Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.

Is the mineral borickyite (Ca,Mg)(Fe3+,Al)4(PO4,SO4,CO3)(OH)8•3-7.5H2O the same as delvauxite CaFe3+4(PO4,SO4)2(OH)8•4-6H2O?

The two minerals borickyite and delvauxite CaFe3+4(PO4,SO4)2(OH)8•4-6H2O have the same formula. Are the minerals identical or different? The minerals borickyite and delvauxite have been characterised by Raman spectroscopy. The minerals are related to the minerals diadochite and destinezite. Both minerals are amorphous. Delvauxite appears to vary in crystallinity from amorphous to semi-crystalline. The minerals are often X-ray non-diffracting. The minerals are found in soils and may be described as ‘colloidal’ minerals. Vibrational spectroscopy enables an assessment of the molecular structure of borickyite and delvauxite. Bands are assigned to phosphate and sulphate stretching and bending modes. Multiple water bending and stretching modes imply that non-equivalent water molecules in the structure exist with different hydrogen bond strengths. The two minerals show differing spectra and must be considered as different minerals.

Wind-energy based path planning for electric unmanned aerial vehicles using Markov Decision Processes

Exploiting wind-energy is one possible way to ex- tend flight duration for Unmanned Arial Vehicles. Wind-energy can also be used to minimise energy consumption for a planned path. In this paper, we consider uncertain time-varying wind fields and plan a path through them. A Gaussian distribution is used to determine uncertainty in the Time-varying wind fields. We use Markov Decision Process to plan a path based upon the uncertainty of Gaussian distribution. Simulation results that compare the direct line of flight between start and target point and our planned path for energy consumption and time of travel are presented. The result is a robust path using the most visited cell while sampling the Gaussian distribution of the wind field in each cell.

Functionalised zinc oxide nanowire gas sensors : enhanced NO2 gas sensor response by chemical modification of nanowire surfaces

Surface coating with an organic self-assembled monolayer (SAM) can enhance surface reactions or the absorption of specific gases and hence improve the response of a metal oxide (MOx) sensor toward particular target gases in the environment. In this study the effect of an adsorbed organic layer on the dynamic response of zinc oxide nanowire gas sensors was investigated. The effect of ZnO surface functionalisation by two different organic molecules, tris(hydroxymethyl)aminomethane (THMA) and dodecanethiol (DT), was studied. The response towards ammonia, nitrous oxide and nitrogen dioxide was investigated for three sensor configurations, namely pure ZnO nanowires, organic-coated ZnO nanowires and ZnO nanowires covered with a sparse layer of organic-coated ZnO nanoparticles. Exposure of the nanowire sensors to the oxidising gas NO2 produced a significant and reproducible response. ZnO and THMA-coated ZnO nanowire sensors both readily detected NO2 down to a concentration in the very low ppm range. Notably, the THMA-coated nanowires consistently displayed a small, enhanced response to NO2 compared to uncoated ZnO nanowire sensors. At the lower concentration levels tested, ZnO nanowire sensors that were coated with THMA-capped ZnO nanoparticles were found to exhibit the greatest enhanced response. ΔR/R was two times greater than that for the as-prepared ZnO nanowire sensors. It is proposed that the ΔR/R enhancement in this case originates from the changes induced in the depletion-layer width of the ZnO nanoparticles that bridge ZnO nanowires resulting from THMA ligand binding to the surface of the particle coating. The heightened response and selectivity to the NO2 target are positive results arising from the coating of these ZnO nanowire sensors with organic-SAM-functionalised ZnO nanoparticles.

The influence of motorcyclist behavior in crash involvement

Introduction: In Singapore, motorcycle crashes account for 50% of traffic fatalities and 53% of injuries. While extensive research efforts have been devoted to improve the motorcycle safety, the relationship between the rider behavior and the crash risk is still not well understood. The objective of this study is to evaluate how behavioral factors influence crash risk and to identify the most vulnerable group of motorcyclists. Methods: To explore the rider behavior, a 61-item questionnaire examining sensation seeking (Zuckerman et al., 1978), impulsiveness (Eysenck et al., 1985), aggressiveness (Buss & Perry, 1992), and risk-taking behavior (Weber et al., 2002) was developed. A total of 240 respondents with at least one year riding experience form the sample that relate behavior to their crash history, traffic penalty awareness, and demographic characteristics. By clustering the crash risk using the medoid portioning algorithm, the log-linear model relating the rider behavior to crash risk was developed. Results and Discussions: Crash-involved motorcyclists scored higher in impulsive sensation seeking, aggression and risk-taking behavior. Aggressive and high risk-taking motorcyclists were respectively 1.30 and 2.21 times more likely to fall under the high crash involvement group while impulsive sensation seeking was not found to be significant. Based on the scores on risk-taking and aggression, the motorcyclists were clustered into four distinct personality combinations namely, extrovert (aggressive, impulsive risk-takers), leader (cautious, aggressive risk-takers), follower (agreeable, ignorant risk-takers), and introvert (self-consciousness, fainthearted risk-takers). “Extrovert” motorcyclists were most prone to crashes, being 3.34 times more likely to involve in crash and 8.29 times more vulnerable than the “introvert”. Mediating factors like demographic characteristics, riding experience, and traffic penalty awareness were found not to be significant in reducing crash risk. Conclusion: The findings of this study will be useful for road safety campaign planners to be more focused in the target group as well as those who employ motorcyclists for their delivery business.

Vibrational spectroscopic study of the mineral creaseyite Cu2Pb2(Fe,Al)2(Si5O17)·6H2O – a zeolite mineral?

Vibrational spectroscopy has been used to characterise the mineral creaseyite Cu2Pb2(Fe,Al)2(Si5O17)·6H2O. The mineral is found in the oxidised zone of base metal deposits and interestingly is associated with copper silicate minerals including ajoite, kinoite, chrysocolla as well as wulfenite, willemite, mimetite and wickenburgite. Creaseyite is a mineral with zeolitic properties. A Raman band at 998 cm−1 is assigned to the SiO stretching vibration of SiO3 units. The Raman band at 1071 cm−1 is assigned to the SiO stretching vibrations of the Si2O5 units. Raman bands are found at 2750, 2902, 3162, 3470 and 3525 cm−1. The band at 3525 cm−1 is attributed to zeolitic water. Other bands are assigned to water coordinated to the metal cations. Vibrational spectroscopy enables aspects of the molecular structure of creaseyite to be determined.

Reply to the discussion by Karen Johannesson on “Rare earth element geochemistry of scleractinian coral skeleton during meteoric diagenesis: a sequence through neomorphism of aragonite to calcite” by Webb et al., Sedimentology, 56, 1433-1463

Webb et al. (2009) described a late Pleistocenecoral sample wherein the diagenetic stabilization of original coral aragonite to meteoric calcite was halted more or less mid-way through the process, allowing direct comparison of pre-diagenetic and post-diagenetic microstructure and trace element distributions. Those authors found that the rare earth elements (REEs) were relatively stable during meteoric diagenesis, unlike divalent cations such as Sr,and it was thus concluded that original, in this case marine, REE distributions potentially could be preserved through the meteoric carbonate stabilization process that must have affected many, if not most, ancient limestones. Although this was not the case in the analysed sample, they noted that where such diagenesis took place in laterally transported groundwater, trace elements derived from that groundwater could be incorporated into diagenetic calcite, thus altering the initial REE distribution (Banner et al., 1988). Hence, the paper was concerned with the diagenetic behaviour of REEs in a groundwater-dominated karst system. The comment offered by Johannesson (2011) does not question those research results, but rather, seeks to clarify an interpretation made by Webb et al. (2009) of an earlier paper, Johannesson et al. (2006).

Fusion of morphological images for airborne target detection

Several track-before-detection approaches for image based aircraft detection have recently been examined in an important automated aircraft collision detection application. A particularly popular approach is a two stage processing paradigm which involves: a morphological spatial filter stage (which aims to emphasize the visual characteristics of targets) followed by a temporal or track filter stage (which aims to emphasize the temporal characteristics of targets). In this paper, we proposed new spot detection techniques for this two stage processing paradigm that fuse together raw and morphological images or fuse together various different morphological images (we call these approaches morphological reinforcement). On the basis of flight test data, the proposed morphological reinforcement operations are shown to offer superior signal to-noise characteristics when compared to standard spatial filter options (such as the close-minus-open and adaptive contour morphological operations). However, system operation characterised curves, which examine detection verses false alarm characteristics after both processing stages, illustrate that system performance is very data dependent.

HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection

The quick detection of abrupt (unknown) parameter changes in an observed hidden Markov model (HMM) is important in several applications. Motivated by the recent application of relative entropy concepts in the robust sequential change detection problem (and the related model selection problem), this paper proposes a sequential unknown change detection algorithm based on a relative entropy based HMM parameter estimator. Our proposed approach is able to overcome the lack of knowledge of post-change parameters, and is illustrated to have similar performance to the popular cumulative sum (CUSUM) algorithm (which requires knowledge of the post-change parameter values) when examined, on both simulated and real data, in a vision-based aircraft manoeuvre detection problem.

Sensitivity of the NMR density matrix to pulse sequence parameters : a simplified analytic approach

We present a formalism for the analysis of sensitivity of nuclear magnetic resonance pulse sequences to variations of pulse sequence parameters, such as radiofrequency pulses, gradient pulses or evolution delays. The formalism enables the calculation of compact, analytic expressions for the derivatives of the density matrix and the observed signal with respect to the parameters varied. The analysis is based on two constructs computed in the course of modified density-matrix simulations: the error interrogation operators and error commutators. The approach presented is consequently named the Error Commutator Formalism (ECF). It is used to evaluate the sensitivity of the density matrix to parameter variation based on the simulations carried out for the ideal parameters, obviating the need for finite-difference calculations of signal errors. The ECF analysis therefore carries a computational cost comparable to a single density-matrix or product-operator simulation. Its application is illustrated using a number of examples from basic NMR spectroscopy. We show that the strength of the ECF is its ability to provide analytic insights into the propagation of errors through pulse sequences and the behaviour of signal errors under phase cycling. Furthermore, the approach is algorithmic and easily amenable to implementation in the form of a programming code. It is envisaged that it could be incorporated into standard NMR product-operator simulation packages.