International Business Machines Corporation

Globalfoundries Inc.

International Business Machines Corporiation

Nir Mashkif

Sergey Zeltyn

Alexander Zadorojniy

Lior Limonad

Segev Eliezer Wasserkrug

Asaf Adi

Ari Volcoff

David Breitgand

Aviad Sela

Alessandro Donatelli

Uri Shani

Amit Fisher

Erez Lev Meir Bilgory

Fabiana Fournier

Sami Sobhe Marreed

Offer Akrabi

Sergey Voldman

Odellia Alfassi

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.

Generating natural languages interface from graphic user interfaces

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.

Utilizing stochastic controller to provide user-controlled notification rate of wearable-based events

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.

Personalized recommender with limited data availability

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.

Method for complex events detection using hidden markov models

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.

Adaptive window size segmentation for activity recognition

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&#x3e;1.

Optimizing performance of event detection by sensor data analytics

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.

Hybrid method for anomaly classification

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.

Cross-domain collaborative configuration management

Embodiments of the present invention may provide the capability to identify a specific object being interacted with that may be cheaply and easily included in mass-produced objects. In an embodiment, a computer-implemented method for object identification may comprise receiving a signal produced by a physical interaction with an object to be identified, the signal produced by an identification structure coupled to the object during physical interaction with the object, processing the signal to form digital data identifying the object, and accessing a database using the digital data to retrieve additional information identifying or describing properties of the object identified.

Physical-interactive IDS (P2D)

Physical interactive IDs (P2D)

A computerized method to provide an enriched unified model data index by generating a unified model data index by indexing shared syntax elements of data artifacts that describe engineering and non-engineering domains, creating instances of an engineering reference model describing model properties that define blocks and relationships between each of the data artifacts for each of the engineering and non-engineering domains, and enriching the unified model data index by adding the relationships having shared syntax elements of at least one instance of the engineering reference model to the shared syntax elements of the unified model data index.

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Ein Carmel, Israel

Nir Mashkif