Abstract: Approaches herein relate to reconstructive models such as an autoencoder for anomaly detection. Herein are machine learning techniques that measure inference confidence based on reconstruction error trends. In an embodiment, a computer hosts a reconstructive model that encodes and decodes features. Based on that decoding, the following are automatically calculated: a respective reconstruction error of each feature, a respective moving average of reconstruction errors of each feature, an average of the moving averages of the reconstruction errors of all features, a standard deviation of the moving averages of the reconstruction errors of all features, and a confidence of decoding the features that is based on a ratio of the average of the moving averages of the reconstruction errors to the standard deviation of the moving averages of the reconstruction errors. The computer detects and indicates that a threshold exceeds the confidence of decoding, which may cause important automatic reactions herein.

Abstract: A multilayer perceptron herein contains an already-trained combined sequence of residual blocks that contains a semantic sequence of residual blocks and a contextual sequence of residual blocks. The semantic sequence of residual blocks contains a semantic sequence of layers of an autoencoder. The contextual sequence of residual blocks contains a contextual sequence of layers of a recurrent neural network. Each residual block of the combined sequence of residual blocks is used based on a respective survival probability. By the autoencoder and based on the using each residual block of the semantic sequence, a previous entry of a log is semantically encoded. By the recurrent neural network and based on the using each residual block of the contextual sequence, a next entry of the log is predicted. In an embodiment during training, survival probabilities are hyperparameters that are learned and used to probabilistically skip residual blocks such that the multilayer perceptron has stochastic depth.

Abstract: Techniques are described that extend supervised machine-learning algorithms for use with semi-supervised training. Random labels are assigned to unlabeled training data, and the data is split into k partitions. During a label-training iteration, each of these k partitions is combined with the labeled training data, and the combination is used train a single instance of the machine-learning model. Each of these trained models are then used to predict labels for data points in the k?1 partitions of previously-unlabeled training data that were not used to train of the model. Thus, every data point in the previously-unlabeled training data obtains k?1 predicted labels. For each data point, these labels are aggregated to obtain a composite label prediction for the data point. After the labels are determined via one or more label-training iterations, a machine-learning model is trained on data with the resulting composite label predictions and on the labeled data set.

Abstract: Techniques related to code dictionary generation based on non-blocking operations are disclosed. In some embodiments, a column of tokens includes a first token and a second token that are stored in separate rows. The column of tokens is correlated with a set of row identifiers including a first row identifier and a second row identifier that is different from the first row identifier. Correlating the column of tokens with the set of row identifiers involves: storing a correlation between the first token and the first row identifier, storing a correlation between the second token and the second row identifier if the first token and the second token have different values, and storing a correlation between the second token and the first row identifier if the first token and the second token have identical values. After correlating the column of tokens with the set of row identifiers, duplicate correlations are removed.

Abstract: Techniques related to code dictionary generation based on non-blocking operations are disclosed. In some embodiments, a column of tokens includes a first token and a second token that are stored in separate rows. The column of tokens is correlated with a set of row identifiers including a first row identifier and a second row identifier that is different from the first row identifier. Correlating the column of tokens with the set of row identifiers involves: storing a correlation between the first token and the first row identifier, storing a correlation between the second token and the second row identifier if the first token and the second token have different values, and storing a correlation between the second token and the first row identifier if the first token and the second token have identical values. After correlating the column of tokens with the set of row identifiers, duplicate correlations are removed.

Abstract: Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.

Abstract: Embodiments monitor statistics from groups of devices and generate an alarm upon detecting a utilization imbalance that is beyond a threshold. Particular balance statistics are periodically sampled, over a timeframe, for a group of devices configured to have balanced utilization. The devices are ranked at every data collection timestamp based on the gathered device statistics. The numbers of times each device appears within each rank over the timeframe are tallied. The device/rank summations are collectively used as a probability distribution representing the probability of each device being ranked at each of the rankings in the future. Based on this probability distribution, an entropy value that represents a summary of the imbalance of the group of devices over the timeframe is derived. An imbalance alert is generated when one or more entropy values for a group of devices shows an imbalanced utilization of the devices going beyond an identified imbalance threshold.

Abstract: In order to form a largely oval circulatory path, in particular a pedal (1a), a crank drive (1) is described which periodically changes the effective lever length of a crank (2). In this case, gear levers (4? or 7) are mounted on a crank (2) at both ends (2a, 2b) which rotate in opposite directions with respect to one another and thus form two further movement axes within the pedal path and are thus adapted to the natural human leg movement in a force-saving and ergonomic manner.

Abstract: A flat coil carrier may include a carrier body. The carrier body may include, on an axial front side, a groove spiral configured to receive a coil wire. The groove spiral may have an axially open groove opening and may include a plurality of radially consecutive groove sections. The plurality of radially consecutive groove sections may each have an axially open groove section opening of a plurality of groove section openings. The plurality of radially consecutive groove sections may each be separated from one another by a common separating wall section of a plurality of separating wall sections of the carrier body. At least one of the plurality of separating wall sections may protrude from the carrier body and may have at least one undercut section including a radially protruding protrusion such that an undercut for the coil wire is formed in the at least one undercut section.

Abstract: A detection device, which includes a scanning module, a detection module operated at a distance from the scanning module, and an evaluation unit. The scanning module includes a laser light source for generating a laser beam, a deflection unit to deflect the beam, and a control unit for controlling the laser light source and the deflection unit, so that the beam is moved in a scanning pattern. The detection module includes a light detector, with which the light of the beam reflected on an object in the beam path is detected and converted into a received signal. The first laser light source is controlled so that the beam is modulated as a function of its deflection and in this way is provided with synchronization marks. The evaluation unit identifies these synchronization marks in the received signal and synchronizes the received signal with the deflection of the beam based on them.

Abstract: Embodiments use Bayesian techniques to efficiently estimate the bit error rates (BERs) of cables in a computer network at a customizable level of confidence. Specifically, a plurality of probability records are maintained for a given cable in a computer system, where each probability record is associated with a hypothetical BER for the cable, and reflects a probability that the cable has the associated hypothetical BER. At configurable time intervals, the probability records are updated using statistics gathered from a switch port connected to the cable. In order to estimate the BER of the cable at a given confidence level, embodiments determine which probability record is associated with a probability mass that indicates the confidence level. The estimate for the cable BER is the hypothetical BER that is associated with the indicated probability mass. Embodiments store the estimate in memory and utilize the estimate to aid in maintaining the computer system.

Abstract: Herein are computerized techniques for autonomous and artificially intelligent administration of a computer cloud health monitoring system. In an embodiment, an orchestration computer automatically detects a current state of network elements of a computer network by processing: a) a network plan that defines a topology of the computer network, and b) performance statistics of the network elements. The network elements include computers that each hosts virtual execution environment(s). Each virtual execution environment hosts analysis logic that transforms raw performance data of a network element into a portion of the performance statistics. For each computer, a configuration specification for each virtual execution environment of the computer is automatically generated based on the network plan and the current state of the computer network. At least one virtual execution environment is automatically tuned and/or re-provisioned based on a generated configuration specification.

Abstract: Embodiments use Bayesian techniques to efficiently estimate the bit error rates (BERs) of cables in a computer network at a customizable level of confidence. Specifically, a plurality of probability records are maintained for a given cable in a computer system, where each probability record is associated with a hypothetical BER for the cable, and reflects a probability that the cable has the associated hypothetical BER. At configurable time intervals, the probability records are updated using statistics gathered from a switch port connected to the cable. In order to estimate the BER of the cable at a given confidence level, embodiments determine which probability record is associated with a probability mass that indicates the confidence level. The estimate for the cable BER is the hypothetical BER that is associated with the indicated probability mass. Embodiments store the estimate in memory and utilize the estimate to aid in maintaining the computer system.

Abstract: A projector is provided, which has a plurality of operating modes, including a first operating mode in which a beam of electromagnetic waves is emitted by the projector. In addition, a detection unit is configured in such a way that in the first operating mode of the projector the presence of an object in the optical path of the beam is detected. Moreover, the projector includes a processing unit which is developed to switch from the first operating mode to a second operating mode that differs from the first operating mode when an object is detected in the optical path of the beam.

Abstract: Herein are computerized techniques for generation, costing/scoring, optimal selection, and reporting of intermediate configurations for a datacenter change plan. In an embodiment, a computer receives a current configuration of a datacenter and a target configuration. New configurations are generated based on the current configuration. A cost function is applied to calculate a cost of each new configuration based on measuring a logical difference between the new configuration and the target configuration. A particular new configuration is selected that has a least cost. When the particular configuration satisfies the target configuration, the datacenter is reconfigured based on the particular configuration. Otherwise, this process is (e.g. iteratively) repeated with the particular configuration instead used as the current configuration. In embodiments, new configurations are randomly, greedily, and/or manually generated.

Abstract: Herein are techniques for analysis of data streams. In an embodiment, a computer associates each software actor with data streams. Each software actor has its own backlog queue of data to analyze. In response to receiving some stream content and based on the received stream content, data is distributed to some software actors. In response to determining that the data satisfies completeness criteria of a particular software actor, an indication of the data is appended onto the backlog queue of the particular software actor. The particular software actor is reset to an initial state by loading an execution snapshot of a previous initial execution of an embedded virtual machine. Based on the particular software actor, execution of the execution snapshot of the previous initial execution is resumed to dequeue and process the indication of the data from the backlog queue of the particular software actor to generate a result.

Abstract: Herein are computerized techniques for autonomous and artificially intelligent administration of a computer cloud health monitoring system. In an embodiment, an orchestration computer automatically detects a current state of network elements of a computer network by processing: a) a network plan that defines a topology of the computer network, and b) performance statistics of the network elements. The network elements include computers that each hosts virtual execution environment(s). Each virtual execution environment hosts analysis logic that transforms raw performance data of a network element into a portion of the performance statistics. For each computer, a configuration specification for each virtual execution environment of the computer is automatically generated based on the network plan and the current state of the computer network. At least one virtual execution environment is automatically tuned and/or re-provisioned based on a generated configuration specification.

Abstract: A detection device, which includes a scanning module, a detection module operated at a distance from the scanning module, and an evaluation unit. The scanning module includes a laser light source for generating a laser beam, a deflection unit to deflect the beam, and a control unit for controlling the laser light source and the deflection unit, so that the beam is moved in a scanning pattern. The detection module includes a light detector, with which the light of the beam reflected on an object in the beam path is detected and converted into a received signal. The first laser light source is controlled so that the beam is modulated as a function of its deflection and in this way is provided with synchronization marks. The evaluation unit identifies these synchronization marks in the received signal and synchronizes the received signal with the deflection of the beam based on them.

Abstract: A method and a projector for projecting an image pixel by pixel includes a control unit controlling a light diode device for emitting visible light, pixel by pixel, in accordance with the image to be projected and controlling an infrared diode device for emitting infrared radiation pixel by pixel based on the image to be projected and on a back radiation model, in such a way that a setpoint back radiation intensity measuring signal to be expected pixel by pixel for the actual back radiation intensity measuring signal has a predetermined value for predetermined pixels; and a radiation intensity detection device detecting pixel by pixel a back radiation intensity of reflected visible light and reflected infrared radiation and generating pixel by pixel an actual back radiation intensity measuring signal based on the detected back radiation intensities.

Abstract: Herein are computerized techniques for generation, costing/scoring, optimal selection, and reporting of intermediate configurations for a datacenter change plan. In an embodiment, a computer receives a current configuration of a datacenter and a target configuration. New configurations are generated based on the current configuration. A cost function is applied to calculate a cost of each new configuration based on measuring a logical difference between the new configuration and the target configuration. A particular new configuration is selected that has a least cost. When the particular configuration satisfies the target configuration, the datacenter is reconfigured based on the particular configuration. Otherwise, this process is (e.g. iteratively) repeated with the particular configuration instead used as the current configuration. In embodiments, new configurations are randomly, greedily, and/or manually generated.