Abstract: Provided are a method and system for realizing a service-based mobile originated short message service. The method for realizing a service-based mobile originated short message service (MO SMS) includes: after receiving a short message transferred from a user equipment, an SMSF entity querying, from a NRF entity, information of a network function that can provide a MO SMS forwarding service; when it is determined, according to a query result, that a corresponding SMS-IWMSC can provide the MO SMS forwarding service, the SMSF entity sending the short message to the SMS-IWMSC; and the SMS-IWMSC sending the short message to a SC, so that the SC sends the short message to a corresponding receiver of the short message.

Abstract: The present disclosure describes methods and devices for generating a vector line drawing. A vector line drawing network may include a machine learning-based model that is trained to convert a raster image to a vector line drawing directly. The vector line drawing network may be trained end-to-end, using supervised learning, where only raster images are used as training data. A vector line drawing is generated stroke by stroke, over a series of time steps. In each time step, a dynamic drawing window is moved and scaled across the input raster image to sample a patch of the raster image, and a drawing stroke is predicted to draw a stroke in a corresponding patch in the canvas for the vector line drawing. The image patches are pasted in the canvas to assemble a final vector line drawing that corresponds to the input raster image.

Abstract: A face search method and apparatus are provided. The method includes obtaining a to-be-searched face image, and inputting the face image into a first feature extraction model to obtain a first face feature. The method further includes inputting the face image and the first face feature into a first feature mapping model for feature mapping, to output a standard feature corresponding to the first face feature, and performing face search for the face image based on the standard feature. Features extracted by using a plurality of feature extraction models are concatenated, and a concatenated feature is used as a basis for constructing a standard feature.

Abstract: The present disclosure describes methods and devices for generating a vector line drawing. A vector line drawing network may include a machine learning-based model that is trained to convert a raster image to a vector line drawing directly. The vector line drawing network may be trained end-to-end, using supervised learning, where only raster images are used as training data. A vector line drawing is generated stroke by stroke, over a series of time steps. In each time step, a dynamic drawing window is moved and scaled across the input raster image to sample a patch of the raster image, and a drawing stroke is predicted to draw a stroke in a corresponding patch in the canvas for the vector line drawing. The image patches are pasted in the canvas to assemble a final vector line drawing that corresponds to the input raster image.

Abstract: A face search method and apparatus are provided. The method includes obtaining a to-be-searched face image, and inputting the face image into a first feature extraction model to obtain a first face feature. The method further includes inputting the face image and the first face feature into a first feature mapping model for feature mapping, to output a standard feature corresponding to the first face feature, and performing face search for the face image based on the standard feature. Features extracted by using a plurality of feature extraction models are concatenated, and a concatenated feature is used as a basis for constructing a standard feature.

Abstract: The present invention discloses an antibody drug conjugate that targets MSLN. The present invention also disclosed a method of making the antibody drug conjugate (ADC). The present invention further discloses a novel MSLN antibody or a functional fragment thereof comprising engineered heavy and light chains.

Abstract: A method (100, 200, 300) for managing network event data are disclosed. The method for managing network event data comprises receiving incoming network event data, the network event data comprising notifications of network events occurring within a network (102). The method further comprises, for individual notified network events within the received network event data, identifying a category of the notified network event (104) and filtering the received network event data on the basis of co-occurrence in the network of network events in individual network categories with network events in other network categories (106). Also disclosed are a Manager (600), a System (700) and a computer program.

Abstract: A method for analysing performance of a network by managing network data relating to operation of the network is disclosed. The method comprises receiving a plurality of network data records from at least one network data source, processing the received plurality of network data records into a plurality of network data documents, each network data document corresponding to a received network data record, assembling the plurality of network data documents into document groups, generating statistical data for terms appearing in the document groups, and for at least one term, performing anomaly detection upon the statistical data for the term in the document groups, and detecting an anomaly in the term. The method further comprises performing at least one of identifying the term as an anomalous term, identifying the document group containing the anomaly as an anomalous group, and/or identifying a document containing the anomalous term as an anomalous document.

Abstract: A method of real-time processing of network events, wherein an event comprises a plurality of attributes. The method comprises maintaining counters, including counters indicating numbers of occurrences and co-occurrences of instances of specific attributes of received network events, maintaining a record of instances of specific attributes of recent events, receiving a first event, increasing respective counters based on the instances of attributes of the first event. The method further comprises calculating relation measure scores between a first instance of the first attribute of the first event and instances of the first attribute of events recently received. The relation measure score depends on a number of co-occurrences in which the first instance of the first attribute and a second instance of the first attribute were received within the measurement window, the total number of occurrences of the first instance and the total number of occurrences of the second instance.

Abstract: The invention relates to a network node and a method for handling alarms in a communications network. The method comprises, for a set S of alarms collected in the communications network during a time period T: —obtaining a first and a second type of alarms and their time of arrival from the set; and applying Kernal Density Estimation on the respective first and second type of alarms, by applying a distribution function to each occurrence of the first and second type of alarms; thus obtaining a respective distribution function for the first and second type of alarms over the time period T.

Abstract: A method of identifying a network object group from a plurality of network objects (x1 to xM) of a communications network comprises grouping network objects based on a function of a plurality of key performance indicators, KPIs (k1 to kN) which are common to the plurality of network objects (x1 to xM).

Abstract: A method of real-time processing of network events, wherein an event comprises a plurality of attributes. The method comprises maintaining counters, including counters indicating numbers of occurrences and co-occurrences of instances of specific attributes of received network events, maintaining a record of instances of specific attributes of recent events, receiving a first event, increasing respective counters based on the instances of attributes of the first event. The method further comprises calculating relation measure scores between a first instance of the first attribute of the first event and instances of the first attribute of events recently received. The relation measure score depends on a number of co-occurrences in which the first instance of the first attribute and a second instance of the first attribute were received within the measurement window, the total number of occurrences of the first instance and the total number of occurrences of the second instance.

Abstract: The invention relates to a network node and a method for handling alarms in a communications network. The method comprises, for a set S of alarms collected in the communications network during a time period T:—obtaining a first and a second type of alarms and their time of arrival from the set; and applying Kernal Density Estimation on the respective first and second type of alarms, by applying a distribution function to each occurrence of the first and second type of alarms; thus obtaining a respective distribution function for the first and second type of alarms over the time period T.

Abstract: A method for analysing performance of a network by managing network data relating to operation of the network is disclosed. The method comprises receiving a plurality of network data records from at least one network data source (102), processing the received network data records into a plurality of network data documents, each document corresponding to a received network data record (104) and assembling the network data documents into document groups (108). The method further comprises generating statistical data for terms appearing in the document groups (110) and for at least one term, performing anomaly detection upon the term statistical data for the document groups (114) and, if an anomaly is detected, performing at least one of identifying the term as an anomalous term (120), identifying the document group containing the anomaly as an anomalous group (122), and/or identifying a document containing the anomalous term as an anomalous document (124).

Abstract: A cell is recommended to a first user equipment for selection during its cell selection process, wherein the recommendation is based on a performance parameter between the cell and a second user equipment, wherein the recommendation is based on a determined degree of similarity between the first user equipment and the second user equipment.

Abstract: A cell is recommended to a first user equipment for selection during its cell selection process, wherein the recommendation is based on a performance parameter between the cell and a second user equipment, wherein the recommendation is based on a determined degree of similarity between the first user equipment and the second user equipment.