Abstract: Embodiments of the present disclosure provide methods, systems, and computer program products for implementing user interface (UI) Task Automations. Disclosed embodiments include receiving an automation structure and inputs and outputs of the structure to create a task automation, and providing multi-modal interfaces to process one or more teaching demonstrations for the task automation, where the teaching demonstrations identify automation processing parameters and operations for the task automation. Interactive contextual guidance are generated to record conditional execution of one or more actions or expressions based on states of one or more UI elements of the teaching demonstrations. Disclosed embodiments include recording, based on the conditional execution of one or more actions or expressions, the teaching demonstrations, synthesizing a UI task automation program of the task automation from the teaching demonstrations, and presenting the UI task automation program for validation.

Abstract: An example system includes a processor to receive an event trace. The processor can transform the event trace into a semantic story using a generated story template. The processor can input the semantic story into a fine-tuned model. The processor can receive a next skill prediction from the fine-tuned model.

Abstract: An approach for automatically generate the Natural Language Interface (NLI) directly from the Graphical User Interface (GUI) code is disclosed. The approach leverages the use of mapping between GUI components to pre-defined NLI components in order to generate the necessary NLI components (e.g., intent example, entities, etc.) from the GUI code representation. The approach can leverage pre-defined patterns in order to generate these intent examples for each kind of NLI components. The created NLI dialog can be used simultaneously with the GUI or as a standalone feature.

Abstract: Method, apparatus and product for utilizing stochastic controller to provide user-controlled notification rate of wearable-based events. The method comprises obtaining events issued by a module based on analysis of multiple sensor readings of one or more sensors of a wearable device. The method further comprises determining by a stochastic controller whether to provide an alert to a user based on the events and based on a user preference, wherein the user preference is indicative of a desired notification rate of the user, wherein the stochastic controller comprises a stochastic model of an environment. Based on such determination, alerts are outputted to the user.

Abstract: An approach for automatically generate the Natural Language Interface (NLI) directly from the Graphical User Interface (GUI) code is disclosed. The approach leverages the use of mapping between GUI components to pre-defined NLI components in order to generate the necessary NLI components (e.g., intent example, entities, etc.) from the GUI code representation. The approach can leverage pre-defined patterns in order to generate these intent examples for each kind of NLI components. The created NLI dialog can be used simultaneously with the GUI or as a standalone feature.

Abstract: In some examples, a system for generating personalized recommendation includes a processor that can perform an initial training for a deep reinforcement learning (DRL) model using domain knowledge, available users data, and an items list. The processor also inputs users data and an items list to the trained DRL model to generate an initial list of recommended items. The processor also inputs the initial list of recommended items and a user profile to a content-based filter to generate a final list of recommendations for a target user.

Abstract: A method for predicting at least one score for at least one item, comprising in at least one iteration of a plurality of iterations: receiving a user profile having a plurality of user attribute values; computing the at least one score according to a similarity between the user profile and a plurality of other user profiles by inputting the user profile and a plurality of items into a prediction model trained by: in each of a plurality of training iterations: receiving a training user profile of a plurality of training user profiles, the training user profile having a plurality of training user attribute values; computing by the prediction model a plurality of predicted scores, each for one of a plurality of training items, in response to the training user profile and the plurality of training items, where each of the plurality of training items has a plurality of training item.

Abstract: Embodiments of the present invention may provide the capability to detect complex events while providing improved detection and performance. In an embodiment of the present invention, a method for detecting an event may comprise receiving data representing measurement or detection of physical parameters, conditions, or actions, quantizing the received data and selecting a number of samples from the quantized data, generating a hidden Markov model representing events to be detected using initial model values based on ideal conditions, wherein a desired output is defined as a sequence of states, and wherein a number of states of the hidden Markov model is less than or equal to the number of samples of the quantized data, adjusting the quantized data and the initial model values to improve accuracy of the model, determining a state sequence of the hidden Markov model, and outputting an indication of a detected event.

Abstract: In some examples, a system for generating personalized recommendation includes a processor that can perform an initial training for a deep reinforcement learning (DRL) model using domain knowledge, available users data, and an items list. The processor also inputs users data and an items list to the trained DRL model to generate an initial list of recommended items. The processor also inputs the initial list of recommended items and a user profile to a content-based filter to generate a final list of recommendations for a target user.

Abstract: A computer-implemented method, computerized apparatus and computer program product for activity recognition using adaptive window size segmentation of sensor data stream. A data stream generated by one or more sensors is obtained. A frequency analysis of the data in a first segment of the data stream is performed. A size of a second segment is determined based on the frequency analysis. Activity recognition is performed for the second segment by extracting one or more features of the data therein and applying a machine learning process on the extracted features to obtain a classification of the data into an activity class.

Abstract: A computer-implemented method, computerized apparatus and computer program product, the method comprising: obtaining data measured by one or more sensors; segmenting the data into a plurality of sliding windows; extracting one or more features from each of the plurality of sliding windows; analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M>1.

Abstract: A computer implemented method, a computerized system and a computer program product for anomaly classification. The computer implemented method comprises obtaining a data set, wherein the data set comprises a plurality of data points. The method further comprises filtering the data set based on an absolute distance criterion and performing anomaly classification on a test data point of the data set, wherein the anomaly classification is based on a relative density criterion. The method further comprises outputting an outcome of the anomaly classification.

Abstract: A system having a wearable devices that, together with a cognitive model, are able to analyze a person to determine if they are in the flow and/or guide the person to get into the flow are disclosed. The system and processes help persons to find their unique formula to achieve flow. By using a cognitive AI engine, the system can describe a space of mental states and the actions that cause transitions between them for each individual.

Abstract: A computer program product, an apparatus and a method for training of an HMM. The method comprises applying a classifier that uses an HMM which was trained based on a training set, on a set of samples to provide an initial prediction; computing a first F1-score of the initial prediction measuring an accuracy of the initial prediction; selecting a misclassified sample by the classifier in the initial prediction; adding the misclassified sample to the training set; training the HMM using the misclassified sample to provide a modified HMM; applying the classifier using the modified HMM on the set of samples to provide a second prediction; computing a second F1-score of the second prediction; and comparing the first F1-score and the second F1-score; in response to a determination that the first F1-score is greater than the second F1-score, removing the misclassified sample from the training set.

Abstract: A computerized method for cross-domain collaborative revision management of a common product. The method comprises: Monitoring a plurality of local revisions for each of a plurality of projects associated with the common product, each of the plurality of projects managed in one of a plurality of domain-specific configuration management tools; creating a collaborative baseline within a collaborative relationship hub synchronizing between a group of the plurality of local revisions selected from each of the plurality of projects; and assembling instructions of generating the common product according to the collaborative baseline.

Abstract: Embodiments of the present invention may provide the capability to detect complex events while providing improved detection and performance. In an embodiment of the present invention, a method for detecting an event may comprise receiving data representing measurement or detection of physical parameters, conditions, or actions, quantizing the received data and selecting a number of samples from the quantized data, generating a hidden Markov model representing events to be detected using initial model values based on ideal conditions, wherein a desired output is defined as a sequence of states, and wherein a number of states of the hidden Markov model is less than or equal to the number of samples of the quantized data, adjusting the quantized data and the initial model values to improve accuracy of the model, determining a state sequence of the hidden Markov model, and outputting an indication of a detected event.

Abstract: A computer-implemented method, computerized apparatus and computer program product for activity recognition using adaptive window size segmentation of sensor data stream. A data stream generated by one or more sensors is obtained. A frequency analysis of the data in a first segment of the data stream is performed. A size of a second segment is determined based on the frequency analysis. Activity recognition is performed for the second segment by extracting one or more features of the data therein and applying a machine learning process on the extracted features to obtain a classification of the data into an activity class.

Abstract: A computer implemented method, a computerized system and a computer program product for anomaly classification. The computer implemented method comprises obtaining a data set, wherein the data set comprises a plurality of data points. The method further comprises filtering the data set based on an absolute distance criterion and performing anomaly classification on a test data point of the data set, wherein the anomaly classification is based on a relative density criterion. The method further comprises outputting an outcome of the anomaly classification.

Abstract: Method, apparatus and product for utilizing stochastic controller to provide user-controlled notification rate of wearable-based events. The method comprises obtaining events issued by a module based on analysis of multiple sensor readings of one or more sensors of a wearable device. The method further comprises determining by a stochastic controller whether to provide an alert to a user based on the events and based on a user preference, wherein the user preference is indicative of a desired notification rate of the user, wherein the stochastic controller comprises a stochastic model of an environment. Based on such determination, alerts are outputted to the user.

Abstract: A computer-implemented method, computerized apparatus and computer program product, the method comprising: obtaining data measured by one or more sensors; segmenting the data into a plurality of sliding windows; extracting one or more features from each of the plurality of sliding windows; analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M>1.