification and possible reuse of low-volume organic solvents waste streams in pharmaceutical manufacturing. Solvent use has a large impact on the life cycle of pharmaceutical processes, as typically 80–90% of the total mass used in the production of an active pharmaceutical ingredient is attributed to solvent use. Solvents are not consumed in pharmaceutical processes so they exit the process as waste. These waste streams are usually disposed of using incineration; therefore, generating significant life cycle emissions from disposal and replacement of virgin solvent. Solvent recovery efforts are generally limited to large-volume waste streams. However, results show that solvents in low-volume streams can also be economically recovered using a versatile multi-campaign solvent recovery skid. Three different solvent waste streams were evaluated, and 85.3% reduction in total emissions and 86.3% reduction in operating costs were achieved. Investment in the solvent recovery system was determined to have a payback period of 4.5 years and a 28% IRR over 10 years. A life cycle impact assessment shows impacts on the human health, ecosystems, and resources categories have been reduced by 82.4, 85.1, and 87.1%, respectively.     

Technologies and logistics for phosphorus recovery from livestock waste

Phosphorus (P) runoff from livestock waste can trigger algal blooms that adversely affect aquatic life and human health. One strategy to mitigate this problem is to install nutrient recovery technologies that concentrate and mobilize nutrients from nutrient-rich regions to nutrient-deficient ones. We present supply chain design formulations to identify optimal types and locations for P recovery technologies. The formulations capture trade-offs in transportation costs, technology efficiency, investment/operational costs, revenue collected from different recovered products (struvite and nutrient cakes), and environmental impact. Our computational framework is used to analyze the impact of different scenarios for market prices of recovered products, recovery yields, and remediation costs. We find that transportation of waste alone (without any processing) can achieve significant reductions in environmental impact at low cost, but cannot achieve economic sustainability in the long run due to the lack of direct revenue streams. Mechanical separation technologies that recover P in the form of nutrient cakes are low-cost solutions that can achieve high environmental benefits and reduced transportation costs, but revenues are also limited due to low values of the cakes. Struvite crystallization in fluidized beds is found to be a highly attractive option under current struvite prices, but economic sustainability is strongly dependent on yield recoveries (which are currently highly uncertain).     

Evolutionary Learning of Dynamic Naive Bayesian Classifiers

Many problems such as voice recognition, speech recognition and many other tasks have been tackled with Hidden Markov Models (HMMs). These problems can also be dealt with an extension of the Naive Bayesian Classifier (NBC) known as Dynamic NBC (DNBC). From a dynamic Bayesian network (DBN) perspective, in a DNBC at each time there is a NBC. NBCs work well in data sets with independent attributes. However, they perform poorly when the attributes are dependent or when there are one or more irrelevant attributes which are dependent of some relevant ones. Therefore, to increase this classifier accuracy, we need a method to design network structures that can capture the dependencies and get rid of irrelevant attributes. Furthermore, when we deal with dynamical processes there are temporal relations that should be considered in the network design. In order to learn automatically these models from data and increase the classifier accuracy we propose an evolutionary optimization algorithm to solve this design problem. We introduce a new encoding scheme and new genetic operators which are natural extensions of previously proposed encoding and operators for grouping problems. The design methodology is applied to solve the recognition problem for nine hand gestures. Experimental results show that the evolved network has higher average classification accuracy than the basic DNBC and a HMM.     

Inductive transfer for learning Bayesian networks

In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with different conditions; or in industrial diagnosis, where there is equipment with similar specifications. In these cases it is common to have plenty of data for some scenarios but very little for others. In order to learn accurate models for rare cases, it is desirable to use data and knowledge from similar cases; a technique known as transfer learning. In this paper we propose an inductive transfer learning method for Bayesian networks, that considers both structure and parameter learning. For structure learning we use conditional independence tests, by combining measures from the target task with those obtained from one or more auxiliary tasks, using a novel weighted sum of the conditional independence measures. For parameter learning, we propose two variants of the linear pool for probability aggregation, combining the probability estimates from the target task with those from the auxiliary tasks. To validate our approach, we used three Bayesian networks models that are commonly used for evaluating learning techniques, and generated variants of each model by changing the structure as well as the parameters. We then learned one of the variants with a small dataset and combined it with information from the other variants. The experimental results show a significant improvement in terms of structure and parameters when we transfer knowledge from similar tasks. We also evaluated the method with real-world data from a manufacturing process considering several products, obtaining an improvement in terms of log-likelihood between the data and the model when we do transfer learning from related products.     

A Survey of Airworthiness and Certification for UAS

The Italian SMAT project is a civilian program supported by Piemonte Region developed for the territorial monitoring and surveillance and for the prevention of natural hazards. In a different way from the previous surveillance programs, the SMAT project is based on the use of several unmanned platforms that are able to cooperate through ground coordination and control station. The flight in non-segregated airspace, needed in order to accomplish the territorial monitoring mission, is a main issue to be investigated and defined, because of the current lack of a common consensus and agreement on the regulations of UAS. The present work outlines the international initiatives that deal with the development of the regulatory framework on airworthiness and certification of UAS and their potential impact on the SMAT project.