Abstract: Example methods and systems to support accessibility to a web page are disclosed. One example method includes examining a document to be rendered to the web page and determining whether an accessibility issue exists in the document. In response to determining that the accessibility issue exists in the document, the example method further includes determining whether a first remediation of the document corresponding to the accessibility issue exists. In response to determining that the first remediation exists, the example method further includes performing the first remediation to the document to generate a first remediated document to be rendered to the web page. After performing the first remediation, the example method includes examining the first remediated document and determining whether an additional accessibility issue exists in the first remediated document.

Abstract: Disclosed is an urban non-metallic pipeline leakage location method, the method comprise the following steps: determining whether leakage occurs in a pipeline through numerical simulation law analysis or a Markov chain-based flow analysis method; for a leaking pipeline, establishing an inverse-transient control equation for non-metallic pipeline gas leakage, and obtaining pressure and flow rate data of each measuring point in different periods of time through experiments and substituting the data into the control equation to analyze experimental data; and defining a nonlinear programming problem of an objective function with a least squares criterion, and applying a sequential quadratic programming method to minimize the objective function, so as to determine the size and position of the leakage.

Abstract: An indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. A plurality of user profiles in the social networking service is accessed. A first machine-learned model is used to learn embeddings for the plurality of different entities in a d-dimensional space. A second machine-learned model is used to learn an embedding for each of one or more query terms that are not contained in the indication of the plurality of different entities in the social networking service, using the embeddings for the plurality of different entities learned using the first machine-learned model, the second-machine learned model being a deep structured semantic model (DSSM). A similarity score between a query term and an entity is calculated by computing distance between the embedding for the query term and the embedding for the entity in the d-dimensional space.

Abstract: Disclosed is an urban non-metallic pipeline leakage location method, the method comprise the following steps: determining whether leakage occurs in a pipeline through numerical simulation law analysis or a Markov chain-based flow analysis method; for a leaking pipeline, establishing an inverse-transient control equation for non-metallic pipeline gas leakage, and obtaining pressure and flow rate data of each measuring point in different periods of time through experiments and substituting the data into the control equation to analyze experimental data; and defining a nonlinear programming problem of an objective function with a least squares criterion, and applying a sequential quadratic programming method to minimize the objective function, so as to determine the size and position of the leakage.

Abstract: Embodiments of the present disclosure provide methods and apparatus for path selection in a storage system. The storage system includes a plurality of storage devices, each storage device being connected to a plurality of different HBA ports of a server via a plurality of paths; the method comprises: monitoring a system-wide performance metric of the storage system to obtain dynamic performance statistics; detecting a performance-related event based on the dynamic performance statistics; and selecting, from the plurality of paths, an active path for a storage device of the plurality of storage devices based on the dynamic performance statistics and a result of the detecting. With the methods or apparatus according to the embodiments of the present disclosure, performance bottleneck can be avoided or timely eliminated, load balance can be achieved, and system resources can be utilized more effectively.

Abstract: In an example, an indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. Then a plurality of user profiles in the social networking service are accessed. A machine-learned model is then used to calculate, based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred, a similarity score between a first node and second node by computing distance between the first node and the second node in a d-dimensional space on which a plurality of entities are mapped, the similarity score generated using a generalized linear mixed model having a global coefficient vector applied to global function pertaining to the co-occurrence counts and a first random effects coefficient vector applied to a random effects per-country function.

Abstract: The disclosure provides a pipeline leak detection device and a leak detection method based on the variational mode decomposition optimized by the particle swarm (PSO-VMD). The acoustic emission signals with leakage and without leakage are collected by the acoustic emission system. Since the decomposition result of the traditional VMD depends on the selection of the parameter preset scale K and the penalty coefficient ?, the PSO is employed to obtain the optimal parameters of the VMD. The optimized parameters are input into VMD to decompose the original signals, and then K intrinsic mode functions (IMFs) can be obtained. After the signal reconstruction for de-noising, based on the energy ratio, the time-domain features are extracted and the support vector machine (SVM) is used to detect the leak.

Abstract: In an example, a plurality of user profiles in a social networking service are accessed. A heterogeneous graph structure having a plurality of nodes connected by edges is generated, each node corresponding to a different entity in the social networking service, each edge representing a co-occurrence of entities represented by nodes on each side of the edge in at least one of the user profiles. Weights are calculated for each edge of the heterogeneous graph structure, the weights being based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred. The heterogeneous graph structure is embedded into a d-dimensional space. A machine-learned model is then used to calculate a similarity score between a first node and second node by computing distance between the first node and the second node in the d¬-dimensional space.

Abstract: Embodiments of the present disclosure relate to a method and a device of managing input/output of a storage device. The storage device at least includes a first I/O port and a second I/O port. The method comprises receiving a first I/O request for the storage device, and determining a type of the first I/O request. Based on the type of the first I/O request, the first I/O request is dispatched to the first I/O port or the second I/O port. If the first I/O request is a read request, the first I/O request may be dispatched to the first I/O port, and if the first I/O request is determined as a write request, the first I/O request may be dispatched to the second I/O port. The method may reuse at least one of the first I/O port or the second I/O port.

Abstract: In an example, a deep learning network is used to calculate a similarity score between a first query in a social networking service and each of one or more suggestable entities in the social networking service. The suggestable entities are determined via a first machine learned model. The deep learning network takes as input the suggestable entities as well as a history of interactions with a graphical user interface of a social networking service by a first member of the social networking service, a history of queries performed via the graphical user interface by the first member, and the first query itself.

Abstract: The subject disclosure is directed to an indexing technology for massive blob/objects, in which a Multi-Level Index and a well-designed hash function work together to reduce the low-latency memory consumption and to finish the Blob lookup/insertion/deletion operations with fixed and limited IO Requests (including read/write). Every blob is uniquely identified by its fingerprint. All the fingerprints are stored in the Multi-Level Index which includes Root Index, Intermediate Indexes and Leaf Index. There may be 0 or 1 or more intermediate indexes. All of these indexes in the Multi-Level Index are built on non-volatile storage. The Insertion Buffer, Deletion Buffer are built in the primary storage or secondary storage, and they are used to resolve the write amplification for the indexes in the Multi-Level Index.

Abstract: Embodiments of the present disclosure provide methods and apparatus for path selection in a storage system. The storage system includes a plurality of storage devices, each storage device being connected to a plurality of different HBA ports of a server via a plurality of paths; the method comprises: monitoring a system-wide performance metric of the storage system to obtain dynamic performance statistics; detecting a performance-related event based on the dynamic performance statistics; and selecting, from the plurality of paths, an active path for a storage device of the plurality of storage devices based on the dynamic performance statistics and a result of the detecting. With the methods or apparatus according to the embodiments of the present disclosure, performance bottleneck can be avoided or timely eliminated, load balance can be achieved, and system resources can be utilized more effectively.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system determines impression discounting features for ordering a set of candidates that match parameters of a search from a recruiter, wherein the impression discounting features include a recruiter-candidate feature indicating interaction between the recruiter and a candidate and a candidate popularity feature indicating interaction between the candidate and a set of recruiters. Next, the system applies a machine learning model to the impression discounting features and features for the set of candidates to produce a first set of scores for personalizing a ranking of the set of candidates for the recruiter. The system then generates the ranking according to the first set of scores. Finally, the system outputs, to the recruiter, at least a portion of the ranking as search results of the search.

Abstract: Embodiments of the present disclosure provide methods and apparatus for path selection in a storage system. The storage system includes a plurality of storage devices, each storage device being connected to a plurality of different HBA ports of a server via a plurality of paths; the method comprises: monitoring a system-wide performance metric of the storage system to obtain dynamic performance statistics; detecting a performance-related event based on the dynamic performance statistics; and selecting, from the plurality of paths, an active path for a storage device of the plurality of storage devices based on the dynamic performance statistics and a result of the detecting. With the methods or apparatus according to the embodiments of the present disclosure, performance bottleneck can be avoided or timely eliminated, load balance can be achieved, and system resources can be utilized more effectively.

Abstract: In an example, a plurality of user profiles in a social networking service are accessed. A heterogeneous graph structure comprising a plurality of nodes connected by edges is generated, each node corresponding to a different entity in the social networking service, each edge representing a co-occurrence of entities represented by nodes on each side of the edge in at least one of the user profiles. Weights are calculated for each edge of the heterogeneous graph structure, the weights being based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred. The heterogeneous graph structure is embedded into a d-dimensional space. A machine-learned model is then used to calculate a similarity score between a first node and second node by computing distance between the first node and the second node in the d-dimensional space.

Abstract: An indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. A plurality of user profiles in the social networking service is accessed. A first machine-learned model is used to learn embeddings for the plurality of different entities in a d-dimensional space. A second machine-learned model is used to learn an embedding for each of one or more query terms that are not contained in the indication of the plurality of different entities in the social networking service, using the embeddings for the plurality of different entities learned using the first machine-learned model, the second-machine learned model being a deep structured semantic model (DSSM). A similarity score between a query term and an entity is calculated by computing distance between the embedding for the query term and the embedding for the entity in the d-dimensional space.

Abstract: In an example, an indication of a plurality of different entities in a social networking service is received, including al least two entities having a different entity type. Then a plurality of user profiles in the social networking service are accessed A machine-learned model is then used to calculate, based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred, a similarity score between a first node and second node by computing distance between the first node and the second node in a d-dimensional space on which a plurality of entities are mapped, the similarity score generated using a generalized linear mixed model having a global coefficient vector applied to global function pertaining to the co-occurrence counts and a first random effects coefficient vector applied to a random effects per-country function.

Abstract: In an example, a deep learning network is used to calculate a similarity score between a first query in a social networking service and each of one or more suggestable entities in the social networking service. The suggestable entities are determined via a first machine learned model. The deep learning network takes as input the suggestable entities as well as a history of interactions with a graphical user interface of a social networking service by a first member of the social networking service, a history of queries performed via the graphical user interface by the first member, and the first query itself.

Abstract: Embodiments of the present disclosure relate to a method and a device of managing input/output of a storage device. The storage device at least includes a first I/O port and a second I/O port. The method comprises receiving a first I/O request for the storage device, and determining a type of the first I/O request. Based on the type of the first I/O request, the first I/O request is dispatched to the first I/O port or the second I/O port. If the first I/O request is a read request, the first I/O request may be dispatched to the first I/O port, and if the first I/O request is determined as a write request, the first I/O request may be dispatched to the second I/O port. The method may reuse at least one of the first I/O port or the second I/O port.

Abstract: A method of sharing recruiting data between business units of an organization is disclosed. It is determined that a first business unit has a contract to use a first set of resources of a social-networking system to manage a first set of profiles. It is determined that a second business unit has a contract to use a second set of resources to manage a second set of profiles. It is determined that the first entity used the first set of resources to create a private data item and to associate the private data item with a profile of the first set of profiles. It is determined that the profile of the first set of profiles and a profile of the second set of profiles correspond to the same person. The second entity is provided with access to the private data item, but not control of the first set of resources.