Intel Corporation

Apple Inc.

Intel IP Corporation

Yi Gai

Jing Zhu

Chih-Yuan Yang

Rath Vannithamby

Ravi L Sahita

Xiaoning Li

Abhay Kanhere

Mingwei Zhang

Aravind Subramanian

Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency. An example method disclosed herein includes retrieving a log file in a first file format, the log file containing feature occurrence data, generating a first unit operation based on the first file format to extract the feature occurrence data from the log file to a string, the first unit operation associated with a first metadata tag, generating second unit operations to identify respective features from the feature occurrence data, the second unit operations associated with respective second metadata tags, and generating a first sequence of the first metadata tag and the second metadata tags to create a first vector output file of the feature occurrence data.

Methods and apparatus to improve feature engineering efficiency with metadata unit operations

Methods, apparatus, systems and articles of manufacture are disclosed to facilitate electronic data security. An example apparatus includes a data storage including a memory adjusted to store data organized according to a data table including columns identifying a first data record and a first security tag associated with the first data record. In the example apparatus, retrieval of data from the data storage involves a bit operation comparing the first security tag with a first privilege tag. In the example apparatus, the data storage provides the first data record when the bit operation comparing the first security tag with the first privilege tag has a non-zero result, and the data storage does not provide the first data record when the bit operation comparing the first security tag with the first privilege tag has a zero result.

Methods and apparatus to provide group-based row-level security for big data platforms

A malicious object detection system for use in managed runtime environments includes a check circuit to receive call information generated by an application, such as an Android application. A machine learning circuit coupled to the check circuit applies a machine learning model to assess the information and/or data included in the call and detect the presence of a malicious object, such as malware or a virus, in the application generating the call. The machine learning model may include a global machine learning model distributed across a number of devices, a local machine learning model based on use patterns of a particular device, or combinations thereof. A graphical user interface management circuit halts execution of applications containing malicious objects and generates a user perceptible output.

Malicious object detection in a runtime environment

Technology for switching from a wireless local area network (WLAN) to a wireless wide area network (WWAN) is disclosed. A multi-radio access technology (multi-RAT) user equipment (UE) can receive WLAN-specific dedicated physical random access channel (PRACH) allocation information from an evolved node B (eNB) to enable the multi-RAT UE to perform an inter-RAT WLAN-to-WWAN handover. The multi-RAT UE can initiate the inter-RAT WLAN-to-WWAN handover at the multi-RAT UE by performing random access with the eNB using the WLAN-specific dedicated PRACH allocation information.

Random access procedure for handover

Systems and methods to support intra-application flow prioritization are disclosed herein. User equipment (UE) may be configured to communicatively couple to an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Node B (eNB). The eNB may transmit packets from the UE to an evolved packet core (EPC), which may transmit schedule packets to an application function (AF) via a network. The AF may provide classification information and prioritization information for a plurality of intra-application flows transmitted between the AF and the UE. The EPC may classify uplink and/or downlink traffic into the intra-application flows and mark and/or schedule the traffic based on the prioritization information. Absolute and/or modular length, payload values, and/or packet type may be used to classify the traffic into the intra-application flows.

Systems, methods, and devices to support intra-application flow prioritization

Embodiments of the present disclosure describe systems and methods for user equipment (UE)-initiated reporting of congestion information. Various embodiments may include systems and methods for reporting congestion information to an evolved node B (eNB) by UEs. In embodiments, the congestion information may be utilized in managing access requests made by the UEs. Other embodiments may be described and/or claimed.

Techniques and configurations associated with user equipment-initiated congestion reporting

Systems and methods to support intra-quality of service (QoS) class identifier (QCI) QoS-aware scheduling are disclosed herein. User equipment (UE) may be configured to communicatively couple to an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Node B (eNB). The eNB may schedule packets within a QCI according to information provided to the eNB by the UE. Packets in a QCI may be classified into one or more flows using the information. The flows may be prioritized based on which are most important to the UE. The UE may provide QoS parameters for each flow. The eNB may be schedule the packets based on which flow each packet is in and the QoS parameters for that flow. The associated QoS parameters may be prioritized, and more important QoS parameters may be met to the detriment of less important QoS parameters.

Systems, methods, and devices to support intra-QCI QoS-aware radio resource allocation

Embodiments describe mechanisms for an eNB, possibly assisted by a UE, to detect and possibly alleviate user plane congestion. In some embodiments, the eNB implements UE assisted packet dropping, where the UE requests the eNB drop packets in a sub-QCI that meet designated criteria, such as exceeding a sub-QCI delay threshold. Other embodiments, detect periods of congestion and send congestion indication to the core network when the congestion period exceeds a threshold or when the user experience is degraded. Some embodiments implement both sets of functionality.

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Hillsboro, OR, United States of America

Yi Gai