Abstract: A system and method are provided for discovering k-nearest-neighbors to a given point within a certain distance d. The method includes constructing an index of geometries using geohashes of geometries as an indexing key to obtain an indexed set of geometries, and calculating a geohash representation of the given point with a resolution equal to a magnitude value of d. The method includes searching for a closest-prefix geometry from the indexed set using the geohash representation of the given point, and identifying geometries from the indexed set having a same prefix as the closest-prefix geometry. The method further includes calculating distances between the given point and the geometries identified from the indexed set having the same prefix as the closest-prefix geometry, and determining k geometries with respective shortest distances less than d from the geometries identified from the indexed set having the same prefix as the closest-prefix geometry.

Abstract: Embodiments for providing rare topic detection using hierarchical topic modeling by a processor. A hierarchical topic model may be learned from one or more data sources. One or more dominant words in a selected cluster may be iteratively removed using the hierarchical topic model. The dominant words may relate to one or more primary topics of the cluster. The learned hierarchical topic model may be seeded with one or more words, n-grams, phrases, text snippets, or a combination thereof to evolve the hierarchical topic model and the removed dominant words are reinstated upon completion of the seeding.

Abstract: Embodiments related to using a foundational model for network packet traces. A technique includes receiving network traffic of a network and extracting features from the network traffic, the features having a function related to communications in the network. The technique includes generating tokens from the features, each of the features corresponding to a respective one of the tokens, training a machine learning model by inputting the tokens, the machine learning model being trained to output contextual embeddings for the tokens, and using the contextual embeddings to determine an anomaly in the network traffic.

Abstract: A computer-implemented method, a computer program product, and a computer system for accelerated learning. A computer partitions an independent variable of input data into partitions. A computer creates data samples in each of the partitions, where each of the data samples has less granularity. For each of the partitions, a computer trains a machine learning model independently on each of the data samples and compares results of training on the data samples. For each of one or more partitions in which results of training on a predetermined number of the data samples are statistically identical at a predetermined confidence level, a computer outputs a result of training on one of the data samples. For each of one or more rest partitions in which no result of training has been outputted, a computer merges each pair of the data samples and uses merged data samples to train the machine learning model.

Abstract: A method for training and fine-tuning an artificial intelligence model is disclosed. In one embodiment, such a method distributes, across multiple chiplets of a package, functionality associated with a deep neural network. The method implements, within a first set of chiplets, frozen layers of the deep neural network. By contrast, the method implements, within a second set of chiplets, trainable layers of the deep neural network. The number of chiplets in the second set may be smaller than the number of chiplets in the first set and may consist of a single chiplet in some embodiments. In certain embodiments, the second set of chiplets has one or more of additional memory capacity and additional processing capacity compared to the first set of chiplets in order to train and fine tune the trainable layers. A corresponding apparatus is also disclosed.

Abstract: Embodiments receive a plurality of faults for at least one microservice in a cloud native based application within a fault set selection server; inject the faults into at least one microservice in the cloud native based application within the fault set selection server; obtain a system state representation for each of the injected faults in the at least one microservice in the cloud native based application using an unsupervised clustering algorithm; derive a fault subset based on the system state representation for each the faults in the at least one microservice in the cloud native based application; and inject the derived fault subset into the at least one microservice in the cloud native based application and logging behavior of the at least one microservice in the cloud native based application with the injected derived fault subset.

Abstract: The present inventive concept provides for a method of unmanned machine synchronization using robotic sensing. The method includes generating at least one physical signal in the vicinity of at least one unmanned machine. The at least one generated physical signal is received by the at least one unmanned machine. At least one task is performed by the at least one unmanned machine based on the at least one received generated physical signal.

Abstract: An embodiment for improved estimating of end-user performance of cloud-based services. The embodiment may collect, for a target cloud-based service, a first dataset including network level metrics, and a second dataset including end-user performance data from one or more monitoring services. The embodiment may combine the collected first dataset and second dataset to generate a curated training dataset. The embodiment may train a machine learning prediction model using the curated training dataset. The embodiment may predict and estimate, using the trained machine learning prediction model, the end-user performance of the target cloud-based service for any target end-user.

Abstract: Aspects of the present disclosure relate to annotating or tagging customer data. In some embodiments, the annotating can include summarizing touchpoints into k-hot encoding feature vectors, mapping the feature vectors onto an embedding layer, predicting a hierarchical data sequence using the embedding layer and the feature vectors, extracting the feature vectors that are most influential in predicting the embedding layer, and outputting the touchpoints associated with the most influential feature vectors.

Abstract: A first computational device with a first visual display and a second computational device with a second visual display are maintained, wherein the first computational device is configured to read information displayed on the second visual display, and wherein the second computational device is configured to read information displayed on the first visual display. Byte streams are exchanged bidirectionally between the first computational device and the second computational device via the information displayed on the first visual display and the information displayed on the second visual display, wherein active radio transmission between the first computational device and the second computational device is avoided.

Abstract: A computer implemented method determines a placement of an application being added to a network. The method includes determining a logical-physical mapping for the application. Sub-graphs in the logical-physical mapping are identified. A stored application in a catalog of applications previously used in the network is located. The stored application includes one of the sub-graphs in the logical-physical mapping. An experiment is generated, performed by a computing device running a neural network model, using the sub-graphs. The experiment includes inducing a physical node mapping for the sub-graph. A cost function associated with a placement of the application being added to the network to one or more physical nodes in the induced physical node mapping is determined.

Abstract: Embodiments are related to improving the bandwidth of classical networks using quantum networks. Sender equipment transfers quantum bits over a quantum communications network to receiver equipment, the quantum bits being used to obtain entry values in a shared dictionary. The sender equipment determines a solution for an optimization problem using the entry values, where data to be transferred over a telecommunications network is expressed by the optimization problem. The sender equipment transfers the solution over the telecommunications network to the receiver equipment, where an equivalence of the data is transferred to the receiver equipment in response to the receiver equipment using the solution, the optimization problem, and the entry values to obtain the data.

Abstract: A computer-implemented method, according to one approach, includes receiving at least one set of qubits at a first client component, and using the at least one set of qubits to index a configuration dictionary to determine a first configuration. The configuration dictionary defines a plurality of different configurations. The method further includes causing the first client component to be configured according to the first configuration. A computer program product, according to another approach, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a first client component to cause the first client component to perform the foregoing method. A system, according to another approach, includes a processor, and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Abstract: Techniques are described regarding log data comprehension in a computing environment. An associated computer-implemented method includes inducting data into a knowledge base associated with a log comprehension machine learning knowledge model in order to configure a common log schema. The method further includes extracting at least one key-value pair from a log file including input data associated with at least one downstream application and deriving at least one value feature and any key signal associated with the at least one key-value pair. The method further includes applying the log comprehension machine learning knowledge model in order to compare data associated with the at least one key-value pair extracted from the log file with knowledge base node key-value pair data. The method further includes creating mapping results formatted according to the common log schema and compatible with the at least one downstream application based upon the model application.

Abstract: A method for determining a correlation of one or more events occurring in a plurality of nodes of a network includes accessing, by a computing device, address information associated with each of the plurality of nodes on the network. The computing device can further access one or more event IDs associated with one or more events occurring on the plurality of nodes. The computing device can further create an association the one or more events occurring on the plurality of nodes with related events occurring on others of the plurality of nodes, the association including the address information.

Abstract: A computer-implemented method, according to one embodiment, includes determining whether a predetermined sequence of knocks has been performed by a requesting node to other nodes of a network along existing paths between the requesting node and the other nodes. In response to a determination that the predetermined sequence of knocks has been performed, a connection is established between the requesting node and a first of the other nodes. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method. A system, according to another embodiment, includes a processor, and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Abstract: An embodiment includes detecting a set of anomalies recorded during a first predefined window of time in log entries for a computer environment. The embodiment also includes generating cluster data representative of a cluster of anomalies from among the set of anomalies, where the cluster is formed using a lattice clustering algorithm that spatially distinguishes the cluster of anomalies from other anomalies in the set of anomalies. The embodiment also includes composing an explanation using log templates generated from log entries associated with the cluster of anomalies.

Abstract: Computer-implemented methods for repairing a software of a computing system are provided. Aspects include receiving a request to diagnose the software, the software including a plurality of software components configured to communicate with each other via application programing interfaces and creating a representation of the software in a virtual reality environment. Aspects also include identifying an error in the software based on error logs generated by the software and identifying one or more of the plurality of software components and the application programing interfaces that correspond to the error. Aspects further include displaying a visual indication of the error in the virtual reality environment, wherein the visual indication is determined based on a type of the error, receiving, via the virtual reality environment, a corrective action from a user, and performing a task in the software based on the corrective action, wherein the task is configured to address the error.

Abstract: Embodiments related to using a foundational model for network packet traces. A technique includes receiving network traffic of a network and extracting features from the network traffic, the features having a function related to communications in the network. The technique includes generating tokens from the features, each of the features corresponding to a respective one of the tokens, training a machine learning model by inputting the tokens, the machine learning model being trained to output contextual embeddings for the tokens, and using the contextual embeddings to determine an anomaly in the network traffic.

Abstract: Data associated with performances of microservices functioning in a distributed computing environment is clustered by executing an unsupervised machine learning algorithm. A representative data is selected from a cluster, selecting performed for a plurality of the clusters. Based on time series data of the representative data associated with the plurality of the clusters, causal extraction is performed. Based on the causal extraction and the plurality of the clusters, a causal graph is constructed. The causal graph is embedded into vector space. Based on the embedded vector space, an artificial neural network model can be trained for managing the distributed computing environment.