In this paper, we investigate a novel application of online planning and scheduling:controlling an automated infeeder for a packaging line of foodand consumer packaged goods. In this system, products arrive continuously at high-speedfrom the end of the production line and need to be arranged into a specific configurationfor downstream primary and secondary packaging machines.In collaboration with a domain expert from the packaging industry,we developed an innovative design for a reconfigurable parallel infeed system usinga matrix of interchangeable smart belts. We also adapted our online model-basedPlantrol planner to this domain. Our planner can control various configurations ofthe new infeed system through simulation both in nominal planning and when runtimefailures occur. We are also building a small physical prototype to validate the newdesign and our software framework.

The increasing volume of information generated on micro-blogging sites such as Twitter raises several challenges to traditional text mining techniques. First, most texts from those sites are abbreviated due to the constraints of limited characters in one post; second, the input usually comes in streams of large-volumes. Therefore, it is of significant importance to develop effective and efficient representations of abbreviated texts for better filtering and mining. In this paper, we introduce a novel transfer learning approach, namely transfer latent semantic learning, that utilizes a large number of related tagged documents with rich information from other sources (source domain) to help build a robust latent semantic space for the abbreviated texts (target domain). This is achieved by simultaneously minimizing the document reconstruction error and the classification error of the labeled examples from the source domain by building a classifier with hinge loss in the latent semantic space. We demonstrate the effectiveness of our method by applying them to the task of classifying and tagging abbreviated texts. Experimental results on both synthetic datasets and real application datasets, including Reuters-21578 and Twitter data, suggest substantial improvements using our approach over existing ones.

In recent years, environmental sustainability has received widespread attention due to continued depletion of natural resources and degradation of the environment. Life cycle assessment (LCA) is a methodology for quantifying multiple environmental impacts of a product, across its entire life cycle — from creation to use to discard. The key object of interest in LCA is the inventory tree, with the desired product as the root node and the materials and processes used across its life cycle as the children. The total impact of the parent in any environmental category is a linear combination of the impacts of the children in that category. LCA has generally been used in "forward: mode: given an inventory tree and impact factors of its children, the task is to compute the impact factors of the root, i.e., the product being modeled. We propose a data mining approach to solve the inverse problem, where the task is to infer inventory trees from a database of environmental factors. This is an important problem with applications in not just understanding what parts and processes constitute a product but also in designing and developing more sustainable alternatives. Our solution methodology is one of feature selection but set in the context of a non-negative least squares problem. It organizes numerous non-negative least squares fits over the impact factor database into a set of pairwise membership relations which are then summarized into candidate trees in turn yielding a consensus tree. We demonstrate the applicability of our approach over real LCA datasets obtained from a large computer manufacturer.

Text and images are two major sources of information in scientific literature. Information from these two media typically reinforce and complement each other, thus simplifying the process for human to extract and comprehend information. However, machines cannot create the links or have the semantic understanding between images and text. We propose to integrate text analysis and image processing techniques to bridge the gap between the two media, and discover knowledge from the combined information sources, which would be otherwise lost by traditional single-media based mining systems. The focus is on the chemical entity extraction task because images are well known to add value to the textual content in chemical literature. Annotation of US chemical patent documents demonstrates the effectiveness of our proposal.

This paper addresses one of the key components of the mining process: the geological prediction of natural resources from spatially distributed measurements. We present a novel approach combining undirected graphical models with ensemble classifiers to provide 3D geological models from multiple sensors installed in an autonomous drill rig. Drill sensor measurements used for drilling automation, known as measurement-while-drilling (MWD) data, have the potential to provide an estimate of the geological properties of the rocks being drilled. The proposed method maps MWD parameters to rock types while considering spatial relationships, i.e., associating measurements obtained from neighboring regions. We use a conditional random field with local information provided by boosted decision trees to jointly reason about the rock categories of neighboring measurements. To validate the approach, MWD data was collected from a drill rig operating at an iron ore mine. Graphical models of the 3D structure present in real data sets possess a high number of nodes, edges and cycles, making them intractable for exact inference. We provide a comparison of three approximate inference methods to calculate the most probable distribution of class labels. The empirical results demonstrate the benefits of spatial modeling through graphical models to improve classification performance.

It is well known that the three water categories science. Methods in biophysics only provide qualitative have different functions. Individually bound water has multiple description of the structure and thus clarifying contacts that stabilize the protein structure. Hydration the collective phenomena of a huge number water has heterogeneous dynamical behavior, contributing to of water molecules is still beyond intuition protein folding, stability and dynamics, and interacting with in biophysics. We introduce a simulation-based the bulk water. Bulk water is free to move and continuously data mining approach that quantitatively model the exchanges with hydration water, and indirectly influences on structure of water surrounding a protein as clusters the protein [Bizzarri and Cannistraro, 2002], [Halle, 2004]. of water molecules having similar moving behavior. Much effort has been devoted to quantitatively model the The paper presents and explains how the advances relative motion (orientation, rotation and velocity) and dynamical of AI technique can potentially solve this properties of individual water molecules in protein challenging data-intensive problem.

In the past few years, sentiment analysis and opinion mining becomes a popular and important task. These studies all assume that their opinion resources are real and trustful. However, they may encounter the faked opinion or opinion spam problem. In this paper, we study this issue in the context of our product review mining system. On product review site, people may write faked reviews, called review spam, to promote their products, or defame their competitors' products. It is important to identify and filter out the review spam. Previous work only focuses on some heuristic rules, such as helpfulness voting, or rating deviation, which limits the performance of this task. In this paper, we exploit machine learning methods to identify review spam. Toward the end, we manually build a spam collection from our crawled reviews. We first analyze the effect of various features in spam identification. We also observe that the review spammer consistently writes spam. This provides us another view to identify review spam: we can identify if the author of the review is spammer. Based on this observation, we provide a two-view semi-supervised method, co-training, to exploit the large amount of unlabeled data. The experiment results show that our proposed method is effective. Our designed machine learning methods achieve significant improvements in comparison to the heuristic baselines.

Multi-task learning remains a difficult yet important problem in machine learning. In Gaussian processes the main challenge is the definition of valid kernels (covariance functions) able to capture the relationships between different tasks. This paper presents a novel methodology to construct valid multi-task covariance functions (Mercer kernels) for Gaussian processes allowing for a combination of kernels with different forms. The method is based on Fourier analysis and is general for arbitrary stationary covariance functions. Analytical solutions for cross covariance terms between popular forms are provided including Mat´ern, squared exponential and sparse covariance functions. Experiments are conducted with both artificial and real datasets demonstrating the benefits of the approach.

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.

With the availability of the huge amounts of data produced by current and future large multi-band photometric surveys, photometric redshifts have become a crucial tool for extragalactic astronomy and cosmology. In this paper we present a novel method, called Weak Gated Experts (WGE), which allows to derive photometric redshifts through a combination of data mining techniques. \noindent The WGE, like many other machine learning techniques, is based on the exploitation of a spectroscopic knowledge base composed by sources for which a spectroscopic value of the redshift is available. This method achieves a variance \sigma^2(\Delta z)=2.3x10^{-4} (\sigma^2(\Delta z) =0.08), where \Delta z = z_{phot} - z_{spec}) for the reconstruction of the photometric redshifts for the optical galaxies from the SDSS and for the optical quasars respectively, while the Root Mean Square (RMS) of the \Delta z variable distributions for the two experiments is respectively equal to 0.021 and 0.35. The WGE provides also a mechanism for the estimation of the accuracy of each photometric redshift. We also present and discuss the catalogs obtained for the optical SDSS galaxies, for the optical candidate quasars extracted from the DR7 SDSS photometric dataset {The sample of SDSS sources on which the accuracy of the reconstruction has been assessed is composed of bright sources, for a subset of which spectroscopic redshifts have been measured.}, and for optical SDSS candidate quasars observed by GALEX in the UV range. The WGE method exploits the new technological paradigm provided by the Virtual Observatory and the emerging field of Astroinformatics.