Abstract: According to an aspect of an embodiment, a method may include receiving vocal input that includes conversational language describing a computer program to be generated. The method may also include generating input text based on the vocal input. The input text may be representative of the conversational language describing the computer program. The method may additionally include generating an intent layout based on the input text. The method may include generating an execution layout based on the intent layout. The execution layout may include computer programming language based on the intent layout. The method may also include generating the computer program based on the computer programming language included in the execution layout.

Abstract: Technologies are described to provide autonomous speech and language assessments. In some examples, a method to conduct an autonomous speech and language assessment includes facilitating authoring of an autonomous speech and language assessment. The autonomous speech and language assessment may be interactions between one or more of a conversational module, an analytical module, and a control module, and the interaction may be specified using a visual programming language. The method may also include modeling the autonomous speech and language assessment as a graph using a conversation markup language, and conducting the autonomous speech and language assessment utilizing a conversational virtual agent.

Abstract: A method may include obtaining data input including one or more words. The method may include determining a set of expressed emotions and a set of expressed emotion probabilities based on the one or more words. The method may include obtaining a set of likelihood probabilities. Each likelihood probability may correspond to a conditional probability of an expressed emotion given an experienced emotion of a group of experienced emotions. The method may include determining a set of experienced emotions of the group of experienced emotions and a set of experienced emotion probabilities based on the set of expressed emotion probabilities and the set of likelihood probabilities. The method may include selecting an experienced emotion of the set of experienced emotions based on the selected experienced emotion corresponding to the highest experienced emotion probability of the set of experienced emotion probabilities. The method may include presenting the selected experienced emotion.

Abstract: A method of generating a report for a digital task is provided. The method may include receiving an identification of a digital task initiated by a user, and electronically monitoring at least one of user interaction with at least one application while the user performs the digital task via an electronic system and a state of the electronic system while the user performs the digital task via the electronic system. Further, the method may include capturing a plurality of snapshots for the digital task. Each snapshot of the plurality of snapshots may include information for at least of one or more user actions and the state of the electronic system while the user performs the digital task. The method may also include generating a report for the digital task including the plurality of snapshots.

Abstract: A method may include obtaining a threshold cognitive load of a user and a threshold user readiness. The method may include obtaining a current cognitive load of the user and a threshold number of messages. The method may include obtaining multiple messages. The method may include obtaining a user readiness. The method may include identifying a group of messages of the multiple messages in response to the user readiness satisfying the threshold user readiness. A number of messages of the identified group of messages may be greater than the threshold number. The group of messages may have the highest priorities of the messages of the multiple messages. The group of messages may be selected such that a sum of the current cognitive load and a total cognitive cost of the group of messages is less than the threshold cognitive load.

Abstract: A method of providing online forum assistance is provided, including receiving a post generated by a user and to be submitted in an online forum. The text of the post is analyzed via at least one processor. The online forum is analyzed via the at least one processor based on one or more machine learning models for the online forum. At least one user profile of at least one other user of the online forum is analyzed via the at least one processor based on the one or more machine learning models for the at least one user profile. At least one of one or more suggestions to the user and one or more modifications to the post are provided based on at least one of the analysis of the text of the post, the analysis of the online forum, and the analysis of the at least one user profile.

Abstract: An embodiment includes a method of learner engagement that includes crowdsourcing a skill proposal. The skill proposal is crowdsourced and includes a skill item that describes an exercise related to an educational concept. The method may include crowdsourcing a real-world problem proposal. The real-world problem proposal may include a real-world problem item that includes one or more technical issues that occur in at least one career. The method may include determining whether the skill item is relevant to the real-world problem item based on application of the educational concept of the skill item to technical issues of the real-world problem item. In response to a determination that the skill item is relevant to the real-world problem item, the method may include pairing the skill item with the real-world problem item.

Abstract: A method of automated inquiry response includes forming clusters that represent a meaning. The method includes assigning a level of sophistication to the clusters and generating sub-clusters within the clusters that represent a sentiment or a level of sophistication. The method includes assigning responses to the sub-clusters that address the meaning of the cluster and are modified based on the sentiment or a level of sophistication. The method includes computing a substantive issue, a sentiment, and a level of sophistication of a received inquiry. The method includes identifying clusters to which the inquiry pertains and generating an order of the identified clusters based on the assigned level of sophistication. The method includes crowdsourcing a comparison of the substantive issue of the inquiry to meanings of the identified clusters. The method includes offering a response associated with one of the identified clusters and with the sub-cluster for the identified sentiment.

Abstract: A method includes acquiring individual assessment data that includes behavior from player of a construction toy. The construction toy includes at least one sensor to detect an activity of the player with the construction toy. The method further includes determining a goal for the player based on the individual assessment data and a set of group assessment data. The method also includes providing a teaching based on the goal. The method includes permitting the player to play with the construction toy with reduced monitoring or intervention by the processing device during a free play period. The method further includes in response to a determination that free play period is over, prompting the player to perform an activity based on the goal. The method also includes in response to a determination that the player has completed the activity, providing a first reinforcement message to the player.

Abstract: A method includes receiving input data pertaining to a user. Based on the input data, the method includes rendering a graphical user interface (GUI) that includes an initial icon arrangement (arrangement) presentable in a virtual environment. The initial icon arrangement includes a set of icons representative of portions of an overall time period represented by the initial icon arrangement. The method includes determining whether a portion of the input data is representable in the initial icon arrangement. If so, the method includes quantifying the second portion of the input data with a target and a timeframe, associating the target and the timeframe with the second portion of the input data, modifying icons in the arrangement that correspond to the timeframe to represent the target, rendering the modified icons as an opportunity layer on the arrangement, and displaying the arrangement with the opportunity layer in the virtual environment.

Abstract: A method of providing an interactive story is provided. The method may include displaying, via a user interface, a segment of a story including a first virtual character. The method may also include activating a characterbot associated with the first virtual character in response to selection of the first virtual character by a user. Moreover, the method may include receiving, via the user interface, a message from the user directed toward the first virtual character. The method may further include generating, via the characterbot, a response to the received message, and conveying the response via the user interface.

Abstract: A system includes a memory and a processor operatively coupled to the memory. The processor being configured to execute instructions to monitor a behavior of a player of a first construction toy. The first construction toy includes at least one sensor to detect a first construction made by the player between the first construction toy and a second construction toy. The processor being further configured to record the first construction made by the player between the first construction toy and the second construction toy. The processor also being configured to identify a preexisting second construction that is similar to the first construction. The processor being configured to group all the constructions. The processor being further configured to determine a behavior variability and a complexity level for the player based on the construction operations and structures.

Abstract: Technologies are described to provide an active learning source code review framework. In some examples, a method to review source code under this framework may include extracting semantic code features from a source code under review. The method may also include training an error classifier based on the extracted semantic code features, and selecting a candidate code section of the source code under review for discrete review. The method may further include facilitating discrete review of the selected candidate code section, updating the error classifier based on a result of the discrete review of the selected candidate code section, and generating an automated review of the source code under review based on the updating of the error classifier.

Abstract: A method includes receiving, via a network, a join request from a client device for a user to access a digital community. The join request includes a membership eligibility assertion. The method further includes verifying the membership eligibility assertion. The method also includes granting access to the digital community based on the verifying. The method includes receiving a question from the client device via a graphical user interface associated with the digital community. The method also includes identifying a solution to the question, wherein the solution includes an asset. The method further includes sending the solution to the client device via the network.

Abstract: A computer-implemented method may include identifying multiple workers, multiple tools, and multiple taxonomy parameters. The method may also include identifying a partially-full first matrix of values representing relationships between the taxonomy parameters and the tools, a partially-full second matrix of values representing relationships between the workers and the tools, and a partially-full third matrix of values representing relationships between the workers and the taxonomy parameters.

Abstract: A method of smart notification scheduling and modality selection includes identifying a notification. The method includes extracting context pertaining to the notification or a user of the client device in response to receiving the notification. The method further includes determining a priority for the notification based on the context. The method also includes determining a delivery schedule for the notification based on the priority. The method includes determining a notification delivery plan based on at least one of the context, priority or delivery schedule. The method further includes transmitting the notification to a display device.

Abstract: A method of estimating one or more conditional probabilities may be provided. A method may include determining one or more states based on user input, and determining a similarity measurement between at least one state pair of one or more state pairs. The method may further include determining a likelihood of probability for the at least one state pair of the one or more state pairs. Moreover, the method may include estimating a conditional probability for the at least one state pair of the one or more state pairs based on the determined likelihood of probability and the determined one or more states.

Abstract: A method of smart notification scheduling and modality selection includes identifying a notification. The method includes extracting context pertaining to the notification or a user of the client device in response to receiving the notification. The method further includes determining a priority for the notification based on the context. The method also includes determining a delivery schedule for the notification based on the priority. The method includes determining a notification delivery plan based on at least one of the context, priority or delivery schedule. The method further includes transmitting the notification to a display device.

Abstract: A method may include receiving a textual input. Topics associated with the textual input and top words associated with each of the topics may be generated by way of Latent Dirichlet Allocation (LDA) topic modeling. Relevance weights may be generated for each emotion associated with the topics generated by the LDA topic modeling. Emotion weights associated with the textual input may be output. The emotion weights may be based, at least in part, on the relevance weights.

Abstract: A method may include generating a personalized rating vector associated with a querying user. The personalized rating vector may relate personalized ratings to options. The personalized rating vector may be generated via element-wise multiplication of a user vector, a relevance weight, and a characteristics vector. The user vector may relate user ratings to the querying user and to the options. The relevance weight may be associated with a characteristic of the querying user. The characteristics vector may relate characteristics ratings to a user characteristic and to the options. The user characteristic may correspond at least in part to the characteristic of the querying user. The method may further include generating a recommendation for the querying user based at least in part on the personalized rating vector, the recommendation associated with at least one option of the options of the personalized rating vector.