Abstract: Provided is a system for detecting an anomaly in a multivariate time series that includes at least one processor programmed or configured to receive a dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, determine a set of target data instances based on the dataset, determine a set of historical data instances based on the dataset, generate, based on the set of target data instances, a true value matrix, a true frequency matrix, and a true correlation matrix, generate a forecast value matrix, a forecast frequency matrix, and a forecast correlation matrix based on the set of target data instances and the set of historical data instances, determine an amount of forecasting error, and determine whether the amount of forecasting error corresponds to an anomalous event associated with the dataset of data instances. Methods and computer program products are also provided.

Abstract: Systems, methods, and computer program products for predicting user preference of items in an image process image data associated with a single image with a first branch of a neural network to produce an image embedding, the single image including a set of multiple items; process a user identifier of a user with a second branch of the neural network to produce a user embedding; concatenate the image embedding with the user embedding to produce a concatenated embedding; process the concatenated embedding with the neural network to produce a joint embedding; and generate a user preference score for the set of multiple items from the neural network based on the joint embedding, the user preference score including a prediction of whether the user prefers the set of multiple items.

Abstract: A method includes receiving a first data set comprising embeddings of first and second types, generating a fixed adjacency matrix from the first dataset, and applying a first stochastic binary mask to the fixed adjacency matrix to obtain a first subgraph of the fixed adjacency matrix. The method also includes processing the first subgraph through a first layer of a graph convolutional network (GCN) to obtain a first embedding matrix, and applying a second stochastic binary mask to the fixed adjacency matrix to obtain a second subgraph of the fixed adjacency matrix. The method includes processing the first embedding matrix and the second subgraph through a second layer of the GCN to obtain a second embedding matrix, and then determining a plurality of gradients of a loss function, and modifying the first stochastic binary mask and the second stochastic binary mask using at least one of the plurality of gradients.

Abstract: Systems, methods, and computer program products for multi-domain ensemble learning based on multivariate time sequence data are provided. A method may include receiving multivariate sequence data. At least a portion of the multivariate sequence data may be inputted into a plurality of anomaly detection models to generate a plurality of scores. The multivariate sequence data may be combined with the plurality of scores to generate combined intermediate data. The combined intermediate data may be inputted into a combined ensemble model to generate an output score. In response to determining that the output score satisfies a threshold, at least one of an alert may be communicated to a user device, the multivariate sequence data may be inputted into the feature-domain ensemble model to generate a feature importance vector, or at least one of a model-domain, a time-domain, a feature-domain, or the combined ensemble model may be updated.

Abstract: A method includes receiving a first data set comprising embeddings of first and second types, generating a fixed adjacency matrix from the first dataset, and applying a first stochastic binary mask to the fixed adjacency matrix to obtain a first subgraph of the fixed adjacency matrix. The method also includes processing the first subgraph through a first layer of a graph convolutional network (GCN) to obtain a first embedding matrix, and applying a second stochastic binary mask to the fixed adjacency matrix to obtain a second subgraph of the fixed adjacency matrix. The method includes processing the first embedding matrix and the second subgraph through a second layer of the GCN to obtain a second embedding matrix, and then determining a plurality of gradients of a loss function, and modifying the first stochastic binary mask and the second stochastic binary mask using at least one of the plurality of gradients.

Abstract: Disclosed are a system, method, and computer program product for user network activity anomaly detection. The method includes generating a multilayer graph from network resource data, and generating an adjacency matrix associated with each layer of the multilayer graph to produce a plurality of adjacency matrices. The method further includes assigning a weight to each adjacency matrix to produce a plurality of weights, and generating a merged single layer graph by merging the plurality of layers based on a weighted sum of the plurality of adjacency matrices using the plurality of weights. The method further includes generating a set of anomaly scores by generating, for each node in the merged single layer graph, an anomaly score. The method further includes determining a set of anomalous users based on the set of anomaly scores, detecting fraudulent network activity based on the set of anomalous users, and executing a fraud mitigation process.

Abstract: Described are a system, method, and computer program product for user network activity anomaly detection. The method includes receiving network resource data associated with network resource activity of a plurality of users and generating a plurality of layers of a multilayer graph from the network resource data. Each layer of the plurality of layers may include a plurality of nodes, which are associated with users, connected by a plurality of edges, which are representative of node interdependency. The method also includes generating a plurality of adjacency matrices from the plurality of layers and generating a merged single layer graph based on a weighted sum of the plurality of adjacency matrices. The method further includes generating anomaly scores for each node in the merged single layer graph and determining a set of anomalous users based on the anomaly scores.

Abstract: Described are a system, method, and computer program product for user network activity anomaly detection. The method includes receiving network resource data associated with network resource activity of a plurality of users and generating a plurality of layers of a multilayer graph from the network resource data. Each layer of the plurality of layers may include a plurality of nodes, which are associated with users, connected by a plurality of edges, which are representative of node interdependency. The method also includes generating a plurality of adjacency matrices from the plurality of layers and generating a merged single layer graph based on a weighted sum of the plurality of adjacency matrices. The method further includes generating anomaly scores for each node in the merged single layer graph and determining a set of anomalous users based on the anomaly scores.

Abstract: Provided is a system for event forecasting using a graph-based machine-learning model that includes at least one processor programmed or configured to receive a dataset of data instances, where each data instance comprises a time series of data points, detect a plurality of motifs representing a plurality of events in the dataset of data instances using a matrix profile-based motif detection technique, generate a bipartite graph representation of the plurality of motifs in a time sequence, and generate a machine-learning model based on the bipartite graph representation of the plurality of motifs in the time sequence, where the machine-learning model is configured to provide an output and the output includes a prediction of whether an event will occur during a specified time interval. Methods and computer program products are also provided.

Abstract: Methods for detecting insider threats are disclosed. A method includes collecting server access data and application access data, based on the server access data and the application access data, determining nearest neighbors of an employee, and based on the nearest neighbors of the employee, determining a peer group of the employee, determining an average rank distance (ARD) of the nearest neighbors based on a ranking of the nearest neighbors in a plurality of time periods, identifying ARD gaps between the nearest neighbors, and generating scores corresponding to the ARD gaps between the nearest neighbors. One or more employees are identified that represent an internal threat to an organization based on the scores corresponding to the ARD gaps.

Abstract: Systems, methods, and computer program products for predicting user preference of items in an image process image data associated with a single image with a first branch of a neural network to produce an image embedding, the single image including a set of multiple items; process a user identifier of a user with a second branch of the neural network to produce a user embedding; concatenate the image embedding with the user embedding to produce a concatenated embedding; process the concatenated embedding with the neural network to produce a joint embedding; and generate a user preference score for the set of multiple items from the neural network based on the joint embedding, the user preference score including a prediction of whether the user prefers the set of multiple items.

Abstract: Methods for detecting insider threats are disclosed. A method includes collecting server access data and application access data, based on the server access data and the application access data, determining nearest neighbors of an employee, and based on the nearest neighbors of the employee, determining a peer group of the employee, determining an average rank distance (ARD) of the nearest neighbors based on a ranking of the nearest neighbors in a plurality of time periods, identifying ARD gaps between the nearest neighbors, and generating scores corresponding to the ARD gaps between the nearest neighbors. One or more employees are identified that represent an internal threat to an organization based on the scores corresponding to the ARD gaps.

Abstract: The present application provides a wireless charging module and a modularized sensing device therewith. A first charging unit is disposed around the inside of the wireless charging module for wirelessly charging a sensing module. Without introducing a transmission line as an electricity transmitting medium, the sensing module can be directly disposed inside and charged by the wireless charging module, which improves flexibility of implementation. Further, since the first charging unit is configured to surround the inside of the wireless charging module shaped as a bowl structure, the sensing module can be simply disposed into the bowl structure and wirelessly charged by the surrounding first charging units, which saves the inconvenience of setting the sensing module to a specific location or area for stable wireless charging and increases effective charging angles.

Abstract: The present application provides a wireless charging module and a modularized sensing device therewith. A first charging unit is disposed around the inside of the wireless charging module for wirelessly charging a sensing module. Without introducing a transmission line as an electricity transmitting medium, the sensing module can be directly disposed inside and charged by the wireless charging module, which improves flexibility of implementation. Further, since the first charging unit is configured to surround the inside of the wireless charging module shaped as a bowl structure, the sensing module can be simply disposed into the bowl structure and wirelessly charged by the surrounding first charging units, which saves the inconvenience of setting the sensing module to a specific location or area for stable wireless charging and increases effective charging angles.

Abstract: A device of detecting a door/window status includes a signal generating unit and a signal sensing module. The signal generating unit is disposed beside a door/window to generate a position signal. The signal sensing module is disposed opposite the signal generating unit and includes a three-axis accelerometer, a three-axis magnetic force gauge and a microcontroller. The microcontroller reads the position signal generated from the signal generating unit and detection signals generated from the three-axis accelerometer and the three-axis magnetic force gauge while the door/window is operating to thereby determine the position and status of the door/window, thereby enhancing home safety. A method of detecting the door/window status with the device is further introduced.

Abstract: The present invention relates to a bidirectional voice transmission system and a method thereof, wherein the system timing of the system is divided into three time slots for carrying a control signal, a broadcast voice signal and a mobile device voice signal. The bidirectional voice transmission system is able to carry out the signal transmission by way of frequency frogging, so as to effectively avoid the signal interference from occurring between any two different signal transmitting and receiving devices. Besides, all of the mobile devices can be added into the bidirectional voice transmission system by way of dynamic building, without using any specific communication interface. The most important is that, the number of node routers can be expanded to about 65,000 in order to apply the bidirectional voice transmission system in a huge range network communication or a wild rescue field.

Abstract: The present invention relates to a wireless communication locating method, the method uses a first wireless receiver, a second wireless receiver and a third wireless receiver (i.e., a first rescuer, a second and a third rescuer) to receive mayday and RSSI signals outputted by a person to be rescued, therefore the position of the person to be rescued can be precisely located after a backend signal-processing platform processes and calculates the mayday and RSSI signals. Through the method, each of rescuers are able to output a prompting signal to the person to be rescued by way of frequency synchronization, so as to give the person to be rescued hopes and a message that rescuers are coming. The method also includes the advantages of high damage resistance ability, high expansibility and can be easily operated, such that the purpose of immediate rescue can be achieved by using the method.

Abstract: The present invention relates to a wireless communication locating method, the method uses a first wireless receiver, a second wireless receiver and a third wireless receiver (i.e., a first rescuer, a second and a third rescuer) to receive mayday and RSSI signals outputted by a person to be rescued, therefore the position of the person to be rescued can be precisely located after a backend signal-processing platform processes and calculates the mayday and RSSI signals. Through the method, each of rescuers are able to output a prompting signal to the person to be rescued by way of frequency synchronization, so as to give the person to be rescued hopes and a message that rescuers are coming. The method also includes the advantages of high damage resistance ability, high expansibility and can be easily operated, such that the purpose of immediate rescue can be achieved by using the method.

Abstract: The present invention relates to a bidirectional voice transmission system and a method thereof, wherein the system timing of the system is divided into three time slots for carrying a control signal, a broadcast voice signal and a mobile device voice signal. The bidirectional voice transmission system is able to carry out the signal transmission by way of frequency frogging, so as to effectively avoid the signal interference from occurring between any two different signal transmitting and receiving devices. Besides, all of the mobile devices can be added into the bidirectional voice transmission system by way of dynamic building, without using any specific communication interface. The most important is that, the number of node routers can be expanded to about 65,000 in order to apply the bidirectional voice transmission system in a huge range network communication or a wild rescue field.

Abstract: An alternate radio transmitter includes a processor, a modulator, a PLL unit and a radio transmission unit. The processor provides a digital signal. The modulator modulates the digital signal, thus providing at least two modulated signals. The PLL unit mixes the modulated signals, thus providing an output signal. The radio transmission unit transmits the output signal.