Abstract: A powder-based three-dimensional printing (3DP) method, device and system, and a computer-readable storage medium. The method includes: analyzing printing images of layers corresponding to a part to be printed to determine a target print image and adding a preset mark to the target print image which includes a print image of a target layer that causes a previous powder layer of the target layer to displace during printing; acquiring an image to be printed of a current layer to be printed; identifying the image to be printed to determine whether the preset mark exists on the image to be printed; and if yes, processing the current layer to be printed and/or a previous powder layer of the current layer to be printed such that the previous powder layer of the current layer does not move with powder spreading of the current layer.

Abstract: Techniques are disclosed for improved video coding with virtual reference frames. A motion vector for prediction of a pixel block from a reference may be constrained based on the reference. In as aspect, if the reference is a temporally interpolated virtual reference frame with corresponding time close to the time of the current pixel block, the motion vector for prediction may be constrained magnitude and/or precision. In another aspect, a bitstream syntax for encoding the constrained motion vector may also be constrained. In this manner, the techniques proposed herein contribute to improved coding efficiencies.

Abstract: This application provides a data transmission method. The method includes: receiving a first PDCCH, where the first PDCCH includes first DCI, the first DCI indicates a first resource, and the first resource is used to carry first uplink data; determining a first time period or a second time period, where the first time period includes a time period for monitoring a second PDCCH, and the second time period does not include the second PDCCH; and monitoring the second PDCCH based on the first time period or the second time period.

Abstract: Some implementations disclosed herein provide techniques and arrangements to enable translating language characters in media content. For example, some implementations receive a user selection of a first portion of media content. Some implementations disclosed herein may, based on the first portion, identify a second portion of the media content. The second portion of the media content may include one or more first characters of a first language. Some implementations disclosed herein may create an image that includes the second portion of the media content and may send the image to a server. Some implementations disclosed herein may receive one or more second characters of a second language corresponding to a translation of the one or more first characters of the first language from the server.

Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: Embodiments of the present application provide communication methods and systems. One method comprises receiving, from a first access network device, signaling destined for a terminal device, wherein the terminal device is in a radio resource control (RRC) inactive mode. A first paging message is sent to a second access network device for paging the terminal device and first indication information is received indicating that the terminal device is connected to a second access network device. After receiving the signaling destined for the terminal device, the first access network device can learn the terminal device has connected to the second access network device, and therefore performs proper subsequent processing.

Abstract: Disclosed are a method and a system for determining a background water temperature of thermal discharge from operating nuclear power plants based on the remote sensing. The system includes a station selection module, a model construction module, a background water temperature calculation module and a temperature rise calculation module; the general idea: constructing linear regression coefficients between water temperature reference station and water temperature estimation stations before the operation of the nuclear power plant based on historical satellite remote sensing water temperature data, and establishing a linear relationship model to calculate the background water temperature of the water temperature estimation of the operating nuclear power plant.

Abstract: Techniques for providing Internet of Things (IoT) security are disclosed. An applicable system includes profiling IoT devices to limit the number of network signatures applicable to the IoT devices and performing pattern matching using a pattern that is appropriate for the profile of a given IoT device.

Abstract: Some implementations disclosed herein provide techniques and arrangements to enable translating language characters in media content. For example, some implementations receive a user selection of a first portion of media content. Some implementations disclosed herein may, based on the first portion, identify a second portion of the media content. The second portion of the media content may include one or more first characters of a first language. Some implementations disclosed herein may create an image that includes the second portion of the media content and may send the image to a server. Some implementations disclosed herein may receive one or more second characters of a second language corresponding to a translation of the one or more first characters of the first language from the server.

Abstract: Techniques for providing Internet of Things (IoT) security are disclosed. An applicable system includes profiling IoT devices to limit the number of network signatures applicable to the IoT devices and performing pattern matching using a pattern that is appropriate for the profile of a given IoT device.

Abstract: Techniques for enforcing policy on multiple levels are disclosed. A multi-level policy includes at least one policy at a low level of abstraction and at least one policy at a high level of abstraction. An Internet of Things (IoT) device is discovered on a network. The IoT device is classified. The set of multi-level policies is applied to the IoT device based on the classification of the IoT device.

Abstract: Techniques for performing Internet of Things (IoT) device identification are disclosed. Information associated with a network communication of an IoT device is received. A determination of one or more confidence scores that represent how well the received information matches respective one or more network behavior pattern identifiers is made. A determination is made that each one of the one or more determined confidence scores is below a threshold. In response to determining that each of the one or more determined confidence scores is below the threshold, a two-part classification process is performed, where a first portion includes an inline classification, and a second portion includes a subsequent verification of the inline classification. A result of the classification process is provided to a security appliance configured to apply a policy to the IoT device.

Abstract: An anomalous behavior detector has been designed to detect novel behavioral changes of devices based on network traffic data that likely correlate to anomalous behaviors. The anomalous behavior detector uses the local outlier factor (LOF) algorithm with novelty detection. After initial semi-supervised training with a single class training dataset representing stable device behaviors, the obtained model continues learning frontiers that delimit subspaces of inlier observations with live network traffic data. Instead of traffic variables being used as features, the features that form feature vectors are similarities of network traffic variable values across time intervals. A feature vector for the anomalous behavior detector represents stability or similarity of network traffic variables that have been chosen as device identifiers and behavioral indicators.

Abstract: Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. Information associated with a network communication of an IoT device is received. A determination of whether the IoT device has previously been classified is made. In response to determining that the IoT device has not previously been classified, a determination is made that a probability match for the IoT device against a behavior signature exceeds a threshold. The behavior signature includes at least one time series feature for an application used by the IoT device. Based at least in part on the probability match, a classification of the IoT device is provided to a security appliance configured to apply a policy to the IoT device.

Abstract: Techniques for establishing a risk score for Internet of Things (IoT) device parameters and acting in response thereto are disclosed. One or more data packets transmitted to or from an Internet of Things (IoT) device are analyzed to obtain event parameters. The event parameters are analyzed to determine a context of the IoT device. A behavior of the IoT device is determined based at least in part on the event parameters and the context. A progressive risk score is obtained for the IoT device. Subsequent to obtaining the progressive risk assessment score, the progressive risk assessment score is updated based at least in part on an analysis of one or more additional data packets.

Abstract: Disclosed are an audio processing method and an electronic apparatus. The audio processing method is applied to a conference system, and the conference system includes at least one audio capturing device. The audio processing method includes: receiving at least one segment of audio captured by the at least one audio capturing device (S1710); determining voices of a plurality of targets in the at least one segment of audio (S1720); and performing voice recognition on a voice of each of the plurality of targets, to obtain semantics corresponding to the voice of each target (S1730). Voice recognition is separately performed on voices of different targets, thereby improving accuracy of voice recognition.

Abstract: Techniques for enforcing policy on multiple levels, including context-based and/or packet-based, as well as one or more of event-based, activity-based, and behavior-based. Higher-level abstraction of policy enables IP endpoint discovery and classification for which predefined multi-level policy can be applied. Management of policy can abstract lower-level parameters in favor of a higher-level of abstraction, which enables integration with an asset management platform.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for dietary tracking with instant dietary feedback using a portable multi-sensor device are described. An example method may include: obtaining a first machine learning model trained based on a plurality of food images and corresponding labels; receiving a series of images from one or more cameras capturing a user taking food out of a food-serving storage; determining, using the first machine learning model based on the series of images, an identification of the food being taken by the user; determining, using a second machine learning model based on the series of images and the identification of the food, an estimated weight of the food being taken by the user; determining and displaying portion-based food information of the food being taken by the user.

Abstract: Techniques for grouping and labeling Internet of Things (IoT) devices are disclosed. A first set of raw events associated with a first IoT device is identified, including a transmission made by the first IoT device. A communication manner of the first IoT device is determined, based at least in part on a communication manner of the first IoT device. The first set of raw events over the first time period is examined to generate one or more formatted events of the first IoT device. The formatted events are used to extract a set of features. Similar processing is performed with respect to a second IoT device. A context-based IoT device grouping model is generated based on at least one of: (1) the features extracted for the first IoT device or (2) the features extracted for the second IoT device. The model is applied to determine that a third IoT device belongs to a particular group. A deviation by the third IoT device from group behavior is detected and an alert is generated in response.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for intelligent dietary tracking are described. An example method includes obtaining a complete coverage of a user’s food intake activities using multiple tools. These tools are respectively designed to collect the user’s food intake activities through different venues. For example, the obtaining may include: obtaining first food intake events of the user collected by an Internet of Things (IoT) system installed as a foodservice establishment; obtaining second food intake events of the user collected by an electronic appliance placed at the user’s residence or office; and obtaining third food intake events of the user from a mobile application installed on a mobile device of the user. The complete coverage of the user’s food intake activities may be used for dietary behavioral analysis or other tracking purposes.