Abstract: An embodiment senses a survey sequence, responsive to the sensed survey sequence, inputs the sensed survey sequence into a prediction model. The embodiment comprises a prediction model wherein the prediction model comprises training a hidden Markov model on a previously administered survey with a corresponding response status as a hidden status and a corresponding linguistic metric as an observation. The embodiment computes a probability of a response status of the sensed survey sequence as an output of the prediction model based at least in part on the previously administered survey.

Abstract: An approach for directing a chat agent for an online discussion is provided. A processor retrieves message data from a messaging program. A processor identifies at least one topic present in the message data. A processor, responsive to a determination that the at least one topic is a primary topic, updates the chat agent with the primary topic. A processor modifies message data based on the determined primary topic.

Abstract: An embodiment for determining presentation content effectiveness using attraction modeling is provided. The embodiment may include receiving presentation content from a meeting host during on online collaborative meeting. The embodiment may also include capturing one or more actions of one or more users during a display of the presentation content to the one or more users. The embodiment may further include creating an audience attention model. The embodiment may also include in response to determining at least one user is distracted from the presentation content, modifying the display of the presentation content for each distracted user in accordance with one or more characteristics associated with each distracted user. The embodiment may further include categorizing the one or more users into one or more groups. The embodiment may also include providing visual feedback to the meeting host.

Abstract: An embodiment performs, by a digital contract modification analysis engine, a modification analysis between a first digital contract and a second digital contract. The embodiment identifies, by the digital contract modification analysis engine, a modification based on the modification analysis. The embodiment determines, by the digital contract modification analysis engine based on user data associated with a user, a risk assessment of the modification.

Abstract: An embodiment of a summarization application divides collected conversation data into media and text components. The application implements respective machine learning mechanisms to enhance modeling operations of the text and media components to identify key elements from the conversation. The application generates a headline banner from a group of key elements based on an analysis involving first predetermined criteria. The application also combines additional key elements to the group of key elements to form a second group of key elements. The application generates a summary from the second group of key elements based on a second analysis involving second predetermined criteria. The application presents, via a display, the headline banner according to a first output of the first key element analysis and the summary according to a second output of the second key element analysis.

Abstract: A method, computer program product, and computer system are provided for evaluating quality of changes to a collaborative document. Data corresponding to text, metadata, and comments associated with a document is received based on detecting a change to the document. The received data is analyzed based on term frequency, collocations including bigrams and trigrams, and colligates associated with the document. The analyzed data is compared to a corpus of documents from a domain associated with the document. A list of frequencies and collocates associated with the data is generated based on the comparison. An agreement score corresponding to agreement between comments, metadata, and document text is calculated based on the list of frequencies and collocates. The change to the document is caused to be displayed to a user based on the calculated agreement score.

Abstract: A method for tuning software container interdependent systems includes receiving, by a computer processor operating a prediction engine, data from one or more software containers. The data includes operations from one or more software applications in the software containers. The prediction engine identifies a plurality of dependencies in the operations. The prediction engine develops a model of interdependencies based on the identified dependencies. The prediction engine determines one or more failure candidates in the identified dependencies. The prediction engine generates a software architecture template based on the identification of failure candidates.

Abstract: According to one embodiment, a method, computer system, and computer program product for augmenting a digital audio representation of a voice is provided. The embodiment may include identifying a current voice waveform of a user. The current voice waveform corresponds to captured speech output of the user. The embodiment may include comparing one or more frequency components of the current voice waveform to one or more corresponding frequency components of a baseline voice waveform of the user. In response to determining that at least one of the one or more frequency components of the current waveform fail a threshold degree of match to at least one corresponding frequency component of the baseline voice waveform, the embodiment may include augmenting the captured speech output via a generative artificial intelligence (AI) voice model trained to produce speech which mimics a voice and a speaking style of the user.

Abstract: The illustrative embodiments provide for signature discourse transformation. An embodiment includes detecting a signature discourse of a user by analyzing the user's discourse using corpus linguistics. The embodiment also includes generating computing a collection of words distinct to the user. The embodiment also includes sensing, using tone analysis and a natural language process model, a tone of the collection of words distinct to the user in the signature discourse utterance. The embodiment also includes mapping a cartesian value of the collection of words distinct to the user. The embodiment also includes deriving a signature discourse transformation (SDT) model from the signature discourse utterances, the collection of words, the tone, and the cartesian value. The embodiment also includes transforming, using the signature discourse transformation, an original discourse by the user into an anonymized text.

Abstract: According to one embodiment, a method, computer system, and computer program product for human-machine interfacing is provided. The present invention may include receiving a personality corpus associated with a personality typology comprising multiple personality types; extracting a plurality of utterances from a user; selecting, by a personality model, a personality type associated with the user based on the utterances and the personality corpus; identifying a compatible personality type of the selected personality type; constructing one or more natural language scripts from a word graph associated with the compatible personality type; and transmitting the one or more natural language scripts to the user.

Abstract: A system, method, and computer program product are configured to: analyze, based on a taxonomy of skills, a plurality of data sources to determine skills of a position, skills of an individual, and trend skills of a profession; create, based on the analyzing, a multi-dimensional skills model comprising a first axis corresponding to the skills of the individual, a second axis corresponding to the skills of the position, and a third axis corresponding to the trend skills of the profession; and determine, using the multi-dimensional skills model, a score of a comparison of the skills of the individual to the skills of the position.

Abstract: Computer-implemented methods for providing source code with predictable performance characteristics are provided. Aspects include performing a static analysis on the source code to identify one or more functions of the source code and a type associated with each of the one or more functions and performing a dynamic analysis on the source code by executing the source code and capturing telemetry data for the execution of each of the one or more functions. Aspects also include calculating a source code execution variability score for each of the one or more functions based at least in part on the captured telemetry data and the type associated with each of the one or more functions. Based on a determination that the source code execution variability score associated with one of the one or more functions is above a threshold value, aspects include remediating the one of the one or more functions.

Abstract: Using a regression analysis on first telemetry data of a container, an embodiment identifies a set of features predicting a resource usage of the container. The container comprises an executing application. Using a time series analysis of data of the set of features, an embodiment predicts a future resource usage of the container at a future time. The data of the set of features is extracted from second telemetry data of the container. At the future time according to the future resource usage, an embodiment adjusts a size of a resource available to the container.

Abstract: Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. For example, embodiments of the present invention can, in response to receiving a request, create a power refactoring model for a codebase based on received telemetry data and static analysis of code within the codebase. Embodiments of the present invention can further optimize existing code regions within the codebase to function with less energy requirements using the created power refactoring model and replace existing code regions within the codebase with optimized code that functions with less energy requirements.

Abstract: According to one embodiment, a method, computer system, and computer program product for adjusting an audible area of an avatar's voice is provided. The present invention may include detecting an available state of a user participating in a mixed-reality environment; generating behavior for a user avatar representing the user in the mixed-reality environment; controlling the user avatar to perform the generated behavior; monitoring the mixed-reality environment to identify if a participant is interacting with the user avatar; and responsive to detecting an interaction between a participant and the user avatar, notifying the user of the detected interaction.

Abstract: At least one target in a speech of a first speaker during an electronic conference is identified. A closest vector between the identified target and a group of possible matches for the target is determined. A most similar match for the identified target is determined based on a current usage context for the identified target, a history of stored information associated with the first speaker and the identified target, and the determined closest vector. The most similar match to a set of participants of the electronic conference is displayed.

Abstract: Using data of a plurality of interactions within a collaborative network, a collaborative network graph is constructed. Each node in the collaborative network graph represents a participant in an interaction, and each edge in the collaborative network graph represents an interaction between participants represented by corresponding nodes. Using a plurality of stochastic simulations of changes to the collaborative network graph, integration of a new node into the collaborative network is simulated. A simulation in the plurality of stochastic simulations producing a largest score improvement between scores computed on the collaborative network graph is selected as a best simulation. Using a plurality of changes to the collaborative network graph included in the best simulation, an integration recommendation is constructed, in which a portion of the integration recommendation corresponds to a change in the plurality of changes.

Abstract: According to one embodiment, a method, computer system, and computer program product for explaining discourse is provided. The embodiment may include identifying one or more skill levels for one or more users. The embodiment may also include identifying a complexity level corresponding to a piece of content. The embodiment may further include determining an explanation level for the piece of content based on the complexity level of the piece of content and a target skill level from the one or more skill levels. The embodiment may also include generating an explanation for the piece of content according to the explanation level. The embodiment may further include providing the explanation to a target user.

Abstract: Participant inclusion determination can include generating a textual analysis of a draft of an electronic communication in response to a sender preparing the draft for conveyance over an electronic communications network. A word relevance score can be computed for each word of the draft based on the textual analysis. Probabilistically weighted distances between the sender, an initial recipient of the electronic communication, and more additional recipient candidates for additionally receiving the electronic communication can be determined. The probabilistically weighted distances can correspond to hierarchical distances within a hierarchical structure corresponding to an organization in which the sender, the initial recipient, and one or more additional recipient candidates are members.

Abstract: According to one embodiment, a method, computer system, and computer program product for explaining discourse is provided. The embodiment may include monitoring a conversation. The embodiment may also include deriving a conversation alignment model based on the conversation. The embodiment may further include identifying a misalignment in the conversation. The embodiment may also include taking an action to align the conversation based on the conversation alignment model.