Abstract: Embodiments of the present disclosure provide a face shield for covering a facial region of a subject. The face shield includes a headband to be worn on a head of the subject. The face shield includes a face protector connectable to the headband for covering the facial region of the subject, wherein the face protector including a foldable structure having a plurality of layers. The plurality of layers of the foldable structure are held together for providing multiple layers of coverage for the facial region of the subject.

Abstract: Aspects of the present disclosure relate to proactive intervention in a software application. Embodiments include selecting, by a proactive intervention system related to an application, an event of a plurality of events related to use of the application for processing based on event priorities associated with the plurality of events. Embodiments further include receiving, by the proactive intervention system, contextual information related to the event. Embodiments further include determining, by the proactive intervention system, a proactive intervention based on the event and the contextual information. Embodiments further include determining, by the proactive intervention system, that the proactive intervention can presently be provided based on intervention availability data. Embodiments further include providing, by the proactive intervention system, the proactive intervention via a user interface associated with the application.

Abstract: Methods and systems for crowd level estimation are provided. The system for crowd level estimation includes an input module and a crowd level estimation module. The input module receives an input image of a crowd and the crowd level estimation module estimates a crowd level of the crowd in the input image in response to a crowd density level.

Abstract: A workload of a database may be evaluated to provide a database migration recommendation. A request for a recommendation to migrate a database to a new host may be received. An evaluation of the workload of a client application of the database at a current host may be performed. The migration recommendation may be returned based on the evaluation of the workload of the client application of the database. In some embodiments, the migration recommendation may trigger an automated migration of the database to the new host.

Abstract: A wireless charging device includes a driver unit configured to generate one of a first AC voltage signal having a first frequency and a second AC voltage signal having a second frequency. Also, the wireless charging device includes a transmitting unit having a first coil and a first capacitor and configured to transmit the first AC voltage signal. Further, the transmitting unit includes a second coil and a second capacitor and configured to transmit the second AC voltage signal. Additionally, the wireless charging device includes a control unit configured to detect a first receiver device operating at the first frequency based on a change in a first voltage in the transmitting unit, and detect a second receiver device operating at the second frequency based on a change in a second voltage in the transmitting unit.

Abstract: Conventionally, biometric template protection has been achieved to improve matching performance with high levels of security by use of deep convolution neural network models. However, such attempts have prominent security limitations mapping information of images to binary codes is stored in an unprotected form. Given this model and access to the stolen protected templates, the adversary can exploit the False Accept Rate (FAR) of the system. Secondly, once the server system is compromised all the users need to be re-enrolled again. Unlike conventional systems and approaches, present disclosure provides systems and methods that implement encrypted deep neural network(s) for biometric template protection for enrollment and verification wherein the encrypted deep neural network(s) is utilized for mapping feature vectors to a randomly generated binary code and a deep neural network model learnt is encrypted thus achieving security and privacy for data protection.

Abstract: A vertical cavity surface emitting laser (VCSEL) chip includes a structured light (SL) VCSEL array, and a fill VCSEL array. The SL VCSEL array includes a plurality of first VCSELs on a substrate. The fill VCSEL array includes a plurality of second VCSELs on the substrate. The fill VCSEL array is positioned orthogonal to the SL VCSEL array on the substrate. Light emitted from the SL VCSEL array may be used to form a bar pattern, and light from the SL VCSEL array and the fill VCSEL array together may be used to form flood illumination.

Abstract: An unlock method and system for an air-conditioning unit. The unlock system includes: a display apparatus; an input apparatus; and a control apparatus in communication with the display apparatus and the unlock system is configured to perform the following operations: generating a dynamic graphic according to at least an identification code and an update code; generating an unlock password according to at least a certificate, the identification code and the update code; receiving an unlock password through the input apparatus; comparing the unlock password received from the input apparatus with the generated unlock password; and granting a corresponding permission if the acquired unlock password is consistent with the generated unlock password.

Abstract: A machine learning (ML) model is described herein that predicts computational resource requirements (e.g., a memory and/or runtime metric) for evaluating an integrated circuit (IC) design (e.g., static verification) based on design features extracted from the IC design and auxiliary features related to the IC design. The model may be used to predict the metric for sub-blocks of the IC design. A platform selector may select one of multiple platforms on which to evaluate the IC design or sub-blocks of the IC design based on the predicted metric(s) and specifications of the platforms. The model may be trained to correlate a combination of design features extracted from training IC designs and auxiliary features related to the training IC designs, with metrics of computational resources used in evaluation of the training IC designs, such as with a multiple-linear-regression-based supervised learning technique.

Abstract: In some aspects, a method for object re-identification may include obtaining a first set of images from a first camera, and a second set of images from at least one second camera; determining a first set of features based on the first set of images, the first set of features lying in a first feature space; and determining a second set of features based on the second set of images, the second set of features lying in a second feature space. The method may additionally include determining a first feature projection matrix and a second feature projection matrix that respectively map the first set of features and the second set of features to a shared feature space; and determining a common dictionary based on the shared feature space.

Abstract: A wireless charging device includes a driver unit configured to generate one of a first AC voltage signal having a first frequency and a second AC voltage signal having a second frequency. Also, the wireless charging device includes a transmitting unit having a first coil and a first capacitor and configured to transmit the first AC voltage signal. Further, the transmitting unit includes a second coil and a second capacitor and configured to transmit the second AC voltage signal. Additionally, the wireless charging device includes a control unit configured to detect a first receiver device operating at the first frequency based on a change in a first voltage in the transmitting unit, and detect a second receiver device operating at the second frequency based on a change in a second voltage in the transmitting unit.

Abstract: The present disclosure is directed to a mechanism for optimized application performance in SD-WAN, and includes the steps of receiving initial traffic packets at a first site for transmission to a second site; determining whether a direct tunnel is established between the first site and the second site based on a state of the second site, the state comprising an active state indicating that a direct tunnel is established between the first and second sites or an inactive state indicating that the direct tunnel is not established between the first and second sites; and in response to determining that the direct tunnel is not established, determining that the initial packets satisfy a configured trigger; forwarding the initial packets to the second site via a backup path; establishing the direct tunnel between the first and second sites; and forwarding subsequent traffic packets to the second site via the established direct tunnel.

Abstract: A system and a method for performing programmable analytics on network data are described. A data layer constructs flow behavior information based on information present within headers of data packets flowing across one or more network devices configured in a computer network. An inline heuristics layer performs one or more inline heuristic operations on the flow behavior information to obtain aggregate statistical information. An integrated analytics layer performs one or more analytical operations on the flow behavior information to obtain network insights. A presentation layer filters and plots information obtained from the data layer, the inline heuristics layer, and the integrated analytics layer, based on a user input.

Abstract: A method may include generating views materializing tensors generated by a convolutional neural network operating on an image. Determining the outputs of the convolutional neural network operating on the image with a patch occluding various portions of the image. The outputs being determined by generating queries on the views that performs, based at least on the changes associated with occluding different portions of the image, partial re-computations of the views. A heatmap may be generated based on the outputs of the convolutional neural network. The heatmap may indicate the quantities to which the different portions of the image contribute to the output of the convolutional neural network operating on the image. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: A system for training a machine learning framework to estimate depths of objects captured in 2-D images includes a first trained machine learning network and a second untrained or minimally trained machine learning framework. The first trained machine learning network is configured to analyze 2-D images of target spaces including target objects and to provide output indicative of 3-D positions of the target objects in the target spaces. The second machine learning network can be configured to provide an output responsive to receiving a 2-D input image. A comparator receives the outputs from the first and second machine learning networks based on a particular 2-D image. The comparator compares the output of the first trained machine learning network with the output of the second machine learning network. A feedback mechanism is operative to alter the second machine learning network based at least in part on the output of the comparator.

Abstract: A system for efficiently parsing semi-structured deep packet inspection traffic data tied to a telecommunications entity. The system is capable of parsing such records at million-records-per-second scale through use of a numerical data model, leverage on proven fundamental algebraic techniques, and shortcuts to label streaming traffic on the fly. In some embodiments, the system may perform parallel accumulation of data traffic into business grade counters using elementary techniques and subsequently identify subscribers exhibiting specific data patterns in real time for contextual targeting of promotional offers. A method of efficiently parsing the traffic data via the system of the disclosure.

Abstract: An exhaust aftertreatment system includes a catalyst, an exhaust conduit system, a first sensor, a second sensor, a reductant pump, a dosing module, and a reductant delivery system controller. The exhaust conduit system is coupled to the catalyst. The first sensor is coupled to the exhaust conduit system upstream of the catalyst and configured to obtain a current first measurement upstream of the catalyst. The second sensor is coupled to the exhaust conduit system downstream of the catalyst and configured to obtain a current second measurement downstream of the catalyst. The reductant pump is configured to draw reductant from a reductant source. The dosing module is fluidly coupled to the reductant pump and configured to selectively provide the reductant from the reductant pump into the exhaust conduit system upstream of the catalyst. The reductant delivery system controller is communicable with the first sensor, the second sensor, the reductant pump, and the dosing module.

Abstract: A system for classifying to apply for a microloan by a user is provided. The system includes user device associated with a user, and loan applying user classification system. The loan applying user classification system 106 collects raw data from at least one of (i) one or more programs on the user device directly; (ii) the one or more programs indirectly through the network or (iii) both. The raw data includes mobile brand, screen height, demographic details, mcc, session timestamp, sessionid, session duration, etc. The loan applying user classification system is configured to (i) pre-process the raw data to obtain pre-processed data; (ii) identify representative set of features (i.e. training dataset) from the pre-processed data; (iii) balance an imbalanced training dataset to obtain balanced dataset; and (iv) generate a classification model using balanced dataset to classify in applying for micro-loan by the user.

Abstract: Scheduling work of a machine learning application includes instantiating kernel objects by a computer processor in response to input of kernel definitions. Each kernel object is of a kernel type indicating a compute circuit. The computer processor generates a graph in a memory. Each node represents a task and specifies an assignment of the task to one or more of the kernel objects, and each edge represents a data dependency. Task queues are created in the memory and assigned to queue tasks represented by the nodes. Kernel objects are assigned to the task queues, and the tasks are enqueued by threads executing the kernel objects, based on assignments of the kernel objects to the task queues and assignments of the tasks to the kernel objects. Tasks are dequeued by the threads, and the compute circuits are activated to initiate processing of the dequeued tasks.

Abstract: Systems, methods, and computer products are described herein for identifying data inconsistencies within database tables associated with an application. A master data inconsistency evaluator receives data including at least one selection parameter within at least one database table. By the master data inconsistency evaluator evaluates the at least one selection parameter by comparing the at least one selection parameter with other database tables associated with the application to identify data inconsistencies. The master data inconsistency evaluator repairs the data inconsistencies to further facilitate an error free transaction.