Abstract: A foldable backrest structure is provided and includes a backrest, a releasing mechanism and a trigger. The backrest is pivoted to a frame of a child carrier. The releasing mechanism is disposed between the backrest and the frame. The releasing mechanism is located at a position misaligned with a position of a pivoting joint of the frame where a rear leg of the frame is pivotally connected to a front leg of the frame from a side view. The releasing mechanism includes a first abutting component. The trigger is disposed on the frame and located outside the pivoting joint of the frame. The trigger is for slidably abutting against the first abutting component to drive the releasing mechanism to release the backrest, so as to allow the backrest to be folded together with the frame when the frame is being folded. Besides, a related child carrier is also provided.

Abstract: A foldable backrest structure (50) adapted for a frame (40) of a child carrier (100), includes a backrest (30), a releasing mechanism (20) and a trigger (10). The backrest (30) is pivoted to the frame (40). The releasing mechanism (20) is disposed between the backrest (30) and the frame (40). The releasing mechanism (20) includes a first abutting component (22). The trigger (10) is disposed on the frame (40) and for slidably abutting against the first abutting component (22) to drive the releasing mechanism (20) to release the backrest (30), so as to allow the backrest (30) to be folded together with the frame (40) when the frame (40) is being folded. The foldable backrest structure (50) of the present invention has advantages of simple structure, easy operation and low manufacturing cost. Besides, the present invention further discloses a child carrier (100) including the aforementioned foldable backrest structure (50).

Abstract: Multi-modal systems, for voice-based mental health assessment with emotion stimulation, comprising: a task construction module to construct tasks for capturing acoustic, linguistic, and affective characteristics of speech of a user; a stimulus output module comprising stimuli, basis the constructed tasks, to be presented to a user in order to elicit a trigger of one or more types of user behaviour, the triggers being in the form on input responses; response intake module to present, to a user, the stimuli, and, in response, receive corresponding responses in one or more formats from responses; an autoencoder to define relationship/s, using the fused features, between: an audio modality to output extracted high-level text features; and a text modality to output extracted high-level audio features; the autoencoder to receive extracted high-level text and audio features, in parallel, to output a shared representation feature data set for emotion classification correlative to the mental health assessment.

Abstract: Techniques performed by a data processing system for analyzing training data for a machine learning model and identifying outliers in the training data herein include obtaining training data for the model from a memory of the data processing system; analyzing the training data using a Siamese Neural Network to determine within-label similarities and cross-label similarities associated with a plurality of data elements within the training data, the within-label representing similarities between a respective data element and a first set of data elements similarly labeled in the training data, the cross-label similarities representing similarities between the respective data element and a second set of data elements dissimilarly labeled in the training data; identifying outlier data elements in the plurality of data elements based on the within-label and cross-label similarities; and processing the training data comprising the outlier data elements.

Abstract: Techniques performed by a data processing system for analyzing the impact of training data changes on a machine learning model herein include training a first instance of a machine learning model with a first set of training data; modifying the first set of training data to produce a second set of training data; training a second instance of the model with the second set of training data; comparing the first instance of the model to the second instance of the model to determine features that differ between the first instance and the second instance of the model; identifying a subset of historical data associated with the features that differ between the first instance and the second instance of the model; and scoring the subset of the historical data to produce a report identifying differences in the output of the first instance and the second instance of the machine learning model.

Abstract: Techniques performed by a data processing system for analyzing training data for a machine learning model and identifying outliers in the training data herein include obtaining training data for the model from a memory of the data processing system; analyzing the training data using a Siamese Neural Network to determine within-label similarities and cross-label similarities associated with a plurality of data elements within the training data, the within-label representing similarities between a respective data element and a first set of data elements similarly labeled in the training data, the cross-label similarities representing similarities between the respective data element and a second set of data elements dissimilarly labeled in the training data; identifying outlier data elements in the plurality of data elements based on the within-label and cross-label similarities; and processing the training data comprising the outlier data elements.

Abstract: Techniques performed by a data processing system for analyzing the impact of training data changes on a machine learning model herein include training a first instance of a machine learning model with a first set of training data; modifying the first set of training data to produce a second set of training data; training a second instance of the model with the second set of training data; comparing the first instance of the model to the second instance of the model to determine features that differ between the first instance and the second instance of the model; identifying a subset of historical data associated with the features that differ between the first instance and the second instance of the model; and scoring the subset of the historical data to produce a report identifying differences in the output of the first instance and the second instance of the machine learning model.

Abstract: A method for dynamically generating a bookmark suggestion within a user interface of a computing device. The method includes accessing a target URL, receiving a request to bookmark the target URL, and determining if the target URL was accessed via a URL redirection function. The method also includes generating a prompt for display on the user interface. The prompt includes a user selectable option to save one of the target URL or a redirection URL associated with the target URL as a desired bookmark target address, based on the target URL being determined to have been accessed via the URL redirection function. The method further includes receiving an indication via the user interface of the desired bookmark target address, and saving the target URL or the redirection URL as the desired bookmark target address based on the received indication.

Abstract: A method for dynamically generating a bookmark suggestion within a user interface of a computing device. The method includes accessing a target URL, receiving a request to bookmark the target URL, and determining if the target URL was accessed via a URL redirection function. The method also includes generating a prompt for display on the user interface. The prompt includes a user selectable option to save one of the target URL or a redirection URL associated with the target URL as a desired bookmark target address, based on the target URL being determined to have been accessed via the URL redirection function. The method further includes receiving an indication via the user interface of the desired bookmark target address, and saving the target URL or the redirection URL as the desired bookmark target address based on the received indication.

Abstract: Connectivity is tested using different locations and contexts selected from different possible failure zones. The failure zones may include: client failure zones; Internet failure zones; and online service failure zones. The results relating to different connectivity tests performed using the different failure zones are correlated and analyzed in an attempt to determine a root cause of the connectivity issue. For example, the root cause may be determined to be a configuration problem of the client, a problem with the client's networking equipment, an ISP problem, an Internet backbone problem; a problem of the online service, and the like. Different contexts may also be used when performing the tests. These results may be compared to the other connectivity test results. The results from the tests may be provided to the client experiencing the problem. Aggregated test results may also be used to detect service wide issues and trigger an alert.

Abstract: Concepts and technologies are described herein for providing contextually-aware discovery of solutions. In some configurations, a computing device may receive a request from a tenant. A request may be any type of request, including a service request or any other type of request for information. To process the request, the computing device may also obtain data that associates characteristics with the tenant. For example, one or more resources may maintain a database that includes a tenant identifier, data indicating the size of the tenant, the length of time a tenant has been in service, or any other type of characteristic of the tenant.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a system that can include logic and at least one processing device configured to execute the logic. The logic can be configured to receive a first task request to execute a first task that uses a resource when performed. The first task can have an associated first level of interactivity. The logic can also be configured to receive a second task request to execute a second task that also uses the resource when performed. The second task can have an associated second level of interactivity. The logic can also be configured to selectively throttle the first task and the second task based upon the first level of interactivity and the second level of interactivity.

Abstract: Connectivity is tested using different locations and contexts selected from different possible failure zones. The failure zones may include: client failure zones; Internet failure zones; and online service failure zones. The results relating to different connectivity tests performed using the different failure zones are correlated and analyzed in an attempt to determine a root cause of the connectivity issue. For example, the root cause may be determined to be a configuration problem of the client, a problem with the client's networking equipment, an ISP problem, an Internet backbone problem; a problem of the online service, and the like. Different contexts may also be used when performing the tests. These results may be compared to the other connectivity test results. The results from the tests may be provided to the client experiencing the problem. Aggregated test results may also be used to detect service wide issues and trigger an alert.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a system that can include logic and at least one processing device configured to execute the logic. The logic can be configured to receive a first task request to execute a first task that uses a resource when performed. The first task can have an associated first level of interactivity. The logic can also be configured to receive a second task request to execute a second task that also uses the resource when performed. The second task can have an associated second level of interactivity. The logic can also be configured to selectively throttle the first task and the second task based upon the first level of interactivity and the second level of interactivity.

Abstract: A computing system includes an authentication layer, the authentication layer being programmed to receive a request for resources of the computing system and to authenticate an identity of a user requesting the resources, and a command layer, the command layer being programmed to execute one or more commands from the request for resources, wherein the command layer logs characteristics associated with one or more of the commands, wherein the computing system monitors each logged command to determine when a threshold is met, and wherein the computing system blocks a subsequent request for resources from the user when the threshold is met.