Abstract: An analytics system jointly predicts values for multiple unobserved individual-level features using aggregate data for those features. Given a dataset, a transformation is applied to individual-level information for the dataset to generate transformed data in a higher dimensional space. Bag-wise mean embeddings are generated using the transformed data. The bag-wise mean embeddings and aggregate data for unobserved individual-level features for the dataset are used to train a model to jointly predict values for the unobserved individual-features for data instances. In particular, a given data instance can be transformed to a representation in a higher dimensional space. Given this representation, the trained model predicts values for the unobserved individual-level features for the data instance, and the data instance can be augmented with the predicted values.

Abstract: The present disclosure provides an end-to-end proctoring system and method for conducting a secure online examination. The system comprises an image capturing device and an audio recording device for capturing and recording a plurality of live face images and a plurality of audio files of one or more users respectively. A processor is programmed to execute one or more module(s) stored in a memory, including, but not limited to, a user face recognition module, an occlusion detection module, a user authentication module, an object detection module, and an audio analytics module. The processor is further configured to control a warning module that may output a notification signal for the one or more module(s) when at least one suspicious activity is determined during the secure online examination. Further, the system with the processor for executing the one or more module(s) is pre-trained on various deep learning-based approaches for conducting a secure online examination.

Abstract: Exemplary methods, apparatuses, and systems include receiving a plurality of read operations. The read operations are divided into a current set of a sequence of read operations and one or more other sets. The size of the current set is a first number of read operations. An aggressor read operation is selected from the current set. A first data integrity scan is performed on a victim of the aggressor and a first indicator of data integrity is determined based on the first data integrity scan. A scaling factor is determined using the indicator of data integrity and a number of program erase cycles for the portion of memory. The set size of read operations is adjusted to a second number of read operations using the scaling factor for a subsequent set.

Abstract: Methods, systems, and apparatuses include generating recovery likelihood metrics for undecodable segments in a stripe of data distributed across a redundant array of storage nodes. The recovery likelihood metrics are based on a determination of a likelihood of recovering the undecodable segment. The undecodable segments are ranked based on the recovery likelihood metrics. The undecodable segments are recovered in an order based on the ranking starting with the undecodable segment with the highest likelihood of recovery.

Abstract: An intake hood system for a heating, ventilation, and/or air conditioning (HVAC) unit includes an intake hood having an inlet configured to receive an air flow into the intake hood, a first support configured to engage a first end of an air filter, and a second support configured to engage a second end of the air filter, where the first support is configured to align with a boundary of the inlet. The intake hood system also includes a lock bar configured to couple to the intake hood while abutting the first support and the second support to secure the air filter within the intake hood.

Abstract: Embodiments of the present invention synchronize multiple synthesizers, such as phase-locked loops (PLLs), in a manner that does not require communication or coordination between the synthesizers. Specifically, each synthesizer is part of a synthesizer circuit that includes a synthesizer (e.g., a PLL), a phase measurement circuit, and a synchronization circuit. A common reference signal (e.g., an alternating clock signal) is provided to the synthesizer circuits. In one exemplary embodiment, in each synthesizer circuit, the phase measurement circuit measures a phase difference between the reference signal and a corresponding output of the synthesizer, and the synchronization circuit adjusts the synthesizer operation based on the measured phase difference in such a way that all of the synthesizers operate in-phase with one another relative to the common reference signal, without having any communication or coordination between the two synthesizer circuits other than provision of the common reference signal.

Abstract: The present disclosure provides a system and method for utilizing an application programming interface (API) for handling multiple requests/models from various users. The API uses a machine learning (ML) model for processing the various requests and producing a plurality of trained models. The system is resilient to a race condition and incorporates an optimized random access memory (RAM) usage. Further, the system efficiently manages the limited available resources by loading and unloading the machine learning models based on their usage, thus maximizing the throughput of the API.

Abstract: A machine may be configured to facilitate partial reductions of an amount by generating and providing a graphical user interface that enables a user to specify one or more partial reductions of the amount, thus defining or otherwise controlling how the amount is to be reduced in stages by such partial reductions. For example, in situations where a total amount is to be reduced in stages by multiple partial reductions, the generated and provided graphical user interface may be operable to specify each stage of reduction by enabling the user to select a corresponding account for each stage of reduction. The corresponding account may be selected from among a set of multiple available accounts, each with a respectively corresponding available amount, any portion or all of which may be available to use in that stage of reducing the total amount.

Abstract: The embodiments set forth techniques for implementing various “prediction engines” that can be configured to provide different kinds of predictions within a mobile computing device. According to some embodiments, each prediction engine can assign itself as an “expert” on one or more “prediction categories” within the mobile computing device. When a software application issues a request for a prediction for a particular category, and two or more prediction engines respond with their respective prediction(s), a “prediction center” can be configured to receive and process the predictions prior to responding to the request. Processing the predictions can involve removing duplicate information that exists across the predictions, sorting the predictions in accordance with confidence levels advertised by the prediction engines, and the like. In this manner, the prediction center can distill multiple predictions down into an optimized prediction and provide the optimized prediction to the software application.

Abstract: The present disclosure relates to a system, a method and a computer-readable tangible non-transitory medium for recommending live streaming data. The method includes identifying a set of live streaming programs according to an attribute of the user and a tag of each live streaming program in the set of live streaming programs, generating a first subset of live streaming programs from the set of live streaming programs according to a first criterion, and generating a second subset of live streaming programs from the set of live streaming programs according to a second criterion. The first criterion is different from the second criterion. The present disclosure can recommend contents in a more efficient way and increase user engagements.

Abstract: Methods, systems, and computer program products for context-based response generation are provided herein. A method includes: obtaining conversation logs comprising agent responses matched to contexts and a set of designated responses that are not matched to the contexts; replacing at least a portion of the agent responses with the designated responses to form modified conversation logs; training a first model, using the modified conversation logs, to output a designated response in the set for a given context and a second model, using the historical conversation logs, to output one of the agent responses for a given context; identifying one or more new responses based at least in part on the output of the second machine learning model for a particular one of the contexts; and retraining the first machine learning model based at least in part on the one or more new responses.

Abstract: A two-dimensional image segment that includes an outline of an object can be determined in a top-down fisheye image. A six degree of freedom (DoF) pose for the object can be determined based on determining a three-dimensional bounding box determined by one or more of (1) an axis of the two-dimensional image segment in a ground plane included in the top-down fisheye image and a three-dimensional model of the object and (2) inputting the two-dimensional image segment to a deep neural network trained to determine a three-dimensional bounding box for the object.

Abstract: Currently, spatial reuse (SR) is a feature used indiscriminately for/across all traffic classes. However, systems and methods are provided for increasing the reliability of voice traffic and Transmission Control Protocol (TCP) Acknowledgement (ACK) messaging. The result is improved quality of voice calls, and enhanced network capacity. When the length of a data section of a packet suggests that subsequent packets are associated with voice traffic or TCP ACKs, such packets are not transmitted when another transmission(s) is occurring under spatial reuse. When a determination is made that the length of a data section of the packet suggests non-voice traffic or TCP ACKs, they can be transmitted simultaneously with other transmissions occurring under spatial reuse.

Abstract: Embodiments of the disclosure provide methods and electronic devices comprising a work function layer comprising a material that forms a weak silicide. The electronic devices comprise a silicon layer with the work function layer thereon and a metal contact on the work function layer.

Abstract: Embodiments of the disclosure provide methods and electronic devices comprising a work function layer comprising a material that forms a conductive oxide with or without titanium. The electronic devices comprise a silicon layer with the work function layer thereon and a metal contact on the work function layer.

Abstract: A method and apparatus for providing Web advertisements to online users is disclosed. A balanced set of negative data points and positive data points is derived from a log of Ad impressions and used to train a classifier. In response to an Ad request signal, a plurality of Ads is retrieved from a database. The Ad request signal indicates a request to provide an Ad for a slot available on a Web page associated with a website. The signal is provided in relation to an access of the Web page by an online user and includes information related to the online user. A choice of an Ad is predicted based on the information related to the online user and the plurality of Ads. The Ad is provided to a Web server to cause display of the Ad on the slot when the Web page is displayed to the online user.

Abstract: A request to retrieve data from a memory device of a memory sub-system can be received from a machine learning (ML) framework executing on a host system, where the data comprises a plurality of logical partitions. A set of parallel I/O threads can be initiated to retrieve the data from the memory device, where each I/O thread of the set of parallel I/O threads retrieves a different portion of the data from a different corresponding logical partition and stores the different portion of the data in a I/O buffer of a set of I/O buffers corresponding to the set of I/O threads in parallel. The different portion of the data can be successively provided from each I/O buffer to the ML framework, where the set of parallel I/O threads is to continually retrieve the data from the memory device until all of the data from the logical partitions has been provided to the ML framework.

Abstract: In order to avoid writing duplicates of blocks of data into a storage platform, any virtual disk within the storage platform may have a de-duplication feature enabled. Or, all virtual disks have this feature enabled. For virtual disks with de-duplication enabled, a unique message digest is calculated for every block of data written to that virtual disk. Upon a write, these message digests are consulted in order to determine if a particular block of data has already been written, if so, it is not written again, and if not, it is written. All de-duplication virtual disks are written to a single system virtual disk within the storage platform. De-duplication occurs over the entire storage platform and over all its virtual disks because all message digests are consulted before a write is performed for any virtual disk. A read for a de-duplication virtual desk reads from the system virtual disk.

Abstract: Provided are systems, methods, and computer-readable medium for a simulation platform that can generate simulated activity data for testing a security monitoring and control system. In various examples, the simulation platform can parse the activity data from a cloud service to generate a template, where each entry in the template describes an action and the fields associated with the action. The simulation platform can further generate a configuration that describes a test scenario. The simulation platform can use the configuration and the template to generate the particular action, including randomizing some or all of the fields of the action. When input into the security monitoring and control system, the system can operate on the simulated activity data in the same way as when the system ingests live activity data.