Abstract: A system for predicting process changes by using key factors in a commercial chemical process, includes: a key factor extraction and individual tag importance backup unit that extracts yield key factors by calculating the importance of each tag, and backs up importance data for each tag; and a yield prediction model training and yield prediction performing unit that performs yield prediction model training by using the importance of each tag accumulated in the key factor extraction and individual tag importance backup unit, and performs yield prediction so as to output a yield prediction result, evaluates performance, and selects an optimal prediction model.

Abstract: The invention relates, in particular, to a quantum-enabled server (S) comprising an enclave (SE), a qubit source (SS), and quantum-computing means (SM), this enclave comprising means for: receiving information from a remote client through a secured communication channel; determining transformation data from said information; transforming at least one qubit received from said qubit source, according to transformation data; providing the at least one transformed qubits to said quantum-computing means.

Abstract: A call model is generated that takes into account location-specific information and target attributes such as throughput per user. A cluster of different machine learning models is utilized to compute dynamic call model characteristics for each location, and merges the outputs into a dynamic call model. Additionally, techniques such as feature vector extraction, clustering algorithms, and ensemble models are employed to improve the accuracy and predictive performance of the machine learning models.

Abstract: Systems and methods include acquisition of data representing one or more user interactions with a user interface of an application, determination of a user workflow from a plurality of user workflows based on the acquired data, determination of one of a plurality of trained models based on the determined user workflow, each of the plurality of trained models associated with a respective one of the plurality of user workflows, and generation of an inference based on the data using the determined trained model.

Abstract: A room communication kiosk integrated within a campus communication system. The room communication kiosk includes a touchscreen device. The touchscreen device is configured to: display one or more user function widgets on the touchscreen; sense one or more user touch gestures interacting with the one or more user function widgets displayed on the touchscreen; and generate a signal responsive to the one or more user touch gestures. The room communication kiosk is further configured to: receive the signal responsive to the one or more user touch gestures; interpret the signal responsive to the one or more user touch gestures based on the one or more user function widgets to determine a kiosk action for the campus communication system; and communicate the kiosk action to the campus communication system over the network interface.

Abstract: Various embodiments of the present disclosure provide reinforcement learning for machine learning using dynamic confidence thresholds. In one example, an embodiment provides for generating a plurality of training datasets for a machine learning model by augmenting a labeled dataset for the machine learning model with a synthetic labeled dataset, generating a plurality of retrained model versions of the machine learning model based on the plurality of training datasets, generating a reward indicator for a retrained model version of the plurality of retrained versions of the machine learning model based on a comparison between a validation dataset for the machine learning model and a respective output dataset for the retrained model version, and modifying the defined confidence threshold based on the reward indicator for the retrained model version to generate a modified confidence threshold for the machine learning model.

Abstract: A neural network model training method, a data processing method and apparatus are disclosed. The neural network model training method includes: training a neural network model based on training data, where an activation function of the neural network model includes at least one piecewise function, and the at least one piecewise function includes a plurality of trainable parameters; and updating the plurality of trainable parameters of the at least one piecewise function in a training process. According to the method, the activation function suitable for the neural network model can be obtained. This can improve performance of the neural network model.

Abstract: Systems, apparatuses and methods provide technology that identifies a plurality of data samples of a data set. In response to a trigger, the technology identifies a first subset of samples of the plurality of data samples that are outside a range associated with a decision boundary of a machine learning model, and removes the first subset of samples from the data set to generate a modified training set. The technology train the machine learning model based on the modified training set.

Abstract: A display method includes an electronic device that obtains preset information, where the preset information includes information about an application installed on the electronic device, and the information includes at least one of a name, an icon, and a package name of the application. The electronic device generates a first window based on the preset information, where the first window includes one of a plurality of desktops of the electronic device. The electronic device sends the first window to the large-screen device to enable the large-screen device to display a first user interface, where the first user interface includes the first window.

Abstract: Systems, methods, and computer-readable media are described for training and implementing logic gate networks for inference tasks. An untrained node network includes a first set of nodes parameterized by differentiable parameters, each associated with a predefined set of potential logic gate operators, and a second set of nodes corresponding to predefined logic gate operators. During training, node outputs of the first set are computed using a first relaxation, and node outputs of the second set are computed using a second relaxation different from the first relaxation. Updated differentiable parameters are applied over multiple training iterations, and a fixed logic-gate network is generated by selecting logic gate operators for at least some nodes in the first set while retaining the predefined logic gate operators for the second set. In some embodiments, the second relaxation imposes structural inductive bias. ASICs and FPGAs implementing fixed logic gate networks are also described.

Abstract: A system for facilitating user interaction with interactive areas includes a memory encoding processor-executable routines. The system also includes a processor configured to access the memory and to execute the processor-executable routines. The processor may identify data a user of an interactive area based on identifying data obtained at the interactive area. The processor may also utilize a trained machine learning model personalized for the user, wherein the trained machine learning model personalized for the user is configured to recognize idiosyncrasies of the user. The processor may also utilize the trained machine learning model personalized for the user in detecting an idiosyncratic task performed by the user interacting with the interactive area to activate a special effect associated with the interactive area based on interactive data obtained at the interactive area. The processor may further instruct the initiation of the special effect in response to detecting the idiosyncratic task.

Abstract: Disclosed is an operating method of a computing device. The operating method of a computing device includes generating quantum mapping basis information based on a fault-tolerant constraint, generating quantum circuits and initial qubit mappings by performing a quantum circuit mapping as much as the number of times based on the fault-tolerant constraint, the quantum mapping basis information, and different random initial qubit mappings, and selecting one quantum circuit and one initial qubit mapping from among the quantum circuits and the initial qubit mappings, respectively.

Abstract: Mechanisms are provided for automatic identification of a reconciliation computer tool for producing coherent reconciled data from base data generated by a computer model. A machine learning training operation is executed on one or more performance prediction computer model(s) (PPCMs) based on first input features of at least one hierarchical dataset, and second input features of a plurality of different reconciliation computer tools. The PPCM(s) generate a prediction of performance of a corresponding reconciliation computer tool based on the first and second input features. Features are extracted from a runtime hierarchical dataset and input into the trained PPCM(s) which generate predictions of performance of a plurality of reconciliation computer tools based on the extracted features of the runtime hierarchical dataset. The reconciliation computer tools are ranked relative to one another based on the predictions of performance.

Abstract: A processor-implemented method with neural network processing includes: determining whether a portion of a population comprising a plurality of instances to which different mixed-precision quantizations are applied for a neural network satisfies convergence criteria; generating, in response to the determination that the portion satisfies the convergence criteria, a new instance using the portion; and updating the population by adding the new instance to the population.

Abstract: In an embodiment, a method includes receiving input data. The method also includes collecting data attributes from the input data based on a type of the input data. The method also includes classifying the input data using the data attributes. The method also includes performing class-imbalance removal on the classified input data, the performing yielding a balanced dataset. The method also includes creating integrated data using the balanced dataset, the integrated data including the balanced dataset integrated with data-attribute intelligence. The method also includes representing at least a portion of the integrated data as structured knowledge for execution of a particular task. The method also includes applying a plurality of extrapolation algorithms to the structured knowledge to yield raw augmented data. The method also includes generating an extrapolated dataset using the raw augmented data. The method also includes constructing a synthetic dataset based on the extrapolated dataset.

Abstract: According to an embodiment, a point process learning method executed by a computer includes: an input procedure of inputting a learning data set including at least first event data representing a series of occurrences of first events; a division procedure of dividing the first event data included in the learning data set by using a prediction time observation area including at least a time series when predicting future event occurrence; and a learning procedure of learning a model parameter including a parameter of an intensity function of a predetermined point process model by using a divided learning data set divided in the division procedure.

Abstract: A method, system, and computer program product are configured to: receive an input machine learning model being developed for deployment in a production environment; create a feature vector based on metadata of the input machine learning model and a data source used by the input machine learning model; determine a risk tier of the input machine learning model by classifying the input machine learning model into one of plural predefined tiers using the feature vector with a model-tiering machine learning model; and provide validation information to an evaluator wherein the validation information is based on the risk tier and is used to validate the input machine learning model in accordance with the risk tier.

Abstract: A method includes in a first iteration receiving a machine learning (ML) network model comprising a plurality of ML operations in high-level code; generating an internal representation (IR) for the ML network model, the IR mapped to one or more components in a multi-processing tile device; generating primitive functions based on the IR; generating an allocation list based on the primitive functions; determining when a tensor data within the allocation list is no longer needed; and inserting a deallocation function associated with the tensor data to the primitive functions to form an updated primitive functions, the inserting frees up a memory space associated with the tensor data when the tensor is no longer needed; and in a second iteration generating a compilation of the updated primitive functions to map the IR to the one or more components in the multi-processing device, wherein the compilation is a low-level instructions.

Abstract: In example embodiments, techniques are provided for customizing a ML model for a specific user absent user-coding. The techniques may provide a “black box” service to the user where the intricacies of ML model training are abstracted, and the user simply provides source data and makes high level selections. The techniques may be used with a variety of types of ML model architectures and ML Pipelines.

Abstract: The present disclosure relates to a method and an apparatus for federated learning of an artificial intelligence model. According to an exemplary embodiment of the present disclosure, a federated learning method of an artificial intelligence model includes: training a first local artificial intelligence model and a second local artificial intelligence model using data sets of a first client and a second client among the plurality of clients; calculating performance values for the first local artificial intelligence model and the second local artificial intelligence model by transmitting the first local artificial intelligence model to the second client and transmitting the second local artificial intelligence model to the first client; comparing the performance values to remove one of the first client and the second client; and training a global model using a client which is not removed.