Abstract: Disclosed herein are a spectrometer device and a method of obtaining spectroscopic information on at least one object. The spectrometer device includes: i. at least one light source for generating illumination light for illuminating the object; ii. at least one broadband detector for detecting detection light in a spectral range; and iii. at least one evaluation unit for evaluating a primary detector signal and a secondary detector signal generated by the broadband detector, for determining responsivity information on the broadband detector from one of the primary detector signal or the secondary detector signal, and for deriving the spectroscopic information on the object from the other of the primary detector signal or the secondary detector signal by taking into account the responsivity information on the broadband detector.

Abstract: Example systems and methods are described for improving Pull Request code visibility and code dependency analysis. One example method involves generating a first call graph representing a plurality of dependencies between sets of code in a plurality of sources, tracking changes made to a subset of a plurality of sets of code, generating a second call graph representing the subset of the plurality of sets of code, expanding the second call graph based on dependencies in the first call graph associated with at least one code set of the subset of the plurality of sets of code, and identifying one or more sets of code that are not in the subset of the plurality of sets of code based on the expanded call graph.

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: Disclosed herein is a method of calibrating a sensing device, including: I. illuminating at least one detector element via at least one first optical path to obtain at least one first detector signal S1; II. illuminating the detector element via at least one second optical path with no sample applied to the sample interface to obtain at least one open port detector signal S2;open; and III. determining at least one item of operation calibration information by using the first detector signal S1, the open port detector signal S2;open and at least one item of factory calibration information, where the item of factory calibration information includes a predefined relation between the open port detector signal S2;open and a reference signal of the second optical path.

Abstract: Disclosed herein are a spectrometer device and a method of obtaining spectroscopic information on at least one object. The spectrometer device includes: i. at least one light source for generating illumination light for illuminating the object; ii. at least one broadband detector for detecting detection light from the object in a spectral range; and iii. at least one evaluation unit for evaluating a primary detector signal and a secondary detector signal generated by the broadband detector, for determining temperature information on the light source from one of the primary detector signal or the secondary detector signal, and for deriving the spectroscopic information on the object from the other of the primary detector signal or the secondary detector signal by taking into account the temperature information on the light source.

Abstract: Disclosed herein is a spectrometer device for obtaining spectroscopic information on at least one object, the spectrometer device including: i. at least one light source for generating illumination light for illuminating the object, the light source including at least one light-emitting diode and at least one luminescent material for light-conversion of primary light generated by the light-emitting diode; ii. at least one detector for detecting light and, thereby, generating at least one detector signal; and iii. at least one evaluation unit configured for deriving the spectroscopic information on the object from the at least one detector signal, wherein the evaluation unit is configured for deriving the spectroscopic information on the object by taking into consideration wavelength correction information.

Abstract: Herein are machine learning techniques that adjust reconstruction loss of a reconstructive model, such as a principal component analysis (PCA), based on importances of features. In an embodiment having a reconstructive model that more or less accurately reconstructs its input, a computer measures, for each feature, a respective importance that is based on the reconstructive model. For example, importance may be based on grading samples that the reconstructive model correctly or incorrectly inferenced. For each feature during production inferencing, a respective original loss from the reconstructive model measures a difference between a value of the feature in an input and a reconstructed value of the feature generated by the reconstructive model. For each feature, the respective importance of the feature is applied to the respective original loss to generate a respective weighted loss, which compensates for concept drift.

Abstract: A computer system includes a detector that monitors DNS communications to detect data exfiltration and/or infiltration that is attempted or has occurred in the DNS communications. DNS communications are stored and parsed to distinguish content that is potentially not public being communicated in a prefix and content contained in a publicly available suffix. The content of the prefix is examined to determine an amount of information conveyed in the prefix based at least in part on a length and a number of unique characters of the prefix. The detector aggregates communications for network sources and/or destinations, and/or for different network groups or characteristics to determine sources, destinations, groups, and/or characteristics associated with aggregate amounts of information that satisfy one or more notification conditions or trigger one or more other corrective actions.

Abstract: Techniques are described herein for using machine learning to learn vector representations of DNS requests such that the resulting embeddings represent the semantics of the DNS requests as a whole. Techniques described herein perform pre-processing of tokenized DNS request strings in which hashes, which are long and relatively random strings of characters, are detected in DNS request strings and each detected hash token is replaced with a placeholder token. A vectorizing ML model is trained using the pre-processed training dataset in which hash tokens have been replaced. Embeddings for the DNS tokens are derived from an intermediate layer of the vectorizing ML model. The encoding application creates final vector representations for each DNS request string by generating a weighted summation of the embeddings of all of the tokens in the DNS request string. Because of hash replacement, the resulting DNS request embeddings reflect semantics of the hashes as a group.

Abstract: Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method accesses a plurality of data points. An individual data point of the plurality characterizes a pattern of activity associated with an account. The method splits the plurality of data points into clusters of similar data points. The method evaluates the clusters to detect that the individual data point has changed clusters in a manner indicative of an anomalous change to the pattern of activity. And, the method generates an electronic alert that the pattern of activity has changed anomalously.

Abstract: Herein for each source logic in a corpus, a computer stores an identifier of the source logic and operates a logic encoder that infers a distinct fixed-size encoded logic that represents the variable-size source logic. At build time, a multidimensional index is generated and populated based on the encoded logics that represent the source logics in the corpus. At runtime, a user may edit and select a new source logic such as in a text editor or an integrated development environment (IDE). The logic encoder infers a new encoded logic that represents the new source logic. The multidimensional index accepts the new encoded logic as a lookup key and automatically selects and returns a result subset of encoded logics that represent similar source logics in the corpus. For display, the multidimensional index may select and return only encoded logics that are the few nearest neighbors to the new encoded logic.

Abstract: The present invention relates to machine learning (ML) explainability (MLX). Herein are techniques for a novel relevance propagation rule in layer-wise relevance propagation (LRP) for feature attribution-based explanation (ABX) for a reconstructive autoencoder. In an embodiment, a reconstruction layer of a reconstructive neural network in a computer generates a reconstructed tuple that is based on an original tuple that contains many features. A reconstruction residual cost function calculates a reconstruction error that measures a difference between the original tuple and the reconstructed tuple. Applied to the reconstruction error is a novel reconstruction relevance propagation rule that assigns a respective reconstruction relevance to each reconstruction neuron in the reconstruction layer. Based on the reconstruction relevance of the reconstruction neurons, a respective feature relevance of each feature is determined, from which an ABX explanation may be automatically generated.

Abstract: Here is dynamic and contextual ranking of reference documentation based on an interactively selected position in new source logic. A computer receives a vocabulary of lexical tokens, a sequence of references that contains a first reference to a first reference document before a second reference to a second reference document, respective subsets of the vocabulary that occur in the first and second reference documents, a new source logic that contains a sequence of lexical tokens, respective measurements of semantic distance between the new source logic and the first and second reference documents, and a selected position in the sequence of lexical tokens. Based on the selected position, the measurements of semantic distance are selectively increased. Based on that increasing the measurements of the semantic distance, a relative ordering of the first and second references is reversed to generate and display a reordered sequence of references.

Abstract: Approaches herein relate to model decay of an anomaly detector due to concept drift. Herein are machine learning techniques for dynamically self-tuning an anomaly score threshold. In an embodiment in a production environment, a computer receives an item in a stream of items. A machine learning (ML) model hosted by the computer infers by calculation an anomaly score for the item. Whether the item is anomalous or not is decided based on the anomaly score and an adaptive anomaly threshold that dynamically fluctuates. A moving standard deviation of anomaly scores is adjusted based on a moving average of anomaly scores. The moving average of anomaly scores is then adjusted based on the anomaly score. The adaptive anomaly threshold is then adjusted based on the moving average of anomaly scores and the moving standard deviation of anomaly scores.

Abstract: Disclosed herein are a method for automated quality control of at least one photodetector, a photodetector for measuring optical radiation and a spectrometer for spectrally analyzing optical radiation provided by at least one object. Further disclosed herein are a computer program and a computer-readable storage medium for performing the method.

Abstract: Disclosed herein is a method of controlling operation of at least one light-emitting system including at least one light-emitting element. The method includes: a) monitoring at least temporarily, at least one electrical parameter of the light-emitting element; b) evaluating the electrical parameter and verifying if the electrical parameter meets at least one predetermined criterion; and c) taking at least one safeguard action depending on the outcome of step b).

Abstract: Approaches herein relate to reconstructive models such as an autoencoder for anomaly detection. Herein are machine learning techniques that detect and suppress any feature that causes model decay by concept drift. In an embodiment in a production environment, a computer initializes an unsuppressed subset of features with a plurality of features that an already-trained reconstructive model can process. A respective reconstruction error of each feature of the unsuppressed subset of features is calculated. The computer detects that a respective moving average based on the reconstruction error of a particular feature of the unsuppressed subset of features exceeds a respective feature suppression threshold of the particular feature, which causes removal of the particular feature from the unsuppressed subset of features.

Abstract: The present invention relates to threshold estimation and calibration for anomaly detection. Herein are machine learning (ML) and extreme value theory (EVT) techniques for normalizing and thresholding anomaly scores without presuming a values distribution. In an embodiment, a computer receives many unnormalized anomaly scores and, according to peak over threshold (POT), selects a highest subset of the unnormalized anomaly scores that exceed a tail threshold. Based on the highest subset of the unnormalized anomaly scores, parameters of a probability density function are trained according to EVT. After training and in a production environment, a normalized anomaly score is generated based on an unnormalized anomaly score and the trained parameters of the probability density function. Anomaly detection compares the normalized anomaly score to an optimized anomaly threshold.

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 computer system includes a detector that monitors DNS communications to detect data exfiltration and/or infiltration that is attempted or has occurred in the DNS communications. DNS communications are stored and parsed to distinguish content that is potentially not public being communicated in a prefix and content contained in a publicly available suffix. The content of the prefix is examined to determine an amount of information conveyed in the prefix based at least in part on a length and a number of unique characters of the prefix. The detector aggregates communications for network sources and/or destinations, and/or for different network groups or characteristics to determine sources, destinations, groups, and/or characteristics associated with aggregate amounts of information that satisfy one or more notification conditions or trigger one or more other corrective actions.