Computing access relevance for path-method generation in OODB and IM-OODB

A path-method (PM) is a mechanism to retrieve or to update information relevant to one class, in an object-oriented database (OODB), that is not stored with that class but with some other class. The PM traverses a chain of classes and connections that ends at the class where the required information is stored. However, it is a difficult task for a user to write PMs. This is because it might require comprehensive knowledge of many classes of the conceptual schema. But a typical user has often incomplete or even inconsistent knowledge of the schema. Currently we are developing a system, called Path-Method Generator (PMG), which generates PMs automatically according to a naive user's requests. One algorithm of PMG uses numerical access relevance between pairs of classes as a guide for the traversal of an OODB schema. In this paper we define the notion of access relevance to measure the significance of the (indirect) connection between any two classes in an OODB and present efficient algorithms to compute access relevance. The manual PM generation in an interoperable multi object-oriented database (IM-OODB) is even more difficult than for one OODB since a user has to be familiar with several OODBs. We use a hierarchical approach for developing efficient online algorithms for the computation of access relevances in an IM-OODB, based on precomputed access relevances for each autonomous OODB. In an IM-OODB the access relevances are used as a guide in generating PMs between the classes of different OODBs.     

Improving the moisture resistance of adobe structures

Mud plasters or soil stuccos are commonly used to protect adobe walls from water erosion. Due to drying shrinkage cracks and high permeability, the commonly used soil stuccos are not durable. Stucco stabilizers such as Portland cement, lime and asphalt are expensive. An experimental study undertaken to evaluate some of the locally available, inexpensive, stucco stabilizers is reported here. Crack-resistant stucco compositions made with a cactus solution are described and techniques to make the stucco surface impervious to water are discussed. The results of durability tests involving simulated rainfall on stucco panels, and stucco applied to adobe walls, are presented. Recommendations for field practice are also given.     

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as—weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases—(i) discovering homogeneous regions, and (ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.     

On simultaneous on-line state and parameter estimation in non-linear state-space models

On-line estimation plays an important role in process control and monitoring. Obtaining a theoretical solution to the simultaneous state-parameter estimation problem for non-linear stochastic systems involves solving complex multi-dimensional integrals that are not amenable to analytical solution. While basic sequential Monte-Carlo (SMC) or particle filtering (PF) algorithms for simultaneous estimation exist, it is well recognized that there is a need for making these on-line algorithms non-degenerate, fast and applicable to processes with missing measurements. To overcome the deficiencies in traditional algorithms, this work proposes a Bayesian approach to on-line state and parameter estimation. Its extension to handle missing data in real-time is also provided. The simultaneous estimation is performed by filtering an extended vector of states and parameters using an adaptive sequential-importance-resampling (SIR) filter with a kernel density estimation method. The approach uses an on-line optimization algorithm based on Kullback–Leibler (KL) divergence to allow adaptation of the SIR filter for combined state-parameter estimation. An optimal tuning rule to control the width of the kernel and the variance of the artificial noise added to the parameters is also proposed. The approach is illustrated through numerical examples.     

Analytic Tangent Irradiance Environment Maps for Anisotropic Surfaces

Environment‐mapped rendering of Lambertian isotropic surfaces is common, and a popular technique is to use a quadratic spherical harmonic expansion. This compact irradiance map representation is widely adopted in interactive applications like video games. However, many materials are anisotropic, and shading is determined by the local tangent direction, rather than the surface normal. Even for visualization and illustration, it is increasingly common to define a tangent vector field, and use anisotropic shading. In this paper, we extend spherical harmonic irradiance maps to anisotropic surfaces, replacing Lambertian reflectance with the diffuse term of the popular Kajiya‐Kay model. We show that there is a direct analogy, with the surface normal replaced by the tangent. Our main contribution is an analytic formula for the diffuse Kajiya‐Kay BRDF in terms of spherical harmonics; this derivation is more complicated than for the standard diffuse lobe. We show that the terms decay even more rapidly than for Lambertian reflectance, going as l^–3, where l is the spherical harmonic order, and with only 6 terms (l = 0 and l = 2) capturing 99.8% of the energy. Existing code for irradiance environment maps can be trivially adapted for real‐time rendering with tangent irradiance maps. We also demonstrate an application to offline rendering of the diffuse component of fibers, using our formula as a control variate for Monte Carlo sampling.     

Unsupervised strategies for shilling detection and robust collaborative filtering

Collaborative filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation by malicious social elements. Lies and Propaganda may be spread by a malicious user who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, malicious user votes and profiles can be injected into a collaborative recommender system. This can significantly affect the robustness of a system or algorithm, as has been studied in previous work. While current detection algorithms are able to use certain characteristics of shilling profiles to detect them, they suffer from low precision, and require a large amount of training data. In this work, we provide an in-depth analysis of shilling profiles and describe new approaches to detect malicious collaborative filtering profiles. In particular, we exploit the similarity structure in shilling user profiles to separate them from normal user profiles using unsupervised dimensionality reduction. We present two detection algorithms; one based on PCA, while the other uses PLSA. Experimental results show a much improved detection precision over existing methods without the usage of additional training time required for supervised approaches. Finally, we present a novel and highly effective robust collaborative filtering algorithm which uses ideas presented in the detection algorithms using principal component analysis.     

Remote sensing and GIS for locating favourable zones of lead-zinc-copper mineralization in Rajpura-Dariba area,Rajasthan, India

This paper reports the results of a pilot study carried out in RajpuraDariba area, Rajasthan, for locating favourable zones of lead-zinc-copper (Pb-Zn-Cu) mineralization using remote sensing, Geographical Information System (GIS) and geostatistical modelling techniques. Remotely sensed data, both aerial and satellite, were used to update the existing geological map. ATLAS GIS software and multivariate geostatistical techniques were used to analyse and integrate different types of geological and geophysical datasets. The Favourability Index (FI) maps prepared during this study show the occurrence of three favourable zones for Pb-Zn-Cu mineralization. They are: (i) around and north of Rawan ka Khera; (ii) isolated spots between Ruppura and Bhupalsagar; and (iii) north of Dhani. Selective geochemical sampling and resistivity profiling carried out in these favourable zones indicated the presence of geochemical anomalies (anomalous concentrations of Zn and Cu) and low/moderate resistivity zones, respectively. Recent drilling carried out by the Department of Mines and Geology (DMG), Rajasthan, at about 2.5 km north of Rawan ka Khera (one of the predicted favourable zones) indicated evidence of Cu mineralization at a depth of about 70 m.     

Seismic strength of adobe masonry

The most important soil characteristies from the viewpoint of the strength of adobe masonry are first studied. Subsequently, based on the acquired knowledge, the effect of some natural additives to the soil is investigated. Simple field tests, devised to identify the most adequate materials for adobe construction and to be easily transmitted to the potential adobe builder are finally proposed.     

Review of anthropometric considerations for tractor seat design

Tractor driving imposes a lot of physical and mental stress upon the operator. If the operator's seat is not comfortable, his work performance may be poor and there is also a possibility of accidents. The optimal design of tractor seat may be achieved by integrating anthropometric data with other technical features of the design. This paper reviews the existing information on the tractor seat design that considers anthropometry and biomechanical factors and gives an approach for seat design based on anthropometric data. The anthropometric dimensions, i.e. popliteal height sitting (5th percentile), hip breadth sitting (95th percentile), buttock popliteal length (5th percentile), interscye breadth (5th and 95th percentile) and sitting acromion height (5th percentile) of agricultural workers need to be taken into consideration for design of seat height, seat pan width, seat pan length, seat backrest width and seat backrest height, respectively, of a tractor. The seat dimensions recommended for tractor operator's comfort based on anthropometric data of 5434 Indian male agricultural workers were as follows: seat height of 380 mm, seat pan width of 420–450 mm, seat backrest width of 380–400 mm (bottom) and 270–290 mm (top), seat pan length of 370±10 mm, seat pan tilt of 5–7° backward and seat backrest height of 350 mm.

Relevance to industry

The approach presented in this paper for tractor seat design based on anthropometric considerations will help the tractor seat designers to develop and introduce seats suiting to the requirements of the user population. This will not only enhance the comfort of the tractor operators but may also help to reduce the occupational health problems of tractor operators.