The Role of a Natural Language Conversational Interface in Online Sales: A Case Study

This paper describes the evaluation of a natural language dialog-based navigation system (HappyAssistant) that helps users access e-commerce sites to find relevant information about products and services. The prototype system leverages technologies in natural language processing and human-computer interaction to create a faster and more intuitive way of interacting with websites, especially for less experienced users. The result of a comparative study shows that users prefer the natural language-enabled navigation two to one over the menu driven navigation. In addition, the study confirmed the efficiency of using natural language dialog in terms of the number of clicks and the amount of time required to obtain the relevant information. In the case study, as compared to the menu driven system, the average number of clicks used in the natural language system was reduced by 63.2% and the average time was reduced by 33.3%.     

Relationship between Hazen–William and Colebrook–White Roughness Values

Despite advances in computing technology and derivation of explicit approximation formulas, the experimentally verified and widely applicable Colebrook–White friction factor formula is often rejected in favor of the limited and less accurate Hazen–Williams equation. The general reluctance of practicing engineers to embrace the Colebrook–White formula may be due to the relatively large available database for Hazen–Williams C coefficient values versus a relatively small database of the equivalent sand roughness ks values required by the Colebrook–White equation. Until now, converting C to ks required knowledge of both the Reynolds number and pipe diameter originally used to determine C. The current effort derives implicit equations relating C to ks that do not require additional information and compare well with published data. The exact solution is approximated with a single explicit equation, accurate to within 4% error.     

A topic-based term frequency normalization framework to enhance probabilistic information retrieval

Many well-known probabilistic information retrieval models have shown promise for use in document ranking, especially BM25. Nevertheless, it is observed that the control parameters in BM25 usually need to be adjusted to achieve improved performance on different data sets; additionally, the assumption in BM25 on the bag-of-words model prevents its direct utilization of rich information that lies at the sentence or document level. Inspired by the above challenges with respect to BM25, we first propose a new normalization method on the term frequency in BM25 (called BM25_QL in this paper); in addition, the method is incorporated into CRTER₂, a recent BM25-based model, to construct CRTER₂^QL. Then, we incorporate topic modeling and word embedding into BM25 to relax the assumption of the bag-of-words model. In this direction, we propose a topic-based retrieval model, TopTF, for BM25, which is then further incorporated into the language model (LM) and the multiple aspect term frequency (MATF) model. Furthermore, an enhanced topic-based term frequency normalization framework, ETopTF, based on embedding is presented. Experimental studies demonstrate the great effectiveness and performance of these methods. Specifically, on all tested data sets and in terms of the mean average precision (MAP), our proposed models, BM25_QL and CRTER₂^QL, are comparable to BM25 and CRTER₂ with the best b parameter value; the TopTF models significantly outperform the baselines, and the ETopTF models could further improve the TopTF in terms of the MAP.     

Polymer electrolyte membranes from fluorinated polyisoprene-block-sulfonated polystyrene: Microdomain orientation by external field

In this study, block copolymer ionomers of the cesium salt (20 mol %) of fluorinated polyisoprene-block-sulfonated polystyrene were spin cast into membranes and annealed under an electric field of ∼40 V/μm at 130 °C for 24 h. The effect of this treatment was a 2.5 times increase in the ionic conductivity as measured by electrochemical impedance spectroscopy, under all humidity conditions measured. This can be attributed to the increased connectivity of the ionic domains of the block copolymers. This E-field alignment technique may thus find application in the fabrication of nanostructured polyelectrolytes with enhanced charge transport capacity.     

Mining of High-Utility Patterns in Big IoT-based Databases

When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors critically, which gives HUIM its intrinsic edge. Due to the flourishing development of the IoT technique, the uncertainty patterns mining is also attractive. Potential high-utility itemset mining (PHUIM) is introduced to reveal valuable patterns in an uncertainty database. Unfortunately, even though the previous methods are all very effective and powerful to mine, the potential high-utility itemsets quickly. These algorithms are not specifically designed for a database with an enormous number of records. In the previous methods, uncertainty transaction datasets would be load in the memory ultimately. Usually, several pre-defined operators would be applied to modify the original dataset to reduce the seeking time for scanning the data. However, it is impracticable to apply the same way in a big-data dataset. In this work, a dataset is assumed to be too big to be loaded directly into memory and be duplicated or modified; then, a MapReduce framework is proposed that can be used to handle these types of situations. One of our main objectives is to attempt to reduce the frequency of dataset scans while still maximizing the parallelization of all processes. Through in-depth experimental results, the proposed Hadoop algorithm is shown to perform strongly to mine all of the potential high-utility itemsets in a big-data dataset and shows excellent performance in a Hadoop computing cluster.

     

Model for Optimal Operation of Water Distribution Pumps with Uncertain Demand Patterns

An optimization model is presented for pump operation based upon minimizing operation costs and indirectly the maintenance costs of pumps considering uncertainty of specified demand (load) curves. The purpose of this model is to determine pump operation to meet the uncertain demands as well as to satisfy the pressure requirements in the water distribution system. In addition, constraints on the number of pump (‘on-off’) switches are included as a surrogate to indirectly minimizing the maintenance costs. This model is a mixed integer nonlinear programming (MINLP) problem using a chance constraint formulation of the uncertain demand constraint. The optimization model was solved using the LocalSolver option in A Mathematical Programming Language (AMPL). The model was first applied to the operation of an example pumping system for an urban water distribution system (WDS) illustrating a reduction in operation costs using the optimization model. The optimization model with the chance-constraint for demand was applied for a range of demand satisfaction uncertainties. A decrease in the operation costs was observed with an increased uncertainty in demand satisfaction, which shows that the model further optimizes the operations considering the relaxed constraints. Model application could be extended to operations of pumping systems during emergencies and contingencies such as droughts, component failures etc.     

Passage extraction and result combination for genomics information retrieval

In this paper, we first propose algorithms for passage extraction to build indices for the purpose of generating more accurate passages as query answers. Second, we propose a basic result combination method and an improved result combination method to combine the retrieved results from different indices for the purpose of selecting and merging relevant passages as outputs. For passage extraction, three new algorithms are proposed, namely paragraphParsed, sentenceParsed and wordSentenceParsed. For result combination, a novel method is proposed, in which we use factor analysis to generate a better baseline result for combination by finding some hidden common factors that can be used to estimate the importance of keywords and keyword associations. Finally, we report the experimental results that confirm the effectiveness and superiority of the factor analysis based method for result combination. Our proposed approaches achieve excellent results on the TREC 2006 and 2007 Genomics data sets, which provide a promising avenue for constructing high performance information retrieval systems in biomedicine.