International Business Machines Corporation

Abhinanda Sarkar

Ashish Verma

Sugata Ghosal

Pradeep Kumar Dubey

Alok Aggarwal

Ashutosh Kulshreshtha

Vipul Bansal

Lakshmi Ramachandran

Manika Kapoor

Arpita Ghosh

The present invention relates to a method, system and computer program product for online negotiations and transactions for electronic commerce spanning international boundaries and includes means for incorporating the effects of the associated uncertainties and risks into decisions related to the assignment of items and the determination of their prices and further means for mitigation of some of these uncertainties and risks. These uncertainties and risk may include those originating from price changes, currency fluctuations, counterparty default, non-conformance to quality and quantity specifications and shipment and payment delays.

Online e-commerce transactions incorporating effects of uncertainty and risk factors

A method of classifying samples to one of a number of predetermined classes involves using a number of class models or classifiers to form order statistic for each classifier. A linear combination of the order statistic (L-statistic) is calculated to determine the confidence of that particular classifier, both in general and for that particular sample. Relative weights are then derived from these confidences, and used to calculate a weighted summation across all classifiers for each class of the likelihoods that a sample belongs to that class. The sample is classified in the class which has the associated weighted summation which is greatest in value.

Decision making in classification problems

A method, system, and computer program product for organizing a set of nodes into a minimum number of connected clusters of bounded size in a wireless transmission system, wherein the method comprises using of bits in packets used in the initial stages of the device discovery procedure, to include information relating to a state of device discovery to achieve the separation of the nodes into those in the transmit-state and the receive-state; defining a Master-designate among the nodes through a statistical procedure and defining remaining nodes as a Slave-designate; defining a cluster including the Master-designate and at least one the Slave-designate, wherein the Slave-designate continuously scans for the inquiry message transmitted from the Master-designate and the Slave-designate transmits the inquiry response to the Master-designate.

Clustering in wireless ad hoc networks

A method of doing meaningful modifications on an image is presented. These modifications can then be used in variety of applications related to image shape manipulation and similar shape retrieval. The method extracts macrofeatures and microfeature from a given shape. Deformations are done on the macrofeatures only. These deformations are either predefined, or are taken from a deformation library, or are calculated from the shape itself, The microfeatures are then added to the deformed macrofeatures to get a deformed shape. The shape deformations then allow user's perception of shape similarity to be learned, which is reflected in the values of parameters in a parameterized shape similarity metric. The user can use one of the deformed shapes as the initial query point, instead of the shape he or she started with. The shape database compression is achieved by storing only the identification of a similar shape and value of global deformations which will generate this shape approximately, instead of storing every shape feature individually.

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New Delhi, India

Abhinanda Sarkar