Abstract: Dynamically controlling the display brightness of a smartphone to reduce power consumption using facial detection via an always-on camera. The smartphone is equipped with a dedicated low-power always-on (AON) camera system that performs facial detection to determine whether the user is actively looking at the screen. When the user is not looking at the screen, the display brightness is automatically lowered to a preset level or by a percentage to reduce display power consumption. When the user's face is subsequently detected, the brightness is increased based on ambient light sensor readings. The AON camera system enables power optimization while also supporting instant face unlock, hands-free convenience features, and enhanced security. By dynamically adjusting the display brightness based on user engagement, battery life is significantly extended without compromising the user experience.

Abstract: A computer-implemented method for web data extraction is provided. The method includes receiving an HTML file containing HTML data and converting the HTML data into an HTML graph. Elements in the HTML file are represented by nodes in the HTML graph and relationships among the elements are represented by meta-paths. The method includes generating feature sets for the nodes in the HTML graph and identifying areas of interest in the HTML graph based on the feature sets of the nodes. The method includes refining the identified areas of interest by segregating sub-structures having recurring patterns or sequences. The method includes extracting data items from the segregated sub-structures, storing the extracted data items and monitoring them over time for any significant updates.

Abstract: A method and a device for creating and training machine learning models is disclosed. In an embodiment, a method for training a machine learning model for identifying entities from data includes creating a first plurality of clusters from a first plurality of data samples in a first dataset and a second plurality of clusters from a second plurality of data samples in a second dataset. The method further includes determining a rank for each of the first plurality of clusters and a rank for each of the second plurality of clusters. The method includes retraining the machine learning model using at least one of the first plurality of clusters weighted based on the rank determined for each of the first plurality of clusters and at least one of the second plurality of clusters weighted based on the rank determined for each of the second plurality of clusters.

Abstract: A method of selecting samples to represent a cluster is disclosed. The method may include receiving one or more clusters by an optimization device. Each of the one or more clusters may include a plurality of samples. The method may determine a count of number of samples to be selected from each of the one or more clusters and may generate an array-based distance matrix for each of the one or more clusters. The method may sort the plurality of samples of the cluster based on a degree of variability of the plurality of samples in the cluster. The sorting may be performed using the array-based distance matrix for each of the one or more clusters. Further, the method may select the determined count of number of samples from the sorted plurality of samples of each of the plurality of clusters to represent the cluster.

Abstract: A method and device for identifying machine learning models for detecting entities is disclosed. The method includes identifying a first entity from within data. A machine learning model trained to identify the first entity is absent in a plurality of machine learning models. The method may include extracting a first set of entity attributes associated with the first entity and matching the first set of entity attributes with each of a plurality of second set of entity attributes. The method may further comprises identifying a second entity from the set of second entities based on the matching. Similarity between a second set of entity attributes associated with the second entity and the first set of entity attributes is above a similarity threshold. The method may include retraining a machine learning model associated with the second entity to identify the first entity based on the first set of entity attributes.

Abstract: A method and a device for creating and training machine learning models is disclosed. In an embodiment, a method for training a machine learning model for identifying entities from data includes creating a first plurality of clusters from a first plurality of data samples in a first dataset and a second plurality of clusters from a second plurality of data samples in a second dataset. The method further includes determining a rank for each of the first plurality of clusters and a rank for each of the second plurality of clusters. The method includes retraining the machine learning model using at least one of the first plurality of clusters weighted based on the rank determined for each of the first plurality of clusters and at least one of the second plurality of clusters weighted based on the rank determined for each of the second plurality of clusters.

Abstract: A transaction card comprises at least one input button that receives a transactional input provided by a cardholder of the transaction card for initiating a secure card-based transaction. The input button is coupled to an electronic chip which when activated generates an electrical signal based on the transactional input. The electrical signal includes encrypted transactional data associated with the transaction card. A signal transmitter that is coupled to the electronic chip receives the electrical signal from the electronic chip, converts the electrical signal to one or more light pulses, and transmits the one or more light pulses to one of a terminal device or a user device for executing the transaction. The transaction is processed by an issuer of the transaction card based on the encrypted transactional data included in the one or more light pulses.

Abstract: A method and a system for managing loyalty points of a user is provided. A plurality of loyalty accounts of the user are identified. Each of the plurality of loyalty accounts stores a first plurality of loyalty points. A conversion factor is determined for each loyalty account. Based on the conversion factor associated with each loyalty account, the first plurality of loyalty points of the corresponding loyalty account are converted to a second plurality of loyalty points. Further, a centralized account of the user is credited with a third plurality of loyalty points. The third plurality of loyalty points include the second plurality of loyalty points associated with each loyalty account. The credited third plurality of loyalty points are redeemable by the user.

Abstract: A computer-implemented method is proposed for determining a new location for a merchant. The method comprises: a) obtaining transaction data for a plurality of cardholders; b) obtaining location data for said cardholders; c) combining the location data with the transaction data; d) analyzing the locations of cardholders performing transactions at a particular merchant or type of merchant to determine any localities within which multiple cardholders performing such transactions are located; and e) assessing such localities on the basis of one or more economic factors to determine whether to recommend said locality as a new location for the merchant or type of merchant.

Abstract: A method and system are proposed for providing recommendations to providers of products in a travel destination, of which products to offer. For a set of consumers for whom travel data indicates that they will in the future travel to the travel destination, transaction level data is used to obtain product preference data which statistically characterizes products the set of consumers prefer. The product preference data is transmitted to product providers in the travel destination in the form of product recommendations. Thus, by offering products according to the recommendations, the product providers can offer products in the travel destination suited to the set of consumers. A particular application is in the case that the product is food, since using the recommendations restaurants can provide dishes matching the tastes of the visitors to the travel destination.