Abstract: The wet masterbatch production line based on high-speed impinging stream reaction is provided, and includes: the mixing unit, configured for mixing and standing carbon black and latex to obtain granular rubber material; the dehydration unit, configured for performing dehydrating treatment on granular rubber material after standing to obtain flocculent rubber material; the conveying and refining unit, configured for conveying and refining dehydrated flocculent rubber material to obtain sheet rubber material; the cooling treatment unit, configured for cooling, and folding and stacking refined sheet rubber material; and the control unit, configured for automatically controlling the mixing unit, the dehydration unit, the conveying and refining unit and the cooling treatment unit.

Abstract: A computer-implemented method, a computer program product, and a computer system for log anomaly detection. A computer receives a windowed log of incoming raw log messages. A computer compares statistical distribution metrics of entities in the windowed log with a statistical distribution extracted from a real-time statistical model for the entities. In response to the statistical distribution metrics being statistically different from the statistical distribution extracted from the real-time statistical model for the entities, a computer tags the windowed log as an entity anomaly. A computer computes a distance between an average word embedding vector in the windowed log and a statistical distribution extracted form a real-time statistical model for word embeddings. In response to the distance being greater than a predetermined threshold, a computer tags the windowed log as a word embedding anomaly. A computer sends to a user an alert with an anomaly severity level.

Abstract: A computer-implemented method, a computer program product, and a computer system for log anomaly detection. A computer receives a windowed log of incoming raw log messages. A computer compares statistical distribution metrics of entities in the windowed log with a statistical distribution extracted from a real-time statistical model for the entities. In response to the statistical distribution metrics being statistically different from the statistical distribution extracted from the real-time statistical model for the entities, a computer tags the windowed log as an entity anomaly. A computer computes a distance between an average word embedding vector in the windowed log and a statistical distribution extracted form a real-time statistical model for word embeddings. In response to the distance being greater than a predetermined threshold, a computer tags the windowed log as a word embedding anomaly. A computer sends to a user an alert with an anomaly severity level.

Abstract: Identifying an log anomaly resolution by generating a knowledge base linking each of a plurality of incidents with historical anomalous log lines, calculating a resolution specificity score for each knowledge base record, identifying a run-time anomalous log line using the knowledge base, predicting a category for the run-time anomalous log line, identifying resolutions according to the category, ranking the resolutions according to the resolution specificity scores, and recommending a resolution according to the ranking.

Abstract: One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line. The one or more computer processors templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines. The one or more computer processors cluster erroneous log templates into erroneous log template clusters and the non-erroneous log templates into non-erroneous log template clusters. The one or more computer processors eliminate the erroneous log template clusters and the non-erroneous log template clusters that exceed a frequency threshold. The one or more computer processors train a log anomaly model utilizing=remaining erroneous log template clusters and remaining non-erroneous log template clusters. The one or more computer processors identify a subsequent log line as anomalous or non-anomalous utilizing the trained log anomaly model.

Abstract: Identifying an log anomaly resolution by generating a knowledge base linking each of a plurality of incidents with historical anomalous log lines, calculating a resolution specificity score for each knowledge base record, identifying a run-time anomalous log line using the knowledge base, predicting a category for the run-time anomalous log line, identifying resolutions according to the category, ranking the resolutions according to the resolution specificity scores, and recommending a resolution according to the ranking.

Abstract: One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line; templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines; cluster erroneous log templates into erroneous log template clusters and non-erroneous log templates into non-erroneous log template clusters; identify one or more log lines as anomalous utilizing a plurality of factors including a log maturity, a number of encountered log template clusters, and a ratio of classified erroneous log lines to classified non-erroneous log lines; responsive to one or more identified anomalous log lines, validate the identified anomalous log lines utilizing a site reliability engineer and human-in-the-loop validation; train a log anomaly model utilizing one or more validated log lines; and identify a subsequent log line as anomalous utilizing the trained log anomaly model.

Abstract: One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line. The one or more computer processors templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines. The one or more computer processors cluster erroneous log templates into erroneous log template clusters and the non-erroneous log templates into non-erroneous log template clusters. The one or more computer processors eliminate the erroneous log template clusters and the non-erroneous log template clusters that exceed a frequency threshold. The one or more computer processors train a log anomaly model utilizing=remaining erroneous log template clusters and remaining non-erroneous log template clusters. The one or more computer processors identify a subsequent log line as anomalous or non-anomalous utilizing the trained log anomaly model.

Abstract: Mechanisms are provided to implement an ensemble of unsupervised machine learning (ML) models. The ensemble of unsupervised ML models processes a portion of input data to generate an ensemble output and the ensemble output is output to an authorized user computing device to obtain user feedback from the authorized user via the user computing device. The user feedback indicates a correctness of the ensemble output. The mechanisms modify at least one feature of the ensemble of unsupervised ML models based on the obtained user feedback to thereby generate a modified ensemble of unsupervised ML models. Subsequent portions of input data are then processed using the modified ensemble of unsupervised ML models.

Abstract: Ceramic foam fiber composites, methods of making ceramic foam fiber composites, and uses of ceramic foam fiber composites. The ceramic foam fiber composites may be made by contacting one or more fiber(s); one or more ceramic precursor(s); one or more pore-forming gas-forming additive(s) (one or more inert gas-generating agent(s)); one or more catalyst(s); and, optionally, one or more additive(s), where the contacting is results in formation of an inert gas and the ceramic foam-fiber composite is formed. A ceramic foam-fiber composite may include a plurality of fibers, where at least a portion or all of the fibers individually comprise a ceramic foam disposed on at least a portion or all of a surface of the fiber. A ceramic foam-fiber composite may exhibit one or more or all of the following: thermal stability, mechanical strength, soundproof/acoustic insulation characteristics. A ceramic foam-fiber composite material may be used as a building material.

Abstract: Mechanisms are provided to implement a hybrid machine learning (ML) anomaly detector comprising an ensemble of unsupervised ML models and a semi-supervised ML model. The ensemble of unsupervised ML models are executed on log data to generate, for each entry in the log data, a predicted anomaly score and corresponding anomaly classification label of the entry. A partially labeled dataset is generated based on a selected subset of entries and other unlabeled log data in the log data. A similarity analysis of the unlabeled log data with entries in the selected subset of entries is performed and anomaly classification labels of the selected subset of entries are propagated to the other unlabeled log data based on the similarity analysis.

Abstract: Systems for forming thermoplastic components are disclosed. A system may include a mold including a first portion and a second portion engaging the first portion. The first portion and/or the second portion may receive material for the component. The system may also include a compressive device positioned adjacent to and contacting the first portion of the mold. Additionally, the system may include a control system in communication with the compressive device. The control system may be configured to displace the compressive device to apply a compressive force to the first portion of the mold, and impose a predetermined pressure on the material for the component. The control system may also be configured to heat the first portion and/or the second portion of the mold.

Abstract: Mechanisms are provided to implement a hybrid machine learning (ML) anomaly detector comprising an ensemble of unsupervised ML models and a semi-supervised ML model. The ensemble of unsupervised ML models are executed on log data to generate, for each entry in the log data, a predicted anomaly score and corresponding anomaly classification label of the entry. A partially labeled dataset is generated based on a selected subset of entries and other unlabeled log data in the log data. A similarity analysis of the unlabeled log data with entries in the selected subset of entries is performed and anomaly classification labels of the selected subset of entries are propagated to the other unlabeled log data based on the similarity analysis.

Abstract: Mechanisms are provided to implement an ensemble of unsupervised machine learning (ML) models. The ensemble of unsupervised ML models processes a portion of input data to generate an ensemble output and the ensemble output is output to an authorized user computing device to obtain user feedback from the authorized user via the user computing device. The user feedback indicates a correctness of the ensemble output. The mechanisms modify at least one feature of the ensemble of unsupervised ML models based on the obtained user feedback to thereby generate a modified ensemble of unsupervised ML models. Subsequent portions of input data are then processed using the modified ensemble of unsupervised ML models.

Abstract: Provided are a method, system and relevant device for realizing a virtual SIM card. The method includes steps of: when a mobile terminal requests to access a communication network, connecting to a cloud service terminal through a wireless network; after the mobile terminal receives an authentication request sent by the communication network, requesting the cloud service terminal to perform authentication calculation and feeding an authentication result obtained by the authentication calculation back to the mobile terminal; and the mobile terminal sending the received authentication result to the communication network. The solution realizes a mobile terminal with a virtual SIM through a cloud service, and on the basis of inheriting the security of a physical SIM card, not only the production and mounting costs of a SIM card slot are reduced, but also the mobile terminal is allowed to be lighter, thinner and more portable.

Abstract: The present invention discloses a method, an apparatus and a system for low-density parity-check (LDPC) coding and decoding. The coding method includes the following steps: constructing each layer of a check matrix of a layered LDPC code used as an error correcting code; when data is initially sent by a data transmitting terminal, performing first-layer-coding of the data to be sent by using the first layer of a check matrix of the LDPC code, sending the first-layer-coded data; when data for (n?1)th retransmission is sent by the data transmitting terminal, performing nth-layer-coding of the data by using the nth layer of a check matrix of the LDPC code, sending the nth-layer-coded data, wherein n is an integer no less than 2. It is possible to reduce the system overhead, decrease the decoding delay, and improve the decoding performance by using the technical solution of the present invention which is also adapted to high-speed data services.

Abstract: The present invention discloses a method, an apparatus and a system for low-density parity-check (LDPC) coding and decoding. The coding method includes the following steps: constructing each layer of a check matrix of a layered LDPC code used as an error correcting code; when data is initially sent by a data transmitting terminal, performing first-layer-coding of the data to be sent by using the first layer of a check matrix of the LDPC code, sending the first-layer-coded data; when data for (n?1)th retransmission is sent by the data transmitting terminal, performing nth-layer-coding of the data by using the nth layer of a check matrix of the LDPC code, sending the nth-layer-coded data, wherein n is an integer no less than 2. It is possible to reduce the system overhead, decrease the decoding delay, and improve the decoding performance by using the technical solution of the present invention which is also adapted to high-speed data services.