Abstract: A given segment of computer code is obtained and modified to produce one or more inefficient versions of the given segment of computer code in comparison to the given segment of computer code. A code parse tree is generated for the given segment of computer code and each inefficient version of the given segment of computer code. Model embeddings are generated based on the generated code parse trees and a diffusion model is trained based on the generated model embeddings.

Abstract: Vehicle license plate auditing is provided. Distance and time traveling information corresponding to a vehicle is extracted from a data network analysis graph model of spatiotemporal traffic flow data based on a license plate number of the vehicle in response to removing irrelevant nodes from the data network analysis graph model. A distance and time mutually exclusive node relationship is determined within the data network analysis graph model of spatiotemporal traffic flow data based on the distance and time traveling information corresponding to the vehicle extracted from the data network analysis graph model of spatiotemporal traffic flow data. An anomalous license plate node is determined in the data network analysis graph model of spatiotemporal traffic flow data based on the distance and time mutually exclusive node relationship. A set of action steps is performed regarding an anomalous license plate number corresponding to the anomalous license plate node.

Abstract: Provided are a metering abnormality analysis method and apparatus, a storage medium, and a computer device. The metering abnormality analysis method includes acquiring preliminary analysis data analyzed by a source-end system and determining at least one data filtering rule based on the preliminary analysis data (101); filtering the monitoring data of the source-end system based on the data filtering rule to obtain target metering abnormality data (102); comparing and analyzing the target metering abnormality data with preconfigured abnormal case data and determining at least one target case data from the abnormal case data (103); and performing multidimensional cluster analysis on the target metering abnormality data to obtain the aggregation level of the target metering abnormality data in multiple data dimensions and analyzing the cause of metering abnormality based on the target case data and the aggregation level (104).

Abstract: Provided are a computer-implemented method, system, and computer program product for multidimensional data recover. A snapshot restore request is received with at least one parameter defining at least one dimension for data recovery. At least one snapshot is reconstructed from a single continuous snapshot stream taken for a certain period of time based on the at least one parameter. A selected snapshot is received for data recovery selected from the at least one reconstructed snapshot. The selected snapshot is restored.

Abstract: A method includes: receiving, by a computer device, a user question; extracting, by the computer device and from a knowledge base, multiple answers to the user question; detecting, by the computer device, a first relationship entity that exists in a subset of the multiple answers to the user question; determining, by the computer device, that the first relationship entity is a condition, the condition being a relationship entity that has different values in the subset of the multiple answers; generating, by the computer device, a first follow-up question that solicits a value of the condition; receiving, by the computer device, an answer to the first follow-up question; and determining, by the computer device, a number of the subset of the multiple answers that have a value of the condition that matches the answer to the first follow-up question.

Abstract: A given segment of computer code is obtained and modified to produce one or more inefficient versions of the given segment of computer code in comparison to the given segment of computer code. A code parse tree is generated for the given segment of computer code and each inefficient version of the given segment of computer code. Model embeddings are generated based on the generated code parse trees and a diffusion model is trained based on the generated model embeddings.

Abstract: The present disclosure relates to systems and methods that enhance log data processing, storage, and querying by dynamically allocating mapping connections between processing/storage components based on characteristics of incoming data and the processing/storage components. More particularly, incoming data from a data stream may be extracted and utilized according to dynamic component mapping to more efficiently process the data. Additionally, active processing/storage metrics may cause dynamic reallocation of the component mappings to balance resource loads.

Abstract: A method, apparatus, system, and computer program product for container migration. A set of processors operates to identify a set of containers for a set of applications for a migration using a set of application performance metrics. The set of processors operates to create a set of tasks following a migration strategy to move the set of containers for the set of applications identified for the migration from a set of current physical host computers to a set of target physical host computers using the set of application performance metrics. The set of processors operates to move the set of containers for the set of applications from the set of current physical host computers to the set of target physical host computers using the set of tasks following the migration strategy.

Abstract: Client data corresponding to a single client can be stored. A decision to proceed with a defragmentation of two or more particular core groups can be determined. A destination core group to receive client data can be identified. At least one additional core group can be initiated to manage interim data write requests from the single client. Each of the two or more particular core groups can be closed to data write requests. A defragmentation of the two or more particular core groups can be performed by merging into the destination core group. The destination core group can be availed to data read and delete requests. The two or more fragmented core groups can be reallocated for other uses. The additional core groups continue to receive read and write requests. The destination core groups further can be merged and defragmented further.

Abstract: Client data corresponding to a single client can be stored. A decision to proceed with a defragmentation of two or more particular core groups can be determined. A destination core group to receive client data can be identified. At least one additional core group can be initiated to manage interim data write requests from the single client. Each of the two or more particular core groups can be closed to data write requests. A defragmentation of the two or more particular core groups can be performed by merging into the destination core group. The destination core group can be availed to data read and delete requests. The two or more fragmented core groups can be reallocated for other uses. The additional core groups continue to receive read and write requests. The destination core groups further can be merged and defragmented further.

Abstract: A method for contrastive learning by selecting dropout ratios and locations based on reinforcement learning includes receiving training data having a positive sample corresponding to a target and negative samples not corresponding to the target. A dropout policy for a neural network is produced based on the training data, where the dropout policy identifies at least one connection between neurons in the neural network to dropout. The training data is encoded, based on the dropout policy, to form embeddings, where the embeddings include multiple positive sample embeddings corresponding to the positive sample and multiple negative sample embedding corresponding to the negative samples.

Abstract: A computer-implemented method, system and computer program product for detecting Unicode injection in text. A language model is trained to determine if text data (e.g., text fragment) conforms with human writing habits using negative and positive samples. Negative samples include samples of text that are not classified as being suspect for containing Unicode characters. Such negative samples include text written by humans. Positive samples include samples of text that are to be classified as being suspect for containing Unicode characters. Such positive samples may be formed by randomly inserting Unicode characters into the corpus of negative samples. After training the language model, the language model is able to determine whether the received text data (e.g., text fragment) is suspect for containing Unicode characters based on whether the text data conforms with human writing habits.

Abstract: A system, method, and computer program product for implementing cognitive natural language processing software framework optimization is provided. The method includes receiving instructions associated with an audible user input of a user. An AI input intention of the user is determined and key information is extracted from the audible user input. The key information is inputted into a generated database table and additional key information is retrieved from a dialog table. A supplementary database table comprising the additional key information is generated and the key information is spliced with the additional key information. A resulting spliced data structure is merged into a final database table and natural language is converted into a request code structure within an SQL structure and an interactive AI interface presenting results of the converting is generated. Operational functionality of an AI device is enabled for audibly presenting results of the conversion.

Abstract: Client data corresponding to a single client can be stored. A decision to proceed with a defragmentation of two or more particular core groups can be determined. A destination core group to receive client data can be identified. At least one additional core group can be initiated to manage interim data write requests from the single client. Each of the two or more particular core groups can be closed to data write requests. A defragmentation of the two or more particular core groups can be performed by merging into the destination core group. The destination core group can be availed to data read and delete requests. The two or more fragmented core groups can be reallocated for other uses. The additional core groups continue to receive read and write requests. The destination core groups further can be merged and defragmented further.

Abstract: A computer-implemented process for modifying a training dataset includes the following operations. The training dataset is benchmarked using a State Of The Art (SOTA) neural network to determine a benchmark for the training dataset. The training set is divided into a plurality of slices. A sequence of a plurality of atomic operations are selected using a selection strategy generator operating on one of the plurality of slices. The sequence of the plurality of atomic operations is applied to modify the one of the plurality of slices to generate a revised one of the plurality of slices. Reverse reinforcement learning is performed on the revised one of the plurality of slices using the benchmark and the SOTA neural network. The training dataset is modified by replacing the one of the plurality of slices with the revised one of the plurality of slices to generate a modified training dataset.

Abstract: Disclosed are techniques for modifying deep learning models (such as neural networks) to run more efficiently in computing environments with limited floating point computation resources. A deep learning model is trained using a set of training data. Input and output values are then recorded from the layers of the trained model when supplied with the training data, which are then used to generate deep forest decision tree models corresponding to individual layers of the trained model. Experimental versions of the trained model are then generated with different layers of the trained model replaced with their corresponding deep forest decision tree models. These experimental versions are then ranked according to the accuracy of their results compared to the results of the trained model. An updated trained model is then generated with one or more layers replaced with their corresponding deep forest decision tree models.

Abstract: Provided is an application method of a Micro Electro Mechanical Systems (MEMS) inertial sensor and an electronic device. An application method of an accelerometer includes: based on an influence of a strain, generated under the action of an external force, of the accelerometer on a detection signal of the accelerometer, adopting the detection signal to reflect the external force. An application method of a gyroscope includes: based on an influence of a strain, generated under the action of an external force, of the gyroscope on a detection signal of the gyroscope, adopting the detection signal to reflect the external force. Further provided is an electronic device adopting the foregoing methods.

Abstract: An example operation may include one or more of executing a machine learning model on training data, where the training data comprises a plurality of word strings, identifying words within the training data that are extracted by the machine learning model during the executing, determining a color for the machine learning model based on the identified words and a predefined mapping of words to colors, and rendering, via a user interface, a label associated with the machine learning model in the determined color for the machine learning model.

Abstract: A hydraulic composite bushing includes: a core shaft, with a continuously spiral fluid channel groove; a rubber member, arranged around the core shaft, and having two recesses formed radially outside of the fluid channel groove and radially opposite to each other; a support ring arranged around the rubber member; an outer cover pressing on the support ring from a radially outer side thereof; and a sealing device provided at each end of a fluid channel tube arranged within the fluid channel groove. Two ends of the fluid channel tube pass through the rubber member radially to extend into two hydraulic chambers respectively, with the hydraulic chambers in communication with each other through the fluid channel tube. One end of the sealing device is arranged inside the flow channel groove and the other end thereof passes through the rubber member to extend into the hydraulic chambers.